Dynamic Time-based Postponement: Conceptual

“You don’t really understand something until you can explain it to your grandmother.” Albert Einstein

DYNAMIC TIME-BASED POSTPONEMENT: CONCEPTUAL DEVELOPMENT AND EMPIRICAL TEST DISSERTATION

Presented in Partial Fullfilment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State Univeristy

By Sebastián Javier García-Dastugue ******* The Ohio State University 2003

Dissertation Committee:

Approved by

Professor Douglas M. Lambert, Adviser Professor Keely L. Croxton Professor Thomas J. Goldsby

__________________________________ Adviser Business Administration

Copyright by Sebastián Javier García-Dastugue 2003

ABSTRACT

The purpose of this research was to present the conceptual development of dynamic time-based postponement and empirically test the concept in the context of the management of short-lived products in a supply chain formed by independent firms. The conceptual development of dynamic time-based postponement was based on a review of the literature. The empirical test was performed with actual data from past limited-time offers in the quick-service restaurant business. Postponement, the deliberate delay of activities, is used to reduce the acquisition, manufacturing and logistics costs while maintaining or increasing customer service levels. Traditionally, postponement is implemented by changing the sequence in which activities are performed. The sequence of activities is changed by modifying the design of the product or the manufacturing processes, or by reconfiguring the supply chain network structure. In this research, postponement is implemented without changing the product, the manufacturing processes or the network structure. Dynamic time-based postponement is an extension of postponement to situation where the extent to which postponement is used is adapted to the business environment. Postponement as described here is time-based because only the time when activities are performed is affected; that is, the sequence in which activities are performed is not changed. Dynamic time-based postponement is dynamic because it represents a method for capturing a number of managerial objectives that change within a short time horizon. Early in the life cycle of the ii

product, speculation is the strategy of choice for positioning inventories across the supply chain. Speculation is used to minimize the risk associated with running out of stock. It is generally more expensive to speculate than not to because inventory is held at a higher cost and products are differentiated. Later in the life cycle, uncertainty of demand is to a considerable extent dissipated and the focus is cost minimization as for a standard product. Finally, close to the end of the life cycle, obsolescence becomes a considerable cost driver, and shorter and more expensive lead times are used in order to reduce inventory investment at risk of obsolescence. The use of shorter lead times allows management to delay activities and to maintain product in a non-committed status for longer. In this research setting, product is maintained in a non-committed status by holding raw materials instead of finished goods in stock and/or by delaying its geographic differentiation. The objective for dynamic time-based postponement is to reduce safety stocks wherever it is possible by determining the tactical locations of safety stocks across the supply chain which might be different at different phases of the life-cycle of the product. Data for the quantitative test were provided by four independent companies in a supply chain operating in the restaurant industry. The four companies were two manufacturers of ingredients, one distributor with nine distribution centers, and quick-service restaurants owned by a franchisor. These four companies had suppliercustomer relationships, worked closely, but were independent from each other and did not share ownership. Collected data included manufacturing runs, interplant transportation, shipments to next-tier customers, sales to end-customers, emergency transshipments, and cost data. These data were integrated into a single database and used to estimate the size of the business opportunity for the supply chain as a whole. They were used also as input to an optimization model used to determine the tactical locations and levels of safety stock across a supply chain. Conclusions were drawn by comparing various scenarios produced with the optimization software. iii

The major conclusions of this research were: 1. The main conclusion of this study was that adapting inventory policies across a supply chain from speculation to postponement in short-time periods has potential to produce cost savings to the supply chain. This allows managers to integrate apparently conflicting objectives such as to guarantee product availability even with uncertain demand, reduce costs as demand becomes more predictable, and minimize obsolescence as the end of the life-cycle draws near. Dynamic time-based postponement represents a method to focus on each of these objectives at different phases of the product’s life-cycle. 2. The assessment of dynamic time-based postponement was presented here in detail. This dissertation includes a detailed description of the data that were collected, how these data from a number of sources were integrated to analyze the supply chain as a whole, the issues that were encountered in this process, and the quantitative analyses performed. This dissertation was meant to provide managers willing to integrate activities beyond the single firm with a roadmap for assessment of the business opportunity and for its implementation. 3. In this research setting, dynamic time-based postponement could produce a cost saving of between $5.3 and $6.9 million annually while improving product availability from approximately 92% to 99.5%. 4. Despite the closeness of the relationships among the members of the supply chain, there was reluctancy to share data. Confidentiality concerns seemed to be the single most critical factor to evaluate supply-chain-wide initiatives. This was a problem even in this research setting, in which companies work as extensions of each other at many organizational levels. There was considerable openness to share transactional data; there was reluctancy to share cost data. Suppliers felt they were at risk of giving away information that could be used to negotiate better deals in the future. iv

5. Dynamic time-based postponement represents an approach to a true implementation of collaborative replenishment. Traditionally, the channel captain sets static inventory policies with which other supply chain members have to comply. Dynamic time-based postponement requires that activities are integrated across key supply chain members to reach its full potential and make the supply chain as a whole more efficient. There are three key differentiating aspects of this dissertation. First, this dissertation is research in supply chain management; that is, research that views a supply chain formed by several independent organizations holistically and that extends beyond a dyad. The second aspect is that postponement is viewed as a dynamic approach based on information sharing and the managers’ willingness to coordinate activities beyond a single firm; and that it is implemented without changing any aspect of the product, the manufacturing process or the supply network structure. The third differentiating aspect is that, in this research, it is recognized that postponement can be implemented by changing only the time in which activities are performed, rather than by changing the sequence of activities.

v

Este trabajo está dedicado a mis padres, Inés y Juan Carlos, quienes siempre se esforzaron por darme lo mejor de ellos (a).

A la memoria de mi padre, Juan Carlos, a quién tendré presente por siempre (b).

a) Dedicated to my parents, who always have provided me with their best. b) To my father who will be in my memory forever.

vi

ACKNOWLEDGMENTS Several people interacted with me during the writing of this dissertation. To all of them I am extremely thankful for their support and their help. A dissertation grants a doctorate to a person, but there is at least a bit of this enterprise that belongs to each of these individuals. I would like to thank to the executives of the companies that participated in this research, who dedicated their time. Without their help this research would not have been possible. They not only provided the data, but explained (sometimes several times) many aspects of their industry. To the members of The Global Supply Chain Forum at the Fisher College of Business, who listened to reports of my progress at several meetings. Their comments, questions, general feedback, and reactions to those presentations certainly made me be better prepared to continue making progress. To Professor Douglas M. Lambert, Director of The Global Supply Chain Forum and Chairman of my dissertation committee; who saw my potential early in the doctoral program; who taught me endlessly; who helped me grow professionally and personally by providing me with challenges in teaching, writing and presenting my research interests; and who let me know when I could do better. There is not a doubt in my mind that this journey would not have been as exiting and stimulating if Professor Lambert had not given me his full support and dedicated time to my education when I needed it. To Professors Keely L. Croxton and Thomas J. Goldsby, who, together with Professor Lambert, formed my dissertation committee. Their guidance through the process of writing this dissertation was invaluable. Both played a central role in vii

helping me ride the emotional roller coaster of writing a dissertation. Their openness and accessibility made this process tolerable. I also want to express my gratitude to Professor Sean Willems from Boston University. Professor Willems not only let me use the software used for the quantitative analysis for this research, but also dedicated his time to answering my questions and guiding me in the use of the optimization software. I will never forget the time, when even though he was working late at night, he spent more than an hour on the phone with me working together to find a solution to a problem. I also want to thank Professors Martha C. Cooper and Walter Zinn, from the Marketing and Logistics Department at the Fisher College of Business. They with the three members of my dissertation committee, provided the environment to learn an enormous amount inside the classroom, doing research, or during informal conversations on the corridors. I had the good fortune to interact with all of the logistics faculty. Unquestionably, this interaction was the best feature of my doctoral program. To my fellow doctoral students who helped to make everyday a fun day and with whom I shared both exiting and frustrating moments. To the staff of the Department of Marketing and Logistics, at the Graduates Office at the Fisher College of Business, and at the Office of International Education. To Ignacio and Victor, who belived in me. Without their motivational sparks, I never would have pursued my doctoral degree. This dissertation represents the end of a wonderful journey and the beginning of another one. I decided to accompany Ignacio in his endeavor to build a world class logistics program in Argentina several years ago, because I believed that being with great people makes great things happen. So far, this belief has been reinforced strongly. To Stan, my past office-mate and my friend, who passionately helped me improve my English, who patiently guided me through learning the academic viii

environment of the United States, and from whose experiences, both good and bad, I learned plenty. To a friend, who was always there (and I am sure that he always will be) to help, listen, or argue... whatever was needed at the time. To this friend who taught me both with his actions and his words; who demonstrated, countless times, how to implement his first law of consulting: “help is defined by those who get it, not those who give it”. To my dear friend Steve. At last, but certainly not least, to Cecilia, who followed me to the other side of the world leaving behind her family ties and putting on hold everything just to be by my side. To the mother of Valentina, our first daughter who is growing up beautifully thanks to Cecilia’s love. To my caring wife, I owe her the most because at the end of the day for the past four years, she was always waiting for me at home.

ix

VITA

Education 1992 .................................................. Bachelor of Arts, Management Information Systems, Universidad C.A.E.C.E., Buenos Aires, Argentina 1996 .................................................. Master in Business Administration, Instituto de Altos Estudios Empresariales (IAE), Universidad Austral, Buenos Aires, Argentina 2002 .................................................. Master of Arts, Business Administration, Logistics, Fisher College of Business, The Ohio State University 2000 - present ................................. Research Associate, The Global Supply Chain Forum, The Fisher College of Business, The Ohio State University

Professional Experience 1997-1999......................................... Change Agent, Cementos Avellaneda, Buenos Aires, Argentina 1997-1999 (part time) .................... Consultant to ING Systems, Buenos Aires, Argentina 1996-1999 (part time) .................... Faculty, Instituto de Estudios para la Excelencia Competitiva, Buenos Aires, Argentina x

1996-1997......................................... Logistics Engineer, Ryder Argentina, Buenos Aires, Argentina 1993-1996......................................... Information Systems Engineer, Solutions Informatiques Françaises, Buenos Aires, Argentina 1992-1993......................................... Information Systems Technical Support, Sud Amércia Seguros, Buenos Aires, Argentina

PUBLICATIONS 1. Croxton, Keely L., Sebastián J. García-Dastugue, Douglas M. Lambert and Dale S. Rogers, “Supply Chain Management Processes,” The International Journal of Logistics Management, Vol. 11, No. 2 (2001), pp. 13-36. 2. Dale S. Rogers, Douglas M. Lambert, Croxton, Keely L., and Sebastián J. GarcíaDastugue, “Supply Chain Management Processes,” The International Journal of Logistics Management, Vol. 13, No. 2 (2002), pp. 1-18. 3. Croxton, Keely L., Douglas M. Lambert Sebastián J. García-Dastugue, and Dale S. Rogers, “Demand Management,” The International Journal of Logistics Management, Vol. 13, No. 2 (2002), pp. 51-66. 4. García-Dastugue, Sebastián J. and Douglas M. Lambert, “Internet-enabled Coordination in the Supply Chain,” Industrial Marketing Management, Vol. 32, No. 3, 2003, pp. 251-263.

FIELDS OF STUDY Major Field: Business Administration Areas of Specialization: Logistics and Management Information Systems xi

TABLE OF CONTENTS

Abstract ................................................................................................................

ii

Dedication ...........................................................................................................

vi

Acknowledgements ............................................................................................

vii

Vita .......................................................................................................................

x

List of Tables .........................................................................................................

xvi

List of Figures ........................................................................................................

xviii

Chapters: 1. Introduction .....................................................................................................

1

Background .............................................................................................. The Business Problem ............................................................................... The Business Opportunity ........................................................................ The Research Purpose ............................................................................. Research Objectives ............................................................................... Research Questions ................................................................................. The Scope of the Research .................................................................... Limitations ................................................................................................. Potential Contributions............................................................................ Organization .............................................................................................

3 4 5 9 11 11 12 12 13 14

References ..........................................................................................

15

2. Conceptual Foundations: a Review of the Literature ...................................

Management of Limited-Time Offers .................................................... Uses of Limited-Time Offers ........................................................................ The Challenge of Managing LTOs ............................................................ Management Throughout the Life Cycle of a LTO ................................ xii

16

17 19 22 23

Forecasting and Initial Ordering ..................................................... Markdowns During a LTO ................................................................. Replenishment Throughout a LTO ................................................... Summary of Management of LTOs ...........................................................

23 25 25 30

Supply Chain Management ..................................................................

31

Modeling Inventory Management in the Supply Chain .................... Postponement ..........................................................................................

40 45

Supply Chain Management Frameworks ................................................ Supply Chain Management and the Management of LTOs ...............

Evolution of the Concept of Postponement .......................................... Two Views of Postponement ........................................................... To Postpone or Not to Postpone?: That Is Not the Question ................................................................... Number of Decoupling Points in the Supply Chain ..................... Postponement: Half a Concept ..................................................... How is Postponement Measured? .................................................. Summary of the Review of the Literature in Postponement ................ Postponement and the Management of Limited-Time Offers ............ Supply Chain Management Opportunities in Postponement .............

33 37

49 53

63 66 67 69 70 71 72

Dynamic Time-based Postponement: Conceptual Development . Summary of Literature Review ...............................................................

75 82

References ..........................................................................................

84

3. Research Design .............................................................................................

95

Research Setting ...................................................................................... Description of the Supply Chain Used for this Research .................... Data Collection........................................................................................ Description of the Analysis...................................................................... The Optimization-based Support System .............................................

95 98 105 108 111

Summary ...................................................................................................

120

References ..........................................................................................

120

4. Data Analysis and Findings ............................................................................

121

Brief Description of the Optimization Algorithm ..................................... Stationary Single-Stage Inventory Model ...................................... Mechanics of the Optimization Algorithm ....................................

Data Collection........................................................................................ Creating a Single Database for the Supply Chain ................................ Importing Files and Structuring Data .............................................. Standardizing Data Formats ............................................................ Building in Traceability of Transactions .......................................... Normalizing the Database ............................................................... Detecting Errors and Improving Data Quality .............................. xiii

114 114 116

121 126 126 131 131 132 132

Documenting the Database ........................................................... Communication with Data Owners ............................................... Description of the Data Collected ........................................................... Forecast ............................................................................................... Actual Transactions Form Information Systems ............................

133 133 134 134 138

Scaling Data of the Supply Chain ......................................................... Initial Diagnostics of the Supply Chain Dynamics ...............................

139 144

Assessing the Cost of a LTO to the Supply Chain ................................

153

Determining the Size of the Visible Business Opportunity .................. Modeling Dynamic Time-based Postponement .................................

157 161

Dynamic Time-based Postponement in this Setting ........................... Developing the Optimization-based Model ....................................... Modeling the Business Opportunity ......................................................

168 171 175

Modeling the Three Periods of Dynamic Time-based Postponement .............................................

183

Benefits from the Implementation of Dynamic Time-based Postponement ............................................. Summary ...................................................................................................

209 213

References ..........................................................................................

213

Inventory Is Built Early in the Life-Cycle of the LTO ................................. Order Placement ......................................................................................... Planning ........................................................................................................ Obsolescence .............................................................................................. Inventory Holding Costs .............................................................................. Obsolescence of Product Left Over ........................................................ Transshipments ............................................................................................. Limits to the Use of Postponement ........................................................... Degree of Predictability of Demand........................................................ Total Logistics Cost Trade-Offs ...................................................................

Description of the Model ........................................................................... Metrics Used in Modeling the Business Opportunity .............................. Assessing the Benefits from Dynamic Time-based Postponement ..... Timeline of a LTO ................................................................................ Forecasting the LTO .......................................................................... The Full Speculation Period ........................................................................ The Maturity Period ..................................................................................... The Plan for Termination Period .................................................................

5. Summary and Conclusions ............................................................................

Summary of Research Purpose .............................................................. Review of Research Questions and Findings ....................................... Is There an Opportunity for Omplementing Dynamic Time-based Postponement?................................................... How Does Having Better Knowledge of Demand Influence Total Logistics Costs and Acquisition Costs for the Supply Chain Members? ............................................................... What Prevents Management from Performing Holistic Analyses of Supply Shain Dynamics? xiv

144 149 149 152

154 154 155

161 163 165

175 177 178 178 179 189 193 198

215

215 216 216

221

How are these concerns addressed? ......................................................

222

Major Conclusions ................................................................................... Managerial Implications ......................................................................... Limitations ................................................................................................. Extension and Future Research Opportunities .................................... A Commentary ........................................................................................

224 228 230 232 235

References .......................................................................................

237

Bibliography ..............................................................................................

239

xv

LIST OF TABLES

Chapter 2 2.1 Limits to the Use of Postponement ..............................................................

48

2.2

Selected Definitions of Postponement .......................................................

50

2.3

Issues Identified in the Literature Review and the Conceptual Development of Dynamic Time-based Postponement ..........................

83

Chapter 3 3.1 Data Collection ..............................................................................................

106

3.2

Determining Days of Exposure Based on Local Information ...................

118

3.3

Determining Days of Exposure Strategically Across the Supply Chain ..

119

Chapter 4 4.1 Site Visits and Interviewees ...........................................................................

123

4.2

Data Collected: Description and Sources .................................................

124

4.3

Number of Restaurants Participating in the Research .............................

140

4.4

Percentage of Product Flow for Which There Was Data Available at Each Tier of the Supply Chain .................................................................

141

4.5

Restaurants Included in the Model .............................................................

144

4.6

Size of the Visible Business Opportunity ......................................................

160

4.7

Supply Chain Modeling Input Parameters .................................................

174

4.8

Typical Timeline for a Limited-Time Offer ....................................................

180

4.9

Initial Coefficients of Variation of Demand Used for Modeling ..............

182

4.10 Modeling Dynamic Time-based Postponement: Scenarios Developed with the Optimization Software ............................

183

4.11 Days of Exposure by Stocking Location - Maturity ....................................

185

4.12 Days of Exposure: Maturity and Full Speculation Periods ........................

191

4.13 Comparison of Coordinating the Use of Speculation across the Supply Chain and Using Speculation Internally without Considering the Imppat on the Rest of the Supply Chain ........

192

xvi

4.14 Comparison of Using Full Speculation for the Entire LTO and Combining Full Speculation and Maturity Periods of Dynamic Time-based Postponement ...................................................

194

4.15 Example of the Impact of Updating the Forecast on Total Inventory Investment and Total Supply Chain Cost ........................

197

4.16 Expected Variability of Demand – Plan for Termination ..........................

202

4.17 Plan for Termination — Supply Chain Cost Excluding Obsolescence as a Percentage of Base-Case ....................................................................

202

4.18 Total Inventory Investment – Plan for Termination ....................................

204

4.19 Break-Even Analysis: Obsolescence Rate – Plan for Termination ...........

205

4.20 The Effect of Using Shorter Lead Times in Days of Exposure by Stage ...

206

4.21 Comparison of Combining Full Speculation and Maturity, and Uisng the Three Periods of Dynamic Time-based Postponement ..

208

4.22 Expected Improvement in Product Availability at Restaurants and Expected Number of Transshipments Needed .................................

209

4.23 Summary of Business Opportunity ................................................................

210

4.24 Expected Cost Savings from the Implementation of Dynamic Time-based Postponement ...................................................

212

xvii

LIST OF FIGURES

Chapter 1 1.1 Adapting the Postponement-Speculation Strategy Throughout the Life Cycle of a Product .....................................................

7

Chapter 2 2.1 Typical Phases in the Life Cycle of a Limited-Time Offer .........................

18

2.2

Collaborative Planning, Forecasting and Replenishment.......................

28

2.3

Supply Chain Management: Integrating and Managing Business Processes Across the Supply Chain ..................

34

2.4

The Demand Management Process ...........................................................

39

2.5

Modeling Inventory Management in the Supply Chain ..........................

42

2.6

Postponement by Changing the Sequence of Activities in the Supply Chain ........................................................................................

57

2.7

Example of Demand Aggregation Effect ..................................................

59

2.8

Postponement by Delaying Changes in Inventory Location (centralization) ................................................................................................

61

2.9

Structuring the Benefits from Postponement .............................................

79

2.10 Inventory Value Increases in the Supply Chain .........................................

81

Chapter 3 3.1 Mapping the Supply Chain: Locations Participating in the Research ..

99

3.2

Information Distortion in the Supply Chain .................................................

104

3.3

The Supply Chain with an Information Hub ................................................

105

3.4

Determining the Size of the Visible Business Opportunity ........................

108

3.5

Developing the Optimized Scenarios .........................................................

109

Chapter 4 4.1 Creating the Database of Past Limited-Time Offers .................................

128

4.2

Data Structure and Building Traceability of Transactions in the Database .............................................................................................. xviii

129

4.3

Creating a Dynamic Supply Chain Map of Product Flow .......................

135

4.4

Data Availability .............................................................................................

139

4.5

Distribution of Restaurants Based on the Standardized Weekly Total Sales .....................................................

143

4.6

Daily Stock Levels by Tier of the Supply Chain — Ingredient A ..............

146

4.7

Daily Stock Levels by Tier of the Supply Chain — Ingredient B ...............

147

4.8

Daily Stock Levels for the Whole Supply Chain Ingredients A and B .....

148

4.9

Order Placement Ingredient A ....................................................................

150

4.10 Order Placement Ingredient B .....................................................................

151

4.11 Proportion of Restaurants that Disengaged Early from LTO-11 ...............

166

4.12 Total Logistics Cost Trade-Offs ......................................................................

169

4.13 Dynamic Time-based Postponement: An overview .................................

170

4.14 Modeling the Business Opportunity .............................................................

176

4.15 Determining the Expected Variability of Demand ...................................

182

4.16 Safety Stock Levels of Ingredient B by Stocking Location — Original Plans .......................................................

188

4.17 Updating the Forecast: Sensitivity of Coefficient of Variation of Demand on Total Safety Stock Cost and Total Safety Stock Investment ..............................................................

197

4.18 The Effect of Sharing Information on Variability of Forecast Errors .........

200

4.19 Plan for Termination — Updating the Forecast .........................................

201

Chapter 5 5.1 Summary of Benefits from Dynamic Time-based Postponement ...........

226

xix

CHAPTER 1 INTRODUCTION Postponement, the deliberate delay of activities in the supply chain, is used to reduce the total cost to the supply chain while maintaining or increasing customer service levels. Traditionally, postponement is implemented by changing the design of the products, the design of the manufacturing processes or the structure of the supply chain network. These changes are static in the sense that once postponement is implemented, activities are performed in that way until the next structural change. The traditional view of postponement usually is implemented by changing the sequence in which activities are performed. In contrast, the objective of dynamic time-based postponement is to delay the execution of activities. It is dynamic because the use of postponement is adapted to environmental factors over a short time horizon. Dynamic time-based postponement offers the greatest potential when executives manage the relationships with other supply chain members and coordinate activities beyond individual firms. Dynamic time-based postponement occurs when supply chain members shift from speculation, performing activities prior to observing demand, to postponement, delaying activities to the latest possible point in time, dynamically based on changing environmental conditions, such as demand uncertainty and total cost trade-offs, or for strategic reasons. In general, the objective with postponement is to delay manufacturing and logistics activities to the latest time possible while maintaining a targeted service level. When uncertainty of demand is high, managers may use speculation by positioning safety stocks close to the end-customer to minimize replenishment time and meet the target for in-stock availability. As uncertainty of demand is reduced, supply chain members may shift 1

their focus to cost minimization and postpone activities by coordinating order placement and tactically locating safety stocks and reducing safety stock levels in the supply chain. Ultimately, this effort leads to reducing acquisition and logistics costs from a total supply chain standpoint. The motivation behind this dissertation is to develop the concept of dynamic time-based postponement and empirically test it. This view of postponement is implemented without changing any aspect of the products, the manufacturing process or the supply chain network structure. It relies on active sharing of demand information across the supply chain as well as managers’ willingness to integrate activities beyond the boundaries of individual firms. Dynamic time-based postponement was empirically tested in the context of products with life cycles of 8 to 10 weeks, such as limited-time offers or seasonal products. As the life cycle of products shortens, coordinating the product flow across firms in the supply chain becomes more critical. Collaborative replenishment, and other quick response systems such as Automatic Replenishment or Collaborative Planning, Forecasting and Replenishment, are frequently cited as ways to coordinate product flow across the boundaries of individual firms. The reach of collaborative replenishment in practice generally is limited to a dyad and, more frequently than not, it is not collaborative because the channel captain sets static inventory policies within which the other supply chain members must operate. Research efforts in the management of limited-time offers (LTO) and seasonal products tend to be focused on the beginning and the end of the LTOs giving little guidance on how to implement collaborative replenishment over the course of the LTO. This research was designed to make a contribution in collaborative replenishment through the development of a methodology to implement dynamic time-based postponement including measurement of the benefits and documentation of the challenges of coordinating replenishment beyond a dyad. 2

The holistic analysis performed in this dissertation suggests a change in business practices. The implementation of dynamic time-based postponement will lead to a shift in who makes some decisions, such as order placement decisions, and a shift in costs, such as inventory holding costs. Issues regarding implementation were documented providing a road map to assist managers in the successful implementation of dynamic time-based postponement.

BACKGROUND Postponement by changing the design of the products, the manufacturing processes or the supply chain network structure has captured considerable attention since the early ’90s, after the benefits from postponement achieved by HP with the redesign of the DeskJet Plus printer were published. This is usually called “designing product and process for postponement” [1] and involves changing the form or identity of products at the latest possible point in the marketing flow as suggested by Alderson [2]. This view of postponement is implemented by changing the sequence in which activities are performed. In his seminal study in postponement, Alderson also suggested that postponement can be implemented without changing the structure of the network or the design of the product by delaying the point in time when activities are performed. This view of postponement has received sparse attention. However, in light of the central role that supply chain management (SCM) has in business and given the extent to which information technology facilitates information sharing among supply chain members, delaying the point in time when activities are performed becomes an opportunity that leaders in SCM should exploit. This opportunity is enabled by both the potential information available in the supply chain and managers’ willingness to share information with other firms. 3

Postponement by delaying activities in time offers significant potential, particularly in situations where knowledge of demand increases (or demand uncertainty decreases) in short periods of time and where inventory carrying costs increase due to increased risk of obsolescence or perishability. In these situations, delaying activities in time might produce considerable cost savings to supply chain members by reducing the costs associated with uncertainty. This is the case with LTOs and seasonal products. Managers can reduce the total supply chain cost of managing a LTO by capitalizing on the knowledge of the demand gained throughout the life cycle of the offer and reevaluating the location and levels of safety stocks holistically across the supply chain for the remainder of the LTO. Past research shows that as the LTO progresses, uncertainty of demand can be reduced by updating the forecast [3]. Each forecast update can be shared with key supply chain members so that each member plans based on lower demand uncertainty. Less uncertainty of demand calls for less safety stock, permitting supply chain members to coordinate inventory deployment in a leaner fashion by delaying manufacturing activities, moving inventory, and changing ownership. Ultimately, these efforts lead to lower costs of managing the LTO for the supply chain as a whole.

THE BUSINESS PROBLEM Management can use LTOs as: a strategic marketing tool; a limited-time price discount to liquidate end-of-season products; a price discount to drive customers into their stores; a price discount to “flush the pipeline”; and/or a way to improve the financial results for end-of-quarter reports. In general, all these types of LTOs affect the normal product flow. Dynamic time-based postponement is possible because the time when changes in demand will occur is predictable even though the size of the changes may not be. 4

During a LTO, an item is offered in a specific configuration and price for a limited time. The item can be a standard product offered at a special price; a set of standard products that are bundled together; a standard product or group of products that are packaged in a distinctive manner for a season; or a product that is specifically developed to be offered during a short period. The use of each type of LTO will affect the revenue, costs and profits of the members of a supply chain differently and managing the LTO efficiently is critical for the success of many firms in diverse industries. Consultants to members of the Grocery Manufacturers of America reported that the expenditure for LTOs is not effective and manufacturers estimate that only 35% of their LTOs are profitable [4]. In addition, it has been estimated that managing LTOs more effectively could lead to an increase in profitability of 2.5% by increasing service levels [5]. An industry expert from the consumer packaged goods industry estimated that roughly 60% of sales are some sort of LTO in the form of a product tailored for the season, a limitedtime presentation, or a bundle customized for a retailer. The members of The Global Supply Chain Forum, a consortium of leading practitioners and academics at the Fisher College of Business, The Ohio State University, were interviewed to assess the importance of LTOs to their companies. They asserted that LTOs can represent more than 30% of revenues, that managing LTOs is substantially more difficult than standard products, and LTOs more frequently than not are unprofitable. All executives interviewed agreed that the use of LTOs is increasing and that there is a lack of tools to manage the product flow of LTOs more effectively because they are managed in the same way as standard items. Dynamic time-based postponement has the potential to facilitate the management of the product flow during LTOs.

THE BUSINESS OPPORTUNITY Increasingly, LTOs that are a customized product offered for a limited period of time, are being used as part of a firm’s marketing strategy. From an inventory 5

management standpoint, the objective is to have high in-stock availability for the product and run out of stock precisely when the offer ends. Managers may know when demand is going to change throughout the LTO and should coordinate activities accordingly across multiple members of the supply chain, planning at each phase of the LTO for the remaining phases. For example, a heart-shaped box of chocolate candies has much less value to the eyes of the sweetheart on February 15th than the day before. This fact shapes the demand of St. Valentine’s Day products. Supply chain members start manufacturing and deploying inventory in advance of the LTO. The candies are available at the stores for a number of days before St. Valentine’s Day. At this point, planning is based on past experience, marketing intelligence, and other forecasting parameters with the expectation that these plans are imprecise. After some demand is observed, managers may use demand data to plan for the rest of the offer. The objective is to have no inventory of St. Valentine’s Day products after February 14th. Management needs to develop an inventory deployment strategy that captures the characteristics of the business environment that change throughout the life cycle of the LTO. In each of the periods, both management objectives and cost considerations should be taken into account to determine the best inventory levels at each of the potential stocking locations in the supply chain. Figure 1.1 represents the life cycle of a LTO which is divided in three periods. The business environment of each period is different and, therefore, a different postponementspeculation strategy fits best. Figure 1.1 summarizes the characteristics of the business environment and the postponement-speculation strategy for each period. Considerable resources are needed to launch a LTO. Investments in marketing, such as advertising and point-of-sale displays, are made before the LTO. Manufacturers reserve capacity to produce the promotional products and retailers count on the LTO to generate traffic in their stores. Thus, during the first 6

Sales Volume

Characteristic of Business Environment

Postponement Speculation Strategy

7

Time

Figure 1.1

• Update forecast with actual data • Stop speculation • Position inventory based on expected variability of demand and the supply chain network structure

• Cost minimization as for a standard product

Growth

Maturity

Period 2

Termination

•Update forecast •Use actual demand •Incorporate local information about the behavior of the demand •Postpone, delay activities •Hold product at least cost •Maintain inventory undifferentiated •Consider using premium lead times

•Obsolescence becomes a key cost driver

Decline

Period 3

Adapting the Postponement-Speculation Strategy Throughout the Life Cycle of a Product

• Forecast a wide confidence interval • Use speculation to position inventory in the supply chain • Positioning inventory forward changes inventory requirements elsewhere

• Considerable investment to launch the limited-time offer • High risk of stockout

Setup

Initiation

Period 1

period of the LTO, not only predictability of demand is low, but also the risk of stockout is high. Managers need to forecast a wide confidence interval and position inventory as close to the end-customer as possible to cope with the risk of stockouts associated with the early phases of the LTO. Therefore, the strategy of choice is speculation. There are two distinctive characteristics of the use of speculation during the first period of the LTO. One is that cost to the supply chain will be higher than if management does not force inventory to be positioned forward in the supply chain. The other characteristic is that using speculation during the initial phase(s) of the LTO is not proportionally as costly as following this strategy throughout the entire LTO. Early in the life cycle there is virtually no risk of obsolescence, so inventory holding costs are lower than closer to the end of the life cycle. In the second period, the perceived uncertainty associated with the performance of the LTO in part is dissipated after observing some actual demand data. Supply chain members reevaluate inventory deployment and the managerial focus becomes cost minimization similar to a standard product. During this period, management does not impose any inventory policy constraints and optimizes inventory locations and levels across the supply chain. During the third period, two factors become dominant: obsolescence and gathering information about termination. If the changes in demand are predictable, then obsolescence could be factored in inventory cost calculations starting from a determined point in time. That is, managers could determine a particular point in time from when supply chain members have to start considering obsolescence. This deterministic view of obsolescence contrasts with defining a stochastic process or a mathematical function to model obsolescence throughout the life cycle of the LTO. The increase in inventory holding costs might affect the original total cost trade-offs and indicate that the use of standard lead times is no longer the least cost alternative. 8

The use of premium, shorter and more expensive, manufacturing and logistics lead times will enable management to postpone by delaying manufacturing and logistics decisions. If manufacturing decisions are delayed, then instead of holding safety stock of finished goods, manufactures will hold safety stock of undifferentiated raw materials. If logistics decisions are delayed, then the transportation of product to locations closer to the end-customer and the geographic differentiation of product will be delayed. In sum, for the third period of the life cycle of the LTO, managers need to update the forecast based on actual demand data and local information about the behavior of the demand. Additionally, since obsolescence becomes a key cost driver, inventory needs to be held at the lowest cost by delaying the forward movement of product as much as possible. This will be achieved by using premium lead times and will enable management to maintain inventory undifferentiated for as long as possible. This research was intended to capture the dynamic nature of management’s decisions during the life cycle of the LTO. It is unlikely that managers plan at one point of time and follow the plan exactly despite changes in the environment. Changing the inventory deployment strategy in such a short time horizon requires the integration of activities beyond the boundaries of a single firm in order to benefit the supply chain as a whole. Dynamic time-based postponement will assist management in the implementation of the combination of speculation and postponement within short time horizons.

THE RESEARCH PURPOSE The purpose of this research was to present the conceptual development of dynamic time-based postponement and empirically test the concept in the 9

context of LTOs. The conceptual development of dynamic time-based postponement was based on the review of the literature presented in Chapter 2 and it is presented in the last section of that chapter. The empirical test of dynamic time-based postponement was performed with actual data from past LTOs in the quick-service restaurant business. As part of this test, a methodology for the implementation of dynamic time-based postponement was developed. The cornerstones for this methodology are to revise the location and levels of safety stocks and to plan for the rest of the offer as many times as needed during the life cycle of the LTO, and to coordinate order placement across the members of the supply chain. It was expected that the empirical test would show that as the LTO progresses safety stock needs to be shifted backwards in the supply chain. This serves as evidence that dynamic time-based postponement works and that it represents a sizable business opportunity. The goal was to integrate recent developments in planning and forecasting LTOs with the application of an optimization-based tool for determining the tactical locations of safety stock across multiple tiers of the supply chain. This approach to management of LTOs provides unique insight because the majority of optimizationbased tools are used as static models. In the context of LTOs, the use of a static model means that the locations and levels of safety stock for the LTO are determined once before the LTO starts and remain the same for the duration of the offer. In this research, the location and levels of safety stocks are assessed several times during the LTO to plan for the rest of its life cycle using the knowledge of the demand gained from earlier phases. This enables supply chain members to plan every phase of the LTO, including disengagement, in contrast to planning for the total duration of the LTO before it starts. By planning the remainder of the LTO as needed throughout its life cycle, the members of the supply chain are able to delay manufacturing and logistics activities. This is in contrast to the way LTOs have been managed in the past, and should lead to more efficient and less costly management of LTOs. 10

RESEARCH OBJECTIVES The specific objectives of this research were: 1. To present the conceptual development of dynamic time-based postponement. 2. To combine data from independent firms in a supply chain, that included two manufacturers, one distributor, and a retailer with multiple retail stores. 3. To determine from these data the size of the business opportunity for the supply chain as a whole. 4. To estimate the potential cost savings for the supply chain based on an optimization-based model that determines the locations and levels of safety stocks across the supply chain. 5. To identify the challenges of implementing dynamic time-based postponement in a multi-company supply chain. 6. To describe other business setting in which dynamic time-based postponement has potential. 7. To identify future research opportunities.

RESEARCH QUESTIONS The research questions for this study were the following: 1. What is the magnitude of the opportunity associated with the implementation of dynamic time-based postponement? 2. How does having better knowledge of demand influence total logistics costs and acquisition costs for the supply chain members? 3. What prevents management from performing holistic analyses of supply chain dynamics? How are these concerns addressed? 11

THE SCOPE OF THE RESEARCH A distinctive characteristic of this research is that it was meant to capture the time-varying behavior of the product and information flows in a supply chain, understanding that a supply chain is formed by a number of independent companies. The analysis of this problem within the boundaries of a single firm is less interesting because of the existence of a central authority, the firm’s top management, who can make coordination of activities simpler. It was expected that this holistic analysis would indicate different inventory positions and levels than those based on traditional decision-making internal to a single firm. Moreover, these differences happen in later phases of the life cycle of the LTO as knowledge of demand is developed. Coordination of order placement is a central element to achieving the expected benefits from dynamic time-based postponement.

LIMITATIONS A limitation of this research, the extent to which the results can be extended beyond the participants of the study, is intimately related to its strengths: the depth of analysis and usability of the methodology developed. Nevertheless, the objective of this dissertation was to present the conceptual development and empirically test dynamic time-based postponement in an actual supply chain context. The companies that participated in the research and provided data might be top performing organizations. The managers from the participating firms not only have demonstrated strong interest in striving for the best for the total supply chain but they have revealed a remarkable commitment to maintaining and fostering the growth of relationships with the other supply chain members. 12

POTENTIAL CONTRIBUTIONS The research makes a number of contributions to knowledge: 1.

It broadens the concept of postponement by showing how postponement can be implemented without affecting the design of the product, the manufacturing process or the supply chain network structure. This means that postponement can be used as a management tool to adapt the supply chain to changing environmental conditions.

2. It represents actual supply chain management research. Most of the research proclaimed as “supply chain management” studies are actually single-firm logistics and operations management studies [7]. This research represents a step forward in the right direction. 3. It illustrates how an optimization-based tool can be used to facilitate supply chain management. Optimization-based tools have been used successfully to incorporate objectivity into decision making [6]. Using an optimization-based model provides objectivity to decisions that effect supply chain members differently and could be used as a starting point to negotiate risk and reward sharing. 4. It includes the development of a method for managing LTOs more efficiently by having the members of the supply chain practice collaborative replenishment. This contrasts with traditional practices in which suppliers and distributors are limited to following the static inventory policies that the buyer considers appropriate. 5. It extends the application of an optimization-based decision support tool to determine the locations of safety stock and levels by including following features: parameters that are updated within short periods of time; inventory carrying 13

cost percentages that vary by location in the supply chain [8]; the increasing risk of obsolescence as the LTO approaches the termination stage; and the use of transshipments and rush orders to avoid customers facing stockouts.

ORGANIZATION The remainder of the dissertation is presented in Chapters 2 through 5. Chapter 2 is a review of the relevant literature to support the development of the research and includes literature in the management of LTOs, management of promotions and products with short life-cycles; supply chain management; modeling inventory management problems in the supply chain; and postponement. This literature has been published in academic journals from a number of fields of study, including supply chain management, logistics, marketing, operations management, operations research, information technology, management, and production economics. Chapter 2 also contains the conceptual development of dynamic time-based postponement. In Chapter 3, the research design is presented. This includes the operationalization of the constructs, the description of the research setting, a description of the optimization algorithm, the data gathering process, and the procedures to be used for the analyses that address the research questions. The results of data collection, including the description of the database developed from each of the research participants, are presented in Chapter 4. Also, the analyses that support the research propositions are part of this chapter. Chapter 5 contains a summary of the results and conclusions, and the managerial implications of the results. Suggestions for future research and a concluding commentary are presented.

14

References [1]

Lee, Hau and Corey Billington, “Designing Products and Processes for Postponement” in Management of Design: Engineering and Management, Kluwer Academic Publisher, 1994, pp.107-122.

[2]

Alderson, Wroe, Marketing Behavior and Executive Action: a Functionalist Approach to Marketing Theory, Homewood, Illinois: Richard D. Irwin, Inc., 1957

[3]

Fisher, Marshall L., Janice H. Hammond, Walter R. Obermeyer and Ananth Raman, “Making Supply Meet Demand in an Uncertain World,” Harvard Business Review, Vol. 72, No. 3 (1994), pp. 83-89.

[4]

Fairfield, Daren, Karen Ribler and Ralph Drayer, “CPFR and Collaboration – Our Industry Opportunity”, Presentation to the Grocery Manufacturers of America IS/LD Conference, April 2001.

[5]

Haedicke, Jack and Jeff Mitchell, “The ROIT Benefits of e-Marketplaces”, Presentation to the Grocery Manufacturers of America IS/LD Conference, April 2001.

[6]

Camm, Jeffrey D., Thomas E. Chorman, Franz A. Dill, James R. Evans, Dennis J. Sweeney and Glenn W. Wegryn, “Blending OR/MS, Judgement, and GIS: Restructuring P&G’s Supply Chain,” Interfaces, Vol. 27, No. 1 (1997), pp. 128-142.

[7]

Tan, Keah-Choon, Vijay R. Kannan, Robert B. Handfield and Soumen Ghosh, "Supply Chain Management: An Empirical Study of Its Impact on Performance," International Journal of Operations & Production Management, Vol. 19, No. 10 (1999), pp. 1034-1052. and Narasimhan, Ram and Soo Wook Kim, "Information System Utilization Strategy for Supply Chain Integration," Journal of Business Logistics, Vol. 22, No. 2 (2001), pp. 51-76.

[8]

Mallen, Bruce, “Functional Spin-Off: A Key to Anticipating Change in Distribution Structure,” Journal of Marketing, Vol. 37 (1973), pp. 18-25.

15

CHAPTER 2 CONCEPTUAL FOUNDATIONS: A REVIEW OF THE LITERATURE The review of the literature relevant to the management of LTOs serves as the theoretical background for this research. The areas reviewed are: management of LTOs such as seasonal and fashion products, supply chain management (SCM), modeling inventory management problems in the supply chain, and postponement. The last section in the chapter contains the conceptual development of dynamic time-based postponement, the first objective of this dissertation. In the first section, the importance of LTOs is assessed and the issues related to the management of LTOs that have been identified and studied in the past are summarized. This section focuses on the management of the product flow throughout the life cycle of LTOs and includes planning and forecasting, ordering decisions, replenishment, markdowns, and disengagement of the LTO. Second, supply chain management is defined and a brief comparison of three frameworks in supply chain management is shown. This research accepts the view of supply chain management provided by The Global Supply Chain Forum and the goal is to contribute to the development of the Demand Management process, one of the eight SCM processes identified by The Forum [1]. The Forum’s view of SCM is the most comprehensive framework available and provides a structure to manage the relationships with other supply chain members. Since the empirical test of dynamic time-based postponement includes the analysis of whether safety stocks are shifted backwards in the supply chain as the LTO progresses, in the third section, the key aspects of modeling inventory management problems in the supply chain are described. This section contains the description of the approach to modeling the inventory management problem 16

described in the business opportunity. The optimization model used for the analysis is described in the next chapter. The fourth section includes a review of research in postponement and addresses studies in the fields of logistics, operations management, and operations research. The fifth section of the chapter presents the conceptual development of dynamic time-based postponement which, together with the empirical test of the concept, is the purpose of this research.

MANAGEMENT OF LIMITED-TIME OFFERS During a limited-time offer (LTO), a specially developed item is available for a limited period. The item can be a product specifically developed for the LTO, a standard product offered in a particular presentation or a bundle of standard products. Limited-time offers are common business practices in many industries such as consumer packaged goods (CPG) (also called fast moving goods in Europe) and apparel in which many products are seasonal or fashion items. The demand for short lived products, such as personal computers and consumer electronics, can be treated as LTOs as well. LTOs are unique in that demand for the products in the offer change in a rather short time horizon. While demand might be difficult to predict, when the changes in the demand will occur can be easier to predict. Figure 2.1 illustrates typical phases in the life cycle of a LTO in which three periods have been identified. Usually, Period 1 is characterized by filling up the pipeline with products and learning how to handle them. During the setup and initiation phases, product starts flowing and supply chain members are learning to deal with the new products; for example, retail store staff needs to learn how to assemble and replenish products in a pointof-sale display; cooks need to learn how to cook the promotional meal. During the growth phase, some demand data are observed which may be the response to advertisements. During the maturity phase, Period 2 in Figure 2.1, 17

Period 1

Period 2

Units of Product Sold

Maturity

Initiation

Decline

Growth Termination & Disengagement

Setup & Training

>2 weeks

Period 3

1 Week

5 weeks

2+ weeks

Time

Figure 2.1 Typical Phases in the Life Cycle of a Limited-Time Offer

the highest demand is observed, the focus is on product availability [2], and advertising is at its highest. Period 3 in Figure 2.1 includes the decline, and termination and disengagement phases, which are characterized by products losing value or risk of obsolescence increasing. Despite the focus on the management of LTOs, the conceptual development and findings contained in this dissertation are readily applicable to standard products for which demand changes in a short time horizon, and when these changes are predictable. The Newsboy Problem offers a starting point to study the management of LTOs [3]. The problem the newsboy has every day is to decide how many newspapers to order for the next morning. This is challenging because if the newsboy runs out of newspapers, there is not time to reorder newspapers and he will lose 18

sales. On the other hand, if the newsboy does not sell all the newspapers he ordered, the remaining newspapers will not sell because they become obsolete the next day. The unsold newspapers add to the costs, reducing the newsboy’s profits. Managing LTOs presents a similar challenge, which depends on the characteristics of the LTO such as the duration of the selling season, the replenishment cycle time, and the speed at which the product loses value as the season comes to an end. In general, managers find it difficult to forecast demand accurately throughout the duration of a LTO. However, managers can predict accurately when demand is going to change. That is, managers may know when demand will increase as well as when it will decrease. This is the fundamental difference between a LTO and other situations in which demand is wholly uncertain. For example, in the case of a special meal offered by a pizza restaurant, advertising shapes the demand; when the featured pizza is advertised on TV, it is expected that demand will be higher than when the pizza is not advertised. Since the changes in demand are predictable, managers should consider these changes when planning. There are different types of LTOs that are used for different objectives; however, the effective and efficient management of any kind of LTO requires proactive demand management and coordination of activities throughout the supply chain. This section on LTOs is organized as follows. First, the uses of LTOs are described. Second, the challenges in managing the product flow for LTOs are presented. In the third section, the latest developments related to the management of LTOs are summarized. The last section presents the area in which this dissertation is intended to contribute.

USES OF LIMITED-TIME OFFERS Some businesses use LTOs as a strategic marketing tool. Others use limitedtime price discounts to liquidate end-of-season products. Many use price discounts 19

to “flush the pipeline” to look fit on end-of-quarter reports, which is, at a minimum, controversial. Retailers in the CPG industry frequently use LTOs in the form of advertising staple goods to consumers at very attractive prices. The retailers’ motivation is to attract consumers to their stores. Although LTOs are popular, there are many questions about how the use of LTOs benefits a supply chain as a whole. For example, in the case of limited-time price discounts at grocery stores, the objective for the retailers is to generate traffic to their stores, expecting that consumers will buy discounted-priced products and others at regular prices. Consumers will buy more of the discounted-price products, but do they increase consumption of these products? What are the benefits for the manufacturer and its suppliers? Consumers advancing purchases but not consuming more will not benefit the supply chain in the long run. The effect of promotions, particularly price discounts, on consumption and their effect on brand loyalty, repurchase intention and category growth received considerable attention from researchers in Marketing. Some agree that the promotion’s effect on consumption stems from higher household inventory levels, which leads to consumers having fewer stockouts at home and, thus, increased usage [4]. In those product categories in which price promotions foster usage, they can be used to make the category grow [5]. The effect of advertised price cuts on the acceleration of purchases might have a disparate effect among types of consumers; the effect is stronger on users that usually consume more than others [6]. Also, it has been found that price promotions have an immediate effect on brand purchase, but their effect on future brand preference is weaker [7], suggesting that price cuts might not have a sustainable beneficial effect. Similarly, price promotions might affect short-term category demand, but rarely exhibit a lasting effect on category growth [8]. The study of the effect of price promotions on consumer behavior is beyond the scope of this research. However, this form of LTO might have major implications 20

on the normal supply chain dynamics. That is, end-customers sensitive to discounts will change their purchasing behavior, affecting the shape of the end-customers’ demand. If this change in the demand is considered for planning and plans are not shared throughout the supply chain, management at each tier will react to the next-tier’s demand, which will result in the amplification of demand. That is, the end-customers’ demand will be amplified as the demand signal is transmitted upstream in the supply chain [9]. Similarly, the use of price promotions to “flush the pipeline” stems from an undesirable management behavior: to make a firm look better on periodic financial reports. From a SCM perspective, the best-case scenario is for this behavior to stop. Fostering next-tier demand disrupts the supply chain dynamics by disturbing the normal flow of products. This practice usually results in having excessive inventory of the products that end-customers do not want and having stockouts of the products that end-customer do want. However, speculative purchases might generate short-term benefits to a firm willing to buy products at a discount or with rebates, and sell the products at the regular price which is referred to as price gaming. Frequently, the practice of pushing inventory forward to “flush the pipeline” is reinforced by incentives to sale representatives that are based on revenues rather than account profitability. This kind of practice hinders the overall supply chain efficiency [10]. Ideally, financial markets would stop rewarding firms’ performance solely on periodic reports that represent a set of infrequent snapshots of an ongoing process. This amplification of demand effect frequently results in having the wrong quantities of products at the wrong places at the wrong time. Therefore, the implementation of price promotions, and any LTO in general, should not be considered as an issue affecting a single firm in isolation, but should be coordinated across the key supply chain members since benefits for one firm might vanish when considered in the supply chain holistically. 21

This research focused on LTOs used as a strategic marketing tool; these LTOs can be a central element for the long-term success of a firm and the supply chain as a whole. Nevertheless, it is expected that managers dealing with any type of LTO or short-lived products will find value in the implementation of dynamic timebased postponement.

THE CHALLENGE OF MANAGING LTOs Managing LTOs effectively is challenging. Orders have to be placed in advance of the highest selling period, which is usually short relative to the time needed to make products flow through the supply chain. For example, an apparel retailer has an average replenishment cycle time from order receipt to delivery of three to four months [11] for a selling season that might be two months long. Moreover, many products become obsolete in a short period or lose value very quickly. For example, some consumer electronics, which may be managed as LTOs, might become obsolete in a few weeks [12]. In some industries, prices are marked down as the end of the season gets closer and, ultimately, products are diverted to secondary channels. It has been estimated that markdowns are generally 40% to 50% in apparel products and 15% to 25% in CPG items [13]. Planning for a LTO is difficult. Achieving the target customer service level throughout the offer is likely to be very costly given that the level of uncertainty of demand usually is extremely high before the LTO is launched. This is particularly true when LTOs are designed to capture the customers’ attention by offering specially designed products which usually are original. Thus, it is uncertain how customers will respond to the LTO. Product availability is a critical element of a LTO. No generalizations can be made about end-customers reactions to stockouts because these reactions depend on situational factors [14], stockouts might render the marketing efforts inefficient [15]. Consumers at a grocery store face average stockout levels of 10% 22

to 30% on regular items [16] and of 20% on advertised items [17]. The high stockout levels facing the end-customer that grocery retailers are experiencing might cost several million dollars a year to manufacturers and retailers [18]. The challenges associated with managing LTOs effectively and efficiently represent a fertile ground for researchers studying ways to integrate activities across members of the supply chain.

MANAGEMENT THROUGHOUT THE LIFE CYCLE OF A LTO The management of LTOs is not a new business problem. There are several contributions focused on different aspects of the problem. These contributions can be structured in three phases in the management of LTOs: beginning, replenishment, and end. The beginning of a LTO is about forecasting and placing the first order; and the end of a LTO deals with markdowns and diverting products to secondary markets. The replenishment phase centers on the management of the product flow throughout the duration of the offer. In general, previous research has focused on the beginning and end of LTOs; dynamic time-based postponement is meant to provide a contribution to the replenishment of LTOs.

Forecasting and Initial Ordering Forecasting for LTOs has received considerable attention from academics. For example, Cooper et al. [19] developed a regression-style model to develop a tactical forecast for planning LTOs from a retailer’s perspective in the CPG industry. The authors’ contribution is not only the model development, but they also indicate that the unit of analysis should be the LTO . Traditionally, shopping-trip data have been used for forecasting LTOs, but these data only represent a sample of the data in the LTO. The fashion industry also presents a fertile ground for research in LTOs. For instance, Eppen and Iyer [20] developed a model to help buyers determine the 23

order size and how much product to divert to secondary channels. Managers develop an initial forecast before the selling season starts and, as the demand signal is observed, the forecast is updated. The authors develop a heuristic to solve an updated version of the Newsboy Problem, which includes a Bayesian model for updating the probability distribution of the problem. The opportunity from updating the forecast throughout the LTO is frequently referred to as the learning effect. This has been defined as “the generation of significantly more accurate forecasts of future demand distributions” [21] and can be combined with postponement. The postponement of decisions, which is explained in more detail in the next section, enables managers to observe the demand of a greater portion of the LTO and to update the forecast for the remainder of the LTO. Updating the forecast with demand data of the actual LTO or selling season results in a much more accurate prediction of demand. For example, Fisher et al. introduced Accurate Response (AR) as an extension of Quick Response (QR) and Just-in-Time (JIT) [22]. AR is aimed at improving forecasting and planning, and combines these improvements with postponement of decisions until some market signal has been received to make the production process more efficient. The authors’ key contribution is to show how managers can identify the items whose sales can and cannot be predicted which enables management to follow different strategies for each group of items. Manufacturing has to start in advance to the selling season and managers will make ordering decision earlier for the items whose sales are easy to predict, and delay manufacturing and logistics decisions for the items for which the uncertainty is high. The learning effect is central to the conceptual development of dynamic time-based postponement presented in the last section of this chapter. 24

Markdowns During a LTO Markdown decisions have received considerable attention from academics. In particular, several models have been developed to support markdown decisions. Smith and Achabal [23] developed a model to estimate clearance pricing when sales are sensitive to inventory levels such as in fashion retail stores. The analysis suggests that it is better to have higher prices at the beginning of the offer and deeper discounts as the season ends. The authors estimated considerable incremental revenues from using the model. Smith et al. [24] developed an integer program to predict how sales will be affected by a combination of environmental factors such as seasonal effect, advertising, and price. The model maximizes the profit from the LTO subject to resource constraints and a set of possible market scenarios. The use of this model assists buyers in retail chains to plan markdowns and advertising throughout the duration of the promotion. Mantrala and Rao [25] present the implementation of a decision support system developed to help retail-store buyers of fashion goods determine order sizes and markdowns. The system uses store policies and sales estimates to determine an optimal order quantity which have to be made before the LTO starts. Throughout the duration of the LTO, the system is used to determine the optimal markdown strategy for the remainder of the LTO based on revised estimates of demand and the current inventory position.

Replenishment Throughout a LTO Managers in a variety of industries responded to the challenge of offering high levels of product availability throughout a LTO by developing procedures to react quickly to changing demand conditions and rely less on forecasts. These procedures are called Automatic Replenishment Programs (ARP) and are focused on a dyad: the buyer-seller relationship. 25

ARPs were developed to restock products based on actual sales at the retailer or usage by the buyer [26] rather than based on historical data of the nexttier’s orders. ARPs are usually industry-wide initiatives, which are promoted by independent associations. The role of these associations is to foster standardization of data and procedures to facilitate communication among supply chain members. In the mid-1980s, apparel retailers and manufacturers developed QR as a strategy to deal with the uncertainty inherent in their industry. The intent of QR is to define manufacturing schedules closer to the selling time. QR is aimed at reducing uncertainty by producing a small initial amount of products, performing consumer preference tests, and re-order based on actual sales to end-customers [27]. A similar approach used by manufacturers and retailers in the CPG industry is called Efficient Consumer Response (ECR). ECR is formed by four strategies: efficient store assortment, efficient replenishment, efficient promotions, and efficient product introduction. The strategies for effective replenishment and effective promotions of ECR are related to the management of LTOs in this study. Efficient replenishment means using information technologies such as EDI and bar coding to transmit sales and inventory levels from the stores to the manufacturers’ plants [28]. Effective management of promotions requires the elimination of undesirable management practices that disturb the normal dynamics of the supply chain. Traditionally, when a manufacturer offers a promotion in the form of a price reduction for an item, the retailers buy more than what they actually expect to sell during the offer. This is called forward buying [29]. When a manufacturer offers a deal in one geographic region and retailers forward buy and transship the products to other regions, this usually is called diverting [30]. Forward buying alone or combined with diverting causes the manufacturer to observe a demand signal considerably different than actual sales to endcustomers. These practices introduce considerable error into the manufacturers’ 26

forecasting processes and they cause the “amplification of demand effect” [31], undermining the normal flow of products in the supply chain. Like QR, the objective of ECR is to follow demand more closely and achieve higher levels of product availability at the retail stores. However, documentation on both QR and ECR does not offer much detail as to how to deal with LTOs beyond using recent transactional data rather than historical data at the buyer-seller level. Managers of the apparel and CPG industries regard Collaborative Planning, Forecasting, and Replenishment (CPFR) as the next evolutionary step for QR and ECR. A review of the CPFR processes developed by the Voluntary Interindustry Commerce Standard (VICS) association (see Figure 2.2) reveals that the Business Model is mostly focused on the “collaborative planning” and “collaborative forecasting” sections, but it lacks definition of how “collaborative replenishment” is implemented. The last step of the nine-step process is Order Generation and is not clear how this step differs from a traditional environment in which replenishment is not collaborative. The Guiding Principles of CPFR suggest that the aim is to integrate the supply chain from end-customer to original supplier, but the CPFR process seems to focus on the dyad. CPFR is a relatively new initiative and it has been implemented in only a few firms. Academic studies in CPFR are scarce; Aviv [32] modeled the effect of collaborative forecasting on the performance of a supplier-retailer dyad and performed a sensitivity analysis to estimate its benefits. The author modeled two settings. In the first, called local forecasting, all static data such as the demand process, the individual forecasting process, and the cost structure, are shared between the supplier and the retailer. In the second model, called collaborative forecasting, the process is jointly maintained and a single forecast is generated. The author concluded that the additional benefits from practicing collaborative forecasting rather than local forecasting depend on whether each member of the supply chain contributes unique knowledge to the forecasting process. 27

Source: Voluntary Interindustry Commerce Standards (VICS) association: http://www.cpfr.org/ ProcessModel.html CPFR © 1998,

Figure 2.2 Collaborative Planning, Forecasting and Replenishment 28

Stank et al. [33] used self-reported data to support the correlation between the levels of CPFR and implementation of operating process change and information system capabilities. Contrary to the normal expectation, the authors did not find a statistically significant association between the use of CPFR and the achievement of performance goals in logistics such as fewer stockouts, higher inventory turns, and reduced inventory holding cost. These are benefits from implementing CPFR which are usually reported as benefits to a dyad: a buyer and a seller. An often quoted example of a successful implementation of CPFR is the case of Wal-Mart and Warner-Lambert [34]. In general, the number of success stories on the implementation of CPFR is limited. Barratt and Oliveira [35] identified possible issues that prevent CPFR for being widely used or that constrain its full potential. The authors contend that despite the existence of a detailed process framework to implement CPFR, its adoption has been limited. They reviewed the literature and surveyed the 220 members of the VICS CPFR contact list to identify the barriers to the implementation of CPFR. The relevant barriers to this dissertation that the authors report are: ineffective replenishment in response to demand fluctuation; ineffective planning using visibility of sales data; difficulty managing the forecast exception/review processes; promotions (or LTOs) are not jointly planned; trading partners are not working together to ensure consistency in delivery performance; and, sales data and order forecasts are not communicated internally. In conclusion, managers have realized that working in isolation might offer a short-term gain for some supply chain members, but this does not lead to the best outcome for the supply chain as a whole and in the long-run the inefficiencies in the supply chain likely will lead to inefficient business performance to the firm. Efforts to develop standard practices are mostly focused on planning, forecasting, and ordering. No detailed description about integrating replenishment activities in the supply chain was found that specially extends beyond a dyad. This may be because CPFR is meant to provide a framework for implementation but is not meant 29

to prescribe implementation. It seems that managers may benefit from a study that describes the benefits from managing LTOs in an integrated fashion across the supply chain. This dissertation contributes to the elimination of some of these barriers by providing an objective method to perform a holistic analysis of the replenishment process throughout the duration of a LTO.

SUMMARY OF MANAGEMENT OF LTOs The review of both academic literature and trade publications reveals three key issues related to the management of LTO. First, the majority of the research is focused on the beginning and end of LTOs, and few efforts have been dedicated to the management of replenishment in LTOs. The second issue is that the so called “collaborative replenishment” actually represents the implementation of static inventory policies set by the channel captain with which suppliers adopt. Furthermore, the current view of collaborative replenishment extends, at most, to a dyad: a buyer and a seller. Finally, the “learning effect” represents an opportunity and extending the reach of the learning effect beyond the individual firm has sizeable potential to the supply chain as a whole. The management of the replenishment of products throughout the LTO is a fertile ground for researchers and practitioners; researchers have the opportunity to develop new methodologies for the management of LTOs and practitioners willing to integrate activities across the supply chain may be able to markedly improve the performance of LTOs. In summary, the key issues identified in the section Management of LimitedTime Offers in which the conceptual development of dynamic time-based postponement stems from are: z

Extending the “learning effect” across the supply chain has potential.

z

Accuracy of predictability of demand is different at each phase of the LTO. 30

z

Static inventory policies are adopted for the management of LTOs.

z

Replenishment through LTOs is a fertile ground for research.

SUPPLY CHAIN MANAGEMENT Supply chain management (SCM) is a cornerstone to the realization of collaborative replenishment. SCM is about managing the relationships with other supply chain members. There is little agreement on what SCM really is; practitioners and educators from different fields of study use the term “supply chain management” as a synonym for logistics, operations management, or purchasing. This confusion might stem from the fact that supply chain management is a broader concept than logistics or operations management, and that logistics and operations management activities occur in the supply chain. Adding to this confusion, since logistics is a boundary spanning function [36], the precise frontier of logistics might not always be clear. The definition of SCM provide by The Global Supply Chain Forum (GSCF) is accepted for this research: Supply Chain Management is the integration of key business processes from end user through original suppliers that provides products, services, and information that add value for customers and other stakeholders [37]. There are other definitions of SCM. Frequently, the definition of SCM is biased toward a functional expertise, for example Simchi-Levi et al. define SCM as follows: Supply chain management is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that 31

merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system wide costs while satisfying service level requirements [38]. This definition resembles the definition of logistics management provided by the Council of Logistics Management: The process of planning, implementing and controlling the efficient, cost effective flow and storage of raw materials, in-process inventory, finished goods, and related information from point-of-origin to pointof-consumption for the purpose of conforming to customer requirements. In fact, Simchi-Levi et al. assert: We do not distinguish between logistics and supply chain management. Indeed, our definition of supply chain management is similar to the definition of logistics management given by the Council of Logistics Management [39]. A research team affiliated with The University of Tennessee offered the following definition of SCM: The systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole [40]. 32

This definition has become recently the definition of SCM accepted by the Council of Logistics Management [41]. This view of SCM agrees with that of the GSCF in some aspects. First, it suggests that SCM is a broader concept than logistics or operations management. Second, corporate functions will still hold the functional expertise while functional activities are integrated across organizations first within the firm and then across firms. However, the definition itself is not sufficient to assist managers in the implementation of SCM. The objective of the GSCF is to develop a set of SCM processes to assist managers in implementation of SCM. Even though the importance of each business processes is contingent upon the role of each firm in the supply chain, having a standard set of business processes will help managers integrate activities across firms.

SUPPLY CHAIN MANAGEMENT FRAMEWORKS The SCM framework provided by The GSCF operationalizes the management of relationships in the supply chain by defining eight business processes to be implemented across organizations (Figure 2.3). The eight business processes are: z

Customer Relationship Management

z

Customer Service Management

z

Demand Management

z

Order Fulfillment

z

Manufacturing Flow Management

z

Supplier Relationship Management

z

Product Development and Commercialization

z

Returns Management A firm operates within a supply chain; the fact that management has not

implemented formally a SCM approach does not mean the firm operates in isolation. Relationships with customers and suppliers exist, products are manufactured and flow to the end-customers, and some data are shared in some way. At present, 33

34

Tier 2 Supplier

Tier 1 Supplier

R&D

PRODUCT FLOW Finance

Marketing

RETURNS MANAGEMENT

PRODUCT DEVELOPMENT AND COMMERCIALIZATION

SUPPLIER RELATIONSHIP MANAGEMENT

MANUFACTURING FLOW MANAGEMENT

ORDER FULFILLMENT

DEMAND MANAGEMENT

CUSTOMER SERVICE MANAGEMENT

CUSTOMER RELATIONSHIP MANAGEMENT

Production

Purchasing

Logistics

Manufacturer

Information Flow

Customer

Consumer/ End-Customer

Supply Chain Management: Integrating and Managing Business Processes Across the Supply Chain

Figure 2.3

Source: Adapted from Douglas M. Lambert, Martha C. Cooper and Janus D. Pagh, “Supply Chain Management: Implementation Issues and Research Opportunities,” The International Journal of Logistics Management, Vol. 9, No. 2 (1998), p. 2 as reported in Croxton, Keely L., Sebastián J. García-Dastugue, Douglas M. Lambert and Dale S. Rogers, "Supply Chain Management Process," The International Journal of Logistics Management, Vol. 11, No. 2 (2001), pp. 13-36.

Supply Chain Management Processes

some companies have formally implemented portions of this SCM framework. Nevertheless, after some inspection, management may find that many activities performed in their companies will resemble parts of this framework. Their next objective would be, then, to formally establish the processes and extend them to key members of the supply chain in which they participate. The framework provided by The GSCF is not the only one available. The Supply-Chain Council publishes and periodically updates the “Supply-Chain Operations Reference-model” (SCOR). Version 5.0, the latest release (a), has five business processes to be implemented first within the firm and eventually extended across the supply chain. The processes are Plan, Source, Make, Deliver, and Return [42]. Plan, source, make, deliver are the original processes and returns is an addition to the framework mentioned in Version 4.0 and described in Version 5.0. This framework is similar to the one provided by the GSCF in some aspects. The most relevant similarity is that the objective of the Supply-Chain Council and SCOR is to provide a standard set of business processes to facilitate coordination among firms in the supply chain. In general, SCOR is operationalized to a greater degree in contrast to the current state of development of the GSCF framework. For example, SCOR includes detailed metrics for the processes. The use of SCOR starts by seeking operational efficiency within the firm and it is meant to be extended to other supply chain members to achieve operational efficiency across the supply chain. Nonetheless, a review of the metrics reveals that the orientation of SCOR is internal to a single firm because, for example, there is no metric that includes considerations external to the firm. The bias toward internal operational efficiency is verbalized in the boundaries defined for the model: SCOR does not attempt to describe every business process or activity, including: sales and

a) SCOR Version 6.0 was released a few days before the publication of this dissertation.

35

marketing, research and technology development, product development, some elements of post-delivery customer support [43]. What SCOR does include is traditionally called operations management. In this case, supply chain management seems to be the new name for operations management in the supply chain. Researchers from Michigan State University (MSU) have dedicated some efforts to SCM as well. Their initial view of SCM was: [The extension of logistics integration]... through external integration, is referred to as supply chain management... [44]. It since has evolved to: The scope of what is involved in a supply chain is clearly broader than logistics [45]. Supply chain management consists of firms collaborating to leverage strategic positioning and to improve operating efficiency [46]. The SCM framework suggested by scholars from MSU includes eight business processes as the foundation of supply chain management; these are: Product Design/Redesign, Process Design/Redesign, Measurement, Capacity Management, Plan, Acquire, Make, Deliver [47]. The MSU framework includes four of the five processes included in the latest release of the SCOR model (b). The process that is not included is returns, added to

b) The SCOR model has been updated periodically since its introduction in 1996. In 2000, when the description of MSU framework was published, SCOR Version 4.0 had four processes: plan, source, make, and deliver. The fifth process, return, was added to SCOR Version 5 in 2001. SCOR Version 6.0, the latest release of the model, was published in 2003.

36

the SCOR model after the article with the description of the MSU framework was published. The MSU framework, however, suggests that SCM is a broader concept than that specified in the SCOR model by including, for example, product design and capacity management. These two processes are included in the GSCF view of SCM as product development and commercialization, and demand management, respectively. The MSU and the GSCF frameworks differ in that, for example, the GSCF members view measurement not as a process, but as a management component since it is an integral part of every process rather than an element by itself [48]. Other authors that defined SCM as something different from operations management or logistics do not provide a framework for its implementation and, thus, the review is beyond the focus in this section of the dissertation. As noted, the GSCF’s view of SCM is broader than operations management and logistics outside the firm. For example, early involvement of customers and suppliers in the product development and commercialization process is central to successful product roll-out [49] which also has been identified in the literature [50]. Product development and commercialization affects, for example, demand management, order fulfillment, and manufacturing flow. In short, the product development process needs to be included in the SCM framework since its effects on other processes need to be considered. Thus, a SCM framework should assist managers with the integration of all activities in the supply chain as well as show them how each business process interacts with other processes and with the corporate functions in which the functional expertise resides. Certainly, this extends beyond logistics and operations management.

SUPPLY CHAIN MANAGEMENT AND THE MANAGEMENT OF LTOs The GSCF’s view of SCM is appropriate for this research given that this framework acknowledges the need for explicitly treating the management of the 37

demand as a business process extending beyond the boundaries of a single firm, which will enable the implementation of dynamic time-based postponement. This research is intended to contribute to the further development of the SCM framework by exploring ways to implement the demand management process. Figure 2.4 shows the demand management process at the strategic and operational levels, the subprocesses for each level, and the interfaces with other processes. At the strategic level, management defines how the operational process is going to be implemented. The strategic process will be executed at regular periods of time, or as part of changes in the environment, business practices or technology, such as the implementation of an advanced planning system or the development of closer relationships with customers or suppliers. The operational process is the actualization of demand management. The demand management process is about forecasting and synchronizing [51]. Particularly, this research is part of the analysis needed at the strategic level for the subprocesses: determine forecasting approaches, plan information flow, and determine synchronization procedures. The objective of the strategic process is to provide a blueprint for implementing the operational process. As part of designing the operational process, managers evaluate alternative solutions. This research can be considered the evaluation of one possible way to implement the operational demand management process for LTOs. Specifically, for the first subprocesses at the strategic level, determine forecasting approaches, the selection of the forecasting algorithm itself, is beyond the scope of this research. However, this study includes recommendations about the data to input into the forecasting process and the frequency of forecasting throughout the life cycle of the LTO as well as how to extend the reach of the “learning effect”, described in the previous section, beyond the individual firm to other members of the supply chain. Second, this research is expected to show what information should flow across supply chain members to synchronize 38

39 Returns Management

Product Development & Commercialization

Supplier Relationship Management

Manufacturing Flow Management

Order Fulfillment

Customer Service Management

Customer Relationship Management

Process Interfaces

Measure Performance

Reduce Variability and Increase Flexibility

Synchronize

Forecast

Collect Data/Information

Operational Sub-Processes

Figure 2.4 — The Demand Management Process

Source:Croxton, Keely L., Douglas M. Lambert, Sebastián J. García-Dastugue and Dale S. Rogers, "The Demand Management Process," The International Journal of Logistics Management, Vol. 12, No. 2 (2002), pp. 51-77.

Develop Framework of Metrics

Develop Contingency Management System

Determine Synchronization Procedures

Plan Information Flow

Determine Forecasting Procedures

Determine Demand Management Goals and Strategy

Strategic Sub-Processes

manufacturing and logistics activities. Finally, the focus of this study is the development of synchronization procedures to efficiently manage LTOs. This was accomplished by a new use of an optimization model to determine the tactical location of safety stock across the supply chain including the setting of inventory levels to the degree of predictability of demand and the risk associated with products becoming obsolete. Finally, it is worthwhile to note that this research would be less interesting if the setting was not a supply chain formed by a set of independent organizations. The objective of this research is not only to find the most efficient solution given the network structure, but to investigate the managerial challenges and concerns a holistic analysis presents to the members of the supply chain. In summary, the key issues identified in the section Supply Chain Management in which the conceptual development of dynamic time-based postponement stems from are: z

SCM is about managing the relationships across members of the supply chain.

z

One of the SCM processes is demand management which is about forecasting and synchronizing.

z

Demand management has potential to extend the “learning effect” beyond the single firm and, thus, to assist in the management of LTOs.

MODELING INVENTORY MANAGEMENT IN THE SUPPLY CHAIN There are numerous studies in modeling inventory management in the supply chain. An integrative review of that literature is beyond the scope of this dissertation. However, the identification of the key characteristics of inventory management models is included to help describe the research setting in which the empirical test of dynamic time-based postponement was performed. The test, as described in 40

Chapter 3 – Research Design, shows that inventory can be shifted backwards in the supply chain as predictability of demand increases. The objective of this section is to describe the main characteristics of inventory problems in the supply chain and describe the published optimization model used in the research. In order to classify inventory system models, the first distinction to be made is whether a model is developed to find the theoretical optimality, or whether the focus is on numerical tractability and applicability [52]. The applicability of a model refers to its usability in practice [53]. Since this dissertation represents a new application of postponement, the focus was on models that can be used in practice. For modeling purposes, a supply chain can be represented by nodes and arcs. A node, also called a stockpoint, represents a processing unit such as a work center, a manufacturing line, or a manufacturing or distribution facility. Each node is a potential location of inventory or stockpoint. This characteristic helps to determine what should be represented as a node when modeling. The simplest inventory model has only one stockpoint which is located between a supply process and a demand process (see Figure 2.5.1). Nodes are connected by arcs, also called links. Practically, any flow of products can be modeled as a set of nodes connected by arcs. The parameters of nodes and arcs are , for example, processing time, added cost to the product, and cost of holding inventory. A supply chain with multiple nodes is referred to as “multi-stage” [54] or “multisite” [55] system. In a multi-stage system, a node that is connected to a downstream node shows that the former is a supplier to the downstream node. The need to understand the dynamics of multi-stage inventory systems stems from the fact that the inventory level needed at one node, or stage, is affected by the behavior of the supplier nodes such as the length of the replenishment time [56]. Modeling the 41

42

Demand Process Flow of Demand

Flow of Product

Flow of Product

Flow of Product

Flow of Product

Stockpoint

Flow of Product

Figure 2.5.6 General System

Stockpoint

Figure 2.5.3 Distribution or Divergent System

Note: The supply and demand processes, and arrows of the flow of demand are omitted.

Stockpoint

Figure 2.5.2 Multi-stage Serial Inventory System

Figure 2.5 — Modeling Inventory Management in the Supply Chain

Source: Adapted from Zipkin, Paul Herbert, Foundations of Inventory Management, Boston, MA: McGraw-Hill, 2000, pp. 7,109 and 110.

Stockpoint

Figure 2.5.5 Spanning Tree or Tree System

Stockpoint

Figure 2.5.3 Assembly or Convergent System

Note: Usually, the supply and demand processes, and arrows of the flow of demand are not included, as in the next figures.

Supply Process Stockpoint Flow of Product

Figure 2.5.1 Simple Inventory Model

product flow in a supply chain is complex, thus, frequently the problem is described as a serial system which is the simplest multi-stage inventory model. In a serial system, each node is supplied by one node and supplies to one other node (Figure 2.5.2). Multi-stage inventory models can have convergent or divergent structures [57]. Figure 2.5.3 shows that a convergent structure, also known as an assembly system, is that of an assembly setting in which many supply nodes provide parts to a node which converts the parts into one end-item. A divergent structure, which is the mirror image of the convergent as shown in Figure 2.5.4 and is also known as distribution systems, is that in which a distribution node is supplied by exactly one supplier node and supplies more than one downstream node. In contrast to convergent structures, in divergent structures there are several raw materials and only one end-item. More complex systems are modeled as a spanning tree, also called “arborescent” [58] or “tree system” [59]. A spanning tree is formed by n nodes and (n-1) undirected arcs (see Figure 2.5.5), which is the minimum number of arcs needed to have a connected network and the maximum number of arcs having no undirected cycles [60]. More general systems, as shown in Figure 2.5.6, include distribution structures that input parts to an assembly portion of the system. Another main characteristic that helps classify inventory system is the ordering policy in use. In terms of how frequently inventory is reviewed, the ordering policy can be either continuous review or periodic review. In a continuous review policy, the inventory level is reviewed constantly and if it the level is at or below the minimum, a new order is triggered. In a periodic review policy, the inventory level is reviewed at fixed intervals rather than continuously. In terms of how the order size is determined, an ordering policy is either fixed order quantity, in which every order has a predetermined size, or order-up-to, in which the size of the order is equal to the number of units needed to reach the maximum inventory level [61]. 43

In general, a continuous review, order-up-to policy is the most cost effective [62] because they are more flexible in two aspects. First, an order can be placed at any point in time. And, second, the size of the order can be determined based on the actual needs rather than being constrained to a predetermined order size. The last factor that characterizes an inventory system is the type of control system used, which can be either centralized or decentralized. A decentralized control system implies that information and decision-making power, in terms of how much inventory to hold or the size of the orders, is distributed throughout the nodes [63]. In a decentralized control system, each node makes decisions based on local information [64], without visibility of end-customers demand or inventory status in nodes other than the ones immediately downstream. In a centralized control system, all information needed flows to a central location and decisionmaking is held by a central authority. The decisions are then transmitted to all nodes in the system, where decisions are implemented [65]. A basic advantage of decentralized over centralized control systems is that in a decentralized control system, each node needs no information other than the inventory status at that node. On the other hand, a centralized control system requires information of the status throughout the system. Nonetheless, the cost effectiveness of a decentralized control system is limited [66]. Furthermore, Lee and Billington contend that, in general, centralized control mechanisms should perform better than decentralized ones [67]. This suggests that decreasing costs of sharing information fosters increasing use of centralized control systems. In summary, the key issues identified in the section Modeling Inventory Management in the Supply Chain in which the conceptual development of dynamic time-based postponement stems from are: z

Inventory models are developed to prove theoretical optimality or to be used in practice. 44

z

Modeling inventory problems is complex; there are several types of networks: serial, assembly, distribution, tree, and general systems.

z

There are several ordering policies; the continuous review, order-up-to policy tends to be the most cost effective.

z

Inventory systems can have centralized or decentralized control policies; a centralized system performs better when it is feasible.

POSTPONEMENT Postponement is central to the effective and efficient replenishment throughout a LTO. Broadly, postponement is a concept that relates uncertainty and risk to time [68]. In this sense, postponement has been studied in several fields. Research efforts in logistics, and operations management / operations research (OM/OR) are generally related and are reviewed in depth later in this section. The fields Finance, Strategy, and Innovation contain research related to the effects of postponing actions or decisions. However, the way in which postponement usually is used in logistics and OM/OR differs from its use in finance, strategy, or innovation. For example, in finance and strategy, the study of the benefits from delaying investment has captured much interest. Traditional financial analysis has problems handling uncertainty and decision flexibility appropriately [69]. “Options” were developed to overcome these problems. Option-based techniques recognize management’s ability to revise its decision when uncertainty is resolved. This is referred to as managerial flexibility [70]. Real options may be used to value management’s flexibility to adapt its future decisions to future market conditions. This flexibility expands investment opportunities by improving potential benefits and limiting potential losses relative to management’s initial expectations under passive management [71]. 45

In a similar vein, in the field of innovation, postponement is considered one possible way that users react to innovation in products or services. Users may postpone adoption to see how the innovation develops over time. Postponement gives the user the chance to gather more information about the new product or service [72]. In these fields of study, postponement is frequently viewed as a “wait and learn” approach. Wait until more information becomes available and learn from the environment to reduce uncertainty in decision-making. This “wait and learn” approach is dynamic because the decision-maker waits until he/she considers has learned enough to make a better decision or until he/she cannot delay the decision any longer. In logistics and OR/OM, the concept of postponement has evolved into two basic ideas. One is to wait and learn to reduce uncertainty; the other is to redesign the product, the manufacturing process or the supply chain network structure to enable management to wait in a noncommittal status for longer. Even though the latter view might include the idea of “wait and learn” in some cases, this is hardly ever assessed explicitly. In logistics and OM/OR, generally postponement is viewed as a way to reduce risk by changing the design of the products, the manufacturing processes or the supply chain network structure, which is a static approach to postponement. In these fields, postponement as a “wait and learn” approach is not considered, unstated, or assumed implicit. The implementation of postponement as a static solution means that postponement thinking is used to analyze the supply chain and as a consequence of this analysis, managers decide to make some changes to the products, the manufacturing processes, or the supply chain network structure. In this sense, in his seminal study of postponement, Alderson asserted: Orderly application of the principle of postponement means the separate consideration of limits to each step of the process. Each step is regarded as a candidate for postponement. The limits of 46

postponability with respect to each step are taken into account. The final outcome of this analysis is the arrangement of the steps in the most effective sequence. Each step has been postponed to the latest feasible point in the sequence [73]. There are two sources of benefits from postponement which are be described later in this section: z The Demand Aggregation Effect: When the sequence of activities is changed, planning is based on aggregate demand. z The Learning Effect: When the time in which activities are performed is delayed, the forecasting horizon is reduced and managers may incorporate more information they gather from the market into their decision-making processes and, consequently, make more accurate decisions. That is, to “wait and learn”. Changing the sequence of activities can permit an activity to be performed later in time in addition to later in the sequence of activities. If that is the case, then the benefits from implementing postponement are confounded by the two sources. Part of the benefits stems from planning the aggregate demand, and the rest stems from reducing the forecasting horizon (assuming that the shorter the forecasting horizon, the higher the forecasting accuracy). In this dissertation, the focus was on estimating the effect of implementing postponement to reduce the forecasting horizon. This effect was isolated because the sequence in which activities are performed was left unchanged. The definition of postponement used for this dissertation was based on Bucklin’s [74] definition of postponement, as follows: Postponement states that activities in the supply chain are to be delayed to the latest possible moment. This definition of postponement includes not only implementations in which the sequence of activities is changed, but also includes the “wait and learn” approach to postponement. 47

Type of Limit

Economic

Technical

Strategic

Legal

Examples of Limits to the Use of Postponement Required economies of scale or cost trade-offs limit the use of postponement. When the cost of producing one unit of product at a time is higher than the benefits from holding no stock, activities will not be delayed until an order is received. For example, one of the processes in the manufacturing of Portland cement involves an oven which works at about 1,500 degrees; the oven needs about three days to reach its operational temperature, and it needs about two days to cool down before a technician using a special suit can get in the over to repair it. Operating the oven is considerably expensive and to make the operation to be cost effective it has to be producing clinker, work-in-process cement, constantly. The high costs related to operating the oven makes it infeasible to delay the manufacturing of clinker until an order from an end-customer is received. Time usually limits the use of postponement. When the time an activity takes is longer than the time the customer is willing to wait for the product, activities cannot be delayed until the customer order is received. For example, when a customer orders lobster at a restaurant, he is willing to wait while the meal is cooked; however, the customer is not willing to wait while they capture the lobster from the sea. The activity of fishing cannot be delayed until the customer’s order is received. The selected customer service strategy limits the use of postponement. For instance, managers of a manufacturing company plan to forbid competitor product to access the retail stores. They plan to follow this strategy by loading the distribution channel with product. This strategy requires that product is manufactured in advance to customers placing an order and, thus, limit the use of postponement. Laws might affect the limits to the use of postponement. For example some sensitive food items have to be stored for a specific length of time to allow for incubation of the product. The minimum incubation times which is required by the Food and Drug Administration in the US, delays shipping until after quality assurance steps are completed. Thus, product has to be manufactured before the customer’s order is received. The legal requirement to hold product during a period of time limits the use of postponement.

Table 2.1 Limits to the Use of Postponement

Postponement should be implemented whenever possible. Nonetheless, several factors limit the use of postponement. Alderson also introduced the idea of “limits as to the postponability” [75]. This means that postponement might be limited by economic, technical, strategic or legal factors that make postponement infeasible. Table 2.1 presents examples of limits to the use of postponement. The reminder of this section is organized as follows. First, the evolution of the concept of postponement is evaluated and leads to the identification of five relevant issues for positioning this dissertation. Second, postponement and the management of LTOs are related by explaining the potential of postponement in the context of LTOs. Last, postponement opportunities for managers willing to implement SCM are described. 48

EVOLUTION OF THE CONCEPT OF POSTPONEMENT The study of the evolution of the concept of postponement serves to show that the “wait and learn” view of postponement remains unexplored. The etymology of postpone is from Latin postponere: post + ponere. That is to place (ponere) after (post). To postpone has two meanings; one is to put off to a later time or to intentionally defer, usually to a definite later time. The other meaning is: to place later (as in a sentence) than the normal position in English, for example to postpone an adjective; or to place later in order of precedence, preference, or importance [76]. The two views of postponement, to delay activities or decision in time, and to change the sequence of events in the supply chain, have been identified in scholarly research. However, over time, the latter view dominated researchers’ attention. In this dissertation, the two views of postponement are referred to as changing the timing and changing the sequence views of postponement, respectively. Table 2.2 shows selected definitions of postponement relevant to studying the evolution of the concept. The column labeled Changing the Timing or Changing the Sequence indicates whether the definition considers both views of postponement as stated by the seminal definition by Alderson, or if it considers just one of them. The next column labeled Number of Decoupling Points indicates whether the definition considers only one decoupling point in the supply chain or allows for many. This dimension is relevant because the authors considering one decoupling point generally focus on postponement opportunities internal to a firm as opposed to the focus in this dissertation which was on postponement across multiple members of the supply chain. The column labeled Only with Order shows whether postponement is considered as an either/or decision. For Table 2.2, postponement is regarded as only with order when the definition states that activities have to be delayed until an order is received. The last column, named Study, indicates whether the study is conceptual, theoretical modeling, a survey, or it is a numerical example (sensitivity analysis). 49

Year

Authors

1957

Alderson

1965

Bucklin

1981

Ballou

1984

Shapiro

1985

Bowersox, Carter and Monczka

1988

Zinn and Levy

1988

Zinn and Bowersox

1990

Zinn

1993

Cooper

1993

Lee

1994

Lee and Billington

1997

Feitzinger and Lee

1998

Pagh and Cooper

1998

van Hoek

Definition of Postponement ... the principle of postponement requires that changes in form and identity occur at the latest possible point in the marketing flow, and changes in inventory location occur at the latest possible point in time [77]. The principle [of postponement] states that changes in form and inventory location are to be delayed to the latest possible moment. The principle of speculation holds that changes in form, and the movement of goods to forward inventories, should be made at the earliest possible time in the marketing flow in order to reduce the costs of the marketing system. [78] Ship as much as possible as far as possible before breaking bulk shipments into smaller quantities, or move materials as far down the distribution channel as possible before committing them to final products [79]. … “postponement” (make to order) and “speculation” (make to stock) [80] Postponement means planned delay of the scheduled performance of an activity as long as possible in the overall MLM [materials logistics management] process [81]. [The principle of postponement] … holds that changes in inventory location will be postponed until the latest possible time in the marketing process [82]. The principal of postponement proposes that the time of shipment and the location of final product processing in the distribution of a product be delayed until a customer order is received [83]. Postponement is the practice of delaying the final configuration of a product until a customer order is received [84]. [Postponement]… allows for some activities normally associated with production to be performed downstream in the supply chain, delaying the point in time when goods become dedicated to particular markets or customers [85]. A key concept in design for supply chain management is delayed product differentiation. This is also known as … simply a postponement strategy [86]. Postponement refers to redesigning the process to delay the point of product differentiation [87]. The key … is postponing the task of differentiating a product for a specific customer until the latest possible point in the supply network (a company’s supply, manufacturing, and distribution chain) [88]. The notion of manufacturing postponement is to retain the product in a neutral and noncommitted status as long as possible in the manufacturing process. The notion of logistics postponement is to maintain a fullline of anticipatory inventory at one or a few strategic locations [89]. Postponement is the operating concept that aims at delaying activities until actual customer orders have been received [90].

Timing or Changing the Sequence Number of Decoupling Points Only with Order Study Both

Allows many

No

Conceptual

Timing

Allows many

No

Conceptual

Sequence

Allows many

No

Conceptual

n.a.

One

Yes

Conceptual

Timing

Allows many

No

Conceptual

Timing

Allows many

No

Modeling

Both

One for each

Yes

Numerical

Timing

One

Yes

Conceptual

Sequence then timing

One

No

Conceptual

Sequence

One

No

Conceptual

Sequence

One

No

Empirical

Sequence

One

No

Conceptual

Both

Allows many

No

Conceptual

Timing

One

Yes

Survey

Table 2.2 Selected Definitions of Postponement 50

Continued

Table 2.2 continued y 1999 1999 2000

MasonJones and Towill Naylor, Naim and Berry Battezzati and Magnani

2000

Brown, Lee and Petrakian

2000

van Hoek

2001

van Hoek

2001

Chopra and Meindl

2001

Stock and Lambert

2002

Bowersox, Closs and Cooper

Work in Progress

Zinn and Cardoso

Postponing the variant differentiation of a product until the latest possible moment reduces the risk and uncertainty imposed by the consumer demands [91]. The aim of postponement is to increase the efficiency of the supply chain by moving product differentiation (at the decoupling point) closer to the end user [92]. [Postponement or delayed differentiation means delaying] … the final customization at plant level or – in the most extreme cases- at distribution level [93]. In product postponement, the products are designed so that the product’s specific functionality is not set until after customer receives it. In process postponement, a generic part is created in the initial stages of the manufacturing process. In the later stages, this generic part is customized to create the finished product [94]. Postponement centers around delaying activities in the supply chain until customer orders are received, rather than performing them in anticipation to future customer orders… [95]. Postponement is an organizational concept whereby some of the activities in the supply chain are not performed until customer orders are received [96]. Postponement is the ability of a supply chain to delay product differentiation or customization until closer to the time the product is sold [97]. … postponing changes in the form and identity of a product to the last possible point in the marketing process and postponing inventory location to the last possible point in time… [98]. [Postponement is the delay] … of final manufacturing or distribution of a product until receipt of a customer order… [99]. The physical movement and the final form of a product should be delayed as much as possible in the manufacturing and marketing processes… Product is not moved until the location of demand is known, and not given its final form until customer preferences are known [100].

Timing

One.

No

Conceptual

Sequence

One

Yes

Conceptual

Sequence

One

Yes

Conceptual

Sequence

One

Yes

Empirical

Timing

One

Yes

Conceptual

Timing

One

Yes

Conceptual

Timing

Allows many

No

Conceptual

Both

Allows many

No

Conceptual

Timing (1)

One

Yes

Conceptual

Both

One for each

Yes

Conceptual

1) The definition suggests that only one view of postponement is being considered by the authors. However, the explanation seems to indicate that both views are being considered

51

The analysis of the definitions of postponement used by academics shows five relevant issues about the evolution of the concept. Each of these five issues is described in greater detail in one of the following sections. The first issue is that, the seminal definition of postponement suggests two views of the concept aligned with the etymology and definition of the term postponement. One is to delay the time when activities are performed, which can be implemented dynamically as described later. The other is to change the sequence of activities in the supply chain, which is implemented as a static solution. Changing the sequence of activities might lead to delaying when an activity is performed. Postponement by changing the sequence of activities has dominated the research related to the implementation of postponement. Second, frequently, postponement is considered a dichotomy: a given supply chain member either practices postponement or it does not; an activity is postponed or it is not. The original objective of delaying activities to reduce uncertainty has been intertwined with the concepts of make-to-stock and maketo-order. In practice, however, postponement is implemented by reducing the degree of speculation. Speculation means to transform the product or change its location at the earliest possible time to minimize the total cost to the channel. The ultimate implementation of postponement is one in which the whole supply chain, from original suppliers to retailers, delays activities until the end-customer places an order. This is, in most cases, a practical impossibility constrained by the limits to the used of postponement (see Table 2.1). Too frequently it is asserted, “postpone until orders from the customers are received”, and we need to ask: “Who is the customer?” Is it the end-customer or the next-tier customer? Postponement does not imply necessarily that the setting is either make-to-stock or make-to-order. Generally, postponement is considered only internal to a firm. Postponement as an interorganizational concept has received little attention [101]; this is particularly true in empirical work. Therefore, the third issue is that many definitions suggest 52

there is only one differentiation point in a supply chain (see Table 2.2). Other definitions consider a supply chain having several differentiation points, suggesting that postponement could be implemented at several tiers in a supply chain at the same time. Furthermore, as contended in the previous paragraph, a firm can implement postponement at different degrees. The goal of SCM should be that management from each supply chain member facilitates the implementation of postponement at every possible tier to the greatest extent possible within the limits to the use of postponement. Fourth, few authors consider the full concept of postponement-speculation as suggested by Bucklin [102]. Bucklin contended that postponement cannot explain the accumulation of inventory at the many tiers in a marketing channel. Therefore, he contended that postponement-speculation should be used to explain the formation of the structure of a marketing channel. Postponement will be used whenever possible to reduce inventory-related costs and speculation will be used to gain economies of scale or reduce replenishment times by making products available in advance to the end-customers placing orders. Last, none of the studies published explicitly addresses how to assess the level of implementation of postponement. There is no benchmark available. The degree to which activities are postponed might be assessed relative to a previous situation, to standard business practices in the industry, or to a competitor’s practices. The most frequent benchmark used to assess the implementation of postponement seems to be a comparison with the previous situation. In the following sections, each of these five issues are described further.

Two Views of Postponement In Alderson’s seminal work [103], he viewed postponement as a twodimensional concept, which is in concert with the etymology and the meaning of postponement. The first part of the definition refers to the position in the supply 53

chain (from raw materials to end products) at which product differentiation happens. This can be referred to as changing the sequence of activities because relocating the point of product differentiation changes the sequence in which activities are performed. Since the early 1980s [104], this view of postponement captured considerable attention in both the fields of logistics and operations management. The other view of postponement is to delay activities to later in time. Delaying activities in time enables manager to reduce uncertainty in decision-making by learning from the behavior of the demand and by making decisions closer to the time when the end-customer places an order. Alderson only considered the delay in time of moving products closer to the end-customer. But, as explained later, any kind of activity in the supply chain can be delayed (and it should be delayed) if total supply chain cost, such as holding costs and risk of obsolescence, can be reduced. As it is explained in the next section, the benefits from postponement by changing the sequence of activities stem from two facts. One is that it enables planning to be performed based on aggregate demand, the demand aggregation effect, and the other is that it reduces the needed forecasting horizon which, in turn, has the potential to increase forecasting accuracy [105], the learning effect. There are three key issues about the relationship between these two views of postponement. First, changing the sequence of activities might lead to delaying activities in time. If that happens, changing the sequence of activities will lead managers to making decisions closer to the time when the end-customer places an order enabling some learning effect. But whether this happens is rarely assessed explicitly. The second issue is that both views complement each other and, thus, in scholarly research they should be considered independently. Otherwise, the benefits from postponement are confounded by the demand aggregation effect and learning effect. The last issue is that changing the sequence of activities is static, 54

while changing the timing of activities can be implemented dynamically based on the level of predictability of demand. Having a better understanding of postponement of activities in time has considerable potential. Postponement by Changing the Sequence of Activities. The focus on postponement by changing the sequence of activities was accentuated after the publication of studies in postponement developed at Hewlett-Packard during the early ‘90s [106]. For Hewlett-Packard , postponement meant redesigning the DeskJet Plus inkjet printer and redesigning some manufacturing processes. The printer was redesigned to include a dual power supply and the manufacturing processes were redesigned to finish assembly in a distribution facility rather than in a manufacturing plant [107]. Implementing postponement by changing the sequence of activities in the supply chain is a structural decision. That is, the products, the processes, or the supply chain network structure is reassessed and changed to implement postponement. Several ways to delay differentiation by changing the sequence of activities have been identified [108]: z

Standardization of components and subassemblies.

z

Modular design.

z

Postponement of operations.

z

Re-sequencing of operations. This view of postponement tends to dominate OM/OR literature, where case

studies are frequently used to complement optimization models. To a lesser extent, this view of postponement has dominated in the field of logistics [109]. However, logisticians have reported fewer empirical findings. Overall, the concept of postponement is about 45 years old, but practical examples are found only since the early 1990s [110] because the implementations of postponement have been enabled by advancements in information technology [111]. 55

Changing the sequence of activities is a structural decision and, as such, usually it follows a structural change in the environment such as a new manufacturing technology, a considerable increase in the level of operation, or a change in the design of the product. Changing the sequence of activities leads to delaying the point at which products are differentiated. In the example of the Hewlett-Packard DeskJet printer, before implementing postponement, the printers were differentiated for different markets when the power supply was assembled. Thus, at the end of the manufacturing process (c), there were printers with a 220 volt power supply for the European and Latin American markets and printers with a 110 volt power supply for the US market. After the product and process redesign, there was only one type of inventory at the end of the manufacturing process; that is, dual-power printers that could be shipped to all markets. Figure 2.6 represents the effect of changing the sequence of activities to delay the point of product differentiation in the supply chain. Figure 2.6.1 represents three raw materials, denoted as S(a), S(b), and S(c), transformed into two finished products, P(1) and P(2). Finished product is then shipped to field warehouses. In each warehouse, inventory of each finished product is held awaiting demand. This setting has been called a push system [112], forecast driven [113], based on forecast [114] or make-to-stock [115]. Figure 2.6.2 represents the same supply chain having implemented postponement by changing the sequence of activities. In this case, semi-finished products are shipped from the plant to the field warehouses, where the final configuration of the products is performed based on the orders placed by the retailer, for example. This scenario has been called pull postponement [116] or order based [117].

c)

This example is not intended to describe the case of HP in detail; it has been simplified to be used as an example.

56

Figure 2.6.1 Before Changing the Sequence of Activities in the Supply Chain Supplier

Manufacturer

Distributor

S(a)

P(1)

S(b) P(2)

S(c)

Material or Product

Manufacturing

Inventory

Figure 2.6.2 After Changing the Sequence of Activities in the Supply Chain Supplier

Distributor

Manufacturer

P(1) P(2)

S(a)

P(1) S(b)

P(2)

S(c)

P(1) P(2)

Material or Product

Manufacturing

Inventory

Figure 2.6 Postponement by Changing the Sequence of Activities in the Supply Chain

Zinn summarized the source of the benefits from moving the point of product differentiation closer to the point of consumption: If postponement is not applied, a firm must rely on past sales of each individual item of a product line to decide how much safety stock should be maintained for that item. In contrast, if postponement is applied and the firm keeps only inventory of unassembled product, the firm may rely on past aggregate sales for the entire product line to decide on the level of safety stock 57

needed. Safety stock savings are thus generated because the product line safety stock is smaller than the sum of individual safety stock for each item [118]. Zinn’s description of the benefits of postponement addressed only the demand aggregation effect. Reducing uncertainty by aggregating demand is frequently referred to as “statistical economies of scale” [119] since the benefits from aggregating demand stem from pooling, or aggregating, the variance of the demand. Pooling the variance of the demand reduces the risk associated with holding inventory. Figure 2.7 shows a very simple example of what pooling variance means. Note that the variance of the demand is 10.8 and 9.4 for Market 1 and Market 2, respectively. If safety stock for each market is needed, each safety stock will be based on the corresponding variance (among other parameters excluded to simplify the explanation). If safety stock is centralized, then the safety stock calculations are based on the variance of the demand for “Market 1 & 2” which is 0.4. In this simple example, the benefit from pooling variance is materialized in the reduction of inventory after centralizing safety stocks. The benefits from postponement by changing the sequence of activities stem from aggregating demand, and from reducing the time between decisionmaking and the end-customers placing orders. Changing the sequence of activities surely enables the aggregation of demand which has been documented, but rarely are the benefits from delaying activities in time assessed. Each view of postponement should be considered as independent initiatives that complement each other. Postponement by Changing the Timing of Activities. The other view of postponement refers to the time when product differentiation happens in terms of form, identity, or place. Product differentiation in terms of form is, for example, when 58

Market 1

Market 2

Market 1 & 2

8 Period 1 2 Period 2 6 Period 3 4 Period 4 10 Period 5 10 Period 6 6 Period 7 2 Period 8 2 Period 9 10.8 Variance Sum of Variances

4 10 6 8 2 2 6 10 8 9.4 20.2

12 12 12 12 12 12 12 12 10 0.4

Pooling Demand Variability

Variability of Demand

14 12 10 8 6 4 2 0 1

2

3

4

5

6

7

8

9

Period

Market 1

Market 2

Market 1 & 2

Figure 2.7 Example of Demand Aggregation Effect

a standard component is transformed into one of many possible finished products. Differentiation in terms of identity is when a product becomes market-specific by, for example, labeling the product with a specific brand. Product differentiation in terms of place happens by moving inventory closer to a market because diverting the product to another market is costly; thus, the firm usually is better off by selling the product in that market. 59

Alderson’s work only considered delaying in time the movement of inventories; and described how form and identity postponement is achieved by changing the sequence of activities. That is, Alderson only considered changing the timing of activities in terms of place, but not in terms of form and identify. However, all activities in the supply chain such as ordering from suppliers, manufacturing, packaging and handling, and transportation can be delayed as long as possible if there is a good reason for doing so, such as improving the forecast accuracy or holding inventory at a lower cost. The empirical study of the intentional delay in moving inventory in the supply chain has received less attention from scholars in contrast to that of changing the sequence of activities. Notwithstanding, the stream of research on centralization of inventories [120] is related to Alderson’s view of postponement. He contended that inventories should be held in the marketing channel far back as possible for maximizing the efficiency of the system [121]. If inventories are held back, inventories are centralized. The concepts of centralization of inventories and postponement have not been linked in the literature until recently [122]. The reason for this might be that centralization of inventories is used to estimate the impact on safety stock from closing warehouses, rather than to estimate the impact on safety stock from delaying the movement of product. Nevertheless, both approaches are conceptually similar. Figure 2.8 exemplifies the effect of centralizing inventories by delaying movement until a later time. Figure 2.8.1 shows big triangles closer to the endcustomer representing large inventories. Inventories that are held at field warehouses and the plant are represented by smaller triangles to indicate that the sizes of these inventories are relatively smaller because inventories are pushed forward in the supply chain. Figure 2.8.2, in contrast, shows inventories centralized at the plant and field warehouse level, resulting in smaller inventories closer to the end-customer. 60

Figure 2.8.1 Before Inventory Centralization Supplier

Manufacturer

Distributor

Retailer

S(a) S(b) S(c)

Material

Manufacturing

Inventory

Figure 2.8.2 After Inventory Centralization Supplier

Manufacturer

Distributor

Retailer

S(a) S(b) S(c)

Material or Product

Manufacturing

Inventory

Figure 2.8 Postponement by Delaying Changes in Inventory Location (centralization)

Usually, because of the demand aggregation effect, the total inventory held in the case of Figure 2.8.2 is smaller than that of Figure 2.8.1. This means that managers may benefit from shipping products from a central warehouse to a regional warehouse later in time. Shipping products two weeks later might enable managers to reduce uncertainty of demand for each region not only by aggregating demand for longer, but by observing the demand during the following two weeks and incorporating what they learned into the decision process to determine how much product to ship to each region. 61

Delaying manufacturing activities (form and identity) offers a similar opportunity to those of delaying logistics activities. Managers may benefit from manufacturing products two weeks later if they can learn from the demand during the next two weeks and manufacture the products that are in demand. This example assumes that delaying decisions has no detrimental effect on product availability. In short, postponement by delaying activities in time enables the demand aggregation effect over a longer time and enables the learning effect, which resembles the view of postponement in the fields of finance, strategy and innovation; that is: “wait and learn”. Relating the “Views” and “Types” of Postponement. The two views of postponement, changing the timing and changing the sequence of activities, should not be confused with the types of postponement reported in the literature. Alderson’s definition of postponement suggests two types of postponement, one refers to the form or identity of product, the other to place, the location of inventory. These types of postponement have been called manufacturing and geographic postponement, respectively [123]. Zinn and Bowersox called form postponement the delay of the point of differentiation in the marketing flow and introduced four types of form postponement: labeling, packaging, assembly, and manufacturing. In the same study, the term time postponement was used to refer to the delay of movement of inventory [124]. In other studies, manufacturing and logistics postponement were used to refer to form and time postponement, respectively [125]. For the purpose of this dissertation, the distinction between types of postponement becomes unnecessary because the focus is on postponement by changing the timing of activities and should include any type of activity in the supply chain that adds costs to the product. These activities include all “types” of manufacturing or form postponement: labeling, packaging, assembly, manufacturing, as well as logistics activities such as transportation, breaking bulk, 62

and handling. In general, the activities that have to be delayed are all activities that increase the value including changing ownership of the product, which is described later in this chapter, and all activities that reduces the effect of pooling variance (statistical economies of scale).

To Postpone or Not to Postpone?: that Is Not the Question The complete concept of postponement-speculation is regarded as having two extremes. On the one hand, there is postponement; on the other, there is speculation. At the extremes, postponement is frequently related to a make-toorder environment and speculation to a make-to-stock setting [126]. Whether a product is defined as made-to-stock or made-to-order depends on whether the product is manufactured or shipped after the customer order is received. Since the ‘80s, many definitions of postponement refer to the time the customer order is received, indicating that postponement occurs only if activities are performed thereafter (see Table 2.2). This seems restrictive. Consumers at the grocery store get products off the shelves; moreover, the demand for a product is affected by the amount of shelf space that is dedicated to that product [127]. Therefore, a category manager forecasts demand for the products on the shelves, deciding not only how much shelf space to dedicate to each product, but how much product to hold in the back of the store for immediate replenishment. Similarly, a customer purchasing a Hewlett-Packard DeskJet printer walks out of the store with the product. Someone decided how many printers to hold in the store. In both examples, inventory is positioned throughout the supply chain in advance of the end-customers placing orders. In short, some degree of speculation is needed to avoid “absurdity” [128]. Alderson described absurdity as the postponement of every step in the supply chain, in which case materials would be delivered to the end-customer in the raw state and the end-customer would transform it into a finished product. 63

If postponement only occurs when products are manufactured after the order from the end-customer is received, the most frequently cited examples of postponement are, in fact, speculation. In these examples products are not madeto-order, but made-to-stock. The majority of the publications about postponement refer to “postponement until the customer order is received” but do not specify who “the customer” is. It seems that “the customer” is the next-tier customer, rather than the end-customer. Nevertheless, assuming that the focus is on postponement internal to a firm, considering postponement only when activities are delayed until the orders from the next-tier customers are received is still restricting. This reasoning indicates that postponement is not a clear-cut concept, but that there are shades of gray in its use. A classic case of postponement is the shipment of “brights”, canned products without the label [129]. At a warehouse closer to the retail stores, the product is labeled with the manufacturer’s brand or with the retailer’s private label. Even though brights are labeled after the order from the next-tier customer (the retail store) is received, some forecasting is needed to estimate how much inventory to hold at the stores. In other words, defining the supply chain from point-of-origin to point-of-consumption, some degree of speculation will be needed as long as end-customers want to get products right off the shelves of the stores. Moreover, this might be true in most postponement cases, even when used internal to a firm. The general contention that postponement is not an either/or decision is supported by past studies in more specific cases and different contexts. For example, studies focusing on delaying the point of differentiation to later in the supply chain indicate that postponement is frequently regarded as speculating to a lesser degree. Lee [130] contends that, at the extremes, companies may be working under a build-to-stock or build-to-order environment but most operate somewhere in the middle of these pure systems. Lee regards speculating less as performing more activities in a build-to-order fashion because his focus is on postponement by 64

changing the sequence of activities in the supply chain. However, holding less safety stock at the retail store or holding materials in a raw state for longer should be regarded as speculating less and, therefore, it is postponement. van Hoek identified this tendency to regard postponement only at the extremes and explains that postponement can be used as a selective strategy. He asserts: [The findings from this research] … indicate how it [postponement] is not an either/or decision but can be a balance. Companies may decide to postpone assembly of certain products (such as high end systems) or assembly of products for certain markets (such as emerging markets) only. Thus [postponement] does not only center around deciding at what level in the chain postponement is to be applied, it is also a matter of to what degree it is applied. [131] Although van Hoek refers to a specific situation, he agrees tacitly with Lee in that postponement can be regarded as speculation to a lesser degree. Whether scholars view the concept of postponement-speculation only at the extremes is frequently present in the definition used for postponement (see the column labeled Only with Order in Table 2). Some view postponement as delaying activities until the customer’s order is received; others as delaying activities to the last possible point in time. The delay of activities is then constrained by the limits to the use of postponement. In conclusion, delaying activities until the end-customers’ orders are received is the ultimate postponement implementation. However, most managers will not be able to implement postponement to such an extent because of constraints set 65

by their businesses, the limits to the use of postponability. The value of postponement depends on several factors including the managers’ ability to capture and process observed demand data to improve manufacturing and inventory positioning decisions. In this sense, delaying activities to some extent rather than until endcustomers’ orders are received has considerable potential.

Number of Decoupling Points in the Supply Chain Most frequently, supply chains are presented as having one decoupling point. In fact, a decoupling point has been defined as the boundary between the part of the supply chain that works based on forecast and the one that works in a maketo-order environment [132]. A decoupling point acts as a buffer between each side of the supply chain [133]. If there is only one decoupling point, there is only one buffer in the supply chain. Depending where the decoupling point is located, it will be in the form of raw materials, subassemblies, or finished products. As conceptually appealing as this view might be, in practice there are many buffers in the product flow from original supplier to end-customer. There will a buffer of finished products on the shelves at the retail stores awaiting customers who want to get the product immediately; there will be a buffer of products to fill a full truck load to gain scale in transportation, etcetera. Having only one decoupling point is a conceptual simplification because, in practice, postponement can be implemented at many tiers in the supply chain at the same time. For example, in the case of postponement by changing the sequence of activities, activities at many tiers can be re-sequenced at the same time. Furthermore, these applications of postponement could be independent. Similarly, in the case of delaying activities in time, coordinating activities across multiple members in the supply chain will enable postponement at many tiers at the same time. More frequently than not, there will be several decoupling points in 66

a supply chain. Moreover, there might be several decoupling points even in postponement implementation internal to a firm. This conceptual simplification of having one decoupling point has been identified, for example, by van Hoek who asserts: The representation of a single point in the supply chain that distinguishes between postponed and forecast driven operations is a too simplistic representation of the postponement concept. [134] No empirical studies about multi-firm implementations of postponement was found. Single-firm empirical studies include the cases of Hewlett-Packard [135] and Benetton [136], or involve a manufacturing firm and third-party logistics providers [137]. In one article on the SMART car which was jointly developed by MercedesBenz and Swatch, the authors present a case study in postponement in which they contend that postponement is implemented across more than one member of a supply chain [138]. In this study, the steps of the assembly process and distribution are described. But, how postponement actually is implemented and the role of the many supply chain members in the actual implementation of postponement is not explained. The existence of multiple differentiation points in the supply chain has been considered for the postponement of logistics activities by centralizing inventory at multiple warehousing levels [139]. Similarly, postponement by product and process redesign has been modeled for the case of multiple points of differentiation [140]. However, no empirical study describing a supply chain with multiple differentiation points was found.

Postponement: Half a Concept The fact that postponement frequently is regarded as a dichotomy, there is postponement or there is not, makes it unnecessary to consider the full concept of 67

postponement-speculation. Bucklin [141] asserted that postponement is only half a concept. Since most of the supply chains hold inventories in the form of raw materials, work-in-process or finished goods, and considering that the concept of postponement was unable to explain why inventories appear in the channel, he contended that postponement should be combined with its converse: speculation. Speculation means to transform the product or change its location at the earliest possible time to minimize the total cost to the channel. For example, when manufacturing economies of scale are needed to make the process cost effective, managers have to speculate on what products will sell. In this case, the scale needed is a limit to the use of postponement, forcing managers to speculate. Bucklin used the complete concept of Postponement-Speculation to empirically explain the formation of a specific channel structure [142]. In some industries, the term speculation has a bad connotation and is viewed as the practice of engaging in risky business transactions. In the field of logistics and operations management, speculative inventories (safety stocks) are used to protect against uncertainty. Postponement and speculation are two possible strategies to follow and neither prevails over the other without considering the business environment. The use of different degrees of postponement or speculation depends on several factors such as the nature of the business, the manufacturing technology in use, the characteristics of the market and the product, and the customer service strategy adopted by the supply chain. Alderson suggested using “limits as to the postponability” [143] as a starting point to assess the feasibility of postponement. The concept of the limits to the use of postponement was further developed into frameworks to assist manager on the assessment of the suitability of postponement or speculation in their businesses [144]. 68

Since Bucklin’s empirical study on the channel structure of the cement industry in California in 1965, no other study shows how the concepts of postponement and speculation work together. Generally, authors explain how postponement was implemented, but the reasons why some degree of speculation is used in the supply chain after implementing postponement are rarely assessed. Nor has there been empirical research showing how postponement and speculation could be used as alternative strategies. As noted earlier, under certain circumstances speculation might be a better alternative than postponement [145]. Moreover, changing market environments might suggest that managers should be able to adapt their businesses from following more of a speculation strategy to gradually using postponement or vice versa.

How is Postponement Measured? If postponement was only a dichotomy and if there was only a single decoupling point in the supply chain, it would be straightforward to assess whether postponement is being used and to benchmark different postponement applications. But, reality is not this simple. No reference was found describing in detail how to assess the extent to which activities are being postponed. Both views of postponement, changing the sequence of activities and changing the timing of activities, could be assessed, for example, relative to the situation before implementing postponement. The Hewlett-Packard DeskJet Plus printer was redesigned to incorporate a dual power supply. This enabled the delay of the identity of the product by moving the decoupling point downstream. The location of the decoupling point before and after implementing postponement represents a measure of the implementation of postponement. In the case of postponement by changing the timing of activities, assessing the extent to which activities are postponed might not be so direct. For example, an extreme case is the complete centralization of inventory into regional 69

warehouses that lead to closing the warehouses located closer to demand centers. Since the supply chain network structure changed by the elimination of a node, it is clear that activities are postponed. However, postponing movement of inventory might not lead to closing any facility, but to holding lower inventory levels through the supply chain, especially closer to the end-customers. Centralizing inventories that are close to the end-customer likely will lead to holding higher inventory levels at the central locations. Nevertheless, total inventory for the supply chain should be lower than before the centralization process. In short, when postponement by changing the sequence of activities in the supply chain is implemented, the extent to which postponement has been implemented could be assessed by comparing the sequence of activities before and after the implementation. In the case of postponement by delaying activities in time, the extent to which postponement has been implemented could be assessed by the time at which activities occurred. This time assessment could be relative to the time when the end-customer placed the order. That is, how much time in advance to the end-customers placing orders have activities been performed?

SUMMARY OF THE REVIEW OF THE LITERATURE IN POSTPONEMENT The five issues related to the evolution of the concept of postponement reviewed in this section serve for the foundation of the conceptual development of dynamic time-based postponement. First, this research focused on the view of postponement as delaying activities in time which has not received much attention, neither theoretically nor empirically. Second, postponement is not a dichotomy. Postponement can be implemented to different degrees rather than just as a pure make-to-order setting. The third issue is that having one decoupling point in the supply chain is not enough. Most likely postponement will be used to different degrees at many tiers in 70

the supply chain at the same time. Since postponement is not and either/or situation, the full concept of postponement-speculation has to be considered to understand the reasons for holding safety stocks. Managers will benefit from the development of ways to measure postponement because there are costs associated with the implementation of postponement [146] and they will need to be able to sell the value of a postponement initiative.

POSTPONEMENT AND THE MANAGEMENT OF LIMITED-TIME OFFERS Some studies in postponement are related to the management of LTOs. In fact, many postponement initiatives have been motivated by the shortening life cycles of products such as that of the Hewlett-Packard DeskJet printer case. Johnson and Anderson [147], based on a sensitivity analysis, concluded that postponement is a very useful strategy for products with short life cycles. Similarly, postponement is one of the strategies used frequently when dealing with fashion products. Such is the case of Sport Obermeyer [148]. The research team working at Sport Obermeyer reported considerable benefits from postponing the manufacturing decisions for the products with higher uncertainty of demand. There are indications that a different postponement-speculation strategy might be more appropriate at the different phases of the life cycle of an LTO. For example, managers may find it easier to predict demand at the maturity phase of the life cycle of an LTO than the growth and termination phases [149] which means different safety stock requirements. Pagh and Cooper offered a more detailed description of the postponement opportunities for LTOs. They assert that the life cycle of the product and characteristics of each phase of the life cycle are relevant factors in the selection of an appropriate postponement-speculation strategy. The focus of the introduction and growth phases is in customer service; thus, an anticipatory manufacturing and logistics strategy is sound. During maturation and decline, the focus is on minimizing risk and cost. Supply chain members will benefit from adopting postponement during 71

these phases of the life cycle of a product. Similarly, Ballou [150] asserted that each phase of the life cycle of the product can mean a different configuration of the logistics network would be more economical. While Pagh and Cooper, and Ballou were not dealing with LTOs specifically, the thinking that the postponement-speculation strategy might need to be adapted to the different phases of the life cycle of products holds for LTOs. In short, there is conceptual support that postponement is a valuable strategy for managing LTOs effectively. However, two gaps have been found in the literature. First, this opportunity has been conceptually described, but no real-life examples are described. Second, in spite of the lack of description of interorganizational implementation of postponement, integrating the management of LTOs across the supply chain members, extending a dyad, can offer considerable benefits to the supply chain as a whole.

SUPPLY CHAIN MANAGEMENT OPPORTUNITIES IN POSTPONEMENT As noted earlier, all empirical studies in postponement have been internal to a firm. Interorganizational postponement has received minimal attention even though it was described briefly in 1965. Bucklin [151] contended that a member in the middle of the marketing channel may postpone by only buying from suppliers offering a short lead-time (backward postponement) or by purchasing supplies only when the next-tier customer places an order (forward postponement). These concepts were theoretically modeled [152]. Buying from a supplier offering a shorter lead-time (backward postponement) allows managers to place an order later. In turn, delaying order placement permits managers to observe demand and, ultimately, make decisions based on better information. Postponement beyond the single firm is challenging. It requires information sharing and the coordination of activities to the extent of regarding the supplier as an extension of the focal firm. Van Hoek [153] quoted a presentation by Professor Donald J. Bowersox during the Annual Conference of the Council of Logistics 72

Management in October 1997 in which he contended that interorganizational postponement is difficult to implement. Maybe, this is a reason why it is rarely implemented. To implement interorganizational postponement, the relationships in the supply chain need to be managed in a different way. Activities need to be integrated beyond the boundaries of individual firms and the management of each firm must see the other members of the supply chain as extensions of their own organizations. Supply chain management (SCM) provides the framework for managing the relationships in the supply chain. SCM, as defined in this study, offers postponement opportunities to managers willing to integrate business processes across the supply chain. The focus of this research was to study how postponement can be used as a management tool to adapt to changing demand conditions. Each tier of the supply chain stocks products “to provide some margin of safety for unexpected variations in demand [from the next-tier customer; however,] during any remaining time that the designated goods are in existence, the efficiency of the system would be enhanced by holding them as far back in the marketing flow as possible” [154]. Therefore, SCM coupled with postponement can make the supply chain more efficient. If it is so evident that postponement is beneficial, why is everyone not doing it? Why does not management consider postponement as an alternative to making the supply chain considerably more efficient? Why has this concept not been implemented? Alderson provides an explanation for this. He suggests that postponement frequently is regarded as an opportunity only after a business has gone through periods of change, such as business growth, product change, or technological change. Another reason is suggested by Lee [155], who shows that there is a cost associated with implementing postponement. This indicates that the marginal benefits from implementing postponement might not justify its implementation efforts until some change in the business occurs. 73

One such business change is the advent of SCM in which managers become aware of and strive for integrating activities beyond the boundaries of their organizations. Implementing the SCM process demand management, as described previously, across key supply chain members will enable managers to determine the extent to which activities in the supply chain should be postponed based on the predictability of the demand and the risk of obsolescence. In summary, the key issues identified in the section Postponement in which the conceptual development of dynamic time-based postponement stems from are: z

The benefits of postponement stem from the demand aggregation effect, the learning effect, and the need for shorter forecasting horizon.

z

Postponement by changing the sequence of activities is a static implementation of postponement.

z

Postponement can be implemented by delaying activities in time without changing the sequence of activities.

z

There are different degrees in the implementation of postponement.

z

Postponement as a supply chain initiative has many decoupling points.

z

Postponement can be viewed as using less speculation.

z

Postponement can be measured by the time between when an activity takes place and the end-customer places an order.

z

Postponement has potential for the management of LTOs.

z

A different postponement-speculation strategy might fit better with each phase of a LTO.

z

SCM serves as a framework for the implementation of postponement across multiple members in the supply chain. 74

DYNAMIC TIME-BASED POSTPONEMENT: CONCEPTUAL DEVELOPMENT The first objective for this dissertation, as stated in Chapter 1, was to present the conceptual development of dynamic time-based postponement. This section presents the development of dynamic time-based postponement which is founded in the literature reviewed in this chapter. Dynamic time-based postponement has two main characteristics that make it different from the traditional view of postponement. First, this approach to postponement is dynamic in nature and, second, it is based on changing the timing of activities as opposed to postponement by changing the sequence of activities. Dynamic time-based postponement is dynamic because the levels of the decoupling inventories are adjusted based on the accuracy in the predictability of demand, which in turn might imply a change in the customer service strategy. Decoupling inventories might be affected when different lead-time alternatives are used at different phases of the life cycle of a LTO. For example, shipping products by vessel in the early phases of a LTO and switching to using airfreight in later phases. Eventually a decoupling point might be eliminated or moved upstream altogether. The decoupling point between two nodes is an inventory buffer large enough to let the downstream node operate independently from the upstream node [156]. In a supply chain, there are likely to be numerous decoupling points. If predictability of demand is low (forecasting errors or uncertainty about the behavior of the demand are high), it is likely to find large inventory buffers between nodes. As the degree of predictability of demand increases (forecasting errors are lower), the size of the decoupling inventories required to hedge for the uncertainty should be reduced as well. In the context of LTOs, in which dynamic time-based postponement was tested, usually forecasting errors are high and risk of stockouts are high during the 75

early phases of the offer indicating that speculative inventories will be located close to the end-customers to guarantee high product availability and low replenishment times. Large decoupling inventories close to the point of consumption the nodes upstream in the supply chain to have relatively small safety stocks and operate independently from the nodes located closer to the end-customer. As the LTO progresses two factors affect the original calculations of safety stocks forcing inventory levels, particularly closer to the end-customers, to decrease. First, the forecast is updated with actual demand data and, therefore, the forecasting accuracy improves. Improving the forecasting accuracy reduces the need for speculative inventories (safety stocks) which enables managers to reduce the levels of the inventories located close to the end-customers. The second factor affecting the original safety stocks calculations is that as the end of the offer draws near, the risk of obsolescence increases. The increasing risk of obsolescence affects the total logistics costs because the costs associated with the risk of products becoming obsolete increases the inventory carrying costs [157]. This suggests that inventory carrying costs might be traded off with transportation costs; for example, less safety stock will be held close to the endcustomer and rush deliveries will be used to guarantee product availability. The other main characteristic of dynamic time-based postponement is that this approach to postponement is based solely on the timing of activities and the sequence in which activities are performed is not affected. Changing the sequence in which activities are performed in the supply chain and delaying activities might be related, but they should be assessed independently because each view offers different improvement opportunities and has different implications for implementation. Moving the differentiation point closer to the end-customer allows the delay of product differentiation by changing the sequence. Nothing is said about the 76

time at which the differentiation activities are performed. However, it is frequently assumed that changing the order of activities leads to performing some activities later in time. Sometimes, this is explicitly stated. For example, Cooper asserts that: [Postponement] … allows for some activities normally associated with production to be performed downstream in the supply chain, delaying the point in time when goods become dedicated to particular markets or customers [158]. Delaying the position of the point of differentiation in the supply chain generates inventory reductions from a demand aggregation effect which reduces the inventory carrying costs. However, delaying the point of differentiation reduces the need to forecast or shortens the forecasting period, if the differentiation happens closer to the time when the end-customers place orders. Performing the differentiation activities closer to order placement also enables the learning effect. Managers are able to learn from the demand and increase forecasting accuracy which leads to reduced acquisition and logistics costs as well. The learning effect is realized because new information is incorporated to the forecasting process and managers are capable of revising their decision. Both changing the sequence of activities and performing activities later in time, are likely to generate cost reductions. But, changing the sequence enables the learning effect and reduces the need to rely on forecasts because the differentiation activities are performed later in time. Figure 2.9 summarizes the relationship between postponement by changing the timing of activities and by changing the sequence, and the benefits from postponement. Postponement by changing the sequence produces a demand aggregation effect (X in Figure 2.9) because the planning is based on undifferentiated products as explained in the section Postponement by Changing 77

the Sequence earlier in this chapter. On the other hand, postponement by changing the timing of activities, performing activities later in time, reduces the forecasting horizon (Y in Figure 2.9) because activities are performed closer to when endcustomers place orders and allows managers to observe actual demand which enables the learning effect (Z in Figure 2.9). The demand aggregation effect, reducing the forecasting horizon, and the learning effect ultimately generate cost reductions ([ in Figure 2.9). These four relationships, represented in Figure 2.9 with bold arrows, between postponement and the benefits from postponement have been addressed in the corresponding sections in this chapter; however, there three other relationships, represented with dashed lines in Figure 2.9, that deserve further explanation. First, the benefits from delaying activities in time or changing the sequence are confounded. The confounding between the benefits from postponement arises when postponement by changing the sequence of activities results in activities being performed closer to when end-customers place orders in which cases changing the sequence leads to postponement by changing the timing of activities (\ in Figure 2.9). All the benefits related to changing the timing in Figure 2.9 are related indirectly to changing the sequence as they are for the case in which changing the sequence of activities leads to performing activities closer to when end-customers place orders. The second relationship is between changing the timing of activities and the demand aggregation effect which is represented by ] in Figure 2.9. Postponement is a concept that relates uncertainty and risk to time, and to aggregate demand is a way to reduce risk by pooling the variance of the demand. If inventory can be retained in a central location for longer, the variance of the demand for that location is being pooled for longer. This has not been recognized 78

Postponement

Changing the Sequence

Timing

5 6

1

Benefits from Postponement

2 3

Demand Aggregation Effect

Reduce Forecasting Horizon

Learning Effect

4

7

Cost Reduction

Figure 2.9 Structuring the Benefits from Postponement

in the literature because postponement is considered static. To pool inventories for longer is aligned with the concept of centralization of inventories. However, centralization of inventories has not been considered in relation to time but as a result of changing the supply chain network structure, which is a static solution. The third, and last, arrow shown in Figure 2.9 is the direct relationship between postponement by changing the timing of activities and cost reduction. Usually postponement is related to cost reductions which are achieved by aggregating demand, by the learning effect, or by reducing the reliance on forecasts. However, delaying the activities in time, time-based postponement, has a direct effect on cost reduction by reducing the costs associated with holding inventory. The direct effect of delaying activities in time and cost reduction is considerably larger when postponement is an initiative for multiple firms in the supply 79

chain. Implementing SCM coupled with postponement promises improvement opportunities for the whole supply chain. Postponement thinking together with a holistic analysis of the supply chain can lead managers to identify improvement opportunities for their supply chains [159]. As product moves forward in the supply chain, its out-of-pocket (cash) value increases [160] as shown in Figure 2.10. From a total supply chain viewpoint, it is better to hold inventory at the supplier tier, where inventory is held at $5, the supplier’s variable manufacturing cost. If held as raw materials at the next tier it is worth $11, the purchase price plush acquisition costs. The finished goods inventory at the manufacturer, wholesaler, and retailer level is held at $25, $62, and $72, respectively, as shown in Figure 2.10. Holding inventory at a lower possible value does not only reduce the direct variable costs associated with holding inventory such as cost of capital for the assets employed, but the costs associated with the risk of products becoming obsolete, particularly in the context of LTOs, increases with time. For a LTO, after it ends, products have no value since they cannot be sold at a discount or diverted to secondary markets. In the Figure 2.10, this means that when the offer ends, the products held by retailers are written off at $72, the total variable cost of the product. If the same product is held as finished goods by the manufacturer, the loss is $25. Therefore, if products have to be written off, let this happen at the least cost which usually is upstream the supply chain. In short, delaying activities in time has a direct effect on reducing the costs associated with products becoming obsolete. Managers who want to make the entire supply chain more efficient should strive to hold inventory at the lowest cost in the supply chain within the limits to the use of postponement. With this scenario, it is likely that safety stock will be shifted upstream in the supply chain and, thus, inventory-related costs will shift from one member to others. Therefore, supply chain members incurring higher inventory carrying costs should be compensated by the members that benefit from this practice. 80

81

$40 $60

Selling price

Selling price

Total variable cost of product $25 Full manufactured cost

Total variable cost of product $62

Other variable costs $14

$70

Other acquisition costs $2

$1

Acquisition cost

$60

$10

Variable cost of product

Wholesalers Wholesalers

Variable cost of material

Product

Information Product

Selling price

$…

Total variable cost of product $72

Other acquisition costs $2

$70

Retailers Retailers

Variable cost of product

Information

Payments

Orders

Inventory Value Increases in the Supply Chain

Figure 2.10

Source: Lambert, Douglas M. and Terrance L. Pohlen, “Supply Chain Metrics,” The International Journal of Logistics Management, Vol. 12, No. 1 (2001), p. 2.

$10

$7

Full manufactured cost

Selling price

$5

Product

Information

Variable cost of product

Suppliers Suppliers

Payments

Payments Manufacturers Manufacturers

Orders

Orders

Delaying activities in time should be beneficial whenever it is possible to do so and regardless of the sequence in which activities are performed. Delaying activities in time enables managers to observe key information from the customers [161] reducing uncertainty and enabling managers to adapt safety stock levels. Postponement should be viewed as an opportunity to learn from the behavior of the demand and other environmental factors by delaying decision-making; that is: “to wait and learn”. This view of postponement has become an improvement opportunity during the last few years, as advancements in information technology enable sharing data electronically at a low cost. Nevertheless, information technology is not sufficient. The lack of managers’ willingness to coordinate activities sometimes prevents the integration of activities across firms’ boundaries [162].

SUMMARY OF LITERATURE REVIEW The review of the literature in the management of LTOs, SCM, modeling inventory management and postponement provide the foundation for the conceptual development of dynamic time-based postponement. There is a need for a comprehensive method to manage replenishment throughout the life cycle of LTOs that views the supply chain as a whole cutting across the boundaries of individual firms. SCM together with postponement can be used to adapt the strategy of the supply chain in short periods of time based on the predictability of the demand. As described in Chapter 3, dynamic time-based postponement was tested using an existing optimization model. The description provided in the section, Modeling Inventory Management in the Supply Chain, facilitates the description of the research setting in terms of modeling the business problem. Chapter 2 is an attempt to relate a considerable amount of literature in four vast areas. Table 2.3 presents the key issues identified at the end of each of the 82

Postponement

Modeling Inventory Management

Supply Chain Management

Management of LTOs

Issues identified in the Literature Review

Dynamic Time-based Postponement

Extending the “learning effect” across the supply chain has potential. Accuracy of predictability of demand is different at each stage of the LTO. Static inventory policies are adopted for the management of LTOs. Replenishment through LTOs is a fertile ground for research SCM is about managing the relationships with other members of the supply chain. One of the SCM processes is Demand Management which is about forecasting and synchronizing. Demand Management has potential to extend the “learning effect” beyond the single firm and, thus, to assist in the management of LTOs. Inventory models are developed to proof theoretical optimality or to be used in practice. Modeling inventory problems is complex; there are several types of networks: serial, assembly, distribution, tree, and general systems. There are several ordering policies; the continuous review, order-up-to policy tends to be the most cost effective Inventory systems can have centralized or decentralized control policies; a centralized system performs better when it is feasible. The benefits of postponement stem from the demand aggregation effect, the learning effect, and the need for shorter forecasting horizon. Postponement by changing the sequence of activities is a static implementation of postponement. Postponement can be implemented by delaying activities in time without changing the sequence of activities. There are different degrees in the implementation of postponement Postponement as a supply chain initiative has many decoupling points. Postponement can be viewed as using less speculation. Postponement can be measured by the time between an activity takes place and the end-customer places an order. Postponement has potential for the management of LTOs. A different postponement-speculation strategy might fit better with each stage of a LTO. SCM serves as a framework for the implementation of postponement across multiple members in the supply chain.

The learning effect is one of the benefits from postponement As predictability of demand increases less speculative inventories are needed and postponement is used. Inventory policies are adjusted based on predictability of demand and cost tradeoffs for the whole supply chain. Dynamic time-based postponement is one alternative to the implementation of collaborative replenishment. Dynamic time-based postponement, as other initiatives that reach beyond a single firm, requires the management of relationships with other members of the supply chain The Demand Management process offers a framework to support the coordination required for the implementation of collaborative replenishment. Coordinating activities, a central element of dynamic timebased postponement, becomes critical for LTOs

Dynamic time-based postponement is a new application based on an optimization model for the placement of tactical safety stocks in the supply chain. The description of the modeling process is presented in Chapter 3. The characteristics of inventory management models will facilitate the explanation of the modeling process.

The benefits from dynamic time-based postponement include these and a direct effect on cost reduction. The use of dynamic time-based postponement can be adapted to changing environmental conditions. Dynamic time-based postponement will be tested in the context of LTOs. Dynamic time-based postponement can be implemented to different degrees by reducing speculative inventories. Decoupling inventories will be tactically placed across the supply chain. This is the way the extent to which dynamic time-based postponement will be measured. For the empirical test of dynamic time-based postponement, the use of postponement is adapted to the degree of predictability of demand. Extending the reach of Demand Management, the SCM process, will enable managers to use postponement as a supply chain initiative.

Table 2.3 Issues Identified in the Literature Review and the Conceptual Development of Dynamic Time-based Postponement 83

sections of the literature review and presents how each of the issues related to the conceptual development of dynamic time-based postponement. The next chapter, the Research Design is presented including the formalization of the research design and the description of the research setting in which the empirical test of dynamic time-based postponement was performed.

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[127] Wang, Yunzeng and Yigal Gerchak, “Supply Chain Coordination when Demand Is ShelfSpace Dependent,” Manufacturing & Service Operations Management, Vol. 3, No. 1 (2001), pp. 82-87; Bookbinder, James H. and Feyrouz H. Zarour, “Direct Product Profitability and Retail Shelf-space Allocation Models,” Journal of Business Logistics, Vol. 22, No. 2 (2001), pp. 183-208 and Stassen, Robert E. and Matthew A. Waller, “Logistics and Assortment Depth in the Retail Supply Chain: Evidence from Grocery Categories,” Journal of Business Logistics, Vol. 23, No. 1 (2002), pp. 125-143. [128] Alderson, Wroe, Marketing Behavior and Executive Action: a Functionalist Approach to Marketing Theory, Homewoord, Illinois: Richard D. Irwain, Inc., 1957. [129] LaLonde, Bernard J. and Raymond E. Mason, “Some Thoughts on Logistics Policy and Strategies: Management Challenges for the 1980s,” International Journal of Physical Distribution & Logistics Management, Vol. 15, No. 5 (1985), pp. 5-15 and Federgruen, Awi and Paul Zipkin, “Approximations of Dynamic, Multilocation Production and Inventory Problems,” Management Science, Vol. 30, No. 1 (1984), pp. 69-84. [130] Lee, Hau L., “Postponement for Mass Customization” in J. Gattorna (ed) Strategic Supply Chain Alignment: Best Practice in Supply Chain Management, Aldershot, Hampshire, England: The Gower Press, 1998, pp.77-91. [131] van Hoek, Remko I., “The Thesis of Leagility Revisited,” International Journal of Agile Management Systems, Vol. 2, No. 3 (2000), pp. 198 and 200. [132] Naylor, J. Ben, Mohamed M. Naim and Danny Berry, “Leagility: Integrating the Lean and Agile Manufacturing Paradigms in the Total Supply Chain,” International Journal of Production Economics, Vol. 62, No. 1,2 (1999), pp. 107-118. [133] Mason-Jones, Rachel and Denis R. Towill, “Using Information Decoupling Point to Improve Supply Chain Performance,” The International Journal of Logistics Management, Vol. 10, No. 2 (1999), pp. 13-26. [134] van Hoek, Remko I., “The Thesis of Leagility Revisited,” International Journal of Agile Management Systems, Vol. 2, No. 3 (2000), p. 198. [135] Lee, Hau L., Corey Billington and Brent Carter, “Hewlett-Packard Gains Control of Inventory and Service through Design for Localization,” Interfaces, Vol. 23, No. 4 (1993), pp. 1-11. [136] Bruce, Leigh, “The Bright New Worlds of Benetton,” International Management, Vol. 42, No. 11 (1987), pp. 24-35. [137] van Hoek, Remko I., “The Role of Third-Party Logistics Providers in Mass Customization,” The International Journal of Logistics Management, Vol. 11, No. 1 (2000), pp. 37-46. [138] van Hoek, Remko I, “Logistics and Virtual Integration,” International Journal of Physical Distribution & Logistics Management, Vol. 28, No. 7 (1998), pp. 508-523. [139] Pagh, Janus D. and Martha Cooper, “Supply Chain Postponement and Speculation Strategies: How to Choose the Right Strategy,” Journal of Business Logistics, Vol. 19, No. 2 (1998), pp. 13-34. [140] Garg, Amit and Christopher S. Tang, “On Postponement Strategies for Product Families with Multiple Points of Differentiation,” IIE Transactions, Vol. 29, No. 8 (1997), pp. 641-650. [141] Bucklin, Louis P., “Postponement, Speculation and the Structure of Distribution CHannels,” Journal of Marketing, Vol. 2, No. 1 (1965), pp. 26-31. [142] Bucklin, Louis P. and Leslie Halpert, “Exploring Channels of Distribution for Cemen with the Principle of Postponement-Speculation,” Proceedings of American Marketing Association, (1965), pp. 696-710.

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[144] Zinn, W. and D. J. Bowersox, “Planning Physical Distribution With The Principle of Postponement,” Journal of Business Logistics, Vol. 9, No. 2 (1988), page 118; Pagh, Janus D. and Martha Cooper, “Supply Chain Postponement and Speculation Strategies: How to Choose the Right Strategy,” Journal of Business Logistics, Vol. 19, No. 2 (1998), pp. 13-34 and Johnson, M. Eric and Emily Anderson, “Postponement Strategies for Channel Derivatives,” The International Journal of Logistics Management, Vol. 11, No. 1 (2000), pp. 19-35. [145] Zinn, Walter and Michael Levy, “Speculative Inventory Management: A Total Channel Perspective,” International Journal of Physical Distribution & Logistics Management, Vol. 18, No. 5 (1988), pp. 34-39. [146] Lee, Hau L., “Postponement for Mass Customization” in J. Gattorna (ed) Strategic Supply Chain Alignment: Best Practice in Supply Chain Management, Aldershot, Hampshire, England: The Gower Press, 1998, pp.77-91. [147] Johnson, M. Eric and Emily Anderson, “Postponement Strategies for Channel Derivatives,” The International Journal of Logistics Management, Vol. 11, No. 1 (2000), pp. 19-35. [148] Fisher, Marshall L., Janice H. Hammond, Walter R. Obermeyer and Ananth Raman, “Making Supply Meet Demand in an Uncertain World,” Harvard Business Review, Vol. 72, No. 3 (1994), pp. 83-89+. [149] Johnson, M. Eric and Emily Anderson, “Postponement Strategies for Channel Derivatives,” The International Journal of Logistics Management, Vol. 11, No. 1 (2000), pp. 19-35. [150] Ballou, R. H., “Reformulating a Logistics Strategy: a Concern for the Past, Present and Future,” International Journal of Physical Distribution & Logistics Management, Vol. 11, No. 8 (1981), pp. 71-83. [151] Bucklin, L. P., “Postponement, Speculation and the Structure of Distribution Channels,” Journal of Marketing, Vol. 2, No. 1 (1965), pp. 26-31. [152] Waller, M. A., P. A. Dabholkar and J. J. Gentry, “Postponement, Product Customization, and Market-Oriented Supply Chain Management,” Journal of Business Logistics, Vol. 21, No. 2 (2000), pp. 133-160. [153] van Hoek, Remko I. and Roland van Dierdonck, “Postponed Manufacturing Supplementary to Transportation Services?,” Transportation Research Part E: Logistics and Transportation Review, Vol. 36, No. 3 (2000), p. 205. [154] Alderson, Wroe, Marketing Behavior and Executive Action: a Functionalist Approach to Marketing Theory, Homewoord, Illinois: Richard D. Irwin, Inc., 1957, page 425 [155] Lee, Hau L., “Postponement for Mass Customization” in J. Gattorna (ed) Strategic Supply Chain Alignment: Best Practice in Supply Chain Management, Aldershot, Hampshire, England: The Gower Press, 1998, pp.77-91. [156] Graves, Stephen C. and Sean P. Willems, “Optimizing Strategic Safety Stock Placement in Supply Chains,” Manufacturing and Service Operations Management, Vol. 2, No. 1 (2000), pp. 68-83. [157] Lambert, Douglas M., The Development of an Inventory Costing Methodology: a Study of the Costs Associated with Holding Inventory, Chicago, IL: National Council of Physical Distribution Management, Chicago, 1976. [158] Cooper, James C., “Logistics Strategies for Global Businesses,” International Journal of Physical Distribution & Logistics Management, Vol. 23, No. 4 (1993), p 14..

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CHAPTER 3 RESEARCH DESIGN The purpose of this chapter is to formalize the empirical test for dynamic time-based postponement. In the first section, the following issues are described: the industry in which the supply chain operated; the use of LTOs; and the role of each member of the supply chain and how inventory deployment decisions were made. At the end of the section, the information flow needed for the implementation of dynamic time-based postponement is described. The data gathering process is shown in he second section. For this study, data from individual firms (each of which has its own information system) were integrated into a single database. This section includes a description of the data collected. The third section, contains a description of the analyses for which the empirical test is performed. A number of scenarios were developed for the LTO analyzed using an optimization-based software. Each scenario includes the inventory positions and levels throughout the supply chain, the total supply chain cost and total inventory investment. The test is based on comparing the scenarios. Next, the optimization model selected to perform the test is described. This section includes the assessment of fit between this optimization model and the business problem and an example of the mechanics of the optimization algorithm.

RESEARCH SETTING In many industries, limited-time offers are a strategic part of doing business. Particularly, executives of restaurant chains use LTOs as part of the marketing strategy 95

and regard the successful management of LTOs as a sustainable competitive advantage. A LTO in the restaurant business frequently means that a meal is specifically developed for the LTO, that is, a menu item is available only during the duration of the offer. Managing LTOs successfully requires mastering not only the product development but the management of the product flow throughout the life-cycle of the offer. Suppliers play a central role in the successful management of LTOs. For example, a novel menu item is often jointly developed with key ingredient manufacturers. The research team of the franchisor and that of the ingredient manufacturer work closely together and new product initiatives may be initiated by either one. The roles of logistics service providers are central to making the product flow throughout the LTO effective and efficient. Managing the product flow for LTO menu-items is challenging. LTO meals might be in the menu at the restaurants for as little as six weeks. Manufacturing and logistics activities have to commence in advance of when the meal is offered to the end-customers. Product availability to the end-customer is a key element for the success of a LTO, and this is a requirement for all supply chain members. The demand changes in short periods of time, influenced by mass-media advertisement and point-of-sale displays. The unique ingredients of the promotional meal usually are copyrighted and they become obsolete when the LTO is over. Unique ingredients have no salvage value after the end of the LTO because they cannot go on sale nor they can be diverted to secondary markets. In sum, coordination of activities across the supply chain has substantial potential because demand changes markedly in a short time horizon. Managers can recognize when the demand is going to change, and there are high costs (risks) associated with holding inventory, 96

Planning an LTO is as much a science as guesswork. This is particularly true for some menu items with tastes or presentations that are new to a market. The acceptance of newly developed meals is tested with focus groups and test markets. The forecast of a LTO sales before it starts is usually inaccurate. For national restaurant chains, the acceptance of a new meal might vary by regions of the country adding to the complexity of making inventory deployment decisions. A key success factor for the effective and efficient management of the product flow of LTOs is to have the ability to adapt the inventory deployment and positioning strategy across multiple members of the supply chain. Since product should always be available to the end-customer, the supply chain members will position inventory close to the restaurants when predictability of demand is low; that is, when the expected forecast errors are high. This speculative strategy will minimize the restaurants’ replenishment time in the event they run short of ingredients for the promotional meals. As actual demand is observed, the demand forecast can be updated incorporating the observed data. As predictability of demand increases and the expected forecast errors are reduced, less speculative inventories are needed throughout the supply chain. Activities can be delayed in time and postponement is used. The more activities are postponed, the more actual demand data are observed. These demand data can be used for the next forecast update, and the cycle begins again. Postponement might be used to a greater extent at each iteration of observing demand, updating the forecast, and making inventory deployment decisions. That is, manufacturing and logistics decisions are made closer to the time end-customers place orders. As the LTO approaches the termination phase, obsolescence risk causes inventory costs to increase. This suggests that total cost trade-offs have to be 97

reassessed. In contrast to the majority of the implementations of postponement, dynamic time-based postponement does not require changing the product design, the manufacturing processes or the supply chain network structure, because it is solely based on managers coordinating activities across the supply chain beyond their individual firms. The first objective for this dissertation was the conceptual development of dynamic time-based postponement which was presented in Chapter 2. The other objective for this research was to empirically test dynamic time-based postponement and show how it can be implemented. The test was performed in an actual business setting which allowed for the documentation of the managerial implications related to the implementation of dynamic time-based postponement. Consequently, the quantitative analyses were based on actual data provided by members of a supply chain participating in the quick-service restaurant business.

DESCRIPTION OF THE SUPPLY CHAIN USED FOR THIS RESEARCH Four independent members of a supply chain participated in the empirical test of dynamic time-based postponement. The sponsor company was a restaurant franchisor owner of more than 2,000 restaurants; the suppliers were a distributor and two ingredient manufacturers. The total number of restaurants was over 6,000 system-wide, including the 2,000 owned by the franchisor. Figure 3.1 shows a map of the supply chain in which the test was conducted. The supply chain was modeled like a network formed by nodes and arcs. The triangles shown in Figure 3.1 represent possible locations for holding safety stocks. Two nodes are connected by an arc. An arc shows that product can flow from the node to the left to the node to the right of the arc. Product that flows through an arc is called a shipment. For example, a shipment can happen between 98

99

Plant

Plant Regional Warehouse Warehouse Finished Goods

Restaurants

Manufacturing Process

Franchiser

Distributors

Endcustomers

Figure 3.1 — Mapping the Supply Chain: Locations Participating in the Research

Possible stocking point

Manufacturer 1

Manufacturer 1

Manufacturer 2

Raw Materials

Manufacturers

a warehouse attached to a manufacturer’s plant and one of the manufacturer’s regional warehouses (see Figure 3.1), or between a distribution center and a restaurant. Note that manufacturing runs are regarded as shipments between the raw materials stocking point and the plant’s warehouse which holds finished goods. The end-customers are connected to restaurants. Sales are considered shipments from the restaurants to the end-customers. In short, all movements of products in the supply chain are regarded as shipments between two nodes connected by an arc. The supply chain used in this research included, two manufacturers, a distributor, and approximately 1200 retail stores owned by the franchisor which participated in the LTO used to develop the optimization model. The four companies were independent from each other and all had the same objective: to make their individual firms profitable. The franchisor regarded the effective and efficient management of LTOs as a strategic initiative to produce a sustainable competitive advantage for the restaurants. In fact, LTOs were being used more frequently; prior to 2002, there were four national LTOs a year on average. For 2004, the plan was to have eight national LTOs. In addition to these eight national LTOs there were plans for regional LTOs directed to specific markets . Menu items offered for a limited-time are available to end-customer for about six to 12 weeks. There were substantial costs associated with managing a LTO such as managerial time, obsolescence of ingredients, and emergency transshipments used to avoid end-customer from facing stockouts. Thus, coordinating replenishment across the supply chain members had substantial potential. Decision making and information had been decentralized in the supply chain. Restaurant managers had considerable decision making power. For example, 100

they had to predict sales and determine how much inventory to hold. Order decisions being decentralized enabled managers to incorporate local information [1] in the estimation of future demand. For example, a restaurant manager may know that there is an elementary school soccer game across the street that will affect demand. At the time this research was conducted, the franchisor was implementing a forecasting system to recommend how much inventory to hold, and determine the size of the orders for the restaurant managers. Nonetheless, restaurant managers were able to override the franchisor’s recommendation. Restaurant managers influenced end-customers’ demand and the demand observed be the distributors. For example, restaurant managers had some flexibility about when to start offering promotional meals as well as when to disengage from the LTO. Their decision depended on several factors such as the performance of the LTO, how frequently LTOs were offered, and the added complexity to the kitchen’s operation due to offering the LTO. In general, restaurant managers decided how much to order and how much inventory to hold at the restaurant. They ordered from the distributors three times a week. As a result, restaurants operated with a periodic review inventory policy. Restaurant managers who faced shortages either placed a rush order to the distributor which had a negotiated additional fixed cost, or got products through a transshipment from another restaurant in the area. The franchisor’s management regarded transshipments as cumbersome and preferred transfers not to happen. The distributor had several distribution facilities (see Figure 3.1) which served restaurants directly in the area through milk-runs. The distributor was responsible for in-stock availability and decided how much inventory to hold at its distribution centers. The distributor’s managers based inventory and ordering decisions on the restaurants’ orders. They did not have visibility of end-customers’ demand data 101

and worked with a continuous review, order-up-to inventory policy. Distributors made deliveries to the restaurants three times a week and inventory levels at the distribution centers were reviewed every time an order was allocated for delivery. When inventory levels reached a predefined reorder-point, an order was placed to the appropriate manufacturer. The manufacturers served the distributor, however, manufacturers also had a close relationship with the franchisor. For example, the product development teams from a manufacturer and the franchisor jointly developed products. Manufacturers quoted a maximum replenishment time to the distributor to replenish products and were responsible for the product availability. Manufacturers had to make a series of decisions, such as how much inventory to hold in the form of raw materials, when to manufacture, how much inventory to hold in the form of finished goods, and where to locate this inventory throughout their internal network of distribution centers. During the LTO, demand data available to the manufacturers were based on orders placed from distributors and perceptions about the behavior of the demand gathered informally by talking to the distributor. They did not have visibility of end-customers’ demand nor restaurants’ orders to the distributors. Management of the two ingredient manufacturers gathered information informally through phone calls from the distributors about actual inventory levels at the distribution centers, and the perceived behavior of the demand from the restaurants to make manufacturing and inventory deployment decisions. As mentioned earlier in this section, restaurant managers had some flexibility to decide the periods in which promotional meals were offered. The franchisor specified the minimum duration for LTOs, and the distributor and the manufacturers were responsible for the product availability to their next-tier customers during this time. If restaurant managers were eager to offer the new promotional meal, this might cause the demand to be higher than expected early in the LTO. Similarly, if 102

restaurant managers prefer to stop offering the promotional meal at the earliest allowed, demand would be lower than expected during the later phases of the offer and, eventually, it would end earlier than planned. If many restaurants disengaged early and activities were not coordinated, product leftover was high at most stocking location in the supply chain. In this setting, management frequently reacted to unexpected changes in the next-tier customers’ demand. A manager from one of the ingredient manufacturers described an occasion in which they decided how much product to manufacture and the level of finished goods to hold as safety stock. But a distributor placed an unreasonably large order. In spite of making deliveries every week to this particular distributor, the one large order from this distributor was equal to a five-week supply. The manufacturer had to fulfill the order which meant that the safety stock the manufacturer was planning to have on hand to serve all distributors was consumed by a single customer. Low predictability of demand and supply chain members reacting to the next-tier’s demand seemed to cause the amplification of demand effect. Figure 3.2 illustrates the amplification of demand effect by showing how end-customers’ demand at the retail level has little variability and as this demand signal is transmitted upstream in the supply chain through successive orders placed , the demand signal becomes more erratic. In this research setting, information visibility is central to the effectiveness and efficiency of all members of the supply chain. For this reason, the franchisor was developing an information system to support information sharing. Figure 3.3 represents the franchisor as a hub through which information is shared to all supply chain members. This model has been described as the “information transfer model” [2]. The information hub was meant to provide information visibility to all members of the supply chain. 103

Source: Lee, H. L., V. Padmanabhan and S. Whang, “Information Distortion in a Supply Chain: the Bullwhip Effect,” Management Science, Vol. 43, No. 4 (1997), pp. 546-558.

Figure 3.2 Information Distortion in the Supply Chain

The information hub could be used to integrate key information of the supply chain such as point-of-sale data, forecasts, order status and cost data. These data could be input to the optimization-based tool, described later in this chapter, to determine the optimal locations and levels of safety stock throughout the supply chain. The optimization-based decision support system eventually could become a functionality of the information hub, and used to coordinate activities across the supply chain. The coordination of activities across the supply chain is necessary to support the implementation of dynamic time-based postponement. 104

Manufacturers

Distributors

Manufacturer 1

Distributor

Restaurants

Franchisor as an Information Hub

Manufacturer 2

Distributor

Figure 3.3 The Supply Chain with an Information Hub

DATA COLLECTION Data for this research were provided by the four independent companies that were members of the supply chain described previously. The objective of the data gathering process was to integrate the data to reconstruct the actual supply chain dynamics, such as inventory levels, shipments, manufacturing runs, and total cost during past LTOs, as well as capture the data needed for the development of the optimization-based model. Table 3.1 shows the sources of data and includes a brief description of each data element as well as which supply chain members provided the data. 105

Data Element

Description

Source

Forecast

Developed prior to the LTO and shared with the supply chain members

Franchisor

Demand

End-customers’ demand at the restaurants

Restaurants

Shipments

From the suppliers’ regional distribution centers to the distributor’s warehouses. Shipments can be either standard or rush orders.

Distributor, and suppliers

Transshipments

Transfers internal to a firm to avoid the next-tier customers from facing stockouts

Restaurants, distributor, and suppliers

Manufacturing Runs

Transformation of raw materials into finished goods, the ingredients for the meals. Manufacturing runs can be either standard or rush orders

Manufacturers

Other management costs

Other direct cost related to the management of an LTO, if any

Restaurants, distributor, and manufacturer

Inventory holding costs

Cost of holding inventory, including cost of capital, insurance, and pilferage

Restaurants, distributor, and manufacturers

Minimum order sizes

Minimum size of an order places to the next-tier supplier. For the manufacturer there might be a minimum order size for shipments and another one for manufacturing runs

Distributor and manufacturers

Product value profile

This is when and how much the products increase in value as the product is moved 1 forward in the supply chain ( )

Restaurants, distributor, and manufacturers

Transportation Costs

Standard cost to transport a case of finished product between two facilities in the supply chain. The cost of transportation of products between two facilities within the same firm will be included here

Distributor and manufacturers

Transfer cost for the restaurants

Estimated opportunity cost per hour for the restaurant manager who is handling the transfer

Restaurants

Table 3.1 Data Collection a)

Pagh, J. D. and M. Cooper, “Supply Chain Postponement and Speculation Strategies: How to Choose the Right Strategy,” Journal of Business Logistics, Vol. 19, No. 2 (1998), pp. 1334; Naylor, J Ben, Mohamed M. Naim and Danny Berry, “Leagility: Integrating the Lean and Agile Manufacturing Paradigms in the Total Supply Chain,” International Journal of Production Economics, Vol. 62, No. 1,2 (1999), pp. 107-118; Twede, Diana, Robert H. Clarke and Jill A. Tait, “Packaging Postponement: a Global Packaging Strategy,” Packaging Technology and Science, Vol. 13, No. 3 (2000), pp. 105-115 and Van Hoek, Remko I, “The Rediscovery of Postponement: a Literature Review and Directions for Research,” Journal of Operations Management, Vol. 19, No. 2 (2001), pp. 161-184.

106

Due to the confidentiality concerns expressed by the supply chain members during the definition of this research, a set of “ground rules” for sharing and publishing data was developed . These rules were :

ƒ

The names of the companies or ingredients will not be published. Manufacturer 1, Manufacturer 2, Distributor, and Restaurants will be used to refer to the supply chain members, and Ingredient A, Ingredient B, etc. to refer to products that go into the end products.

ƒ

No reference to the geographic location of the nodes in the supply chain will be published.

ƒ

A description of the supply chain structure needs to be presented. For example, “Manufacturer 1 has a manufacturing facility whose manufacturing lead time is W days. Finished goods are shipped to the distribution center located near the manufacturing site. Products from this distribution center are shipped to three regional warehouses from which the Distributor’s warehouses are served, the replenishment lead times to the three regional warehouses are X, Y, and Z days”.

ƒ

No data will be shared in detail with any company other than the owner of the data except when explicitly requested by the owner of the data.

ƒ

Results of the analysis based on the shipments can be published.

ƒ

No detailed shipment data will be shown. The results will be the volume, in cases, that was located at the various nodes of the supply chain at any given point in time. For example: the amplification of demand effect for the supply chain, showing how the variability of the end-customers’ demand increases as it is transmitted upstream, can be presented.

ƒ

Costs will be published as a single cost figures or as percentages (%) of the total cost of a limited-time offer. The integration of the data collected and the analyses are described next. 107

DESCRIPTION OF THE ANALYSIS The quantitative analyses for dynamic time-based postponement were based on a number of scenarios studied. First, the size of the business opportunity was assessed. Figure 3.4 shows the data that were used to estimate the business opportunity. Transactional data were used to find the inventory levels at all the locations in the supply chain. The value profile of products and the transportation costs were used to estimate the variable costs at every location in the supply chain which together with the inventory holding costs rates of each stocking location were used to estimate the actual avoidable costs of the LTO.

Transactional Data •Point-of-Sale Data •Shipments •Transshipments •Manufacturing Runs

Other Data • Inventory Locations and levels by unit of time • Remaining Inventory

•Value Profile of Products •Transportation Costs •Inventory Carrying Costs

Estimated Size of the Business Opportunity

Figure 3.4 Determining the Size of the Business Opportunity 108

The procedure for developing the scenarios based on the optimization model is shown in Figure 3.5. The optimized scenarios used the original forecast and demand data of the LTO. The optimization model was used to determine the tactical location of safety stocks. The optimization problem is presented and described in the section The Optimization-based Support System. For the LTO

Final Calculations

Other Data

Transactional Data • Forecast • End-customers’ Demand

End-customers’ Demand

One Iteration per Period

Initial Data Input

analyzed, the actual initial forecast, developed before the offer was launched,

Observed Demand During a Phase of the LTO

• Value Profile of Products • Transportation Costs • Lead Time • Minimum Order Size • Inventory Carrying Cost

Expected Forecast Errors

Update Forecast and Forecast Errors

Optimization

Inventory Locations and Levels by Unit of Time

Costs of Reacting to Out-ofBound Demand Situations

Remaining Inventory

Calculate cost of the stage of the LTO

Optimized Scenario Time-phased Cost of the LTO

Figure 3.5 Developing the Optimized Scenarios 109

and an expected forecast error calculated for the purpose of this test were used to feed the optimization-based decision support system to determine the inventory positions and levels for the duration of the LTO. The optimization model used data such as the value profile of the products, the negotiated transportation costs, and the inventory carrying costs for each member of the supply chain. LTOs were divided into phases and periods. A phase in this case was a week long and it was the unit of time for which demand was forecasted. A period of a LTO was determined by predictable changes in the demand and is formed by one or more phases. For example, the initial period included the initial inventory deployment needed for training the cooking crew at the restaurants. The second period began when mass-media advertisement starts and it was expected that demand would increase markedly. The third, and last, period was disengagement of the LTO. The life cycle of a LTO is illustrated in Figure 2.1 in Chapter 2. At the beginning of each period, an updated forecast was developed based on the initial forecast and the actual demand observed at that point in time. The development of the forecast-updating process was beyond the scope of this research. Therefore, the benefits from updating the forecast were shown, in Chapter 4, by means of a sensitivity analysis. The updated forecast and an estimated forecast error are fed to the optimization model again to determine the new inventory positions and levels throughout the supply chain for the next phases until the end of the LTO. This iteration was repeated for each period identified for the LTO. This is represented in Figure 3.5 in the middle section, titled: One iteration per period. In this research setting the process represented in Figure 3.5 was performed three times. For the last forecast update, obsolescence caused inventory carrying costs to increase, which made more expensive and faster lead-time options potentially attractive in order to reduce the inventory investment given the risk of obsolescence. 110

THE OPTIMIZATION-BASED SUPPORT SYSTEM Dynamic time-based postponement was tested by performing analyses based on an optimization model designed to determine inventory locations and levels in a supply chain. The model developed by Graves and Willems [3] was selected for the test. This model (which will be referred to as ‘G&W2000’) has several features that made this the preferred optimization model. These features should be considered within the context of the review of the literature in postponement presented in Chapter 2, from which the following issues were identified: z

Postponement traditionally is implemented by changing the structure of the supply chain and the effect of delaying activities in time has not been addressed explicitly.

z

There is more than one decoupling inventory in a supply chain.

z

Usually, postponement is regarded as internal to a firm.

z

The full concept of postponement-speculation is seldom considered.

z

No explicit description of how to measure postponement exists. Each of these issues are now described. First, G&W2000 facilitates studying

postponement in time by assisting management in the determination of when and how much product needs to be positioned throughout the supply chain. Second, the model might determine that at the optimum, several decoupling inventories are needed and it will output the locations and sizes of the decoupling inventories. Third, withe the appropriate data, the analyses can extend beyond a single firm as is the case of the research setting for this dissertation. Fourth, the use of G&W2000 showed how postponement can be used to different extents by showing lower safety stock levels at each phase of the LTO. The use of a higher degree of postponement translates into a lower degree of speculation which 111

means lower the levels of inventory. Lastly, using this model enabled the measurement of postponement as the difference between the size and locations of safety stock across the supply chain for the scenarios modeled. In addition to the optimization model itself, Graves and Willems provided a framework for modeling a supply chain, of which the optimization model is part. This modeling framework fits remarkably well with the business problem as described in Chapter 1. Specifically, the business problem addressed in this dissertation is the management of inventory of products for limited-time offers. The demand for this type of product is uncertain and safety stocks are needed to guarantee the target product availability. As noted, the model was designed to determine where to place decoupling inventories (safety stocks) throughout a supply chain and how much product to hold at each location. In order to assess the fit between the research setting and the selected optimization model it was necessary to evaluate whether the assumptions of the model limited any aspect of the business problem. The assumptions are presented below (in italics) and following each is a description of whether that assumption limited the analysis: z

The supply chain can be modeled as a spanning tree. The supply chain in this research setting can be modeled as a spanning tree.

z

The demand for each end item is a stationary demand process. Demand for end items can be considered stationary within a period of time or phase of the LTO. Demand is stationary when the predictability of demand does not change over time. The phases of the LTOs can be defined in such a way that this assumption did not limit the analysis.

z

There are no capacity constraints (in terms of manufacturing and transportation, for example). The supply chain in this research does not face any kind of capacity constraints for the fulfillment of ingredients for LTOs. 112

z

The supply chain is a pull system. The objective was to position inventory based on end-customers’ demand, making this supply chain a pull system.

z

Each stage is a potential inventory location and operates under a periodic review, based-stock control. That is, the order size is equal to the lead time demand. Not all nodes in the supply chain operated with a periodic review, base-stock inventory policy, but the objective was to implement this policy at all nodes if the test shows that dynamic time-based postponement works.

z

The service time between stages is fixed. That is, the quoted replenishment time of a stage to its customers has zero variance. Based on interviews with managers of all supply chain members participating in the study, service times were fixed. They commented that rarely was a delivery late by more than a few hours and when it did happen, there was no disruption in the normal operation.

z

All non-raw-material stages have deterministic processing times. That is, the time needed by these stages to fulfill an order is known and has zero variance. The variability of the processing times were small and did not exceed the quoted service time. Variability of processing time for the ingredients for a LTO rarely produced a disruption in the normal operation.

z

There are no backorders. This is the standard policy during LTOs. If, for some reason, a supply chain member cannot fulfill an order, managers implemented corrective measures such as working overtime or transshipping products.

z

Demand is bounded. This means that managers are able to establish a maximum demand. Establishing an upper-bound for the demand replaces estimating the expected average demand and demand variance. The use of a demand bound means that managers will plan for that maximum demand and in the event the upper-bound is exceeded, managers will 113

implement corrective measures to fulfill the orders. As mentioned before, the successful management of LTOs was regarded as a strategic competitive advantage not only by the franchisor, but also by the other supply chain members. Managers from all members of the supply chain already demonstrated that they implemented measures to solve out-of-bound demand situations.

BRIEF DESCRIPTION OF THE OPTIMIZATION ALGORITHM In this section, the optimization model is described briefly in order to put the analysis in context. Readers interested in an in-depth description of the model are referred to the source publications of the development of the dynamic programming optimization algorithm.

Stationary Single-Stage Inventory Model The demand process, the key equations of the safety stock model, and the optimization problem as described in the seminal publication are described next. Demand Process. External demand is observed at demand stages and is based on a stationary process with a daily average (µ ). The demand of the internal j

stages, which are all stages that are not demand stages, is the sum of the demand from each of the next-tier customers multiplied by the value of the corresponding arc. The model does not require any assumption about normality of demand. It is assumed though that managers are able to produce a meaningful upper bound of demand which will be used for inventory planning. One way to produce the upper bound of demand is to assume that demand is normally distributed and use the average demand, standard deviation of demand, and safety factor to produce the upper bound as follows. 114

(1)

D j (τ ) = τµ j + kσ τ Where

D (τ) Upper bound of demand over the days of exposure at stage j j

τ

Days of exposure

k

Safety factor

σ

Standard deviation of demand

The number of days of exposure (t) depends on the stage’s processing time; the inbound service time, which is the maximum time it takes for the stage to get replenished from all its suppliers; and the quoted service time, which is the time promised to the next-tier customers. Specifically, τ = SI + T – S j

j

(2)

j

Where τ

Days of exposure

SIj

Inbound service time

Tj

Stage’s processing time

Sj

Service time quoted to next-tier customers

Safety Stock Model. The expected inventory level at a stage at the end of each period is the expected safety stock for the stage. At the end of each period, it is expected that the stage used all cycle stock. Thus, the expected inventory level at stage j at the end of time t is: E[I ] = D (SI + T - S ) – (SI + T – S ) µ j

j

j

j

j

j

j

j

j

= D (τ) – µ τ j

(3)

j

The expected inventory at stage j at the end of period t is equal to the upper bound of demand minus the average demand over the exposure time. 115

Optimization Problem. The optimization problem is designed to find the optimal service times; that is, the quoted service time to the next-tier customers. N

P = min ∑ h j [ D j ( SI j + T j − S j ) − ( SI j + T j − S j ) µ j ] j =1|

subject to:

Sj - SIj ≤ Tj for j = 1, 2, … N SIj - Si ≥ 0 (for all (i, j) ∈ A Si ≤ si for all demand nodes j Sj, SIj, ≥ 0 and integer for j = 1, 2, … N Where: Dj(τ)

Upper bound of demand over the exposure time

hj

Annual holding cost rate

SIj

Inbound service time

Tj

Stage’s processing time

Sj

Service time quoted to next-tier customers

mj

Expected average demand

si

Maximum service time quoted to the next-tier customers

Thus, the optimization problem is to minimize the summation of the holding cost percentage multiplied by the inventory level at all stages in the supply chain.

Mechanics of the Optimization Algorithm The mechanics of the optimization algorithm are based on dynamic programming. Dynamic programming is used to solve sequential decision problems [3]. A dynamic program starts by solving the problem for the last stage and works backwards solving the problem for previous stages one at a time. In the context of the supply chain modeled here, the optimization algorithm determines the stock level for the demand generation stages (restaurants) and works backward to the warehousing stages (distribution centers of distributors and manufacturers) and on to the manufacturing and procurement stages (manufacturers). 116

In a traditional setting, inventory levels are set based on the information available to each stage. This information is the stage’s processing time (stage time), the time it takes for the stage to get replenished from its suppliers (the inbound service time), and the time the stage has promised the next-tier customers it will take to have the product available to the next stage (the quoted service time). The stage time plus the inbound service time minus the quoted service time equals the time of exposure as shown in equation 2 (since in this research the unit of time was measured in days, it is called days of exposure hereafter). Tables 3.2 and 3.3 shows an example for optimizing a supply chain formed by seven stages: procurement, manufacturing, interplant transportation, warehousing (at the manufacturer’s regional distribution center), transportation to the distribution center, and demand (at a restaurant). Each stage is connected to the stage to the right as shown in the tables. Table 3.2 shows the days optimal of exposure based on only the local information that was available to the stages. Since each stage focuses on the next-tier customer, the quoted service time for all stages was zero in order to model the fact that each stage promises immediate product availability. This forces all stages to hold safety stock. All stages have days of exposure equal to its stage time and all stages provide immediate product availability to the next-tier customer. Table 3.3 shows the output of the optimization, which is the determination of the locations of safety stocks based on minimizing the holding costs. The optimization algorithm calculates the cost of possible solutions (safety stock levels and locations) and outputs the least cost option. These calculations includes, for example, all risk pooling opportunities. Table 3.3 shows that safety stocks are located at the distribution centers to provide product availability to the stores, and the furthest back in the supply chain as possible. Note that this solution is possible because some stages, those that do not hold safety stocks, quoted service times to the next-tier customers of at least one day. The stages that had the inbound service time higher than zero means that those stages will wait to receive their orders. 117

118

14 0 14

0 7 0 7

0 1 0 1

All Stages hold safety stocks

Table 3.2

0 2 0 2

0 2 0 2

Determining Days of Exposure Based on Local Information

M aximum Inbound Service Time Stage Time Quoted Service Time Days of Exposure

0 1 0 1

0 1 0 1

Local Information is Used to Determine Safety Stock Locations and Levels (in Days of Exposure) Warehousing Demand Transportation Warehousing Transportation M etric Procure M anufacture Interplant RDC DC Stage

119

14 0 14

0 7 0 7

2 1 3 0

Only these Stages Hold Safety Stock

Table 3.3

0 2 2 0

3 2 5 0

Determining Days of Exposure Strategically Across the Supply Chain

M aximum Inbound Service Time Stage Time Quoted Service Time Days of Exposure

5 1 0 6

0 1 0 1

Safety Stock Locations and Levels (in Days of Exposure) are Optimized for the W hole Supply Chain Transportation Warehousing Warehousing Demand M etric Procure M anufacture Transportation Interplant RDC DC Stage

These stages can wait because they hold enough safety stock to cope with uncertainty of their demand. It is also assumes that the management of those stages are willing to wait for their orders. Since demand is considered independent across time, the algorithm considers the effect of pooling demand variability across time. Safety stocks are determined using equation (1) which includes the square root of the days of exposure at each stage. This square root represents the pooling of variance. Since the square root of the sum is smaller than the sum of the square roots, the total safety stock requirement across the supply chain is smaller with the solution shown in Table 3.3.

SUMMARY In this chapter, the formalization of the research design was presented including a description of the research setting and the data required for the quantitative analysis of dynamic time-based postponement. The procedures used for the estimation of the business opportunity and for the development of the modeling scenarios were also presented. This chapter included a description of the optimization software that was used for testing dynamic time-based postponement and an example of the mechanics of the optimization algorithm. In the next chapter, the results of the data collection process are presented, as well as the description of the quantitative analyses performed.

References [1]

Anand, Krishnan S. and Haim Mendelson, “Information and Organization for Horizontal Multimarket Coordination,” Management Science, Vol. 43, No. 12 (1997), pp. 1609-1627 and Nault, Barrie R, “Information Technology and Organization Design: Locating Decisions and Information,” Management Science, Vol. 44, No. 10 (1998), pp. 1321-1335.

[2]

Lee, Hau L. and Seungjin Whang, “Information Sharing in a Supply Chain,” International Journal of Manufacturing Technology & Management, Vol. 1, No. 1 (2000), pp. 79-93.

[3]

Graves, Stephen C. and Sean P. Willems, “Optimizing Strategic Safety Stock Placement in Supply Chains,” Manufacturing and Service Operations Management, Vol. 2, No. 1 (2000), pp. 68-83.

120

CHAPTER 4 DATA ANALYSIS AND FINDINGS In this chapter, the results from the data collection and analysis are presented. Chapter 4 begins with the report of the data collection. This includes data collected from interviews with managers as well as hard data collected from the information systems of the four companies participating in this research. Second, an initial diagnostic on the dynamics of the supply chain is presented. This includes an account of the planning process, a description of order placement throughout the life cycle of the limited-time offer, and an assessment of the business opportunity. Third, the modeling of the business opportunity is presented. The quantitative analyses for this research were conducted using a commercially available optimization software and the model of the supply chain developed using this software is described. Fourth, dynamic time-based postponement is described as a method for managing the product flow throughout the life cycle of the limited-time offer. Finally, the assessment of the benefits of dynamic time-based postponement in this research setting is presented.

DATA COLLECTION Data were collected for two purposes. One was to assess the actual cost of past limited-time offers (LTO) for the supply chain as a whole. The other purpose was to gather the data necessary to develop the optimization-based model used for the quantitative analyses. 121

The supply chain that participated in this research was formed by four independent firms: two manufacturers, a distributor, and a quick-service restaurant franchisor (a). Even though the supply chain members maintained a stable longterm relationship, they did not have an “open book policy”. That is, they did not share what is considered, by management of each firm, confidential information such as cost data. In an attempt to facilitate data collection, managers from all companies were assured that no data considered confidential would be shared with any other participant in this research. Furthermore, a set of “ground rules” for sharing data was negotiated with all participants (b). The data collection process started with getting acquainted with the quickservice restaurant business, and learning about the management of food ingredients for the promotional meals. In addition, the managers involved attended a presentation in which the purpose and the scope of this research was explained. Next, managers from the four companies were interviewed and facilities (manufacturing plants, distribution centers, and restaurants) of the corresponding four companies were visited. During these visits, several members at different organizational levels within each company were interviewed. This enabled the researcher to get acquainted with manufacturing, logistics and commercialization aspects of LTOs. The objective was to interview as many managerial levels as possible to obtain an integrated view of the business situation. Table 4.1 summarizes the visits to each company and the role of the interviewees within their organization. For confidentiality reasons, the names of the companies have been replaced with names that show the firms’ position in the supply chain . The column labeled visit indicates the location where the interviews took place. The column labeled Interviewee indicates the position of the manager a)

Refer to the section Description of the Supply Chain in Chapter 3 for more details on the supply chain members and their roles.

b)

The set of ground rules for sharing data was presented in Chapter 3.

122

Firm

Visit Corporate Headquarters

Manufacturer 1 Plant

Plant Manufacturer 2

Distributor

Franchisor / Restaurants

Regional Distribution Center Corporate Headquarters Distribution Center

Corporate Headquarters

Restaurants

Interviewee • VP Supply Chain • Director of Logistics • National Account Manger • Manufacturing Manager (Product Line X) • Manufacturing Manager (Product Line Y) • National Account Sales Manager • Plant Manager • Director of Logistics & Customer Support • Distribution Center Manager • Director Inventory Control • VP & General Manager • VP Supply Chain Management • Director, Supply Chain Management (product line 1) • Director of Supply Chain Information and Analysis • Manager Marketing Planning • Manager Marketing Analysis and Information • Commodity/Product Manager • Supply Chain Management Analyst • Project Manager Operations • Restaurant Managers (5)

Presentation Only

• VP Sales

• VP Finance & Administration

• VP National Accounts • Director, Supply Chain Management (product line 2) • Director, Strategic Sourcing • Director of Distribution • Manager, Purchasing Systems and Analysis • Purchasing Manager

Table 4.1 Site Visits and Interviewees

with which an interview was held. Table 4.1 also shows the positions of managers who participated in the presentation and discussion of the research project, but were not interviewed individually. Data were provided by the four firms that participated in the research. Within each of the participant firms, data came from several sources. Table 4.2 shows each data element gathered and the firm that was meant to provide the data. Some data elements were not provided by the original owner of the data because: 123

Data Element Forecast Demand Shipments

Transshipments Manufacturing Runs Manufacturing Cost Warehousing Handling Costs Inventory Holding Cost Rates Transportation Cost Transportation Lead Time Transshipment Cost Transshipments Lead Time

Owner

Alternative Source

Type

Franchisor Restaurants Manufacturer 1 Manufacturer 2 Distributor Manufacturer 1 Manufacturer 2 Distributor Restaurants Manufacturer 1 Manufacturer 2 Manufacturer 1 Manufacturer 2 Manufacturer 1 Manufacturer 2 Distributor Manufacturer 1 Manufacturer 2 Distributor Restaurants Manufacturer 1 Manufacturer 2 Distributor Manufacturer 1

Actual Actual Actual Actual Actual Actual Not applicable Actual Actual Actual Actual Estimated Estimated Estimated Not applicable Estimated Actual Actual Actual Actual Actual Actual Not applicable Calculated

Manufacturer 2

Calculated

Distributor Manufacturer 1 Manufacturer 2 Distributor Manufacturer 1 Manufacturer 2 Distributor Restaurant

Actual Researched Not applicable Researched Calculated Not applicable Calculated Actual

Franchisor Franchisor Expert Expert

Comments

The franchiser provided costs based on their cost models. Estimated based on discussions with industry experts.

Used negotiated transportation cost by case of product.

Expert

Expert Expert Expert Expert

Calculated based on distances between locations and average speed. Based on locations, industry experts provided contract carrier rates. Calculated based on distances between locations and average speed.

Table 4.2 Data Collected: Description and Sources 124

there were confidentiality concerns; data were not available to the contact persons; or, data were not at the level of disaggregation requested. Therefore, estimates were used in these instances. In Table 4.2, the column titled Alternative Source indicates the data elements that were not provided by the original owner of the data and who provided them. In Table 4.2, the column Type indicates whether the actual data were used. If the column Type does not read “actual”, then data came from one of the following alternative sources which were used in the order shown: z

An estimate provided by another supply chain member. For example, in the case of the variable manufacturing cost, the franchisor’s management had conducted research and developed a cost model for the major ingredients. Thus, this cost model was considered an appropriate proxy for the actual data.

z

A calculation based on other data available. For example, transportation lead times and costs were calculated based on distances between locations, which is information available to the public, and an average speed. The average speed used was provided by a well-known transportation service provider.

z

An estimate based on data available in the market. For example, interplant transportation data for one of the manufacturers were not available to the contact person. Based on the location of the manufacturing plants and regional distribution centers, transportation brokers provided estimates for these costs and lead times.

z

An estimate based on experts’ opinions in the industry. For example, if the data missing were warehousing data, then warehousing experts provided these estimates based on their experience managing the operation of similar warehouses. 125

CREATING A SINGLE DATABASE FOR THE SUPPLY CHAIN The data came from four companies and, within each company, some data resided in different information systems. The challenge at this stage was to consolidate all of the data into a single database. This task required the consideration of the following: z

Importing files and structuring data.

z

Standardizing data formats.

z

Building in traceability of transactions.

z

Normalizing the database.

z

Detecting errors and improving data quality.

z

Documenting the database.

z

Communicating with data owners. Each of these considerations is described next.

Importing Files and Structuring Data In all, 43 files were received from the four supply chain members. The majority of these files were in the same data structure; however, the import process required the development of 11 queries to automate it. Missing data or data errors were found in some files; thus, automating the import process reduced time when files had to be imported again. The import process of a file had to be repeated, for example, because it was found that the data set was incomplete and had to be sent again, because transactions not included in the file had to be added, or because the fields requested or sent were incorrect. The import process was used for more than just importing text files into a relational database. It was used to structure data to perform the analyses. Since the purpose of this study was to analyze inventory locations and levels, the supply chain was viewed as a series of nodes, where inventory can be held, and arcs (or links) between the nodes. In order to analyze the dynamics of the supply chain, all 126

product flow activities were viewed as shipments between two nodes. The transportation of product between a distribution center and a restaurant was considered a shipment between two stocking locations of finished goods. A manufacturing run was viewed as a shipment from the raw materials inventory to the finished goods inventory. A sale, an end-customer ordering a meal at a restaurant, was viewed as a shipment between a restaurant and an end-customer (which is the demand generation process and does not hold inventory). The import process, illustrated in Figure 4.1, served to structure the data in four tables in a relational database. The data structure of each of the four is shown in Figure 4.2 . These four tables are Shipments, Nodes, Arcs and Products. Figure 4.1 depicts the import process. The top section of the figure represents the files provided by the four companies participating in the study. Figure 4.1 also shows that a crossvalidation process was implemented in order to identify any inconsistencies in the data. For example, the franchisor identified stores in one way and the distributor used a different way of identifying the stores. The cross-validation process served to ensure that identifiers match among the various data sources. In addition, one of the companies had two headquarter locations and each of these two offices had its own computer server running the same information system. Nonetheless, the data formats used in these two information systems were not the same. The crossvalidation process was used to identify the right data to use, and to assure that the data used were correct. This step was particularly important to verify that what was shipped from one node was received by the correct destination. Figure 4.1 also portrays the way in which the nodes and the arcs in the supply chain network were identified from the transactions in the table Shipments. This process was required because data availability depended on the firm. That is, both manufacturers provided manufacturing runs and shipments of all ingredients manufactured. Part of this product was shipped to the distribution centers included in the analysis, and the rest was shipped to other distribution centers. The distributor 127

128

Distributor Transfers

5

2

Manufacturer 1 Shipments

Figure 4.1

Identify Nodes to Include in the Analysis

4

Table Nodes

Grouping

1

Table Shipments

3

3

Manufacturer 2 Shipments

Manufacturer 1 Manufacturing Runs

Structure Data Append Queries Append Queries Append Queries

(32 + 1) Files

Distributor Invoices Manufacturer 2 Manufacturing Runs

Table Arcs

Manufacturer 2 Interplant

Creating the Database of Past Limited-Time Offers

Normalize Database

What data to use?

Cross Validation

Table Products

Stores

Restaurants

Transfers

Restaurants

Sales

Restaurants

Original Files

Traceability

Figure 4.2 Data Structure and Building Traceability of Transactions in the Database 129

provided data of shipments to all restaurants served by each of the nine distribution centers included in this research. However, there are restaurants that were served by other distributors which were not included. Furthermore, point-of-sale data were available for the 1,200 franchisor-owned restaurants that participated on the LTO used for the analysis. Some of these restaurants were served by the distribution centers included in the research; others were served by other distribution centers. Therefore, the nodes of the network to include were identified from the transactions available in the database (X in Figure 4.1). In short, the process for identifying the nodes of the supply chain that could participate in this study was a discovery process based on availability and quality of data. All data were consolidated in a table called Shipments. Table Shipments displayed for how much of what product was shipped from where to where and when this happened. Therefore, the table Shipments had a reference to each location in the supply chain. Transactions of table Shipments were grouped by location (both origins and destinations); and then were used to create table Nodes (Y in Figure 4.1). The table Nodes was used to add all data necessary to describe each of the nodes, such as the value that the node adds to the product, and the inventory holding cost rate used at the node. After verifying and validating the table Nodes, the table Arcs was produced from the relationship between table Shipments and table Nodes (Z in Figure 4.1). Table Arcs was used to include all data that characterized each arc such as the lead time. At this point of the import process, tables Nodes and Arcs had all the nodes and all the arcs of the supply chain network; and table Shipments had all transactions provided by each of the four companies participating in the study. But not all these data were needed for the analysis. The manufacturer, for instance, provided data of shipments to all distributors, the ones participating in this study and others. This is 130

also the case with the distributor and the restaurants. Therefore, the nodes included in the study were identified in table Nodes. This somewhat tedious but critical process helped to identify missing transactions and data-quality problems in the source files.

Standardizing Data Formats Standardizing data formats means that the same identifiers are used in all files. Each company referred to the next-tier customer in a different way and product identification was different for each information system. The process of standardizing data formats makes it possible to establish relationships among data from different sources as well as to perform calculations. Usually this required the study of the data structure used by each firm, and the development of standards. Standardization involves not only standardizing identifiers but also the standardization of other aspects such as units of measure. For example, product quantity might be stored in cases, pounds, tons, or pallets. Having disparate data formats does not only occur when integrating data from different companies. Frequently within one company, different standards are found. This is particularly true when there are legacy systems. The import process was used to standardize other formats such as units of measure.

Building in Traceability of Transactions Because data were manipulated and sometimes converted, it was necessary to identify the source of each transaction in case a problem was encountered later. Each transaction needed to include the name of the company that provided it; the information system from which the transaction came; whether it was an invoice, the delivery of an order, a transshipment, etcetera; and, the identification of the transaction used at the source. Figure 4.2 indicates the fields that were included in the table Shipments for traceability. Traceability is critical to be able to talk to each individual firm participating in the research in “their own language”. 131

Normalizing the Database Database normalization is used to reduce redundancy. The database used in this study contained more than 1.5 million records and removing redundancy expedited queries to the database. The detailed explanation of database normalization [1] is beyond the scope of this study. In short, normalization is used to achieve functional dependency; that is, to identify the data attributes that completely determine all other data. Having redundancy in the data is not necessarily bad, but it slows down queries to the databases and increases the chances of data inconsistency.

Detecting Errors and Improving Data Quality Detecting errors is not a onetime task, nor does it have a definite end, unfortunately. The errors that were detected and corrected ranged from data missing to incorrect data. An example of data missing is when the point-of-sale data provided did not include one of the menu items; a promotional meal comes in three menu items, and only two were provided. An example of receiving incorrect data happened when the dates for all manufacturing runs by one of the manufacturers were represented as only occurring on Mondays, when they occurred throughout the week in actuality. The majority of data quality issues were identified by matching transactions during the cross-validation process. An example of cross-validation is to sum the amount of product delivered to the restaurants and from this summation, subtract all sales. If the calculation produces suspiciously high numbers for product leftover then there could be a problem with the programming or in the data. In the case of the menu item missing, when management of the firm that provided sales data were consulted, they quickly identified that a menu item was missing in the source files and corrected the problem. 132

Documenting the Database Documentation is another key task that helps not only to keep track of assumptions and estimations, but helps identify programming errors quickly. Manipulating large amounts of data with a relational database is challenging because making relationships among tables of data seldom produces an error message. Instead, a query might result in wrong output which might go unnoticed. Having a detailed description of the programming and the relationships among the tables of data facilitates the identification of errors when there are any. Another aspect of documentation is the identification of who provided what estimation, what assumptions were made, and what was the rationale behind the calculated data. Some data were provided by managers from the participant firms but other had to be calculated or estimated.

Communicating with Data Owners Detecting errors and identifying the data needed in order to improve the quality of the database took about three months of one person working full-time. A central issue related to improving data quality and validating the process by which the database was created was to be able to communicate with the owners of the data with which there appears to be a problem. Initially, tables were used to summarize and present data. Next, static graphics (produced in presentation software such as Microsoft PowerPoint®) were developed. But as data were improved, these graphics proved difficult to maintain because it was a manual process. The best option identified to maintain the map of the supply chain representing the product flow during a LTO was to use Microsoft Visio and link the supply chain map to the relational database. In other words, a graphical representation of the supply chain was drawn in Microsoft Visio, and text boxes were linked to database records to show data that resided in the database. Doing 133

so enabled updating the map automatically when data were modified in the database. An example of the supply chain map used is shown in Figure 4.3. In the figure, the numbers in bold below each node indicate the amount of product leftovers at that location after the LTO was over. These numbers were calculated as the difference between inbound and outbound shipments at each location. The numbers on the lines connecting two nodes, the arcs, indicate the number of cases shipped between each pair of locations and the number of transactions (or shipments) that were used to ship that many cases of product. Linking a database to graphical representations of a supply chain as shown here has potential in supply chain mapping applications. At an operational level, this kind of supply chain map might provide an on-line graphical view of the status of the product flow in the supply chain. Transactional data coupled with usermaintained data such as forecasts and capacity availability of resources could provide valuable information at a tactical or strategic level.

DESCRIPTION OF THE DATA COLLECTED In addition to actual transactions that came from several information systems of the four companies, each supply chain member provided data about lead times and costs of the activities to be included in the optimization model, the franchisor also provided the forecast used for the LTO. This section includes a description of the forecasting process used by the franchisor, the forecast data collected, and the description of transactional data that came from information systems.

Forecast The forecasting process that the franchisor used for LTOs had two parts. There was a quantitative forecast process that was used to predict total weekly sales for each restaurant. The other part was provided by the marketing managers at the franchisor. They produced an “expert’s forecast” which was the expected 134

135

56418 (75)

Key

Finished Goods - 10 0

110 (1)

7485 (41)

7816 (53)

13519 (74)

6817 (60)

Regional DC - 9 228

330 (2)

Regional DC - 7 722

22591 (53)

Regional DC - 8 49 1408 (5)

80 (1) 2893 (11)

4495 (22)

1760 (11)

1680 (11)

3850 (13)

2178 (8)

43 (1)

1416 (16)

5312 (18)

2473 (2355)

3650 (3590)

1722 (1697)

1586 (1537)

3571 (3454)

2048 (2009)

15 (1)

1170 (1108)

4082 (3916)

Other

DC #9 - 19 194 Nodes not included in the analysis

DC #8 - 17 620

DC #7 - 16 9

DC #6 - 12 -1

DC #5 - 20 63

DC #4 - 15 30

DC #3 - 18 6

DC #2 - 14 741

DC #1 - 13 100

3292 (3213)

7 (23)

5 (14)

132 (2117)

4 (10)

11 (33)

289 (4545)

10 (35)

1 (2)

28 (589)

1 (3)

7 (21)

87 (1550)

3 (8)

7 (21)

190 (4357)

7 (23)

3 (7)

77 (1266)

3 (11)

3 (8)

182 (2266)

4 (7)

14 (31)

465 (6635)

8 (24)

7 (24)

282 (5822)

End Customer

End Customer

End Customer

End Customer

End Customer

End Customer

End Customer

End Customer

End Customer

Other End Customers

7076 (119578)

31.6406

29.4688

3.6531

24.4312

50.7469

14.2785

26.5937

114.8031

78.5861

Other Restaurants

146 (143)

305 (302)

29 (31)

95 (92)

216 (215)

85 (83)

197 (186)

524 (501)

314 (317)

Figure 4.3 — Creating a Dynamic Supply Chain Map of the Product Flow

Cases Shipped (Number of Shipments)

Node’s Name - Node’s ID Cases Left Over

-

Raw Materials - 11

56418 (24)

25 (3)

LTO-11 - Manufacturer 2 Ingredient B

3691 (15)

percentage of total sales produced from promotional meals. Since the expert’s forecast is expressed as a percentage of total sales, it was referred to as LTO Mix. This section includes a description of the two parts of the forecast for the LTO, as well as the planning process. Forecasting Total Sales for Restaurants. The forecasting process for total sales was based on the moving average method. Each restaurant manager produced a forecast as follows: z

Add weekly total sales for the last 6 weeks.

z

Subtract the highest and the lowest weekly total sales numbers.

z

Divide the resulting number by 4. Using a moving average does not seem to be the best fit with this business

environment. Restaurants’ total sales showed trend and seasonality, which is an indication that other techniques, such as exponential smoothing, might perform better. However, a moving average forecast is economical and simple which enables restaurant managers to produce their own forecast independently. Expert’s Forecast (LTO Mix). Based on marketing intelligence such as past experience, focused groups, and test markets, marketing managers at the franchisor developed the LTO mix. The LTO mix was the expected sales of the promotional meals as a percentage of total sales. The LTO mix was a single percentage number for all restaurants for each phase of the LTO, from initiation to termination. Each phase lasted a week, and from initiation to termination there are eight phases in a LTO. It is important to note that a LTO does not have a “lift” effect on restaurants’ total sales. This was expressed by the franchisor’s management and corroborated by the data. LTOs were a key component of the success of the business and, virtually, there was a promotion running all the time. If the promotion did not include a 136

specifically developed meal, it was called a “value meal”. The result was not an actual “lift”, but a fraction of the total sales of a restaurant was in the form of the LTO du jour. Planning Limited-time Offers across the Supply Chain. After producing the LTO Mix, a marketing manager from the franchisor issued a memo to all restaurant managers and suppliers involved in the LTO. In this memo, the details of the LTO were specified. The memo included the characteristics of the LTO such as name, suppliers involved, ingredients and portioning, cost of ingredients and selling price. The elements of these memos of interest to this section were the forecast LTO Mix by phase (or week) and the forecast of total sales for an average restaurant. The forecast of total sales for the average restaurant was used only for the initial pipeline fill. After this formal communication, all coordination among the supply chain members was informal. The memo was sent to all affected managers about 10 weeks before the LTO was initiated, and it is used by manufacturers and distributors to plan for the LTO. The average total sales and the LTO mix was used to estimate total LTO sales during the entire promotion. Manufacturers were asked to manufacture product for 40% of the expected demand before the LTO was initiated. It was corroborated that competitors have similar practices. In fact, one competitor asked the manufacturer to produce 100% of the expected sales in advance. Since the forecast for a LTO was developed 10 weeks before initiation, using the described forecasting process implied that the total weekly sales figure used together with the LTO mix was not “last week’s” moving average. Manufacturers and distributors started procurement, manufacturing and logistics activities several weeks before orders from restaurants were observed. Thus, planning, and initial manufacturing and inventory deployment were not based on “last week’s” total sales, but the sales figure of the week before the memo was sent. 137

Actual Transactions from Information Systems At the highest level of aggregation, the unit of analysis was the LTO. During a LTO a specially developed sandwich was offered in several menu items during a limited time. For example, the promotional sandwich might be offered alone, in a combo, and in different meal sizes. The promotional meal was made of standard products and specifically developed ingredients for the LTO. The analysis in this research was focused on the ingredients particular to the promotional sandwich since the management of these products posed the challenges described in the section The Business Opportunity in Chapter 1. The promotional sandwich included in this study used two specifically developed ingredients. Each of the two manufacturers supplied one of these two ingredients. The distributor, who managed the nine distribution centers, supplied the restaurants with not only the ingredients for the promotional sandwiches, but other products. The ingredients for the promotional meals and other products flowed from the manufacturers to the distributors by the truckload and orders were placed once a week. The distributors replenished restaurants using milk-runs three times a week. Both manufacturers provided data for all manufactured product for the LTO analyzed. However, only part of all manufactured products was shipped to the nine distribution centers included in the analysis. Figure 4.4 is a summary of the data availability. The call-outs represent percentages over the total product manufactured. Figure 4.4 shows that from all manufactured product, 45% was shipped to the nine distribution centers included in the analysis. Point-of-sale data were available for 19% of all restaurants of which slightly more than half were served by the distribution centers in the analysis. Additionally, point-of-sale data from some of these restaurants were incomplete. Restaurants with good data quality were manually selected. These restaurants represent 6.5% of the product manufactured. 138

All Restaurants

19%

100%

Restaurants Point-of-Sale Data

45%

6.4% Distributor

FIGURE 4.4 Data Availability

SCALING DATA OF THE SUPPLY CHAIN To conduct this supply-chain-wide research, it was necessary to determine the fraction of the business of each supply chain member that would be analyzed. Data from each supply chain member had to be scaled to the portion of the supply chain for which there were data available. Based on data available, summarized in Figure 4.4, the largest portion of the supply chain that could be analyzed was determined by the number of restaurants for which there was data available. Cost and inventory data from each supply chain member were multiplied by the corresponding scaling factor in order to perform calculation of the same magnitude. 139

There are two approaches that can be used to determine these scaling factors. One is based on the number of restaurants for which there are data available. The other is based on the amount of ingredients that these restaurants ordered as a percentage of the total manufactured. If there is no systematic bias on the selection of the restaurants for which there are data available, the results of these two approaches should be equivalent. Table 4.3 shows the number of restaurants owned by the franchisor owned when LTO-11 was held and the number of restaurants that was available to participate in this research. Table 4.3 should be read as follows: from the 1,777 franchisor-owned restaurants when LTO-11 was held, 1,202 had some point-of-sale data for LTO-11; 689 of these 1,202 were served by one of the nine distribution centers participating in this research; from these 689 restaurants, 387 had good quality point-of-sale data. The final count of the restaurants included in the analysis was 384 because three restaurants were discarded for other data-quality-related reasons. Table 4.3 also shows that these 384 restaurants represented 6.4% of the number of restaurants system-wide. Table 4.3 is a break-out of Figure 4.4.

Count

Percentage

Percentage of System-wide Restaurants

Franchisor-owned Restaurants

1,777

100.0%

29.6%

Franchisor-owned Restaurants with Some Point-of-Sale Data for LTO-11

1,202

67.6%

20.0%

Franchisor-owned Restaurants with Sales served by Selected Distribution Centers

689

38.8%

11.5%

Franchisor-owned Restaurants with Point-of-Sale Data

387

21.8%

6.5%

Restaurants Available for the Research

384

21.6%

6.4%

Franchisor-owned Restaurants Description

Table 4.3 Number of Restaurants Participating in the Research 140

Even though the restaurant count provides a measure of the portion of the supply chain, another meaningful measure is the percentage of product flow. Using the percentage of product flow for scaling the results of the analyses to the whole supply chain helps to avoid any possible selection bias on the sizes of the restaurants. As mentioned before, all manufacturing runs were included in the data provided. The number of cases manufactured of each of the ingredients was used as a base to scale the rest of the product flow. Table 4.4 shows that from the total product produced by each manufacturer, 45% of Ingredient A and 48% of Ingredient B were shipped to one of the nine distribution centers. Also, Table 4.4 shows that 18% of Ingredient A and 19% of Ingredient B that was manufactured was sold through franchisor-owned restaurants that were replenished from any of the nine distribution centers of the Distributor. Finally, Table 4.4 shows that the 384 restaurants available to participate in this research represent 6.1% and 6.6% of all Ingredient A and Ingredient B manufactured, respectively.

Ingredient A

Ingredient B

100%

100%

Product Shipped to Included Distribution Centers

45%

48%

Sales as a Percentage of Product M anufactured all Franchisor-Owned Restaurants

18%

19%

Sales as a Percentage of Product Manufactured - Available Restaurants

6.1%

6.6%

Product M anufactured

Table 4.4 Percentage of Product Flow for Which There Was Data Available at Each Tier of the Supply Chain 141

Note that both, the number of restaurants available to participate in the research (384 restaurants) as a percentage of the system-wide count and the amount of product shipped to the same 384 restaurants as a percentage of total manufactured product, are similar. These percentages can be used to estimate the system-wide costs and determine the size of the business opportunity. For example, if product left over in all nine distribution centers is 100 cases of one of the ingredients; then the estimated product left over system-wide, accounting for all distribution centers, is about 222 cases (100 cases/45%). It is assumed that the nine distribution centers are a representative sample and that the rest of the distribution centers will behave in a similar way. It is also assumed that the 384 restaurants are a representative sample of all restaurants, both franchisor- and franchisee-owned. These assumptions seem reasonable because both approaches to determining the scaling factors produce similar results. Additionally, the validity of these two assumptions was corroborated with the franchisor’s management. A concern was whether the franchisor-owned restaurants performed better than the average restaurants. However, it was confirmed that there was no difference in the performance of franchisee-owned versus franchisor-owned restaurants. The restaurants that were included in the model did not show a systematic bias in terms of performance. The ultimate measure of the restaurants’ performance is total sales. Therefore, weekly total sales were standardized for confidentiality reasons and a Student t-test was performed on the restaurants available to participate in the research. From the 384 restaurants only two were outside the confidence interval at 99% level. Figure 4.5 shows the distribution of the standardized weekly total sales during the weeks of the analyzed LTO. Figure 4.5 shows that selected restaurants were normally distributed, lightly skewed to the right. This is expected because there are some restaurants that perform better than average. Generally, this is due to the fact that these restaurants are better located. 142

For the purpose of modeling, a representative fraction of the supply chain needs to be modeled in order to learn about its dynamics and extend the results. After consulting with an expert in the use of the optimization software used for this research, it was determined that modeling approximately 150 restaurants would be sufficient to learn about the potential benefits of implementing dynamic time-based postponement in this setting. These 150 restaurants could not be any restaurant, the model had to include a representative number of restaurants for each of the distribution centers in the research. Therefore, the 384 restaurants participating in the research, were grouped by distribution center. The number of restaurants by distribution center was divided by four and rounded to the next integer. The result was the number of restaurants by distribution center to include in the optimization model. Table 4.5 shows the result of this process. The total number of restaurants included was 177 which represent 3.1% of the supply chain both in terms of the number of restaurants and as a fraction of the total product manufactured. Therefore, 3.1% was determined as the scaling factor for the restaurants.

80 70

Frequency

60 50 40 30 20 10 0

-1.4

-0.7

0

0.7

1.4

2.1

2.8

N umber of Standard D eviations from the Mean

Figure 4.5 Distribution of Restaurants Based on the Standardized Weekly Total Sales 143

Restaurants Percentage of Restaurants in Percentage of All with Data Restaurants Restaurants the Model All Restaurants Distribution ----------------------- Available with Data ----------------------- in Model Center --------------------- ------------------Available ------------------- ----------------------[A] [B] [C] [B/A] -- [C/A] 12 13 14 15 16 17 18 19 20

37 130 147 34 12 108 60 70 91

Total

689

23 72 82 18 11 49 34 43 52 384

62% 55% 56% 53% 92% 45% 57% 61% 57%

10 32 37 9 4 28 16 18 23 177

27% 25% 25% 26% 33% 26% 27% 26% 25%

Table 4.5 Restaurants Included in the Model

INITIAL DIAGNOSTICS OF THE SUPPLY CHAIN DYNAMICS Based on the interviews held at all the members of the supply chain, four key characteristics of the supply chain dynamics appeared: z Inventory is built early in the life-cycle of the LTO. z Order placement is not coordinated across the supply chain. z The planning process is done once, before the LTO starts. z Obsolescence is the single greatest concern to all supply chain members, particularly to distributors, but also to manufacturers. Next, each of these characteristics is described.

INVENTORY IS BUILT EARLY IN THE LIFE-CYCLE OF THE LTO Managers of the franchisor were uncertain about the performance of the LTO when planning and they required manufacturers to build speculative inventories. Manufacturers must produce 40% of total expected sales for the LTO. Product was held by the manufacturer waiting for distributors to place orders. 144

Figures 4.6, 4.7 and 4.8 show the extent to which inventories were built in advance to the beginning of the LTO. The gray area in the figures represents the length of time for which sales were forecasted. Before and after this 8-week long period, there were two phases: training and disengagement which were between one and four weeks long each, and were not coordinated or planned by the franchisor. Figures 4.6 and 4.7 show that the manufacturers held most of the inventory available in the supply chain before the LTO started. Figure 4.6 represents the amount of Ingredient A that each member of the supply chain held; Figure 4.7 shows the same for Ingredient B. Figure 4.6 and 4.7 show that not only the manufacturers performed manufacturing activities early, but also inventory deployment throughout the supply chain started several weeks before the LTO started. Figure 4.6 shows that for Ingredient A, manufacturing activities started six weeks before the training phase of the LTO (which was 2 weeks before the LTO meals were available to endcustomers). Shipments to distributors started the week after manufacturing had started. As expected, due to the amplification of demand effect, the shapes of the curves are smoother upstream in the supply chain and show spikes downstream in the chain. Figure 4.7 illustrates a similar effect for Ingredient B. In this case, product was manufactured and shipped to distributors much earlier. The first shipments to distributors were 18 weeks before the first shipments to restaurants. This might have happened in order to use trucking capacity available at that time. Figure 4.8 depicts inventory levels by week for each of the two ingredients summed over all stocking locations in the supply chain. This figure represents a summary of the description provided in this section and shows how inventories were built up early in the LTO. Figure 4.8 characterizes the different behaviors of the manufacturers. Ingredient A was manufactured more frequently and in smaller batch sizes. The bulk of Ingredient B was produced before the LTO was initiated. The behaviors of the manufacturers were related to the different manufacturing technologies required to produce the ingredients and other circumstances such as capacity available. 145

146

5000

6000

0

1000

2000

3000

4000

Cases of Ingredient

24

'0 1

25 26

'0 1 27

'0 1 28

'01 29

'0 1 30

'01 '0 1

' 01

33

'0 1

Week - Year

32

Figure 4.6

Franchisor-Ingredient A

31

34

' 01

35

'0 1

36

'0 1

Daily Stock Levels by Supply Chain Tier — Ingredient A

Manufacturer #1-Ingredient A

' 01

The LTO was planned for a period of eight weeks which excluded Training and Disengagement

'01

38

'0 1

39

'01

Distributor-Ingredient A

37

40

'0 1

147

0000

2000

4000

43

0

2000

4000

6000

8000

Cases of Ingredient

'0 0

45

'0 0

47

49

'0 0 51

'0 0 53

'0 0 1

'0 1 3

'0 1 5

'0 1 7

'0 1 9

'0 1 11

'01 15

'01

17

'0 1

19

Week - Year

13

Figure 4.7

Distributor-Ingredient B

'0 1

'0 1

21

'0 1

23

'0 1

25

'01

27

'01

29

Daily Stock Levels by Supply Chain Tier — Ingredient B

M anufacturer #2-Ingredient B

'00

The LTO was planned for a period of eight weeks which excluded Training and Disengagement

31

'0 1

33

'0 1

35

'01

37

Franchisor-Ingredient B

'0 1

'0 1

39

'0 1

148

Number of Cases

43

0

5000

10000

15000

20000

25000

Cases of Ingredient

'0 0

46

'0 0 49

52

'0 0

'01 4

'0 1

Ingredient A

1 7

'01 '01 13

'0 1

16

'01

19

'0 1

Figure 4.8

Week Number - Year

10

22

'01

'01

28

'0 1

31

Ingredient B

25

'01

34

'0 1

Daily Stock Levels for the Whole Supply Chain — Ingredients A and B

'00

The LTO was planned for a period of eight weeks which excluded Training and Disengagement

37

'01

40

'01

ORDER PLACEMENT Order placement was not systematically coordinated across the supply chain. For instance, management of each stocking location decided how much product to hold. Order placement was coordinated informally based on phone calls between managers of the distribution centers and the manufacturers. Despite the level of effectiveness with this type of coordination, it was time consuming and sometimes lead to managers overreacting. If a distributor overreacted or failed to act proactively, manufacturers had to work extra time or postpone other customers’ requests, to fill the order.

PLANNING Traditionally, the planning process was done once for each LTO, 10 weeks before it began. As the LTO progressed, knowledge about the performance of the LTO was gained by the franchisor’s management, but not used for planning or shared with other supply chain members. This meant that management of each firm based inventory decisions on the orders placed by the next-tier customers which resulted in the amplification of demand effect (c). Figures 4.9 and 4.10 show, for Ingredient A and B respectively, the extent to which demand variability increased as orders traveled backwards in the supply chain. That is, while the end-customers’ orders followed a smooth demand pattern, demand variability increased upwards in the supply chain. Note that Figures 4.9 and 4.10 show some gaps between markers. These gaps are due to the fact that the events shown in the figures are discrete events and do not necessarily happen every week. For example, Figure 4.9 indicates that on week 25 and 26, there were shipments to distribution centers, but on week 27 there was not.

c)

Chapter 3 includes a description of the amplification of demand effect.

149

300

350

400

450

0

50

100

150

200

250

Cases Cases of Ingredient

150

24

25

'01 26

'0 1 27

'01 28

M anufacturing Runs-Ingredient A Shipm ents to Restaurants-Ingredient A

'01

'0 1

The LTO was planned for a period of eight weeks which excluded Training and Disengagement

29

'0 1 31

'0 1

'01

33

'01 W eek - Year

32

34

'01

Figure 4.9

Sales to End-custom ers-Ingredient A Transfers between Restaurants-Ingredient A

'01

Order Placement Ingredient A

30

35

'01

36

37

'0 1

38

'01

39

'0 1

40 Shipm ents to Distributors-Ingredient A

'0 1

'01

Cases

151

600

700

800

Order Placement Ingredient B

Figure 4.10

Shipm ents to Restaurants-Ingredient B M anufacturing Runs-Ingredient B

W eek - Year

Shipm ents to Distributors-Ingredient B

'0 1 7 ' 0 1 8 ' 0 1 9 '0 1 0 ' 0 1 1 '0 1 2 ' 0 1 3 '0 1 4 ' 0 1 5 ' 0 1 3 '0 1 4 ' 0 1 5 '0 1 6 ' 0 1 7 '0 1 8 ' 0 1 9 ' 0 1 0 '0 1 1 ' 0 1 2 '0 1 3 ' 0 1 4 ' 0 1 5 '0 1 6 ' 0 1 7 '0 1 8 ' 0 1 9 '0 1 0 ' 0 1 3 ' 0 0 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 2 1 1 1 1 0 0 0

The LTO was planned for a period of eight weeks which excluded Training and Disengagement

Transfers between Restaurants-Ingredient B Sales to End-custom ers-Ingredient B

02

0

100

200

300

400

500

Cases of Ingredient

Figure 4.9 and 4.10 show that even though a considerable fraction of all product manufactured had been produced before the LTO started, there were manufacturing runs throughout the LTO. For Ingredient A (shown in Figure 4.9), there were manufacturing runs every week until two weeks before the end of the LTO. Similarly, Figure 4.10 shows that the majority of Ingredient B was manufactured early, and that there were five small manufacturing runs between phases three and seven.

OBSOLESCENCE Ingredients used for a promotional meal were developed jointly by the franchisor and the manufacturer, and were copyrighted. Product remaining after the LTO ends had no residual value. Aside from preventing stockouts to the endcustomers , obsolescence was the single greatest concern to all supply chain members, particularly to distributors and manufacturers. This fact was supported by the interviews and data, as shown in the section titled Determining the Size of the Business Opportunity later in this chapter. Product leftovers was a lesser concern to restaurant managers because they had the power to stop offering the promotional meal, to disengage from the LTO, within a specified time-window. They could stop ordering ingredients and maintain point-of-sale displays until they used their inventories. When ingredients were consumed, restaurant managers simply removed the promotional menu items and the point-of-sale displays from the restaurants. Two characteristics of the supply chain dynamics influenced the amount of product remaining at the end of the LTO. One was that all members of the supply chain were responsible for product availability to the next-tier customers, the demand visibility beyond the next tier was limited and based on informal communication. Thus, inventory levels were set only using local information available to management of each stocking location. Local information included replenishment lead times, expected demand, and perceived demand uncertainty. Therefore, inventories were not optimized across the supply chain. 152

The other characteristic of the supply chain dynamics that influenced the amount of product leftover was that LTOs had a disengagement phase during which the restaurant managers decided independently whether to continue offering the meal for up to four more weeks, approximately. Restaurant managers based their decisions on several factors such as how well the LTO was performing at their store, whether another LTO was about to start, in which case the cooks needed to start training, and how well the operation was running in the kitchen. Nonetheless, restaurant managers did not share their decision with other members of the supply chain in any way other than by ceasing to order product. Obsolescence represented an important cost component to the franchisor’s suppliers. Nevertheless, LTOs were considered strategic to the sustained success of the business and the franchisor did not want the suppliers to bare the additional costs of the LTOs by themselves. On the contrary, the franchisor regarded the participation of the other supply chain members key to the success of LTOs. The franchisor had implemented a system of allowances to protect suppliers against costs incurred in the form of obsolescence. The franchisor paid for the product left over up to a certain dollar amount. Product became obsolete before it perished. Despite the fact the products were food items, which were perishable, obsolescence occurred first. That is, the shelf-life of the products was three months, but the LTOs were shorter.

ASSESSING THE COST OF A LIMITED-TIME OFFER TO THE SUPPLY CHAIN Three major cost categories were included in this research to measure the size of the business opportunity. These three cost categories provided insight into the different trade-offs required at different phases of a LTO. Sufficient data were available to estimate these costs for the four companies participating in the study. The cost categories are: 153

z Inventory holding costs. z Obsolescence of product left over. z Transshipments. These costs could be reduced if LTOs were managed differently. Thus, in this research, these costs do not include costs that would have been incurred anyway such as the transportation and manufacturing costs of the products that were sold. The three cost categories are described next.

INVENTORY HOLDING COSTS Inventory holding costs were determined by the direct variable cost of the products held at each location, the inventory holding cost rate used by the firm that owned the inventory and the length of time inventory was held at each stocking location. For a standard product, obsolescence would be prorated as an annual cost and included in the inventory holding cost rate. In this setting, obsolescence became a critical concern close to the end of the LTO. Because obsolescence cost was unusually large and spiked at the end of the life cycle of the promotion, it was removed from inventory carrying costs and dealt with separately.

OBSOLESCENCE OR PRODUCT LEFT OVER In some settings, obsolesce is modeled as a stochastic mathematical function which increases the holding cost rate with the life cycle of the product [2]. In this setting, the time when product became obsolete was deterministic. The time of the changes in demand were predictable, and obsolescence costs represented a considerable portion of the business opportunity. Obsolescence was, to some extent, a necessary evil. LTOs were strategic, and customer service requirements for promotional meals were stringent: no endcustomer will leave the store without the ordered promotional meal. The forces of obsolescence and high product availability clash, but each one dominated at 154

different phases of the life-cycle of the LTO. More specifically, obsolesce was a non-issue early in the life-cycle of the LTO, but it made managing the termination and disengagement phases challenging. On the other hand, product availability requirements were the same throughout the duration of the LTO; but if restaurants ran out of stock after the end of maturity (week six of the LTO) and could not get replenished on time, restaurant managers could decide to remove the promotional meal from the menu. If an end-customer ordered a promotional meal after the menu item was removed, that was not considered a stockout. Instead, this means the LTO finished. Minimizing the amount of product left over was a difficult task; it required close coordination among the supply chain members. Any means to minimize uncertainty of demand helped to reduce the level of obsolescence. The average obsolescence rate across all stocking locations in the supply chain was 84.9% and the median was 84.3%. Obsolescence rates at each stocking location were calculated as the ratio of product left over at the end of the LTO over the inventory level at the end of phase eight, which was the last phase that was planned. Restaurants could offer the meal for up to four weeks beyond phase eight. Despite these obsolescence rates, product was manufactured close to the end of the LTO. For instance, Figure 4.10 indicates that even one week before disengagement there was a manufacturing run. The fact that there were manufacturing runs this close to the end of the life cycle of the product and that there were high levels of obsolescence indicate that it was not that there was no product available in the supply chain, but that the product was in the wrong place.

TRANSSHIPMENTS Transshipments occurred at all three tiers of the supply chain. Manufacturer 1 had one manufacturing facility with a warehouse attached to it and distributed products from that regional distribution center. Manufacturer #2 had three regional 155

distribution centers, including the warehouse attached to the plant where Ingredient B was manufactured. Management of Manufacturer 2 faced the decision of how to deploy inventory within their distribution network. Also, the Distributor could transship products between distribution centers in the event of running short of stock. Restaurant managers transshipped products between restaurants to avoid the end-customers facing a stockout. As reported by managers at the franchisor’s headquarters and confirmed in the field by interviewing restaurant managers, it was extremely rare that end-customers faced stockouts of promotional meals. They might face a stockout of other meals, but restaurant managers reported that they were especially proactive when it came to avoiding stockouts of the promotional meals. From the site visits to restaurants, it appeared that, in general, restaurants did not carry excess inventory for two reasons. First, restaurants paid the fee to the franchisor based on revenue, not profit. Therefore, inefficiencies were absorbed by restaurant owners. In the case of the restaurants owned by the franchisor, the restaurant manager’s bonus was tied to profitability. Thus, restaurant managers were motivated to run their business as efficiently as they possibly could and this means, among other things, not carrying excess inventory. In addition, the restaurant manager did not have an out-of-pocket cost associated with transshipments because they handled them themselves. There was a hidden cost, which was the opportunity cost of not being at the restaurant serving the end-customers and supervising the restaurant’s operation. The second reason for not holding inventory was that restaurants did not have much storage space. This was particularly true for refrigerated storage space. Therefore, emergency transshipments between restaurants were used to prevent end-customers from facing stockouts. Assisting a fellow restaurant manager in avoiding a stockout was in the best interest of every restaurant manager because restaurant managers helped each other. Furthermore, during a field visit, a restaurant manager explained that if he 156

had some storage space to spare he might order more product than he actually expected to sell in order to have enough inventory on-hand to assist other restaurant managers in need of some extra ingredients. Even though the franchiser’s management would rather have no transshipments, there was an information system in place to assist with the accounting associated with sharing ingredients. This system supported the transfer pricing between restaurants.

DETERMINING THE SIZE OF THE BUSINESS OPPORTUNITY Based on the transactions provided by all supply chain members, the product flow for LTO-11 was reconstructed. The product flow for the LTO was built from the amount of product that was shipped from and received by each location each day between the first and the last transaction of the LTO. In this way, it was possible to estimate daily inventory levels for each stocking location in the supply chain. Having daily inventory levels by location enabled the estimation of inventory holding costs and the amount of product left over at each location. Daily inventory levels at each stocking location were calculated as the sum of the inbound shipments minus the outbound shipments for each day. If some product was lost, rather than shipped to a customer, then this was not accounted for. However, none of the participants in the study identified spoilage and pilferage as a major concern. As product moves forward in the supply chain, it increases in value. For example, if the direct variable manufacturing cost of a case of product is $10, the product is held in inventory at the plant’s warehouse at this cost. If moving the product to regional distribution center #1 costs $1 then the same case of product is held at the regional distribution center at $11. Similarly, if the transportation cost to regional distribution center #2 is $2, the same case of product is held in inventory at $12. If a case of product is transshipped to regional distribution center #1 from regional distribution center #2 and the cost of transhipment is $ 0.50, then that case 157

of product should be held in inventory at $13.50. This implies that product at regional distribution center #1 should be held at different costs. As cumbersome as this is to explain, it is necessary to develop an accounting system to keep track of how much of each product is held at what cost. To simplify this, it was assumed that all product at each location was held at the same cost, and the cost of the transshipments were considered avoidable costs. Therefore, rather than taking into account the time effect of holding transshipped inventory at a higher cost and the negative effect that incurring these extra costs has on profit margin, transshipment costs were added as a lump-sum to calculate the business opportunity in terms of potential savings. In short, the business opportunity was the sum of all costs that could be reduced. For the majority of the transshipments, there were no data available to determine the exact cost of the transshipments other than the number of transshipments through the supply chain. Therefore, transshipment costs were estimated. For the manufacturers and distributors, the distance between the origin and the destination of the transshipment was used to estimate the truckload cost per unit of product. The truckload cost was used because the products included in this research are only two of many other products. Furthermore, the volumes of LTO products represented a small percentage of the total. Supply chain members who reported transshipments said that in the majority of the transshipments, there was no extra cost incurred. In less than ten percent of the cases, the cost incurred was due to the additional stop charged by the trucking company because the stop “was on the way of the truck”. No manager reported the managerial time required to deal with the transshipment as a cost. However, handling the transshipment does take managerial time. Data of the time, or cost, related to the management of a transshipment were not available. Failure to estimate a cost for these efforts, encourages transshipments. It is a common practice in modeling to estimate a “penalty” cost, to discourage an undesired 158

type of event. That was the case with transshipments in this setting. A $10 management time cost was added per transaction to all transshipments except for those between restaurants. For example, if 10 cases were transshipped at a transportation cost of $0.50 per case, the total cost of this transshipment was estimated at $15; that is, 10 cases at $0.50 per case, plus $10 for management’s time. For restaurants, $30 per transaction was used to represent the opportunity cost of not being at the restaurant. The appropriateness of the size of these penalty costs incorporated in the modeling process were validated with faculty and executives experienced in modeling in addition to the franchisor’s management. These penalty costs seemed reasonable and perhaps conservative. The cost of obsolescence was calculated by determining the final inventory levels at each location and the direct variable cost at which the product was held at each location. The final inventory levels were determined by considering all inputs and outputs to each location provided in the data. Table 4.6 presents the business opportunity; that is, the dollar amount that can be reduced with the implementation of dynamic time-based postponement. The cost estimates based on data available needed to be scaled to the portion of the supply chain that was modeled with the optimization software, which was 3.1% of the supply chain. For example, Table 4.5 shows that the amount of Ingredient A left over at the plant was $ 2,764. Since the manufacturers provided transactions for all product manufactured, scaled to the portion of the supply chain modeled the cost of product left over resulted in $86 ($2,764 / 100% * 3.1%). In the case of Ingredient B left over at the distribution centers, the dollar amount was $ 69,361. Since, there were data available for 48.3% of all products manufactured, this dollar amount was scaled and resulted in $4,449 ($69,361 /100% * 3.1%). Adding all estimates, shown in the bottom section of Table 4.6 (Business Opportunity - Totals), the total business opportunity for the portion of the supply chain modeled was $32,034. This amount scaled to the number of restaurants in the 159

Member of the Supply Chain Manufacturer 1Plant Manufacturer 2Plant Manufacturer 2RDC

Product

Cost for LTO before

Cost Component

Scaling Factor

1

Scalling ( ) Ingredient A Ingredient B Ingredient B

Ingredient A Distributor-DC Ingredient B

Ingredient A Franchisor-R Ingredient B

Inventory Holding Cost Left Over Product Inventory Holding Cost Left Over Product Inventory Holding Cost Left Over Product Transshipments Inventory Holding Cost Left Over Product Transshipments Inventory Holding Cost Left Over Product Transshipments Inventory Holding Cost Left Over Product Transshipments Inventory Holding Cost Left Over Product Transshipments

$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

Cost for LTO for the Portion of the Supply Chain in the 2

Model ( )

1,135 2,764 80 0 9,567 22,465 200 755 1,447 50 5,319 69,361 6,914 811 4,142 4,230 1,985 7,155 7,200

100% 100%

$

35

$

86

100% 100% 100% 100% 100%

$ $ $ $ $

2 297 696 6

45.3% 45.3% 45.3% 48.3% 48.3% 48.3% 3.1% 3.1% 3.1% 3.1% 3.1% 3.1%

$ $ $ $ $ $ $ $ $ $ $ $

52 99 3 341 4,449 443 811 4,142 4,230 1,985 7,155 7,200

Business Opportunity - Totals Cost Component

Inventory Holding Cost Left Over Product Transshipm ents Total System -wide Cost per year

Cost for LTO for the Portion of the System-wide Cost 4 Supply Chain in for LTO ( ) 3 the Model ( ) $ 3,523 $ $ 16,627 $ $ 11,883 $ $ 32,034 $ (8 LTOs/Year) $

113,650 536,365 383,325 1,033,340 8,266,718

1) This cost includes inventory holding costs, the cost of left-over product (obsolescence), and the cost of transshipments for both manufacturers, the distributor and 177 restaurants. 2) This column shows the cost for the LTO scaled to the portion of the supply chain represented by 177 restaurants. 3) This cost includes inventory holding cost excluding obsolescence, the cost of left-over product (obsolescence), and transshipments scaled to the portion of the supply chain represented by 177 restaurants. 4) This cost includes inventory holding costs excluding obsolescence, the cost of left-over product (obsolescence), and transshipments scaled to all restaurants in the system.

Table 4.6 Size of the Business Opportunity 160

system resulted in $1,033,340 ($32,034 / 3.1%). This cost estimate was based on one LTO, but there were eight LTOs a year. Thus, the total business opportunity was of $8.26 million, shown at the bottom of Table 4.6.

MODELING DYNAMIC TIME-BASED POSTPONEMENT The two main characteristics of dynamic time-based postponement are that it is dynamic, which means that the degree of postponement changes over time, and that it is focused on delaying the time when activities are performed, which contrasts with changing the sequence of activities. Showing that the sequence of activities was not changed was simple. Using the same optimization model, shown in Figure 4.14, for all modeling scenarios guarantees that the same sequence of activities was used. Showing that the degree of postponement was adjusted with time requires some explanation. If the extent of the use of postponement is dynamic, then some things in the environment change. Otherwise, the environment is stationary, and management decisions also could be stationary. However, the business environment under study was not stationary. The factors that change at the different phases of a LTO were the following: z

Limits to the use of postponement.

z

Degree of predictability of demand.

z

Total logistics cost trade-offs. The importance of each of these factors is related to the phases of the life-

cycle of the LTO. Each of these factors is described next.

LIMITS TO THE USE OF POSTPONEMENT As described in Chapter 2, a priori postponement should be implemented whenever it is possible and to the greatest extent possible. When the use of 161

postponement is limited, there is going to be speculative inventories. That is, whenever activities are not postponed until an order from an end-customer is received, activities will be performed with the belief that the products are going to be sold. Performing activities in advance of end-customers placing orders produces speculative inventories. Table 2.1, in Chapter 2, summarizes the limits to the use of postponement. Two types of limits are of particular interest to inventory management in this research setting. One is a strategic limit and it is related to the customer service strategy. The other is the cost effectiveness of delaying activities.

Strategic Limits to the Use of Postponement In the context of this research, early in the LTO, the biggest risk is to have a shortage of the ingredients for the promotional meal. A shortage of ingredients early in the LTO might damage the performance for the reminder of the LTO. In the event of a shortage affecting several restaurants, the managerial efforts to set up and launch the LTO, and advertisement investment will not produce the expected return. Therefore, the franchisor’s management wanted to position inventory close to the restaurants during the initiation of a LTO. Early in the life cycle of the LTO the cost of having speculative inventories was small relative to the cost of running out of stock. In addition, early in the LTO, the cost of carrying speculative inventories did not include any cost of obsolescence because the products had low risk of becoming obsolete at this point of the life cycle. Obsolescence, which in this setting was the single most expensive component of the costs of holding inventory , was not a concern yet. Because the end of the LTO was far in the future (relative to the length of the life cycle of a LTO) and, since approximately 40% of the expected amount of ingredients needed were positioned forward in the supply chain, management was, to a great extent, confident that it all would be needed. 162

In short, managements’ objectives limited the use of postponement because the balance between the perceived risk associated with running out of stock and the cost of carrying speculative inventories strongly tipped toward having speculative inventories as a way to avoid the risk of running out of stock.

Economic Limits to the Use of Postponement The proposition for dynamic time-based postponement stems, in part, from the contention that total logistics costs trade-offs could be different at different phases of the life cycle of a product [3], which was described in Chapter 2. Therefore, the economic limits to the use of postponement also might be different at the different phases of the LTO. In this setting, obsolescence costs should be traded off with the additional cost of using shorter, and more expensive, manufacturing and transportation lead times.

DEGREE OF PREDICTABILITY OF DEMAND Past LTOs may be used to develop an expectation about the forecast accuracy for a future LTO. Based on past research, and confirmed by data from past LTOs, after observing some demand of a LTO, the forecast could have been updated, reducing uncertainty of demand, and, ultimately, reducing safety stock levels throughout the supply chain. For the quantitative analysis, reducing uncertainty of demand occurred twice during the LTO. First, it was assumed that the forecast was either quantitatively updated, as shown in past research [4], or combined with an updated judgmental forecast. The development of the procedure for updating the forecast was beyond the scope of this research. Therefore, for the first update, it was assumed that there was a way to reduce the variability of the forecast errors, which reduced the need for safety stocks. The benefits from updating the forecast were estimated through a sensitivity analysis, which is shown later in this chapter. 163

The second update was based on information of disengagement. That is, it was assumed that there was an economical system by which restaurant managers revealed when they would disengage from the LTO and that this information was shared effectively. When information, such as the expectation of the behavior of a market, is transmitted, information might be degraded in the process. That is, it might be difficult to produce an abstraction of a person’s perception in order to be transmitted and processed by a computerized information system. Information might be lost in this process. This is called attenuation of information sharing [5] and the degree of attenuation has to be considered to evaluate the feasibility of information sharing. In brief, if there is a high degree of attenuation, then it might be better to decentralize decision making among the owners of the information (the frontline). If information can be shared with low or no attenuation, then the organization might perform better by centralizing decision rights. In this research setting, no decision rights were moved. However, this description is important to support the fact that the information sharing needed to plan for the termination of the LTO effectively is subject to no attenuation. The information that needed to be shared by the restaurant managers was the identification number of their restaurants and the week number in which they were planning to stop ordering from the distributors. When restaurant managers did not share this information, the franchisor’s management forecasted sales for each and every restaurant. If this forecast was shared, it was aggregated as it was transmitted backwards in the supply chain. The standard deviation of past forecast errors was used to determine safety stocks for each restaurant. This resulted in the supply chain, the distribution centers and the manufacturers in this setting, holding both cycle and safety stocks for all restaurants, despite the fact that restaurant managers knew that their average sales and variability of sales of the promotional meals would be zero upon disengagement, because they were discontinuing the LTO. 164

The alternative was that restaurant managers share the date that they plan to disengage from the LTO at their stores. Doing so and using this information for forecasting would result in avoiding holding both cycle and safety inventories for those restaurants. Figure 4.11 shows the actual proportion of restaurants still engaged in LTO-11 during its last phases. The figure shows that during phase six, all restaurants were engaged in the LTO. During phase seven, for only two distributions centers (numbers 17 and 20) all of their restaurants were engaged in the LTO. While for distribution center number 15, less than 50% of the restaurants served continued to offer the promotional meals. During phase eight, four out of nine distribution centers had to serve less than 50% of their original number of restaurants; and no distribution center had all their restaurants still engaged in the LTO. Figure 4.11 suggests that disengagement information needs to be shared at the end of phase five, which enables the supply chain to plan for the termination of the LTO. The system for sharing disengagement information can be, for example, a web-based information system which only has to be capable of recording a restaurant identification number and the week number of disengagement. Another technically feasible solution is to use a telephone system by which using a touchtone phone restaurant managers can report this information. In either case, data captured are recorded in a database, and the forecast is updated and shared accordingly.

TOTAL LOGISTICS COST TRADE-OFFS As described in Chapter 2, in the postponement section in the literature review, a total cost analysis might indicate a different least cost option at the different phases of the life-cycle of a product. LTOs are not different in this regard. Cost trade-offs are different at different phases of the life cycle of a LTO. Specifically, the single most important cost component that could be reduced is obsolescence. During the early phases, obsolescence is not a matter of concern. Management 165

100%

Proportion of Restaurants

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Phase of LTO

6

DC #12 DC #17

7

DC #13 DC #18

DC #14 DC #19

8

DC #15 DC #20

DC #16

Figure 4.11 Proportion of Restaurants that Disengaged Early from LTO-11

may develop a wide forecast confidence interval, rather than a point-forecast, and plan for the lower bound of this confidence interval. Doing so virtually guarantees that all manufactured product will be sold. However, as the LTO gets closer to the end, obsolescence becomes a key element of the total logistics cost trade-off analysis by increasing inventory holding costs. In this research setting, there were two feasible ways to identify when the total logistics cost had to be revaluated. One was to trigger the need to reevaluate total cost when the number of days of supply in stock, both cycle and safety stocks, was close to the number of days left in the LTO. The other way to determine when to reassess the total logistics cost trade-off was based on data availability about 166

disengagement. That is, when there are new data sufficient to update the forecast for the last time before the end of the LTO. It was assumed that there were lead-time options, such as using premium transportation, that were more expensive than the standard option, but that would provide shorter lead times. Shorter lead times imply lower inventory levels, both cycle and safety stocks. Lower inventory levels imply less capital at risk of obsolescence. Therefore, the cost trade-off that needed to be made was between the additional cost of premium lead times, premium transportation costs and “rush” manufacturing orders, and less obsolescence. If the amount of capital freed by reducing lead times multiplied by the expected rate of obsolescence was larger than the additional cost of using shorter lead times, then the supply chain would be better off by using the more expensive lead times for the last phases of the LTO. In the context of supply chain management, “rush manufacturing orders” does not seem to be the right term to call manufacturing runs with shorter lead times. Demand management, the supply chain management process, implemented across the members of the supply chain requires the behavior of the end-customer demand and inventory deployment tactics to be visible to manufacturers. Therefore, manufacturers should be able to plan for these manufacturing runs without building four or five days of planning into the replenishment cycle time quoted to the nexttier customers. If manufacturing managers are provided with updated data regularly, they will be able to plan internally as they receive updated information. Furthermore, shortening the replenishment cycle time quoted to the next-tier customers is most likely going to allow the next-tier customer to make a more informed decision because the decision is made closer to when end-customers place orders. Therefore, the order placed to the manufacturer will be more accurate. A more accurate order translates, on the one hand, to avoid ordering less than needed, which might result in an emergency order in the future. On the other hand, a more accurate 167

order translates to avoid ordering more than needed. If a distribution center orders more than needed, then inventory is pushed closer to the end-customer and inventory is geographically differentiated. Figure 4.12 shows the total logistics cost trade-offs and indicates with dark gray arrows the cost components that will be affected when reassessing the tradeoffs in this research. Since inventory holding costs increased markedly as obsolescence increased, additional inventory holding costs had to be traded-off with the higher cost of using premium manufacturing and transportation lead times and the lowered warehousing (throughput) cost. When premium lead times are used, fewer products will flow through the supply chain and, therefore, less warehousing costs will be incurred. Note that warehousing costs are throughput-related, and should not be confused with the fixed cost of warehouses which does not vary with the level of activity. Figure 4.12 also indicates with light gray arrows other costs that, in general, might be affected. In this research setting, however, the focus was on transportation and lot quantity costs which were related to production setups and order acquisition costs, and on customer service which was considered constant throughout the duration of the LTO.

DYNAMIC TIME-BASED POSTPONEMENT IN THIS SETTING In this research setting, dynamic time-based postponement, summarized in Figure 4.13, was designed to be implemented as a three-period method. Dynamic time-based postponement allows management to capture the changing aspects of the business environment throughout the life cycle of the LTO. In each of the periods, both management objectives and cost considerations were taken into account to determine the best inventory levels at each of the potential stocking locations in the supply chain. 168

Customer Service Level

Inventory Carrying Costs

Transportation Costs

Lot Quantity Costs

Warehousing Costs

Order Processing Source: Douglas M. Lambert, The Development of an Inventory Costing Methodology: a Study of the Cost Associated with Holding Inventory, National Council of Physical Distribution Management, Chicago, 1976, p.7.

Figure 4.12 Total Logistics Cost Trade-Offs

During the first period, Full Speculation, speculative inventories were positioned at the distribution center tier. This complies with the franchisor’s objective to position inventories as close to the end-customer as possible to cope with the low predictability of the performance of the LTO. There were two distinctive characteristics of the Full Speculation period. One was that cost would be higher than if management did not force inventory to be positioned forward in the supply chain. The other characteristics was that following the Full Speculation strategy during the initial phase(s) of the LTO was not proportionally as costly as following this strategy throughout the entire LTO. Early in the life cycle there was virtually no risk of obsolescence, so inventory holding costs were low. 169

In the second period, Maturity, the perceived uncertainty associated with the performance of the LTO in part was dissipated after observing some actual demand data. Supply chain members reevaluated inventory deployment and the managerial focus became cost minimization similar to a standard product. That is, the expected demand variability was calculated based on past LTOs and, if possible, using the observed demand during the early phases. Management did not impose any inventory policy constraints, such as holding speculative inventories at the distribution centers, and optimized inventory locations and levels across the supply chain. The third, and last, period is Plan for Termination. Two factors become dominant during this period: obsolescence and gathering information about disengagement. In contrast to the traditional way in which obsolescence is incorporated into inventory models, in this setting, obsolescence should be included

Dynamic Time-based Postponement: a three-period method – 1st Period: Full Speculation • Forecast a wide confidence interval for the entire LTO • Push inventory forward in the supply chain to minimize risk of stockout

– 2nd Period: Maturity • Minimize total cost as for a standard product – Stop speculation

• Update forecast

– 3rd Period: Plan for Termination • Update Forecast – Gather disengagement information

• Obsolescence becomes a critical consideration • Reassess total cost – Trade-off inventory carrying costs and transportation costs – Avoid obsolescence risk/cost by using premium transportation

Figure 4.13 Dynamic Time-Based Postponement: An Overview 170

in cost calculations starting from a determined point in time because the life-cycle of the product is predetermined. That is, managers could determine a particular point in time when supply chain members have to start considering obsolescence. This deterministic view of obsolescence contrasts with defining a stochastic process or a mathematical function to model obsolescence throughout the life cycle of the LTO. Dynamic time-based postponement is intended to capture the dynamic nature of management’s decisions. It is unlikely that managers plan at one point of time and follow the plan exactly despite changes in the environment. In this research setting, managers had the flexibility to adapt inventory levels periodically. In fact, the life cycle of the product was “managed” in this research setting. The life cycle of a LTO was managed by using advertising, and by removing the promotional meal from the menu whenever the restaurant managers’ found it appropriate. The decline phase in the life cycle of a LTO was marked by the end of advertising and the termination phase started when restaurant managers decided unilaterally to stop offering the promotional meals. The information about when the restaurant managers will stop offering the LTO is valuable to the rest of the supply chain because it affects inventory deployment and, consequently, has considerable impact on the amount of inventory at risk of obsolescence.

DEVELOPING THE OPTIMIZATION-BASED MODEL The commercial software used for the analysis is based on an optimization algorithm developed by Graves and Willems [6] (G&W2000) and introduced in Chapter 3. Using existing optimization software allowed the focus of this research to be on the conceptual development and the empirical aspects of dynamic timebased postponement. Another reason for using commercial software was to use 171

the same tools that were available to management. In other words, dynamic timebased postponement should be an available solution to management willing to coordinate activities across the supply chain, rather than available only to those who understand complicated mathematics or those who have proprietary software. Using the modeling approach of G&W2000, a supply chain is modeled as a chain of stages. Each stage represents an activity or a set of activities after which inventory can be held unless otherwise specified. Therefore, a stage can be all necessary activities to procure a raw material, to transport product between locations, or to serve demand. Two stages are connected by an arc. Stages and arcs have properties which determine the behavior of the model. The behavior of the model also depends on properties of the supply chain, such as the modeling horizon. Table 4.7 summarizes the properties used for the supply chain, stages and arcs. Table 4.7.1 shows the input parameters for the supply chain, which are the modeling horizon, the base time unit, and whether review periods are used. Table 4.7.2 shows the input parameters for the stage. Input parameters vary by the type of the stage; some parameters apply to all stages, some are specific to demand stages, and others are particular to internal stages. Internal stages are all stages other than demand stages. The input parameters are stage cost, holding cost, and stage time, which apply to all stages, and are described in the column Description. Time constraints also could apply to all stages, but are activated where needed. The Service Time constraint refers to the maximum and minimum replenishment time quoted to the next-tier customer. The maximum service time quoted to the customer is used to protect the supplier stage; the minimum service time is used to protect the next-tier customer. For example, stores need to have product available on-the-shelf, with no delay, when end-customers order meals. Thus, the maximum service time at the stores is set to zero. The exposure time refers to the number of days for which safety stock has to be held. Therefore, for example, 172

since no safety stock is held in the transportation stages, their maximum exposure time parameter is set to zero. The concepts of service time and exposure time, and the optimization algorithm were explained in Chapter 3. Table 4.7.2 also shows the parameters for demand stages. These are the average daily demand, the coefficient of variation of demand, the service level (measured as probability of no stockout), and a measure of autocorrelation of demand. The coefficient of variation of demand (COVd) is preferable to the standard deviation of demand because it is a coefficient; that is, it has no unit of measure. Thus the same coefficient of variation for all restaurants could be used for each phase while accounting for the size of the restaurant. The use of COVd facilitated data input and minimized errors. Customer service level or product availability can be measured in several ways, each of which results in different inventory levels and has different managerial implications. As described in Chapter 3, the modeling approach assumes that all demand below an upper bound will be filled; and that if demand is higher than this, managers will resort to other ways to fill demand. For instance, restaurant managers will transship ingredients from another restaurant. Therefore, the measure of customer service level used is probability of no stockout, which measures the number of times managers need to resort to exceptional actions to avoid a stockout. This measure is appropriate in this setting because once a stockout occurs the effort (and cost) of the transshipment of one unit or a several units of ingredient was almost the same. Autocorrelation of demand describes the relationship of daily demand at each store over time. Demand variability is calculated from the standard deviation of the forecast errors and it is assumed that the forecast method used is regressionbased. In this situation, the forecast error can be included as one regression term and demand considered to be independent over time. Because the franchisor was implementing a forecasting system at the time this research was conducted, considering demand to be independent over time was appropriate. 173

The other input parameter for internal stages is the pooling factor which is the extent to which demand streams from several locations served by the same stage are pooled. The concept of variance pooling was described in Chapter 2. Finally, arcs have a single input parameter, shown in Table 4.7.3. The value of an arc indicates how many units of supply are needed for each unit produced by the customer stage. For example, the unit for a manufacturer of an ingredient is a case, but restaurants produce 200 meals from a case of product; therefore, the value of the arc is 0.005 (1/200). Parameter Modeling Horizon Base Time Unit Review Periods

Description The length of time that is modeled What is the unit of time modeled: day, week, etc. Whether review periods are enabled

Value 1 year Day Enabled

Table 4.7.1 – Input Parameters of the Supply Chain Stage Type

All Stages

Demand Stages

Parameter Stage Cost Holding Cost Stage Time Max and Min Service Time

Description The added cost to a unit of product Cost rate at which inventory is held The time it takes to process one unit Constraint to the time quoted to the next-tier customer

Max and Min Exposure Time

Constraint to the exposure time of a stage.

Review Period

Length of the review period

Demand

Average demand per base time unit

Coefficient of Variation of Demand

Measure of demand variability

Service Level Autocorrelation of Demand

Internal Stages

Pooling Factor

Comments

Min=Planning and Max=Promise Used to set minimum inventory levels during Speculation Only if the review period is longer than the base time unit The variability of forecast errors was used.

Measured as probability of no stockout Measure of correlation of demand over time (autocorrelation), The extent to which demand uncertainty accrues over time. How demand uncertainty is pooled at a central location.

Table 4.7.2 – Input Parameters of the Stages Parameter Value

Description Number of units of the supplier node that are needed per unit at the customer node

Table 4.7.3 – Input Parameters of the Arcs

Table 4.7 Supply Chain Modeling Input Parameters 174

MODELING THE BUSINESS OPPORTUNITY In this section, the model developed to test dynamic time-based postponement is described. Then, the metrics used during modeling are defined. Finally, the method for assessing the benefits from dynamic time-based postponement is described

DESCRIPTION OF THE MODEL Figure 4.14 shows a snapshot of the model. Note that despite the similarity to a network model, in which the icons represent facilities or nodes of the network, in the G&W2000 modeling approach the “nodes”, called stages, represent an activity or a set of activities. However, many of the icons shown in the figure coincide with facilities because there was no need to disaggregate the activities that take place inside each facility, such was the case of plants, distribution centers, and restaurants. Each of the icons shown in Figure 4.14 is a stage in the model. The stages with a small triangle inside represent possible locations to hold safety stock. There are some stages where safety stock cannot be held; these are transportation stages. They are identified in Figure 4.14 in the section labeled “Transportation” or by the text-box that reads “Interplant Transportation”. The other stage in which inventory cannot be held is identified with a text-box that reads “Dummy Stage”. A dummy stage is one that is needed to make the model work; it is a modeling artifact. In this case, the dummy stage is needed to make the time constraints work. The left-most stages shown in Figure 4.14 are the procurement stages after which safety stocks of raw materials can be held. The raw material safety stocks would be physically located at the plants. The next section, from left to right in the figure, represents the manufacturing of the ingredients for the promotional meals. The manufacturer at the top has a regional distribution center that was included in this research; thus, there is an interplant transportation stage between the plant 175

Interplant Transportation

Dummy Stage

Demand Generation Warehousing (DCs) Transportation

Stores Distributors

Warehousing (RDCs) Manufacturing

Manufacturers

Procurement

Stage can hold safety stock

Stage cannot hold safety stock

Figure 4.14 Modeling the Business Opportunity 176

and the regional distribution center. The next section in Figure 4.14 shows all transportation stages between the manufacturers and the distribution centers. Since each transportation segment has its own cost and lead time, each one is modeled as a stage. The stages in the next section to the right represent the distribution centers. Note that there are two stages for each distribution center. This was required because there are two inventory stacks in each distribution center, one for each ingredient. Furthermore, recall that the arc between two nodes describes the number of units from the supplier stage that are needed for each of the units of the customer stage. Since the portioning ratios of ingredients are different for the two ingredients, the value of the arcs between distribution centers and restaurants are specific to the ingredient that flows through the arc. The rightmost section is for the demand generation stages, the stores or restaurants. Note that each restaurant is linked to each of the two stages that represent the corresponding distribution centers.

METRICS USED IN MODELING THE BUSINESS OPPORTUNITY The ultimate performance metric of interest in this setting was supply chain cost. Supply chain cost was defined as the sum of cost of goods sold and inventory holding cost. The cost of goods sold is the sum of the cost of the throughput for all the stages in the model. Therefore, in this model, cost of goods sold included the variable, out-of-pocket cost of manufacturing, transportation, warehousing, and acquisitions costs. The cost of throughput varies with the level of activity. The inventory holding cost is the time value of the capital invested in inventory; thus, the inventory holding cost for the supply chain was defined as the sum of the inventory holding cost incurred by each of the stages in the model. Obsolescence is accounted for outside the optimization model, as described earlier. 177

Another metric used is total inventory investment, which was defined as the sum of the capital invested in inventory (both cycle inventory and safety stocks) at all stages. The cost of this capital is accounted for in inventory holding cost; however, obsolescence costs are accounted for outside the model. Total inventory investment was used to perform a break-even analysis to evaluate the trade-off between obsolescence, and premium transportation and manufacturing. Finally, there are measures of time for each stage, most of which have been described in Chapter 3 in the section where the optimization algorithm was described. These measures of time are the stage time, the inbound service time, the service time, and the number of days of exposure.

ASSESSING THE BENEFITS FROM DYNAMIC TIME-BASED POSTPONEMENT The optimization-based model was used to produce several scenarios. For each scenario, only the information that would have been available to managers at the time they would have run the model was used. The objective was to be able to recreate what management would have done throughout the life cycle of the LTO. First, the time-line of a LTO is reviewed. Second, the forecasting and forecast updating processes used to generate the input to the model are explained. Third, the modeling scenarios that were developed are described.

Time-line of a LTO The first event in a LTO that matters to this research is the franchisor’s formal communication to all managers involved, containing planning information. Informal communications occurred as needed both before initiating the LTO and throughout the life cycle of the promotion. Even before the formal communication, through informal contacts, the other supply chain members learned about the LTO. 178

The first formal communication happened 10 weeks before the LTO was initiated. At this time, the marketing department at the franchisor published the forecast of the LTO mix. The LTO mix was the expected fraction of total sales in the form of promotional meals. Other elements of the formal communication were relevant dates such as the first and the last dates of availability of ingredients to the restaurant, when the advertising starts and ends, and the earliest and latest date for disengagement. The formal communication also included information not directly related to this research, such as description of meals, portioning of ingredients, costs, and key contacts. Table 4.8 is an example of the typical time-line for a LTO; this particular one is for LTO-11 which was analyzed in this research. The column labeled Phase Description shows the phases for which there was advertisement. Table 4.8 includes the disguised forecast of the LTO mix. Restaurant managers use the column labeled Forecast Mix with their own forecast of total sales to determine the forecast in units for the LTO at their own restaurants. Therefore, the first necessary step in modeling dynamic timebased postponement in this setting was to produce a forecast for LTO-11.

Forecasting the LTO The first step in planning for the LTO was to produce a forecast. Producing a forecast for a LTO implies that the expected average sales and the expected variability of demand are produced for each restaurant included in the model. Determining Expected Average LTO Sales. The point-forecast, the expected average LTO sales, was determined by multiplying the expert’s forecast of the mix by each restaurant’s forecast of total sales. This forecast was produced during the planning phase which happened 10 weeks before the LTO is initiated (see the column labeled Week Number in Table 4.8). Note that the forecast was for the entire LTO; therefore, the same total sales figure by restaurant had to be used for all phases of 179

Week

Phase Number

Phase Name

Phase Description

-10

Planning

Planning

-3 Æ 0

Training

Before Initiation

Disguised LTO Mix (Expert's Forecast)

Disguised Forecast in Units for the System-wide Average Restaurant

1

1

Initiation

1st week pre-media

9.37%

133

2

2

Growth

Half week media

11.47%

318

3

3

Maturity #1

1st full week media

12.07%

371

4

4

Maturity #2

2nd full week media

12.57%

416

5

5

Maturity #3

3 rd full week media

12.57%

416

6

6

Maturity #4

Half week media

11.97%

363

7

7

Decline

1st full week post media

10.37%

221

8

8

Termination

2nd full week post media

9.37%

133

Disengagement

After Termination

9+

Table 4.8 Typical Timeline for a Limited-Time Offer

the LTO. The total sales figure multiplied by the expected LTO Mix for each phase of the LTO produced the expected LTO Sales, which varied by phase. The forecast of total sales used for planning purposes, and included in the initial formal communication described earlier, was based on the system-wide average and developed in the Marketing function at the franchisor’s headquarters. However, restaurant managers forecast total sales for their own restaurants every week. Restaurant managers were instructed to forecast using the modified moving average method described earlier in this chapter. Determining a Measure of Demand Variability. A measure of variability of demand for each demand stage was needed for modeling. To determine the COVd’s, the variability of forecast errors of six past LTOs were used. Forecast errors were computed as follows: 180

1. Forecast the number of meals to be sold by restaurant by phase (week). 2. Determine the actual number of meals sold by restaurant by week. 3. Calculate the percentage forecast errors by restaurant for each phase as:

PFE R,Ph =

(Actual R, Ph - Forecast R, Ph ) Actual R, Ph

where:PFE R,Ph: Percent Forecast Error for restaurant R and phase Ph R: Restaurant Ph: Phase 4. Calculate the standard deviation of the percentage forecast errors for each phase across all restaurants. The standard deviation of the weekly percentage forecast errors was calculated for each of the phases of the LTOs. Using this measure for the expected variability of demand has two benefits. First, safety stock requirements are lower when using the standard deviation of forecast errors rather than the standard deviation of demand [7]. Second, using a measure of variability of the forecast errors allows one to assume that the correlation of demand over time is zero [8]; ignoring the effect of autocorrelation of demand would make the number of stockouts larger than expected [9]. Figure 4.15 shows the standard deviation of the weekly percentage forecast errors for seven past LTOs. Based on these data, the expected variability of demand for LTO-11 was estimated. Figure 4.15 indicates with an arrow for each phase the expected COVd used as the initial input for modeling LTO-11. Table 4.9 shows the coefficients of variation of demand used as input to the optimization model. To confirm that this approach gave meaningful estimates, the actual standard deviation of the percentage forecast errors for LTO-11 were calculated and shown in Figure 4.15. 181

Standard Deviation of Forecast Errors

6 5 4 3 2 1 0 1

2

3

4

5

6

7

8

LTO Phase LTO-00

Note:

LTO-01

LTO-02

LTO-03

LTO-04

LTO-10

LTO-11

The arrows indicate the expected variability of demand for which inventory deployment would be planned.

Figure 4.15 Determining the Expected Variability of Demand

Phases of Initial Coefficient the LTO of Variation of 1 2 3 4 5 6 7 8

3.00 1.50 0.70 0.40 0.50 0.70 1.20 2.00

Table 4.9 Initial Coefficients of Variation of Demand Used for Modeling 182

MODELING THE THREE PERIODS OF DYNAMIC TIME-BASED POSTPONEMENT In this section, the modeling scenarios used for describing the three periods of dynamic time-based postponement are presented. The first necessary step was to develop a scenario that represents the current practice of the supply chain in this research setting. For the second period of dynamic time-based postponement, Maturity, inventories are optimized as for a standard product. Therefore, the Maturity scenarios functioned a baseline from which the other two periods of dynamic timebased postponement, Full Speculation and Plan for Termination, were modeled. Table 4.10 summarizes the optimization scenarios developed to assess the benefits from the implementation of dynamic time-based postponement. Scenario M1, shown in the table with gray background, represented the lead times option actually used and it was the least cost option of the four Maturity scenarios. M1 used standard lead times for both transportation and manufacturing.

Period of Dynamic Time-based Postponement Full Speculation Maturity

Plan for Termination

Scenario

Premium Transporation Lead Times

Premium Manufacturing Lead Time

Premium Manufacturing Cost

S1 M1 M2 M3 M4 T1 T2 T3 T4

No No Yes No Yes No Yes No Yes

No No No Yes Yes No No Yes Yes

20% 20% 20% 20%

Table 4.10 Modeling Dynamic Time-based Postponement: Scenarios Developed with the Optimization Software 183

Forecast #

1 2 (Sensitivity Analysis)

3

M2 modeled the use of premium transportation lead times, which were shorter but more expensive than those used for M1. Since transportation was by truck, the use of premium transportation meant that a team of drivers was used when the lead time for a single driver was longer than 10 hours. Consequently, the use of premium transportation did not reduce all lead times. M3 modeled the use of premium manufacturing which meant that the lead times were reduced by most of the planning time the manufacturers incorporated into the promised time to their customers. For example, one manufacturer quoted to the next-tier customer 10 days from order placement to available for transportation. The production time, the minimum time needed for production, was approximately three days. Therefore, the premium manufacturing lead time was set to five days. Based on discussions with managers and academics expert in the field, the premium for using shorter manufacturing lead times was set to 20% of the variable manufacturing cost. The fourth scenario for the Maturity period is M4, which included the use of both premium transportation and manufacturing lead times. Table 4.10 summarizes all scenarios included in the quantitative analyses of dynamic time-based postponement. The forecast used for modeling all four Maturity scenarios, labelled forecast #2 in Table 4.10, was the same that had been used for the Full Speculation period. To assess the benefits from updating the forecast for the Maturity period, a sensitivity analysis was used. This analysis served to estimate the impact of reducing COVd on the supply chain cost and inventory investment. Table 4.11 presents the resulting number of days of exposure for all potential stocking locations in the supply chain and has three sections. The first section is the top row and shows the days of exposure for the restaurants which are the same for both ingredients and for all scenarios. Days of exposure is a measure of the size of the safety stock. If the number of days of exposure at one location is zero, then no safety stocks are held there. Furthermore, days of exposure for the restaurants remain unchanged across all four scenarios because of the need to offer immediate product 184

Ingredient B

Ingredient A

Scenario

Stage Restaurants Procure Manufacture DC-13 DC-14 DC-18 DC-15 DC-20 DC-12 DC-16 DC-17 DC-19 Procure Manufacture DC-12 DC-16 DC-17 DC-19 Warehousing-RDC DC-13 DC-14 DC-18 DC-15 DC-20

M1 (Standard Lead Times)

M2 (Premium Transportation Lead Times)

M3 (Premium Manufacturing Lead Times)

M4 (Premium Transportation and Manufacturing Lead Times)

3 0 17 5 6 7 6 6 7 8 7 6 14 7 5 7 8 7 3 7 7 8 6 6

3 0 17 5 6 6 6 6 6 7 6 6 14 7 5 6 7 6 2 6 6 7 6 6

3 7 5 5 6 7 6 6 7 8 7 6 14 4 5 7 8 7 3 7 7 8 6 6

3 7 5 5 6 6 6 6 6 7 6 6 14 4 5 6 7 6 2 6 6 7 6 6

Note: Scenario M1 represents the baseline for comparing the Full Speculation and Plan for Termination periods of dynamic time-based posponement.

Table 4.11 Days of Exposure by Stocking Location - Maturity 185

availability to the end-customers (restaurants only could have a quoted service time of zero days to the end-customer since no end-customer is willing to wait for a day or more to get the meal), their review period was two days, and they had to place orders one day before delivery. This resulted in three days of exposure. The second section shows the days of exposure at each of the stages involved in the product flow of Ingredient A. The third section is the bottom part of the table and presents the days of exposure for the stages involved in the product flow of Ingredient B. Note that some stages in Table 4.11 are indented to represent the product flow. That is, for Ingredient A, all warehousing stages (all distribution centers) are served directly after the manufacturing stage. While for Ingredient B, the stages DC-12 to Warehousing-RDC are served by the plant’s warehouse, after the Manufacture activity, while the other five stages are served by the stage named Warehousing-RDC. Table 4.11 indicates that comparing scenarios M1 and M2, the use of premium transportation lead times affected some distribution centers and the regional distribution center operated by Manufacturer #2. But transportation lead times are not shortened enough to affect the manufacture and the procure stages. Comparing scenarios M1 and M3, Table 4.11 shows that by shortening the manufacturing lead times, the manufacture stages and, in the case of Ingredient A, also the procure stage, activities would be delayed if scenario M3 was used. In the case of Ingredient A, part of the safety stocks of finished goods are pushed backwards and held in the form of raw materials instead. Scenario M4 shows a similar effect but on both distributors and manufacturers. These results should not be generalized to other supply chains, they depend on the supply chain network structure and on the lead-time alternatives available to use. It is important to highlight that using the same days of exposure for all phases of the LTO does not imply that the inventory levels at each location will be the same for all phases. Safety stock level at a specific stocking location depends not 186

only on the number of days of exposure, but also on the safety factor and the standard deviation of demand (d) (which is the COVd multiplied by the average demand). The safety factor used to determine the safety stock levels is the same for all locations for all phases of the LTO, but the standard deviation of demand is specific to each location and depends on the phase of the LTO. Therefore, despite the fact that the number of days of exposure is stationary, safety stock levels vary by phase. Figure 4.16 shows the planned safety stock levels of Ingredient B by location. This plan was developed 10 weeks before the LTO was initiated. When the forecast was updated, the expected standard deviation of demand for each restaurant was updated and, consequently, target safety stock levels for each location for each of the remaining phases of the LTO changed. The Full Speculation period of dynamic time-based postponement was modeled using one scenario: S1. S1 was based on M1, the least cost option of Maturity, and incorporated a constraint on the inventory policy at each distribution center. Specifically, in S1, distribution centers were forced to hold 20 days of safety stock. Scenarios S2, S3 and S4 are not presented because S1 was the least cost alternative, and it is the lead-time option that would be used. The Plan for Termination period includes the same four scenarios of Maturity but with an updated forecast based on the information about disengagement shared by the restaurant managers at the end of phase 5. The reminder of this section is organized as follows. First, the Full Speculation period is described. Second, the Maturity period is described including the assessment of the benefits from updating the forecast after some demand is observed; that is, using forecast #2. Third, the description of the Plan for Termination period is presented which includes the forecast update based on the restaurant managers sharing the time of disengagement of the LTO.

d)

See equation (1) in Chapter 3 on page 114.

187

188

25

30

1

Manufacturer 2-RDC

2

5

DC-20

DC-19

Phase of LTO

4

Manufacturer 2 - Plant & RDC

3

DC-18

DC-17

6

DC-16

DC-15

7

DC-14

Safety Stock Levels of Ingredient B by Stocking Location — Original Plans

Figure 4.16

These inventory levels represent the original plans developed beforethe limited-time offer started

Procure Ingredient B

0

5

10

15

20

Cases of Ingredient

DC-13

8

DC-12

The Full Speculation Period The Full Speculation period resembled the traditional inventory management practice of positioning inventories close to the restaurants. Speculation is a strategy frequently used to ensure high customer service levels. In some industries, the term “speculation” has a bad connotation and viewed as engaging in risky business transactions. This is the case in commodity trading. However, in the field of logistics, speculative inventories (safety stocks) are used to protect against uncertainty. Speculation is achieved by positioning inventory closer to the end-customer and, generally, is more expensive than minimizing costs because more inventory is held at a higher cost and because inventory is differentiated geographically. In this research, the Full Speculation period was compared with the results of the Maturity Period, which represented the baseline prescribed by the optimization software. The Full Speculation period is modeled by incorporating constraints to the minimum inventory levels, measured as days of exposure. These constraints forced inventory levels to be higher than or equal to a minimum. Since these constraints represented the management’s strategy, it was important to identify the cost of following this strategy. The franchisor required the suppliers to manufacture and deploy about 40% of the expected sales for the entire LTO. Therefore, to produce the Full Speculation period, this requirement was translated in to setting the minimum number of days of exposure to 20 days for all distribution centers. In the modeling approach used for this research, days of exposure are “shared” among the supply chain members. That is, the optimization model prescribes where and how much safety stock to hold. If the safety stock locations and /or levels are forced, then the model indicates the optimal inventory locations and levels for the rest of the supply chain given the constraints. This was the case in this research setting for the Full Speculation period. Given that safety stocks were positioned forward, at the distribution centers, the regional 189

distribution centers and the manufacturers needed to hold less safety stocks. Table 4.12 shows the number of days of exposure held at each location during the Full Speculation period, S1, side-by-side with the days of exposure of M1, the base scenario. Table 4.12 shows that the number of days of exposure at the distribution centers is 20 for both ingredients. For Ingredient A, the table shows that during the Maturity period, 17 days of exposure are held in stock at the Manufacture stage and no safety stock is held in the form of raw materials at the Procure stage. Since in the Full Speculation period inventory is pushed forward to the distribution centers, no safety stocks are held in the form of finished goods at the Manufacture stage. But, some inventory is pushed backwards and now 7 days of safety stock are held at the Procure stage in the form of safety stocks of raw materials. In short, during the Maturity period, the Manufacture stage for Ingredient A, which represents the plant where the ingredient is manufactured, operates in a make-to-stock environment, while during the Full Speculation period, the plant operates in a maketo-order environment, In a traditional setting, postponement-speculation is used internally. If speculation was the strategy of choice, then safety stock levels would be increased at a particular location, but other inventories would have remained unchanged elsewhere. In this research setting, this internal view would have led to holding 20 days of safety stock at the distribution centers while leaving unchanged safety stock of finished goods at the regional distribution center and plants, as well as safety stock of raw materials at the plants. Table 4.13 indicates that the additional inventory holding costs of being internally focused would have been between 8.92% and 9.77% more expensive than the situation in which inventory locations and levels were optimized across the supply chain given the speculative strategy. Table 4.13 shows the results for four obsolescence rates. As described before, the actual average obsolescence rate 190

Scenario ---------------------------------

Ingredient B

Ingredient A

------------------------------

M aturity (Base Scenario M1)

Full Speculation (S1)

3 0 17 5 6 7 6 6 7 8 7 6 14 7 5 7 8 7 3 7 7 8 6 6

3 7 0 20 20 20 20 20 20 20 20 20 14 0 20 20 20 20 3 20 20 20 20 20

Stage Stores Procure M anufacture DC-13 DC-14 DC-18 DC-15 DC-20 DC-12 DC-16 DC-17 DC-19 Procure M anufacture DC-12 DC-16 DC-17 DC-19 Warehousing-RDC DC-13 DC-14 DC-18 DC-15 DC-20

Table 4.12 Days of Exposure: Maturity and Full Speculation Periods

was 84.9% of the inventory available at the end of phase eight. Therefore, an expected obsolescence rate of 84% was used as a starting point to determine the cost to the supply chain. Since it was expected that obsolescence rates would decrease as a result of the implementation of dynamic time-based postponement, the cost to the supply chain was calculated using obsolescence rates of 84%, 66%, 48% and 30%. 191

192

Table 4.13

$ 46,067 $ 36,494 $ 26,920 $ 17,347

100.0%

100.0%

100.0%

100.0%

$ 50,176 $ 39,793 $ 29,411 $ 19,028 108.92% 109.04% 109.25% 109.69%

Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Internally without Considering the Impact on the Rest of the Supply Chain

Comparison of Coordinating the Use of Speculation across the Supply Chain and Using Speculation

Speculation all eight phases - locations and levels of safety stock are optimized across the supply chain

Speculation all eight phases - Not Optimzing Safety Stocks across the Supply Chain (speculative stocks are position at DCs, other safety stocks are left unchanged across the supply chain)

Percent of Minimum Cost Alternative

Dollars

Expected Inventory Holding Costs to the Supply Chain for LTO-11 for the Portion of the Supply Chain in the Model

The Maturity Period The focus of the Maturity period is on cost minimization as for a standard product. The constraints on safety stock locations and levels measured in days of exposure by location were removed. It addition, it was assumed that forecast accuracy could be improved and updating the forecast affected supply chain members to different extents. These two factors are described next. Removing Constraints on Inventory Policies. At this point of the life cycle of the LTO, there are two possible options: either to continue with the speculative strategy throughout the duration of the LTO, or to switch to Maturity periods of dynamic time-based postponement. Removing the constraints on the number of days of exposure at the distribution centers makes it necessary to reassess inventory policies throughout the supply chain. Using Full Speculation for the entire LTO should cost more than combining it with Maturity starting with phase four. Since in this research setting inventory requirements are low, the cost of using Full Speculation for the entire LTO was estimated at approximately 0.5% more than combining Full Speculation and Maturity. This percentage represents the inventory holding costs relative to manufacturing, transportation and warehousing costs. Table 4.14 shows that the inventory holding cost associated with using Full Speculation for all eight phases would have been approximately 5% higher than using Full Speculation for the first three phases and Maturity for the remainder of the LTO. Updating the Forecast. As presented in Figure 3.5 (Developing Optimized Scenarios) in Chapter 3, the forecast is updated and the inventory levels reevaluated after observing some actual demand. The franchisor’s management would update the forecast (average sales and expected forecast errors), and inventory locations and levels would be reassessed using the optimization model. For the purpose of assessing the potential of dynamic time-based postponement, when updating the 193

194 $ 25,633

$ 26,920

Table 4.14

$ 34,727

$ 36,494

$ 16,538

$ 17,347

100.0%

105.12%

100.0%

105.09%

Combining Full Speculation and Maturity Periods of Dynamic Time-based Postponement

100.0%

105.02%

100.0%

104.89%

Percent of Minimum Cost Alternative Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Comparison of Using Full Speculation for the Entire LTO and

Full Speculation is Combined with Maturity (Full Speculation Is Used for the First Three Phases, $ 43,822 Maturity Is Used for the Remainder of the LTO)

Full Speculation Is Used for All Eight Phases of the LTO (Locations and Levels of Safety Stock Are Optimized across $ 46,067 the Supply Chain)

Dollars Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Expected Inventory Holding Cost to the Supply Chain for LTO-11 for the Portion of the Supply Chain in the Model

forecast, the average sales figures to input to the model remained unchanged in order to enable the comparison of scenarios produced by the optimization runs. For this research, the focus was the locations and levels of safety stocks. Therefore, the benefits from updating the forecast were evaluated by means of sensitivity analysis on demand variability. To update the forecast, it was assumed that managers were able to improve the forecast and reduce the variability of the forecast errors. Note that in the setting of a LTO or a product with a short life cycle, the forecasting horizon is shorter at each forecast update because the life cycle is predetermined. This contrasts with most ongoing business situations where each time the forecast is updated, the forecasting horizon is the same. In the case of rolling forecasting horizons, forecast accuracy does not necessarily decrease. However, the fact that the forecast horizon is shorter for each forecast update makes it reasonable to assume that forecast accuracy can be improved. The development of the forecast updating process itself was beyond the scope of this research. To show the benefits from updating the forecast, a sensitivity analysis of the COVd (coefficients of variation of demand) was performed. Figure 4.17 shows the results of the sensitivity analysis of reducing variability of demand for all restaurants. Since reducing variability of demand did not affect the number of days of exposure, the locations of the safety stocks were unchanged. The sizes of the safety stocks, of course, did change. The relationship between reducing variability of demand and safety stock cost was linear as shown in Figure 4.17. Table 4.15 shows for phases six and eight (other phases showed similar results), the impact that updating the forecast had on supply chain costs and total inventory investment. Supply chain cost included direct out-of-pocket costs and the cost of holding inventory, assuming there was no obsolescence. When risk of obsolescence is a concern, reducing inventory investment becomes a key objective as in Plan for Termination, the last period of dynamic time-based postponement. 195

Table 4.15 shows that the reduction in supply chain cost from reducing variability of demand was modest. Four factors made the supply chain cost slightly sensitive to changes in variability of demand. First, lead times of the various activities in this supply chain were short. For example, manufacturing cycle times, from order placement to product available for shipping, were 10 and 7 days for Ingredients A and B, respectively. Second, the ratios of number of meals to a case of each of the ingredients were small. For Ingredient A, this ratio was one case to 380 meals; for Ingredient B, the ratio was one case to 320 meals to a case. Thus, high variability of demand in number of meals at a restaurant translated to relatively low demand variability in cases to the distribution centers. Together, short lead times and small portioning ratios lead to the next factor. Third, even with the highest variability of demand of all phases (COVd=300%), safety stock levels throughout the supply chain were low. A big reduction in a small number is still a small number. However, updating the forecast, reduces inventory investment. Reducing inventory investment becomes a critical issue closer to the end of the LTO in order to reduce capital at risk of obsolescence. Fourth, the review period for the distribution centers was seven days, and, with the exception of manufacturing processing times, the longest lead time was three days. Therefore, the review periods were considerably long relative to the lead times. In spite of the size of the benefits from improving forecast accuracy, it is interesting to see which members of the supply chain would have benefited from updating the forecast. Since safety stocks were located throughout the supply chain considering no firms’ boundaries, some locations did not require safety stock. Locations with high days of exposure would benefit more from updating the forecast. Updating the forecast produces a reduction in safety stock levels. However, updating the forecast has managerial implications beyond reducing inventory holding costs. Reducing safety stocks provides management with increased flexibility. 196

$40,000

$35,203

$35,000

$27,380

Dollars

$30,000 $25,000

$19,557

$20,000 $15,000 $10,000

$4,377

$3,126

$5,000

$5,627

$0

COV=50% of actual

COV=70% of actual

Safety Stock Cost (Annualized)

COV=90% of actual

Safety Stock Investment

Figure 4.17 Updating the Forecast: Sensitivity of Coefficient of Variation of Demand on Safety Stock Cost and Safety Stock Investment for the Supply Chain.

Phase 6 Iteration Current 10 9 8 7 6 5 4 3 2 1

Change 100.00% 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% 60.00% 55.00% 50.00%

TII Change 0.00% -3.00% -6.00% -9.01% -12.01% -15.01% -18.01% -21.01% -24.01% -27.02% -30.02%

TSCC Change 0.00% -0.01% -0.01% -0.02% -0.03% -0.03% -0.04% -0.05% -0.05% -0.06% -0.07%

Phase 8 Iteration Current 10 9 8 7 6 5 4 3 2 1

TII: Total Inventory Investment

Change 100.00% 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% 60.00% 55.00% 50.00%

TII Change 0.00% -4.06% -8.11% -12.17% -16.22% -20.28% -24.33% -28.39% -32.44% -36.50% -40.55%

SCC Change 0.00% -0.02% -0.04% -0.06% -0.07% -0.09% -0.11% -0.13% -0.15% -0.17% -0.19%

SCC: Supply Chain Cost

Table 4.15 Example of the Impact of Updating the Forecast on Total Inventory Investment and Total Supply Chain Cost (Phases Six and Eight) 197

If inventory can be reduced at a stocking location, then the manager responsible for making inventory decisions at that location can reevaluate inventory decisions more frequently. In other words, decisions are made closer to when end-customers place orders. In this research, inventory levels were influenced by the length of the review periods. Review periods moderated the benefits from updating the forecast, however, the stages with the longest lead times benefited from updating the forecast. This inventory reduction and the resulting increased flexibility would not have happened if static inventory policies had been used. In conclusion, the “time” aspect of the view of postponement developed in this research stems from recognizing the notion that holding less inventory translates into making manufacturing and logistics decisions closer to the time when endcustomers place orders. In turn, delaying manufacturing and logistics decisions increases managerial flexibility. However, the increased flexibility will happen only if during the time that activities are delayed, management is capable of improving decisions by gathering new information and coordinating activities with the other supply chain members.

The Plan for Termination Period Plan for Termination is the last period in dynamic time-based postponement and had, in this setting, two components. First, the capabilities of the demand management process implemented across the members of the supply chain were used to gather demand information, plan for the reminder of the LTO, and share the plan across the supply chain. Second, the cost of obsolescence was included, the end of the LTO was closer and the total cost trade offs are reassessed. Demand Management for the End of the LTO. The second forecast update was based on disengagement information. The restaurant managers decided when they would disengage from the LTO and this information was used for the Plan for 198

Termination period of dynamic time-based postponement. Restaurant managers owned local information which was valuable to all supply chain members. Figure 4.15 shows the standard deviation of the percentage forecast errors without sharing this information, assuming that all restaurants will continue the LTO until it ends. Note the extent to which variability of forecast errors increase on phases six, seven, and eight of the LTO. Without sharing information, the various members of the supply chain would need to plan inventory deployment for these levels of uncertainty. Based on the assessment of the business opportunity, this is a costly option because high variability of forecast errors implies high safety stock levels, which in turn imply high inventory investment at risk of to obsolescence. If the managers of the restaurants that disengaged from the LTOs on phases six, seven, or eight had reported their decision, then the variability of forecast errors would have been smaller on the last phases. Figure 4.18 shows with a solid line, the average variability of forecast errors of six LTOs prior to LTO-11. In other words, the solid line is the average of all lines shown in Figure 4.15 except that of LTO-11. The shape of this curve is consistent with the contention that forecasting the middle section of the life cycle of a product is easier than planning for its beginning and end [11]. The dashed line of Figure 4.18 is the same average of variability of past forecast errors, but incorporating disengagement information for those restaurants that disengaged between phases six and eight. For phases one to five, both curves are considerably similar. But for the later phases, variability of forecast errors would have been smaller if disengagement information had been shared. Figure 4.19 shows the variability of forecast errors for six LTOs prior to LTO-11 and for LTO-11 assuming that the forecaster knew when restaurants would disengage from each LTO. In Figure 4.19, the updated expected variability of demand at different phases of the LTO are indicated with dark gray arrows. The values of COVd that were used for the initial planning of the LTO are indicated with light gray arrows. 199

Standard Deviation of Percentage Forecast Error

3 2.5 2 1.5 1 0.5 0 1

2

3

4

5

6

7

8

LTO - Phase All LTOs with Information Sharing

All LTOs without Information Sharing

Figure 4.18 The Effect of Sharing Information on Variability of Forecast Errors

The last forecast update was produced by identifying the restaurants that disengaged early and removing these restaurants from the calculations of the forecast errors for the weeks after they actually disengaged. Failing to remove these restaurants increased the forecast error because sales were expected for these restaurants but actual sales were nil. The values of COVd used for the Plan for Termination period are based on past LTOs and assumes that disengagement information was shared. If a restaurant manager reports that the restaurant will disengage from the LTO at the beginning of phase six, for example, then the average demand and the COVd for that restaurant becomes zero in phase six and subsequent phases. Similarly, if a restaurant manager informs that the restaurant will disengage on phase seven, then average demand and COVd is unchanged for phase six and becomes zero for phase seven and thereafter. 200

S tandard D eviation of P ercentage Forecast E rrors

6 5

Original COVdemand

Updated COVdemand

4 3 2 1 0 1

2

3

4

5

6

7

8

Phase of LTO LTO-00

LTO-01

LTO-02

LTO-03

LTO-04

LTO-10

LTO-11

Figure 4.19 Plan for Termination — Updating the Forecast

The right-most column of Table 4.16 shows the COVd used to model the forecast update which was used for the Plan for Termination period of dynamic time-based postponement. The dark gray area at the bottom of the column represents that if the restaurant manager reported the time of disengagement, then zero was used instead of the calculated COVd. Making the restaurant managers’ local knowledge about disengagement available to the rest of the supply chain allows more effective planning for the termination of the LTO. Table 4.17 shows the supply chain cost of the four lead time options as a percentage of the supply chain cost of the least-cost scenario without this forecast update (scenario M1). Supply chain cost excluding obsolescence was reduced by 15% and 40%, approximately, for phases seven and eight, respectively. 201

Phases of the LTO

COVd Forecast Update #3 Initial Coefficient of Variation of Store Store Demand Still Engaged Disengaged

1 2 3 4 5 6 7 8

3.00 1.50 0.70 0.40 0.50 0.70 1.20 2.00

0.40 or 0 0.45 or 0 0.50 or 0

Table 4.16 Expected Variability of Demand – Plan for Termination

Scenario

Phases of the Limited-Time Offer 1

2

3

4

5

6

7

8

100%

100%

100%

100%

100%

100%

100%

100%

T1

85.23%

59.66%

T2

85.25%

59.67%

T3

85.80%

60.06%

T4

85.82%

60.07%

M1

Table 4.17 Plan for Termination — Supply Chain Cost Excluding Obsolescence as a Percentage of Base-Case 202

This cost reduction represents the benefits from sharing updated plans across the supply chain. Additionally, at this point of the life-cycle of the LTO, obsolescence becomes one of the key cost drivers. Obsolescence. Obsolescence is one of the components of inventory carrying cost [10]. For a standard product, a product that is offered year-round, the obsolescence rate can be estimated and incorporated as a component of the inventory carrying cost. Frequently, obsolescence is modeled as a stochastic process or a mathematical function where the holding cost percentage increases with time. For this business setting, the duration of the life cycle of the product was predetermined. In this kind of situation, it seems more suitable to consider obsolescence separately from the standard inventory holding cost. If the cost of obsolescence is included in the inventory holding cost rate, using that rate for all phases of the LTO might lead to holding less inventory. This will be the case if obsolescence increases inventory carrying costs to the point that the total cost trade-offs are affected. In this research setting, obsolescence becomes a concern when the number of days of supply in inventory, both cycle and safety stocks, is close to the number of days left in the life cycle of the product. Restaurants had to be engaged in the LTO during the advertisement period, phases two to six. Restaurant managers had the option to continue the LTO for at least four more weeks. Ingredients had to be available to replenish restaurants at least up to phase eight. After phase eight, restaurants were replenished only if there was product available. Phases six, seven and eight of the LTO were used to show the reassessment of the logistics trade-off. Table 4.18 summarizes the percentage of the total inventory investment that would be needed if the forecast had been updated as described earlier. Table 4.18 indicates total inventory investment for phases seven and eight after updating the forecast as a percentage of those for the original plans. Total inventory investment would have been reduced by 62.5% and 76.4%, respectively. 203

Phases of the Limited-Time Offer

Scenario 1

2

3

4

5

6

7

8

100%

100%

100%

100%

100%

100%

100%

100%

T1

47.40%

23.60%

T2

46.00%

23.1%

T3

45.90%

23.00%

T4

44.80%

22.50%

M1

Table 4.18 Total Inventory Investment – Plan for Termination

Despite the fact that sharing disengagement information produced cost reductions to the supply chain even when using standard lead times, the use of premium lead times for the last phases of the LTO might produce additional cost reductions. The use of shorter lead times reduces inventory levels which translates into reducing money at risk of obsolescence. This risk reduction comes at the price of using premium lead times where possible. At this point, the total logistics tradeoffs should be reassessed and it is necessary to determine the expected obsolescence rate. Table 4.19 presents the results of a break-even point analysis to determine when the supply chain is better off using premium lead times to reduce inventory investment. Table 4.19 shows that if more than 21.9% of inventory investment of phase seven and more than 25.3% of that of phase eight were likely to become obsolete, then it would be preferable to use shorter and more expensive lead times in order to reduce inventory investment and, consequently, reduce the exposure to obsolescence. In sum, updating the forecast with the information gathered from the restaurant managers about the time of disengagement would have reduced supply 204

Scenario Supply Chain T1 Cost T4 Difference in SCC Total Inventory T1 Investment T4 Difference in TII Breakeven Point

TII: Total Inventory Investment

Phase 7 $48,085 $48,415 ($330) $26,939 $25,434 $1,505 21.9%

8 $20,257 $20,392 ($135) $11,910 $11,376 $534 25.3%

SCC: Supply Chain Cost

Table 4.19 Break Even Analysis: Obsolescence Rate – Plan for Termination

chain cost considerably. Furthermore, if the expected obsolescence rates were more than 21.9% and 25.3% for phases seven and eight respectively, then it would have been more cost effective to switch to using shorter lead times. Doing so would have enabled the delay of activities and holding safety stock in a noncommittal status for longer. Table 4.20 shows side-by-side the locations and levels of safety stocks (measured in days of exposure) from the use of standard and premium lead times . Table 4.20 shows that for Ingredient A, the distribution centers 18, 12, 16, and 17 would have held one day less in safety stock, while the manufacturer would have switched from holding 17 days of finished goods, to holding five in finished goods and seven in raw materials. Raw materials are undifferentiated and eventually could be used for other products. For Ingredient B, safety stock measured in days of exposure would have been reduced for all but three stocking locations. These three are distribution center 12, 15 and 20. The manufacturer would have reduced safety stock by one day at the regional distribution center and three days of finished goods at the plant. Moving safety stock backwards in the chain or reducing safety 205

Ingredient B

Ingredient A

Scenario T1 (Standard Lead Times)

T4 (Premium Transportation and Manufacturing Lead Times)

3 0 17 5 6 7 6 6 7 8 7 6 14 7 5 7 8 7 3 7 7 8 6 6

3 7 5 5 6 6 6 6 6 7 6 6 14 4 5 6 7 6 2 6 6 7 6 6

Stage Stores Procure Manufacture DC-13 DC-14 DC-18 DC-15 DC-20 DC-12 DC-16 DC-17 DC-19 Procure Manufacture DC-12 DC-16 DC-17 DC-19 Warehousing-RDC DC-13 DC-14 DC-18 DC-15 DC-20

Table 4.20 The Effect of Using Shorter Lead Times in Days of Exposure by Stage 206

stock levels represents an increase in flexibility because the supply chain can respond faster to changes in demand. It also allows decisions to be made closer to when end-customers place orders which is the objective of postponement. Using the three periods of dynamic time-based postponement would reduce inventory holding cost in comparison to that of combining Full Speculation and Maturity, the first two periods of dynamic time based postponement. Combining Full Speculation and Maturity means that Full Speculation was used for the first three phases of the LTO and Maturity was used for the remaining five phases. The use of dynamic time-based postponement in full means that Full Speculation was used for the first three phases, Maturity was used for the next three phases and Plan for Termination was used for the last two phases of the LTO. Table 4.21 shows the inventory holding cost the supply chain would have incurred in both cases. Table 4.21 shows that the inventory holding cost for combining Full Speculation and Maturity would have been between 361% and 408% higher than that of using the three periods. It has to be realized that management would not have continued to use the original plans. The inventory holding cost calculations for the case of combining Full Speculation and Maturity were based on the belief that all restaurants would continue the LTO until the end of phase eight. Actually, even when disengagement information is not used to update the plans and shared across the supply chain, managers would realize that demand was decreasing and act accordingly. Despite the fact the original plans would not have been followed, the side-by-side comparison of these two cases highlights the importance of the Plan for Termination period of dynamic time-based postponement. Failing to execute the Plan for Termination period effectively implies that the supply chain would be subject to the overreactions and late reactions that were observed in traditional practice. 207

208 $ 6,633

$ 25,633

Table 4.21

$ 8,680

$ 34,727

$ 4,585

$ 16,538

100.0%

408.47%

and Using the Three Periods of Dynamic Time-based Postponement

100.0%

400.06%

100.0%

386.46%

100.0%

360.70%

Percent of Minimum Cost Alternative Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Comparison of Combining Full Speculation and Maturity,

Dynamic Time-based Postponement (Full Speculation Is Used for First Three Weeks, Maturity Is $ 10,728 Used for the Next Three Weeks, and Plan for Termination Is Used for the Last Two Weeks)

Full Speculation is Combined with Maturity (Full Speculation Is Used for the First Three Phases, Maturity $ 43,822 Is Used for the Remainder of the LTO)

Dollars Sensitivity to Obsolescence Rate 84% 66% 48% 30%

Expected Inventory Holding Cost to the Supply Chain for LTO-11 for the Portion of the Supply Chain in the Model

BENEFITS FROM THE IMPLEMENTATION OF DYNAMIC TIME-BASED POSTPONEMENT It was expected that the implementation of dynamic time-based postponement in this research setting would improve product availability and reduce the costs identified in the business opportunity. The cost components included in the business opportunity (see Table 4.6) are inventory holding costs, obsolescence, and transshipments. Improving product availability would reduce costs but would not affect revenue or customer service levels because there were no stockouts facing endcustomers. Instead, when a restaurant was running out of stock, product was transhipped from another restaurant in the neighborhood. Therefore, an increase in product availability would result in a cost reduction. Table 4.22 summarizes the actual product availability at the restaurants measured as the probability of no stockout. Table 4.22 shows that the actual product availability was 91.4% and 92.3% for Ingredients A and B respectively, and that 285 transshipments between restaurants were needed to avoid end-customers from facing a stockout. Since the optimization

Type of Transatcion

Product

Number of Lead-times between distribution centers and restaurants Number of Transshipments between Restaurants

Ingredient A Ingredient B Ingredient A Ingredient B

Total Number of Transshipments between

Count of Actual PNS Invoices for LTO-11 from DCs to 1 () Stores 1637 1870 141 91.4% 144 92.3% 285

Objective PNS ( )

Expected Transfers

99.5% 99.5%

8.2 9.3

1

18

1) PNS stands for "Probability of No Stockout"

Table 4.22 Expected Improvement in Product Availability at Resturants and Expected Number of Transshipments Needed 209

results were based on a 99.5% probability of no stockout, and assuming that the restaurants would place the same number of orders to the distribution centers, 18 transshipments between restaurants would be needed between the two ingredients if dynamic time-based postponement was used. The size of the business opportunity was presented in Table 4.6 and estimated at $8,266,718 for the supply chain, considering all restaurants in the franchise system. As a summary, the totals by cost component of the business opportunity are shown on Table 4.23 as well. Table 4.23 shows, that the cost of obsolescence for the four supply chain members, for the portion of the supply chain included in the optimization model is $16,627. This amount scaled to the system (including all franchisor- and franchisee-owned restaurants ) equals $ 536,355. The costs that can be reduced are $ 32,033 for to the portion of the supply chain in the model. This

Business Opportunity - Totals Cost for LTO for the Portion of the System-wide Cost Cost Component 2 Supply Chain in the for LTO ( ) 1 Model ( ) 3,523 $ 113,645 Inventory Holding Cost $ 16,627 $ 536,355 Left Over Product $ Transshipments $ 11,883 $ 383,323 Total $ 32,033 $ 1,033,323 System-wide Cost per year (8 LTOs/Year) $ 8,266,581 1) This cost includes inventory holding cost excluding obsolescence, the cost of left-over product (obsolescence), and transshipments scaled to the portion of the supply chain represented by 177 restaurants. 2) This costs includes inventory holding costs excluding obsolescence, the cost of left-over product (obsolescence), and transshipments scaled to all restaurants in the system.

Table 4.23 Summary of Business Opportunity 210

cost is equal to $ 1,033,323 system-wide for one LTO. Since there were eight LTOs a year, and assuming that LTO-11 was representative of the other seven, the business opportunity for the four members of the supply chain is $ 8,266,581. Only part of this amount can be saved because inventories still will be held, transshipments will be used to avoid end-customers from facing stockouts, and it is reasonable to expect that some product will become obsolete. Table 4.24 summarizes the expected costs from implementing dynamic time-based postponement for these three categories. Based on the output of the scenarios developed with the optimization model, it was estimated that the inventory holding cost excluding obsolescences would be $ 1,172, shown in Table 4.24. Additionally, it was expected that restaurants would need 18 transshipments. The estimated cost of these transshipments was $ 540. Obsolescence costs depend on the obsolescence rate, which in the past averaged 84.9% across all stocking locations. That is, from the inventory available at all stocking locations at the end of phase eight, 84.9% became obsolete. Obsolescence represented a considerable portion of the expected costs for the LTO. But it was expected that the obsolescence rates would be reduced due to the increased coordination required to implement dynamic time-based postponement. Therefore, obsolescence costs were calculated for four obsolescence rates: 84% (the historic average obsolescence rate), 66%, 48%, and 30%. Adding the three cost components, the expected cost for LTO-11 for the portion of the supply chain in the model was calculated for each of the obsolescence rates. The resulting costs were scaled to all restaurants in the system (for the whole supply chain formed by the two manufacturers, the nine distribution centers and all franchisees- and franchisor-owned restaurants) and then multiplied by eight, which was the number of LTOs that were held per year. These calculations result in expected annual cost savings for the four members of the supply chain of between $5.3 and $6.9 million, depending on the obsolescence rate the supply chain would achieved. 211

212 64.8%

71.2%

231,378

7,173

$

77.6%

6,415,694

$1,851,024

$

$

5,125

$

84.0%

6,944,142

$1,322,576

$ 165,322

$

Expected Cost Savings from the Implementation of Dynamic Time-based Postponement

Table 4.24

Expected Cost Reduction as a Percentage of Past Cost

5,887,246

$

5,358,798

Expected Annual Cost Savings System-wide for the four $ members of the supply chain

297,434 $2,379,472

$

9,220

Expected Annual Cost for the Supply Chain System-wide $2,907,920 (8 LTOs per Year)

363,490

$

Sensitivity to Obsolescence Rate 84% 66% 48% 30% $ 9,556 $ 7,508 $ 5,461 $ 3,413 $ 540 $ 540 $ 540 $ 540 $ 1,172 $ 1,172 $ 1,172 $ 1,172

Expected Cost for the Supply Chain System-wide $

540

Investment at Risk of Obsolescence $ 11,376

11,268

$

Transshipment Cost

Expected Cost $ for the Portion of the Supply Chain in the Model

$1,172.09

Inventory Holding Cost

Expected Cost based on O ptimization M odel for the Four M embers of the Supply Chain for LT O -11

Table 4.24 also shows that the expected cost reduction as a percentage of actual cost was between 64.8% and 84.0%. Despite the fact that these percentages might appear to be very high, it has to be recognized that these cost calculations did not include unavoidable costs such as the cost of goods sold. In order to verify that these cost savings were reasonable, the total cost for the LTO for the supply chain as a whole was determined considering both avoidable and unavoidable costs. Then, the estimated cost savings were calculated as a percentage of this total cost. The expected cost savings represented between 5% and 6% of the total cost for the limited-time offer.

SUMMARY The quantitative analyses performed as part of this research were presented in this chapter as well as the assessment of the business opportunity for the implementation of dynamic time-based postponement, the methodology to gather information and plan for each of the three periods of dynamic time-based postponement, and the expected benefits from its implementation. Chapter 5 contains a summary of the results developed in Chapter 4, as well as the managerial implications, limitations, and suggestions for future research. Finally, a commentary is offered.

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214

CHAPTER 5 SUMMARY AND CONCLUSIONS The purpose of this chapter is to present the summary of the findings and the conclusions of the research. First, the research purpose is reviewed and each research questions is addressed individually. Second, the major conclusions are presented. Third, the managerial implications are presented. Fourth, the limitations of this research are described. Fifth, the future research opportunities are provided. Finally, a concluding commentary is presented.

SUMMARY OF RESEARCH PURPOSE The purpose of this research was to present the conceptual development of dynamic time-based postponement and empirically test the concept in the context of the management of short-lived products in a supply chain formed by independent firms. The conceptual development of dynamic time-based postponement was based on the review of the literature. The empirical test was performed with actual data from past limited-time offers (LTO) in the quick-service restaurant business. Dynamic time-based postponement is an extension of the concept of postponement. In this research, the use of postponement and speculation is combined within the life cycle of the product to fit the business environment. Postponement as described here is time-based because only the time when activities are performed is affected. That is, the sequence in which activities are performed is not changed. Dynamic time-based postponement is dynamic 215

because it represents a method for capturing a number of managerial objective that change within a short time horizon. Early in the life cycle of the product, speculation is the strategy of choice for positioning inventories across the supply chain. Speculation is used to minimize the risk associated with running out of stock. It is generally more expensive to speculate because inventory is held at a higher cost and products are differentiated. Later in the life cycle, uncertainty of demand is to a considerable extent dissipated and the focus is cost minimization as it is for a standard product. Finally, close to the end of the life cycle, obsolescence becomes a considerable cost driver, and shorter and more expensive lead times are used in order to reduce inventory investment at risk of obsolescence. Using shorter lead times, managers can delay activities and maintain product in a non-committal status for longer. In this research setting, product is maintained in a non-committal status by holding raw materials instead of finished goods in stock and/or by delaying its geographic differentiation. The objective of dynamic time-based postponement is to reduce safety stocks wherever possible by determining the optimal locations of safety stocks across the supply chain, which might be different at different phases of the life-cycle of the product.

REVIEW OF RESEARCH QUESTIONS AND FINDINGS In this section, the findings of the research are reviewed with respect to each of the three research questions presented in Chapter 1.

1. IS THERE AN OPPORTUNITY FOR IMPLEMENTING DYNAMIC TIMEBASED POSTPONEMENT? Implementing dynamic time-based postponement in the supply chain that served as the research setting resulted in estimated cost savings of between 64.8% 216

and 84% (the actual percentage depends on the obsolescence rate incurred) of the estimated business opportunity and translates into a potential annual savings of between $ 5.3 and $ 6.9 million. The size of the business opportunity was determined from the summation of transshipment costs, inventory holding costs and obsolescence costs. These cost savings represent between 3% and 4% of the total cost of the limited-time offer (LTO) including acquisition, manufacturing, transportation, warehousing and inventory holding costs. The estimated cost reduction is accompanied by an improvement in product availability at the restaurants which reduces the number of transshipments between restaurants that are needed to prevent end-customers facing stockouts. The actual service level experienced was approximately 92% measured as a probability of no stockout. In other words, for 8 out of 100 replenishment lead times to the stores, a restaurant manager had to arrange an emergency transshipment from another restaurant so that end-customers would not face a stockout. The customer service level used in this research was 99.5%, also measured as the probability of no stockout. This means that in one out of every 200 replenishment lead times to the restaurants, a restaurant manager would need to transfer product from another store. The number of transshipments between restaurants was expected to be reduced from 285 to 18 for the 177 restaurants in the model for the entire duration of the LTO. This results in a 93.5% reduction of transshipments and accounts for $2.8 million of the cost savings. Reducing the number of transshipments between restaurants was presented as a key concern by the management of the franchisor. The fact that the restaurant managers can focus their time on serving end-customers is considered a key success factor for the restaurant business. In this research, the potential of dynamic time-based postponement was determined by updating the forecast and using premium (shorter and more expensive) lead times as the end of the LTO draws near. The forecast would be 217

updated twice. One forecast update would be executed centrally by the franchisor’s management after the third week of the LTO; the other would incorporate information about disengagement from the LTO, which is knowledge about the behavior of the demand only available to the restaurant managers. The first forecast update can be either a combined judgmental and quantitative forecast, as the original forecast developed by the marketing function of the franchisor; or only a quantitative-based forecast. A quantitative forecast updating process could integrate the prior belief of the marketing managers in terms of the performance of the LTO and the observed demand during the initial phases of the LTO. The forecast updating process itself was outside the scope of this research; however, Bayesian-based forecasting techniques are available that might serve well for this purpose [1]. The effect of updating the forecast in situations where activities are tightly coordinated across supply chain members might have considerable impact both in terms of cost and inventory investment to the supply chain. Updating the forecast and the inventory deployment plans, and sharing the updated plans is different than sharing raw point-of-sale data. By sharing raw data it is assumed that each and every member of the supply chain has the same forecasting and planning capabilities. If this is not the case, sharing forecasts and coordinating inventory deployment tactics might have more potential than sharing just raw point-of-sale data. The supply chain as a whole might be better off by having an activity performed by the firm that is best at it [2]. If not all supply chain members have the same level of sophistication in terms of forecasting and inventory planning, then sharing point-of-sale data will bring fewer benefits to the less sophisticated and, ultimately, to the supply chain as a whole. The other forecast update would incorporate the restaurant managers’ actual decisions about when they will disengage from the LTO. Using this information 218

about disengagement and coordinating activities across the supply chain would produce a reduction in total inventory investment for the whole supply chain of approximately 53.6% during phase seven and of 76.4% during phase eight. This reduction in total inventory investment does not include using shorter lead times, which would result in even lower inventory investment. This forecast update would completely remove uncertainty of demand for those restaurants that disengage early and enables management from all supply chain members to prepare for the termination of the LTO. Planning for the termination of the LTO would reduce the need for rush orders, such as the manufacturing runs shown in Figure 4.10 for Ingredient B which occurred up to one week before the disengagement phase. Also, preparing for termination would reduce unnecessary transportation of products to forward stocking locations in the chain, which increases the total inventory investment at risk of obsolesce in addition to geographically differentiating the product. If shorter lead times were used, the reduction in inventory investment would be of 55.2% and 77.5% for phases seven and eight, respectively. Using shorter and more expensive lead times would be more costly before considering obsolescence. Using shorter lead times would increase total supply chain cost before obsolescence, but would reduce the need for both cycle and safety stocks, which reduces total inventory investment. The cost of capital associated with this investment was accounted for in the output of the optimization-based model, but the capital loss due to obsolescence was not. Therefore, the additional cost was traded-off with the expected loss due to obsolescence. If obsolescence rates were higher than 25% on average across the supply chain, then the supply chain as a whole would be better off by switching to shorter lead times in order to reduce inventory investment and, consequently, the exposure to obsolescence.

219

The actual rate of obsolescence was calculated as the amount of product left over divided by total inventory at each of the supply chain members at the end of phase eight. Obsolescence rates were 84.9% of the inventory on hand at the end of phase eight on average across the supply chain. The median obsolescence rate was 84.4%. Therefore, if the same obsolescence rate was incurred, the supply chain would been better off by switching to shorter and more expensive lead times. Obsolescence rates are expected to decrease with the implementation of dynamic time-based postponement. However, the extent of this improvement depends on managers’ ability to “flush the pipeline” as the end of the LTO approaches. Switching to shorter lead times will allow inventories to be held in a noncommittal state for longer. For example, Figures 4.9 and 4.10, Order Placement for Ingredients A and B respectively, show that there were manufacturing runs almost every week up to the week before disengagement. However, obsolescence rates averaged 84.9%. This is an indication that there was product available in the supply chain, but the product was not available where it was needed. Using shorter lead times would enable delaying the time in which product was differentiated geographically. For instance, Table 4.20, The Effect of Using Shorter Lead Times on Days of Exposure, shows the extent to which switching to the shorter lead times would allow inventories to be “pushed backwards” in the supply chain. For instance, the manufacturer of Ingredient A would switch from holding 17 days of finished goods to holding five days of finished goods and seven days of raw materials. Similarly, the manufacturer of Ingredient B would switch from holding seven and three days of finished goods at the plant’s warehouse and the regional distribution center, respectively, to holding four and two days. In addition, the distribution centers with the longest transportation lead times would require less safety stocks. Moving inventory backwards in the supply chain or reducing safety stock implies that 220

manufacturing and transportation decisions are made closer to when endcustomers place orders, which is the objective of using postponement.

2. HOW DOES HAVING BETTER KNOWLEDGE OF DEMAND INFLUENCE TOTAL LOGISTICS COSTS AND ACQUISITION COSTS FOR THE SUPPLY CHAIN MEMBERS? Sharing knowledge about the expected behavior of the demand is a requirement for the implementation of dynamic time-based postponement. This information sharing enables the coordination of activities across the supply chain throughout the limited-time offer. Information sharing is designed in the subprocess called Plan Information Flow at the strategic level of the demand management process [7]. In this subprocess, the sources of data are determined as well as what output information is transferred to whom in the supply chain. Generally, having better knowledge of the demand is likely to reduce costs and/or to increase revenues. In this setting, there are no stockouts facing the endcustomers. Thus, there are no revenue implications from having a more accurate forecast. Based on the numerical analysis, the first forecast update represented a potential cost reduction of up to 0.5% of supply chain cost for every 10% reduction in the coefficient of variation of demand for the restaurants. This estimate did not include obsolescence costs. The benefits from the second forecast update were estimated as the savings in obsolescence costs. These savings in obsolescence costs were calculated as the difference between actual obsolescence costs, and the obsolescence costs that would be incurred if standard lead times were used and if the obsolescence rate was the same as in LTO-11. If this were the case, the second forecast update would generate annual cost savings of approximately $1.7 million for the supply chain. Inventory requirements throughout this supply chain would be low even with the original expected uncertainty of demand. There are several factors that reduce 221

the effect of updating the forecast in this research setting. First, even when demand variability is high at the restaurant, the ratios of meals per case of ingredients are 380/1 and 320/1 for Ingredients A and B respectively. Second, demand for promotional meals at a restaurant is approximately half a case of each of the ingredients a day. These two factors, demand characteristics and aggregation ratios, translate into relatively low variability of demand faced further upstream in the chain, even when demand variability at the restaurants is high. The third factor is that lead times are short. Restaurants get replenished from distribution centers every two or three days; the quoted service time to distribution centers is 10 and seven days for Ingredients A and B respectively; and transportation times are short as well, the longest being two or three days. Updating the forecast diminishes the benefits from switching lead times. The benefits from switching lead times are higher when required inventory levels are high because there is more inventory investment at risk of obsolescence. At the extreme, if management were able to predict demand perfectly, then no safety stock would be needed throughout the supply chain. Consequently, no inventory would become obsolete and there would be no need to use more expensive and faster transportation. In short, having better knowledge of demand does influence total logistics and acquisition costs, but the size of the impact depends on the original inventory requirements. The larger the inventory requirements, the higher the benefits from improving knowledge of demand.

3. WHAT PREVENTS MANAGEMENT FROM PERFORMING HOLISTIC ANALYSES OF SUPPLY CHAIN DYNAMICS? HOW ARE THESE CONCERNS ADDRESSED? Three reasons why management did not perform holistic analysis of the supply chain dynamics were found. These are: management time constraints, information availability (not necessarily data availability), and confidentiality. 222

Generally, managers’ scarcest resource is their own time. They can only dedicate so much of their attention to each of the issues with which they are dealing. The analysis described in this dissertation required considerable time to obtain and improve data quality; and to make reasonable judgment calls when data were wrong, inconsistent, or not available. Information availability is another hurdle to performing supply-chain-wide (multi-firm) analyses. In this dissertation, the term information was purposely differentiated from data. Generally, every organization has a plethora of data. Data refers to a raw state of transactional records. In contrast, information is used to make decision. Information requires data to be available in a timely manner and to be accurate, complete, consistent, and integral. This research started by collecting the following data from all participant firms: manufacturing runs, shipments between locations in the supply chain network (including emergency transshipments), and sales to end-customers. The data were transformed into information by: understanding the behavior of the supply chain as a single entity; calculating actual customer service levels and costs; and estimating obsolescence rates for each of the members of the supply chain. The transition from data to information required considerable effort, which is generally the case. Confidentiality seems to be the single most critical factor that prevents intercompany analyses. This was a problem even in this research setting, in which the companies worked as extensions of each other at many organizational levels. The relationships between the franchisor and each of the other members of the supply chain were not traditional seller-buyer relationships, but had developed into partnerships [3]. Despite the closeness of the relationships, members of the supply chain, in general, were not willing to share some sensitive data. 223

Confidentiality concerns stemmed from the belief that if the seller reveals the actual profit margin, the buyer will use this to obtain price reductions. Information technology might assist managers who are willing to perform holistic analyses across the supply chain, but are concerned about sharing sensitive data. For example, an optimization-based information system can be designed to allow each supply chain member to input data which can only be accessed by allowed users. This might provide the best of both worlds, to be able to perform supply-chain-wide analyses and to maintain confidentially sensitive data. It can be argued, though, that there is always a system administrator who will have access to all the data. In addition, although supply chain costs decrease, individual firms might face increased costs. Therefore, managers of those firms that face increased costs may still be concerned whether their firms will be rewarded appropriately, so they may try to game the system. That is, after experimenting with the mechanics of the optimization, managers could try to input data that would produce the results they expect. In conclusion, as was indicated in the literature [4], managers’ willingness to share information remains a hurdle to the integration of activities across the supply chain.

MAJOR CONCLUSIONS The research described represents the first attempt to estimate the benefits from postponement by changing the timing of activities rather than by changing the sequence in which activities are performed. Postponement was used dynamically based on characteristics of the business environment such as risk of stockout and obsolescence costs, which changed during the life cycle of the product. Additionally, this research represents the first attempt to assess the benefits from the implementation of postponement across a supply chain formed by independent firms and that extends beyond a dyad. 224

The methodology to assess the potential benefits from implementing dynamic time-based postponement was presented in detail. The research includes a description of the data that were collected, how these data from multiple members of the supply chain were combined into a single database, the problems that were encountered in this process, and the quantitative analyses performed which includes an assessment of the expected cost saving from the implementation of dynamic time-based postponement. Supply chain management has received considerable attention, however, sparse research efforts has been dedicated to the development of empirical studies in the context of a supply chain formed by independent firms. In general, empirical research in supply chain management has been about a firm’s internal operations and distribution network or, at most, extended to a dyad. The research setting used for this dissertation was formed by four independently-owned firms: two manufacturers, a distributor with nine distribution centers, and a quick-service restaurant franchisor that had approximately 2,000 company-owned stores and a network of more than 4,000 franchisee-owned stores. The estimation of the benefits from dynamic time-based postponement was based on data collected from the four participants. These data included: manufacturing, transportation and warehousing costs; manufacturing and transportation leadtimes; daily inventory levels at all locations in the supply chain; the inventory holding costs used by each firm; shipments; and point-of-sale data. Figure 5.1 summarizes the potential benefits from dynamic time-based postponement for the supply chain as a whole and for each of its members. The supply chain as a whole would benefit by generating annual cost savings of between $ 5.3 and $6.4 million to be shared among its members. For confidentiality reasons, breaking out these supply-chain-wide savings by firm was beyond the scope of the research. The implementation of dynamic time-based postponement could facilitate the coordination of activities beyond the boundaries of the firm which would improve coordination of the flow of other products. 225

226

• Reduce inventory holding costs (excluding obsolescence). • Reduce obsolescence costs at end of LTO. • Reduce the chances to over- or under-react to demand. • Make more efficient use of management time.

• Reduce inventory holding costs. • Reserve capacity, rather than react to rush orders. • Minimize “delay of other customer’s orders” to fill the franchisor’s orders. • Reduce the number of requests to change the orders placed by the distributors. • Make more efficient use of management time.

• Reduce transshipments (which is actually a hidden cost). • Increase the manager’s time available at the restaurant. • Reduce obsolescence.

Restaurants

Summary of Benefits from Dynamic Time-Based Postponement

Figure 5.1

• Make both franchisor- and franchisee-owned restaurants more profitable. • Reduce operating costs of franchisor-owned restaurants. • Reduce transshipments between restaurants by 93.5%. – Enable restaurant managers to focus on serving end-customers. – Minimize the risks associated with handling food products.

– Minimize the risk associated with stockouts, which is particularly high early in the LTO. – Make LTOs cost effective for all supply chain members.

• Comply with the marketing strategy for LTOs.

Franchisor

Distributors

Manufacturers

• Generate annual cost savings of between $ 5.3 and $6.4 million. • Use shared information to coordinate activities beyond the firms’ boundaries. • Make LTOs more effective and efficient.

Benefits for the Supply Chain as a Whole

In addition to the annual savings for the supply chain as a whole to be shared among its members, the implementation of dynamic time-based postponement had potential benefits for each of the firms participating in the study. Figure 5.1 shows that, for example, manufacturers would require less emergency manufacturing runs and the related delaying of other customers’ orders in order to fill the orders for the ingredients of the promotional meals. Frequently, distributors modified the orders they placed to the manufacturers when they realized that the orders were higher or lower than what was needed. Minimizing the chances that the distributors over- or under-react to changes in demand would benefit distributors and manufacturers by reducing the changes to the manufacturing orders and minimizing obsolescence at the end of the LTO. Figure 5.1 indicates that another benefit for distributors would be to reduce obsolescence costs at the end of the LTO. Despite the fact that the reduction of obsolescence costs would be a benefit for all members of the supply chain, distributors were particularly concerned with reducing obsolescence costs at the end of the LTOs. This concerned stemmed from the fact that distributors had to offer immediate product availability to restaurants, but manufacturer 1 and 2 offered distributors ten and seven days, respectively, from order placement to available for transportation. Restaurants would benefit by reducing obsolescence and the number of transshipments. Reducing transshipments would allow restaurant managers to dedicate more time to monitoring the restaurant’s operation rather than handling transshipments. This benefit would materialize only if the franchisor monitored the use of transshipments and penalized the managers for using them. There might be a conflict of interests between the franchisor and the restaurant managers. Restaurant managers wanted to reduce costs and carrying inventory had an outof-pocket cost. On the other hand, a transshipment was a hidden cost. Therefore, restaurant managers might prefer a transshipment to holding the required inventory to avoid the transshipment. Additionally, a transshipment would happen only when 227

restaurant managers knew the product was needed, but when placing the order to the distributor they were uncertain whether the product would be needed. The franchisor would benefit by, foremost, reducing the risk associated with running out of stock early in the life cycle of the LTO, while reducing the total cost of LTO. The franchisor would benefit by making restaurants more profitable. The franchisor was not only interested in increasing the profitability of the franchisorowned restaurants, but had vested interest in helping run profitable restaurants to attract new franchisees. Lastly, the franchisor wanted to reduce transshipments between restaurants to enable managers to focus on the restaurant’s operation and to minimize the risk associated with the transportation of food products.

MANAGERIAL IMPLICATIONS The majority of the past research in postponement was devoted to improving the understanding of postponement by changing the sequence of activities. The emphasis in this dissertation was on the delay of activities in time. Dynamic timebased postponement is designed to help managers combine speculation and postponement during the life-cycle of short-lived or seasonal products. Dynamic time-based postponement is demanding in terms of management time because its successful implementation depends on management’s ability and willingness to coordinate activities closely across the supply chain. Supply chain management will help managers achieve the required coordination across supply chain members to successfully implement dynamic time-based postponement. This research was conducted within the supply chain management framework. Supply chain management is the integration of key business processes from end user through original suppliers, that provides products, services and information that add value for customers and other stakeholders [5]. Within a firm, supply chain management combines a set of organizational capabilities which 228

leads to better performance by integrating activities with other supply chain members rather that by being internally focused. Organizational capabilities represent the ability of an organization to rearrange and reorganize resources [6]. The integration of business processes with key supply chain members is required for dynamic time-based postponement to reach its full potential. Dynamic time-based postponement was conceived as a methodology to synchronize activities across the supply chain. Therefore, it was developed as part of demand management, one of the eight supply chain management processes. The demand management process is about forecasting and synchronizing [7]. Central to the demand management process is finding ways to reduce demand variability and to increase flexibility [8]. The integration of supply chain management processes within each individual firm and across the members of the supply chain is required not only to rearrange resources within each firm, but to do so across all member firms. This integration allows managers to view the supply chain holistically and tackle the business opportunity as one rather than a set of independent entities. Finding the best solution for the whole supply chain contrasts with the traditional setting in which the most powerful member of the supply chain, the channel captain, imposes static inventory policies based on internal objectives. Static inventory policies are easier to implement and control. However, static inventory policies are, at most, internally optimal and unlikely to be optimal for the supply chain as a whole. Combining data from all members of a supply chain and using commercially available optimization-based software enables management to position inventory optimally across the supply chain, removing subjectivity. There are two implications associated with removing subjectivity. First, the best solution is determined for the supply chain as a whole. That is, managers of each and every member of the supply chain contribute to building a model of their business situation and the optimal location of safety stocks are determined for the supply chain as a whole. 229

Second, the procedure is explicit to all supply chain members. Some members will be required to do something extra, such as offering shorter lead times and others will benefit by, for example, holding less inventories. An assessment of the previous situation and the prescription of the optimization-based model should be used as a starting point to negotiate implementation issues, and the sharing of risks and rewards. Finally, this research provides an example of what collaborative replenishment can be. Collaborative replenishment is a component of Collaborative Planning, Forecasting, and Replenishment (CPFR). CPFR is frequently a topic in the literature and in presentations at professional meetings, but there is very little in the way of explanation of what collaborative replenishment really means.

LIMITATIONS When developing an optimization model of a business situation, assumptions and simplifications are made. This research is no exception. Some assumptions were inherited when it was decided to use the G&W2000 modeling approach used in the quantitative analysis. Each of the assumptions in G&W2000 is addressed individually in Chapter 3. The business environment was also simplified in order to set the scope of the research. The assumptions and simplifications, most of which can be found in other research, included: demand is normally distributed, variability of manufacturing and transportation lead times are deterministic, transportation and manufacturing costs are linear in relation to quantity, and predictability of demand at all restaurants is the same. Each of these assumptions is described next. Even though the optimization algorithm does not require the assumption of normality of demand, in this research this assumption was made for the safety stock calculations (see Equation 1 in Chapter 3) in order to simplify the description of the analysis. Despite the fact that variability of lead times might be a concern in other 230

business settings, in this research setting, variability of lead times were at most a few hours and always less than a day; therefore, they could be considered deterministic without detriment. This fact was corroborated with all supply chain members interviewed. Costs were considered linear to limit the scope of the research. This assumption did not seem to limit results in any way. In fact, two of the three members of the supply chain who were responsible for transportation used linear transportation costs and the prices of both ingredients were linear. Lastly, predictability of demand at all restaurants was considered the same. Further analysis might indicate that demand at some restaurants is easier to predict than at others. This analysis was considered to be outside the focus of this research. Other assumptions were made that are specific to the modeling of the business setting. The phases of the LTO were modeled as separate optimization problems. It was assumed that it is possible to transition from one phase to the next and that doing so is immediate. Each phase of a LTO was one week long and for each phase, inventory levels have been determined for each location. Therefore, by modeling each phase as a separate problem, it is assumed that given the inventory available at each location at a particular phase, it is possible for each location to place orders for the appropriate quantity that will result in the target inventory levels for the next phase. Despite this limitation, the longest review period was that of the distribution centers which was seven days long. Distribution centers placed orders once a week. For the portion of the life-cycle of the LTO from setup to maturity, when demand is increasing, this assumption does not seem to affect results markedly because onhand inventory levels would be increasing. However, for the decline to disengagement phases, when demand and inventory levels would be decreasing, distribution centers might be carrying sufficient inventory to fill demand during the remainder of the LTO. If this was the case, management at the distribution centers would stop ordering product until their inventories were depleted. 231

Another assumption was made by modeling each phase of the LTO as a stationary problem. This implies that each restaurant expects the same average daily demand for the seven days of the week. Actual daily demand showed weekly seasonality; the highest demand is observed on Saturdays and the lowest on Tuesdays. Due to the fact that the ratios of a case of each ingredient to the number of promotional meals were small (1/380 and 1/320 for the ingredients analyzed), and considering that the highest daily demand of this type of meal was approximately 170 meals a day, it was expected that weekly seasonality would have limited impact on the inventory levels prescribed by the optimization. Despite these limitations, it was believed that none of the assumptions and simplifications affected the conclusions regarding the use of dynamic time-based postponement.

EXTENSIONS AND FUTURE RESEARCH OPPORTUNITIES This research represents the first assessment of dynamic time-based postponement. The study should be replicated in different research settings such as the apparel industry, or seasonal products such as candies or sports equipment. The apparel industry presents many of the same characteristics as a limited-time offer, but the number of orders that can be placed to manufacturers is limited and lead times could be as long as nine months. Frequently, retailers can place only one order to a manufacturer for the whole season. This kind of problem is known as the newsboy problem. Dynamic time-based postponement could be used to complement the analysis of the newsboy problem to determine the impact to the whole supply chain of increasing the number of orders that can be placed to manufacturers. For example, two scenarios could be modeled. One would represent the actual practice in which only one order is placed for the whole season. This scenario 232

should include the expectation of sales at regular price, clearance sales, sales through secondary channels and lost sales due to the inability to adapt decisions to follow demand more closely. The other scenario would include the same factors, but it should incorporate the fact that decisions could be revised. This means that the original order could be adjusted within certain limits. This analysis would result in the size of the business opportunity to be shared among the members of the supply chain who made this new scenario possible. Seasonal products such as candies and sport equipment are similar to fashion products, but candy prices are lower and replenishment cycle times are shorter than for the apparel industry. The value of sport equipment products is higher than candies and apparel, and replenishment cycle times tend to be shorter than in the apparel industry. In the sports equipment industry, orders can be placed more frequently than in the apparel industry, but not as frequently as in the candy business. High product value and the ability to place more than one order might allow management to identify several lead time options which suggests that dynamic time-based postponement could have potential. Testing dynamic time-based postponement in several business setting will provide a better understanding of the implementation issues. An optimization model represents a goal for which management would strive. In this research, this goal was potential cost savings to the supply chain. It was a goal because the costs savings are based on the output of a model and in the model all activity are performed exactly as planned. That is, there is no contingencies, no human errors, and no miscommunication or late communications between managers. As a next step, a simulation-based model could be developed using the output of the optimization as the target inventory policies. This analysis might provide insights in terms of the dynamics in the supply chain. A simulation model can be designed to replicate the managers’ decision-making processes and would result in a closer representation of an actual LTO environment. For 233

example, despite the inventory levels set by the optimization before the LTO starts, if managers observe that demand is lower (or higher) than expected, they might not strictly follow the plans. Instead, they might adapt decisions to their new expectations. A simulation model could be developed to capture this dynamic nature of decision-making. The development of a case study of an actual implementation of dynamic time-based postponement would provide insights about the intercompany relationships and the management concerns that hinder the development of supply chain-wide initiatives. The potential contributions of the development of a case study includes: the identification of specific management concerns; how these concerns are presented to the other members of the supply chain, how these concerns are received, addressed and solved; how risks and rewards are determined and shared among firms; and, the impact of the experience on the degree of closeness of the relationships between firms. Another future research opportunity is to reassess past studies in postponement by changing the sequence of activities, and to identify each of the benefits of postponement presented in Figure 2.9. Figure 2.9 shows that postponement allows demand aggregation, reducing the forecasting horizon, and the learning effect. These three sources of benefits from postponement ultimately provide cost savings. The benefits of each might depend on environmental factors. This research opportunity has potential to result in a prescriptive framework to assist management in the determination of the benefits from postponement. If the majority of the benefits from postponement stem from the learning effect, then the priority should be to develop the capability of information sharing and the infrastructure to support information visibility. If most of the benefits stem from reducing the forecasting horizon, then the focus should be on improving forecasting techniques. If the benefits stem primarily from the demand aggregation effect, then efforts should be concentrated on commonality of parts and subcomponents. 234

A COMMENTARY The implementation of dynamic time-based postponement has considerable potential in supply chain management. Dynamic time-based postponement requires that activities (and decisions) across the supply chain are coordinated. This coordination is required not only during the planning period, before the product is launched, but it is required for coordinating replenishment throughout the life of the product. This coordination needs to be supported by the appropriate information sharing. Information sharing is enabled by information technology and telecommunications. The development of information technology received considerable attention from trade publications and scholarly research. Much has been written about the development of seamless information sharing from the end-customer to the original supplier [9]. Numerous initiatives, such as Transora in the consumer packaged goods industry, Covisint in the car manufacturing industry, and ChemConnect in the chemical industry, have based their businesses on offering the required connectivity to share information across supply chain members. In 2002, a large number of Fortune 500 firms invested substantial amounts of money on the so-called Electronic Information Hubs, Electronic Marketplaces or Electronic Exchanges [10]. However, by 2002, only a few of these firms were still interested in the electronic information hubs. And those who were using them were just using basic features such as electronic auctions for purchasing standard non-key purchases, pooling resource (such as sharing transportation) or bartering. By the end of 2001, many of these initiatives failed and the focus turned to be the implementation of supply-chainwide rather than industry-wide initiatives [11]. These failures might be related to managers reluctancy to share data and the lack of a value proposition to them. Information represents power in the supply chain. It is important to recognize that managers can be reluctant to share data because they are concerned about giving away part of their information asymmetry. Information asymmetry occurs 235

when information is available to some but not all members of the supply chain. When there is information asymmetry, the one who has the information may use it to increase benefits at the expense of others [12]. Generally, it is not technology, but the concern about losing power that limits information sharing. If this is the case, management focus should be on how to guarantee that the information shared will be used for good rather than to benefit at someone else’s expense. For example, in a buyer-seller relationship, the seller may be reluctant to share cost data. The buyer may believe that if the seller learns the size of the profit margins the buyer is obtaining from the seller’s business, the buyer will think margins are too high and would try to negotiate better prices. This might have been the reason why manufacturers did not provide actual variable manufacturing costs, as indicated in Table 4.2. This kind of concern should be addressed as part of building a long-term relationship. It is unlikely that technology will replace management’s ability to build relationships on trust. The other factor that limits information sharing is related to the quality of data. Maintaining data quality receives less attention and managerial resources than it deserves. Generally, this is particularly the case for non-IT managers. One possible explanation is that it is difficult to find a relationship between the costs incurred due to data problems. On the other hand, it is difficult to determine the benefits from maintaining and improving data quality. However, problems related to data quality have been estimated to cost US businesses from $ 600 billion [13] to $ 800 billion a year [14]. Poor data quality can cause any enterprise to fail. For example, data quality issues such as accuracy, timeliness, completeness, consistency, reliability and accessibility, are critical factors in the successful implementation of enterprise-resource planning information systems [15]. Data are used at many organizational levels for decision making. Also data are used by most corporate functions. For instance in logistics and production, most data come from the recording of transactions such as sales, handling of 236

product at a warehouse, inventory adjustments, and transportation. The people responsible for entering the data that are used even at the highest organizational levels are usually at the lowest in the organizational hierarchy. They might even be temporary workers. For example, at a retail grocery store, the cashier at the checkout is responsible for scanning the bar codes of all products. The cashier may know that two items, with different bar codes, are priced the same and decide to scan the same product twice. These incorrect data are used for a variety of tasks such as forecasting, and planning manufacturing, transportation and marketing activities. Once data are in an information system, it is unlikely that their quality will be improved. Therefore, considerable effort should be placed on training the people who enter data. This is supported by the fact that data entry by employees is the most frequently mentioned source of data quality problems [16]. Dynamic timebased postponement, as any other supply chain initiative that requires close coordination of activities and information sharing, will be hard to implement if the appropriate information systems foundation (including adequate data quality) is not established.

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