improve lead time with the help of lean production principles mainly by reducing the ..... Figure 4-10 : Order profiles against Capacities for Rapier machines for Taffeta items ........ 43 .... inventories, excessive overhead and quality cost. ..... located closer to the end of the manufacturing line, customer demand is fulfilled from.
EXPLORING IMPLEMENTATION OF LEAN PRACTICES IN CUSTOMIZED PRODUCT MANUFACTURING ENVIRONMENT: A CASE STUDY ON AVERY DENNISON
MASTER OF BUSINESS ADMINISTRATION IN MANAGEMENT OF TECHNOLOGY
Illankoon, I.M.P.K, Department of Management of Technology` University of Moratuwa December 2009
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EXPLORING IMPLEMENTATION OF LEAN PRACTICES IN CUSTOMIZED PRODUCT MANUFACTURING ENVIRONMENT: A CASE STUDY ON AVERY DENNISON
By
Illankoon, I.M.P.K,
Supervised by Dr. Chandana Perera
The dissertation was submitted to the department of Management of Technology of the University of Moratuwa in partial fulfilment of the requirement of the degree of Master of Business Administration
Department of Management of Technology University of Moratuwa December 2009
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Abstract In today’s competitive business world, companies require shorter lead times, low costs and high customer service levels and faster cash flows, to survive. Because of this, companies have become more customer focused. The result is that companies have been putting in significant effort to reduce their inventory levels. The purpose of this master thesis is to analyse the adaptation of lean inventory in customised product manufacturing environment where high variable and low volume products are produced. The methodology is based on an action based case study at Avery Dennison Lanka (Pvt) Ltd, which is a label manufacturer. All processes from receiving an order to delivery of the order were mapped in a Value Stream Map and ideal state is also mapped applying standard lean applications. The variability of products and dynamics of orders were analysed and they were compared with the capacity mix. The after state was implemented with reduced inventory levels and the company experienced several problems due to reduced inventory levels. Some problems were manageable through standard lean applications. Due to high variable nature of the products, some of the elements in the future stare could not be managed as planned. This thesis critically reviews the difficulties and their relevance with high variable nature of the product.
This thesis develops some recommendations to help reduce non-value adding time and improve lead time with the help of lean production principles mainly by reducing the inventories. It also recommends some extended application in order to get the maximum benefits of lean inventory in a customised product manufacturing environment.
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Acknowledgement Deepest thanks goes to Mr. Ravindra Nanayakkare, Director Operations, Avery Dennison South Asia for his interest and comprehension on importance of lean practices in the apparel accessories industry and providing the researcher several opportunities to undertake lean exercises both locally and regional level .
The support and interest of Mr. Upul Karunanayake, Director Operations Avery Dennison Sri Lanaka and Mr. Deepa Liyanage Production Manager, Weaving division and all other staff members are highly appreciated for their active participation in implementing the recommendations, even to tryout some experimentation in the manufacturing facility. The contribution of all the officers who provided the author the correct information is very much acknowledged.
Special thanks go to Dr. Chandana Perera, Senior Lecturer, Department. of MOT, Univeristy of Moratuwa who supervised this study and gave valuable guideline for accomplishing this study.
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Table of Contents
Chapter 1:
Introduction .................................................................................................. 1
1.1
Background to the study ....................................................................................... 1
1.2
Problem definition ................................................................................................ 3
1.3
Objectives of the study ......................................................................................... 4
1.4
Significance of the study ...................................................................................... 5
1.5
Scope and Limitations .......................................................................................... 6
Chapter 2:
Literature Review ......................................................................................... 7
2.1
Lean perceived as a Universal Application ........................................................... 7
2.2
Challenges of Lean Concepts................................................................................ 9
2.3
Lean Concept as a customized approach ............................................................. 13
2.4
Extensions of Lean Concepts .............................................................................. 16
2.5
Little’s Law ........................................................................................................ 20
Chapter 3:
Methodology .............................................................................................. 21
3.1
Data Collection .................................................................................................. 21
3.2
Research Design and analytical procedure .......................................................... 22
3.3
Introduction to the case at Avery Dennison ........................................................ 24
Chapter 4:
Results and Discussion ............................................................................... 25
4.1
Before Lean initiatives - Process and Performance analysis ................................ 25
4.2
Design of Ideal State with standard lean tools ..................................................... 32
4.3
Identification of difficulties of standard lean tools .............................................. 41
4.3.1
Difficulties in identifying detailed product families..................................... 41
4.3.2
Difficulties in levelling by product type and quantity .................................. 42
4.3.3
Difficulties of phased withdrawal ............................................................... 45
4.3.4
Difficulties of establishing the Supermarket ................................................ 47
4.3.5
Difficulties of implementing a FIFO ........................................................... 48
4.3.6
Difficulties in levelling the capacities ......................................................... 48
4.3.7
Risk assessment of standard lean tools ........................................................ 49
4.4
Empirical examination of after state ................................................................... 50
4.5
Root cause analysis of variations compared to ideal state.................................... 53
4.6
Unique benefits of lean inventory in the customised environment ....................... 56
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Chapter 5:
6:
Conclusions and Further Recommendations ................................................ 57
5.1
Customized WIP controllers ............................................................................... 59
5.2
IT assisted planning solution .............................................................................. 59
5.3
Communicating capacity availabilities to customers ........................................... 60
5.4
Search for flexible technologies .......................................................................... 60
5.5
Product Redesigning........................................................................................... 60
5.6
Further Research opportunities ........................................................................... 61
References ................................................................................................................. 62
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List of Tables Table 4-1: Before state KPIs .............................................................................................. 30 Table 4-2 : Calculation of cycle times for before state and ideal state ................................. 39 Table 4-3 : Comparison of KPIs before and ideal states ..................................................... 40 Table 4-4 : In-house capability matrix ................................................................................ 42 Table 4-5 : Comparison of KPIs before, ideal and after states ............................................ 50 Table 4-6 : Comparison of time factors before, ideal and after states .................................. 52
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List of Figures Figure 3-1: Schematic diagram of the research design ........................................................ 23 Figure 4-1 : Process Flow Chart of weaving process .......................................................... 26 Figure 4-2 : Before State Value Stream Map ...................................................................... 27 Figure 4-3 : Delivery reliability comparison against number of orders Delivered ............... 30 Figure 4-4 : Weekly on Time Delivery (OTD) performance against idling capacity ........... 31 Figure 4-5 : Reasons for Downtimes .................................................................................. 32 Figure 4-6 : Ideal State Value Stream Map ......................................................................... 34 Figure 4-7 : Work place diagram with inventory controls ................................................... 37 Figure 4-8 : Spaghetti diagram for before state and after state ............................................ 38 Figure 4-9 : Comparison of daily demand with daily capacities .......................................... 38 Figure 4-10 : Order profiles against Capacities for Rapier machines for Taffeta items ........ 43 Figure 4-11 : Order profiles against Capacities for Rapier machines for Satin items ........... 44 Figure 4-12 : Variations of product Specifications ............................................................. 45 Figure 4-13 : Daily production quantity & picks variation before lean implementation ...... 46 Figure 4-14 : Order Quantity Analysis ............................................................................... 47 Figure 4-15 : After State Value Stream Map ...................................................................... 51 Figure 4-16 : Demand variation for different warp colours ................................................. 54 Figure 4-17 : Variation of Cut and Fold daily output .......................................................... 55 Figure 4-18 : Demand for different cut and fold methods ................................................... 55 Figure 5-1 : SWOT Analysis to identify further extension .................................................. 58
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Acronyms C&F
:
Cut and Fold
CL
:
Cellular Layout
CM
:
Cellular Manufacturing
FIFO :
First in First out
FKS
:
Flexible Kanban System
FL
:
Functional Layout
GT
:
Group Technology
IMVP :
International Motor Vehicle Program
JIT
:
Just in Time
KPI
:
Key Performance Indicators
MDI
:
Managing Daily Improvements
MTO :
Make to Order
OEE
:
Overall Equipment Effectiveness
OPP
:
Order Penetration Point
OTD :
On Time Delivery
PPM :
Picks Per Minute
TKS
Traditional Kanban System
:
VCMS :
Virtual Cell Manufacturing System
VG
Virtual Groups
:
VSM :
Value Stream Mapping
WCM :
World Class Manufacturing
WIP
Work in Progress
:
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Chapter 1:
Introduction
The introduction to the thesis is organised to provide a background to the study, define the problem and present the objectives of this research.
1.1 Background to the study After 1930, due to external forces, such as changing markets, changing customer needs, the pressures coming from competitors and customers requiring new products initialized the needs for variation from mass production. The technoware created a variety of universal machines to accommodate the batch changeovers instead of long continuous runs. During the transition stage, the orgaware was transformed into cells (a family of similar parts) to promote faster movements from stage to stage so reducing the lead-times. Grouping similar parts into part families based on shapes or production process reduces the complexity created by large-scale production and facilitates the production of a variety of products. The cell structure makes it easy for new production planning techniques called group technology or cellular manufacturing. The cell structure accelerated the interactions between all the components, specially orgaware and humanware, by small self-organizing units or groups that look like a factory within factory, enabling management to distribute control and respond quickly.
Before 1980, customers tolerated long lead times which enabled producers to minimize product cost by using economical batch sizes. Later, when customers began to demand shorter lead times, they were able to get them from competitors. This is when the problem arose and companies started to look for changes to be more competitive. In an attempt to reduce lead time, businesses and organizations found that in reality 90% of the existing activities are non-essential and could be eliminated (Harrington, 1996). As soon as manufacturers focused on processes, they found waste associated with changeovers, quality defects, process control, factory layout, and machine down time. So they tried to find ways to reduce or eliminate waste. By eliminating the non-value adding activities from the processes and streamlining the information flow, significant optimization results can be achieved.
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The assertion of lean is that non lean companies are launching batch sizes that are larger than required. These large batches create excess WIP and Finished Goods inventory that extend cycle time and mask waste. This waste may take the form of setup time, downtime, non-standard work, defects, transportation and other process deficiencies which cause large inventories, excessive overhead and quality cost. In the process of recognising inventory as a waste, reducing batch sizes is identified as a key initiative. Batch production is slow and costly because the large quantity of in-process cannot move to the next step until the entire batch is completed. Smaller batch sizes will reduce waiting time as inventory and dramatically reduce the lead time.
Application of the standard lean prescription would recommend gradually reducing the batch size until one of the workstations can no longer keep up with demand. This is generally caused because a workstation spends too large percentage of time in setup (due to smaller batches and hence more setups) or in maintenance/repair, and too little time actually producing product.
It is elaborated in lean manufacturing that maintaining lean inventory shortness the process lead time. However, small reduced inventory levels lead to create new problems such as waiting due to machine unavailability, more frequent material movements, having no multiple skills, waiting due to reworks, increased number of changeovers etc. It is argued that creation of these problems is nothing else than exposing the problems that are already covered with high level of buffers or inventory levels. Lean manufacturing identifies these problems as opportunities and suggests various techniques to address the problems while keeping low inventory levels.
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1.2 Problem definition It is suggested in lean management that processing small batches leads to shorten the lead times. Reduced inventory levels expose problems in the manufacturing process that would have been managed through higher buffer levels. Lean manufacturing suggests series of standard tools to manage the problems exposed by reduced inventory. However, in a customized product manufacturing environment, order profiles are dynamic and managing small batch sizes become more challenging.
The research problem is how to adopt standard lean tools to manage problems exposed due to reduced inventory levels in a customised product manufacturing environment.
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1.3 Objectives of the study The core of this research being the analysis of the process of implementing lean inventory in customised product manufacturing environment, this research consists of four major objectives.
Objective 1: To investigate why are the standard lean tools difficult to implement in a customised product manufacturing environment and to verify the difficulty of implementing standard lean tools in customised product manufacturing environment.
Objective 2: To analyse whether it creates disadvantages by implementing standard lean tools in a customised product manufacturing environment.
Objective 3: To analyse whether it creates unique advantages by implementing standard lean tools in a customised product manufacturing environment.
Objective 4: To identify requirements of extended applications to optimise inventory levels in a customised product manufacturing environment.
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1.4 Significance of the study Not all manufacturing organisations are same in the product nature and in the pattern of customer orders. But, any organisation would be benefited with faster lead times and reduced inventory levels that would improve both financial and operational performance while increasing customer satisfaction. The outcome of the thesis discuses the applicability of standard lean concepts to tackle problems exposed by lean inventories and to convert them to opportunities. The research also reviews the needs for further applications in orders to gain the maximum benefits of reduced inventory in a customised product manufacturing environment. The findings will be useful for organisations that are planning to begin the lean journey particularly in a customised product environment.
The outcome of the thesis presents the possibility of improving operational and financial performance under customised manufacturing environment, only by reducing the inventory levels through uncomplicated and simple lean applications. Outcomes will motivate the organisations with customised products to find the best lean tools and additional applications that would be utilised to strengthen the outcome of lean inventory.
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1.5 Scope and Limitations As a case study, this research focuses on customized product manufacturing environment of one manufacturing organisation. Real-time cases have been studied for a period of 5 months referring to WIP reduction attempts. Because of the time limitation, the investigation focused on one production section. The findings could bias towards the technology involved in the single production section considered. Longer term impacts and challenges of lean inventory would divert form the cases analysed in this research.
Since the analysis is based on an empirical study of one case, the issue of generalising is relevant. Single case study approach would not offer generic statistical sense. However, it is capable of developing and refining generic concepts and frames of reference. The use of the case study methodology together with action based research increased the possibility for the researcher to gain a fundamental understanding of the phenomenon of interest rather than establishment of a correlation or cause effect relationships.
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Chapter 2:
Literature Review
The success of lean systems and the desire for manufacturing competitiveness have stimulated a great deal of research. Most research efforts to date have focused on descriptions of Just in Time (JIT) philosophy, applications perspectives of Cellular Manufacturing and the performance evaluation of kanban systems. Applicability of lean concepts in different manufacturing settings has been occasionally discussed mainly using simulations and mathematical models. The literature review section of this thesis is organised in such a way that it will first review the literature on the universal applicability of lean despite of the environment. Previous literature on criticism of Lean concept and questionable applications of lean are then reviewed. It is evident that previous research has been concluded perceiving lean as a flexible approach that needs to be customised as per the different factors involving under varying circumstances. Some literature including simulation modelling on adopting lean in to different environments is then reviewed. The literature review also presents some theoretical material in order to support the academic framework of this research.
2.1 Lean perceived as a Universal Application In The “Machine that Changed the World”, Womack et al. (1990) claimed that lean production would replace mass production and what was left of the crafts in all industrial sectors, becoming the global standard for the production systems of the twenty-first century. Lewis, M.A. (2000) referring to the original International Motor Vehicle Program (IMVP) has reviewed the universal applicability of lean. IMVP was a five-year (1985-1990) collaborative investigation (academics from various institutions funded by 36 automotive industry firms contributing to a $5 million research fund) into the performance of the global motor industry (Womack, 1990). The IMVP study has revealed the existence of a 2:1 productivity difference between car assembly plants in Japan and those in the West. The performance differential was ascribed to lean production practices that improved productivity through reduced lead times, material and staff costs, increased quality etc. Lewis comments that enhancing productivity has universal appeal, regardless of whether it is Toyota seeking to survive the oil price shock of 1972-1973 or any Western manufacturer faced with increasingly intensive global competition. Lewis referred to the original IMVP,
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literature by Womack and Jones and annual Global Lean Summits to discuss the continuation of lean production as a more or less universal set of management principles for the production of both goods and services. Lewis comments that many have become convinced that the principles of lean production can be applied equally in every industry across the globe and that the conversion to lean production will have a profound effect on human society. Bartezzaghi (1999) comments that lean production is the end-point of the process leading out of the Fordist-Taylorist paradigm.
Hum and Ng (1995) in their study within the Singaporean manufacturing sector have observed successful application of Lean within different manufacturing settings. The greatest benefits realized were reduction of WIP and finished goods inventory level. Other substantial benefits were reduction of lead time, improvement of shopfloor control, reduction of floor space, and product quality improvement. Hum and Ng highlights that all the companies involved in this study indicated substantial benefits. They comment that while the majority of the companies came from the electrical/electronics industry, JIT practices appeared to have spread among many of the other manufacturing industries as well. As per the literature these companies took slightly more than a year to explore whether JIT would be suitable for their operation. Hum and Ng (1995) hypothesize that a sequenced, standardized package of JIT practices could be identified since much learning of JIT has already taken place through its adoption and practice in many developed economies. Though they could not identify such a common implementation sequence for the JIT practices in their study within the Singaporean Industries, they suggest that such general patterns could be exploited to help speed up the diffusion and implementation of JIT practices. During the pre-implementation phase, the JIT companies did not experience any serious problem. The main problem encountered was reconciling the differences between the JIT system and the existing manufacturing system. (Hum and Ng, 1995)
Uncertainties such as machine failures, demand fluctuations, stochastic set-up times, random yields, are most significant characteristics of many contemporary manufacturing systems. Great deal of research has revealed that these uncertainties make it particularly difficult for companies to deliver products at the right quantity, at the right place, and at the right time.
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2.2 Challenges of Lean Concepts Womack (1990) suggests that the lean principles are applicable to any industry. Pettersen (2009) argues that if this is correct, then the Japanese should logically have distributed the knowledge of these principles throughout all domestic Japanese industry. Pettersen referring to Seminars by Shu Yamada, University of Tsukuba, Linko¨ping University argues that this does not seem to be the case. The only “true” lean producers in Japan are confined to the automobile industry whereas other areas of industry are performing at the same level as (or worse than) western competitors (Pettersen, 2009). Pettersen referring to literature by Keys and Miller back in 1984, implies that the principles constituting Lean Production have not received any wide-spread attention outside the auto-industry.
Cooney (2002) has also critically reviewed the universal applicability of lean production concept. He argues that the lean does not provide answers to some operations issues that the batch production does. Cooney suggests that lean is not a system with universal applicability, as its proponents claim. He questions the inherent limitation of the lean model. The central practice of the lean model JIT flow is dependent upon production levelling within the enterprise and within the manufacturing chain. Cooney suggests that when JIT cannot be achieved due to business conditions or the nature of the buyer-supplier relationships, then batch or mass flow may be a more practical form of manufacturing. From the management perspective, lean production provides only a partial model of manufacturing system, if it cannot account for the range of circumstances faced by the management. If lean production fails to provide full account of pressure on management, then its claim to universality can be questioned. Cooney identifies production systems such as the batch system to have enduring value, because they provide a solution to some of the problems that management face, when determining production methods and work methods of the enterprise.
Lewis (2000) reveals a number of concerns with the lean production model as it was initially derived and he summarised the issues under three main categories. First concern classified by Lewis is based on the criteria used in the IMVP study. Much of the interest in lean production principles was based upon the IMVP, claim that Japanese manufacturers were twice as effective as their Western competitors. However, Lewis criticises the
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measurement process used in the IMVP study. Lewis referred to some non dominant measurements in the IMVP study and commented that USA was not performing as badly as the headline IMVP figures suggested. Lewis argues that IMVP highlighted the significance of the Toyota production system but that the remaining Japanese manufacturers exhibited levels of performance merely comparable to the rest of the world.
Lewis presents the second concern as there has been a great deal of debate about how lean production principles will impact upon established production models. Lewis criticises that the demands placed upon workers by lean systems have been highlighted as a problem with respect to ongoing staff recruitment. Third concern presented by Lewis is that establishing the causal linkages between inputs and outcomes is notoriously difficult in any complex system. Lewis suggests that any description of the Japanese organisations achieved these superior outcomes must be filtered through any number of interpretative filters. Lewis highlights that although benchmarking studies have benefited from close attention to actual practice, many have largely ignored wider economic and market conditions. Lewis gives some examples referring to difficulties faced by Nissan (forced to merge with Renault), Honda and Mazda (bought by Ford) and suggests that the lean production model may have reflected particular market conditions at a specific point in time.
Katayama and Bennett (1996) argue that when Womack and his colleagues conducted their research, it was “during conditions of a bull stock market and low interest rates”. They recommend that lean production is incapable of responding to large oscillations in aggregate demand volumes, arguing that the Japanese economy at the time of the IMVP study was exhibiting specific conducive characteristics, creating conditions of high and stable domestic demand. Miyai (1995) also proposes that a weakness of lean is its inability to accommodate the variations or reductions in demand for finished products which are occurring in many Japanese companies. The limits of lean production have been highlighted by Bartezzaghi (1999) in terms of its undesirable effects, including the lack of young labour willing to work in the factories, the excessive product variety, the extreme pressure on suppliers, the inability to find funds for new product development and the increase in traffic and congestion in the cities. Both Bartezzaghi and Katayama underlined the inability of the lean system to respond to oscillations in aggregate demand volumes.
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Hendry (1998) suggested that most of the advice on becoming a World Class Manufacturing (WCM) company in the literature is aimed at the make-to-stock mass producer or mass customize sector and does not apply in full to the Make to Order (MTO) sector. He proposes some guidelines for company improvement that are designed to be appropriate to the MTO that makes a strategic decision to retain a high variety of products and therefore prefers to retain the job shop layout. The proposals suggest ways in which the current guidelines in the literature can be adapted and provide a more appropriate set of criteria for assessing the performance of companies in this sector. Case study evidence were given illustrating reasons for retaining a job shop layout and showing that major improvements can still be achieved if this choice is made.
Hum and Ng (1995) in their study within the Singaporean Manufacturing sector have also commented that there were several problems the JIT companies encountered during implementation process. Main problems identified by them are interfacing the JIT system with the existing manufacturing system, lack of internal JIT expertise, difficulties in reducing the set-up time, and poor information and data accuracy. JIT systems were originally designed for deterministic production environments with a smooth and stable demand and constant processing times; their performance is optimum in that environment. (Gupta, Turki and Perry, 1999). Once implemented, JIT systems face the uncertainties inherent in any manufacturing system, including variations in processing time and demand, equipment malfunctions, as well as known or planned interruptions such as preventive maintenance.
Stockton and Lindley (1995) suggest that rearranging processing equipment, within high variety/low volume manufacturing environments, will enable material movements to be controlled using kanban signals. Process Sequence Cell Layouts involve allocating items of equipment to cells according to their position within the operation route of components. Each cell, therefore, represents a stage in the processing sequence of all components manufactured within a company. (Stockton and Lindley 1995) The procedure for identifying process sequence cells has been described and applied to organizations that manufacture wide ranges of products in small to medium batch sizes. They comment that the majority of processing equipment used by the company can be allocated to individual process cells without capacity problems arising. However, Stockton and Lindley have also discussed the limitations and challenges of the traditional Group Technology (GT) concept in a high 11
variety low volume environment. Stockton and Lindley explains number of reasons why the traditional approach of identifying and adopting group technology cells is inadequate for many high-variety/low volume organizations. •
Often group technology is not applicable since it is impossible to identify groups of
components from which to form cells. •
Hybrid systems are often necessary, which consist of both GT cells and a functional
layout which processes those components not assigned to cells. •
The advantages to be gained from using flow processing techniques are, therefore,
only achieved on a limited number of part types. In addition, complex production control procedures are still required to manage the functional layout. •
Cells when formed often cannot fully process all the components assigned to them.
Hence components need to leave the cell to be processed then returned to the cell for further processing. The greater the variety of part types within the system the greater is the chance of this occurring. •
The formation of cells and the assignment of parts to such cells reduce the flexibility
of a manufacturing system by restricting such cells to a limited variety of parts and restricting parts to a limited set of process routes. In addition volume constraints are imposed on the cell, making it difficult to either increase or decrease production volumes quickly and economically. •
The process of preparing numerical codes for components and inputting these into a
computer is tedious, error prone, time consuming and costly and often leads to long delays in implementing systems. •
The variety of part types that need to be processed frequently results in traditional
kanbans not being able to cope with such conditions. (Stockton and Lindley, 1995)
Wainwright (1996) using queuing theory in the analysis of plant layouts suggests that a flow line layout, specifically tooled to produce a limited range of products in high volumes, is highly viable and will outperform general machine tools in either Functional Layout (FL) or Cellular Layout (CL) modes. Wainwright comments that in the low product volume environment, the necessary flexibility can be achieved only through the application of FL, which will outperform CL.
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2.3 Lean Concept as a customized approach Burcher and Dupernex (1996) suggest that the underlying philosophy of Lean manufacturing can be applied universally within manufacturing, but the tools used to enable the philosophy need to be applied in a context-sensitive manner. For repetitive batch manufacturers, the kanban approach may not be appropriate. Toyota itself does not manufacture cars using kanbans, it uses highly engineered production lines. The items controlled by kanbans are generally for line side production replenishment. What is appropriate for repetitive manufacturers is the encouragement of innovation through the use of technology and methodology (Burcher and Dupernex, 1996). Bartezzaghi (1999) also defend the need to maintain the central aspects of lean production, but to modify some of the features determined by the economic and social climate of previous decades. For example, while maintaining the same basic philosophy, the process design and production strategies in Toyota’s new plants differ significantly from those used in the older factories. (Bartezzaghi, 1999)
Integration of Setup time reduction and JIT practices in job shop environments has seldom been subjected to previous studies. Studies by Li (2005) established elementary schemes for coordinating the major Setup time reduction approaches with the other job shop JIT practices. Li presents the major JIT practices for reforming job shop manufacturing as pull systems, Cellular manufacturing, Operations Overlapping, set-up/processing time variability reduction and Setup time Reduction. Li has conducted simulation experiment to investigate how to coordinate the job shop JIT practices to achieve improved production performance in a job shop environment with the pull system. His study has established a conceptual framework to apply job shop JIT practices as an integrated approach for reforming job shop manufacturing. The findings from the simulation experiment further verify the constructs of the relations of job shop JIT practices, and reveal systematic resolutions for coordinating these practices to achieve a coherent system. Li comments that contrast to flow shops, the more variable set-up/processing times in job shop environments constitute a major obstacle for effective implementation of pull systems. Li suggests job shop manufacturers with systematic guidelines for exercising the job shop JIT practices as an integrated approach to accomplish remarkable performance improvement.
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Literature by Gupta, Turki and Perry (1999) refer to case studies on implementing flexible kanban systems. They identify JIT as a system that originally designed for deterministic production environments with a smooth and stable demand and constant processing times. JIT systems face the uncertainties inherent in any manufacturing system, including variations in processing time and demand, equipment malfunctions, as well as known or planned interruptions such as preventive maintenance. They identify a broader scope of JIT as the overall goal of the JIT production philosophy is to reduce or eliminate the variations that can lead to these problems. The ability to minimize variations may be effected by a variety of factors including distributor and supplier. Turki and Perry (1999) also comment that the overall goal of the JIT production philosophy is to reduce or eliminate the variations that can lead to operation problems.
Burcher and Dupernex (1996) proposed a methodology to assist repetitive batch manufacturers in the adoption of certain aspects of the lean production principles. The methodology concentrates on the reduction of inventory through the setting of appropriate batch sizes, taking account of the effect of sequence-dependent setups and the identification and elimination of bottlenecks. It uses a simple Pareto and modified Economic Batch Quantity -based analysis technique to allocate items to period order day class based on a combination of each item’s annual usage value and set-up cost. The period order day class is determined by the constraint limits in the three measured dimensions: capacity, administration, and finance.
Andries and Gelders (1995) discuss the challenges of implementing lean inventory as the product variation increases. Their main point of discussion is reengineering efforts in the product design so the Order Penetration Point (OPP) would be optimized in order to minimize the inventory levels. As per the discussions by Andries and Gelders, factors are contradictory from a producer’s point of view: variety means that the number of different products is high, while availability means that the products should be kept in stock, in a traditional perspective. When the number of different products increases, the absolute number of products in inventory increases and the inventory value also increases. The reliability of the forecasts will decrease with the number of products to be forecasted. As a consequence, the amount of unsold and obsolete inventory will increase together with the stock-outs. Moreover, increased diversity means production in small batches which increases the proportional part of set-ups and lowers overall productivity. Andries and 14
Gelders discusses that this negative spiral of increasing logistic costs and decreasing efficiency can be avoided.
The answer presented by Andries and Gelders is a redesigning of the logistic chain towards responsiveness and lean production. They suggest that the spiral can be reversed by a good localization of the OPP, a time-based strategy downstream of the OPP and the use of concepts like commonality, load and throughput-oriented order release. When the OPP is located closer to the end of the manufacturing line, customer demand is fulfilled from inventory. Inventory replenishment and production planning are based on forecasts of demand during lead time. The safety stocks are high because they are proportional to uncertainty about customer demand and production lead times. When the OPP is moved to the beginning of the line the concept moves towards production to order. In Production to order, inventory of finished goods is low but customer service is critical. Manufacturers who assemble to order typically have their OPP situated somewhere in the middle of the supply chain. (Andries, 1995)
Andries and Gelders conclude that companies must be creative, both in their product development and in production and distribution in order to survive in the competitive market. Logistics departments have to evolve and adapt to the new requirements. Total quality management and business process re-engineering are the new buzzwords and concepts of change, but neither will succeed unless they are based on strong and modern logistic foundations.
Strategic importance of Lean concepts over the operational benefits has frequently been discussed. Wainwright (1996) suggests that selecting a suitable plant layout some manufacturing trade-off is necessary. He comments that depending on the product market, a greater share may be achieved through a faster delivery performance than that provided by competitors, enabling a premium price to be charged, thus offsetting the higher manufacturing costs. Hum and Ng (1995) comment that most of the companies subjected to their studies were concerned primarily with the operational benefits of JIT such as the reduction of inventories, floor space requirements, and warehouse space requirements. Bhasin and Burcher (2006) suggest that “it is a long-term plan for actually implementing a lean enterprise” that whilst benefits are evident, companies need to view lean as a long term
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strategy. Focus of lean needs to switch to the supply chain, product development, administration and behaviour if the full benefits are to be realised.
2.4 Extensions of Lean Concepts There are some literature based on simulation studies and mathematical modelling proposing extensions to common Lean tools. These extensions are suggested as viable in variable demand and customized products environments.
Yan (1995) presented an algorithm to carry out the gradient estimation and stochastic approximation to find the optimal number of circulating kanbans for a manufacturing system with general machine breakdown and stochastic demand. However the model does not cover multiple machines and re-entrant production flow conditions. Yan believed that it is possible to apply the techniques to complex manufacturing systems but great deal of experiments will be required before the practical implementation.
Houghton and Portougal (1995) presented an approach for optimizing Just in Time (JIT) processing schedules in a batch manufacturing environment. They presented a model with a pre-emptive priority for schedules with minimum holding costs. Solution properties were derived which demonstrated that the model gives JIT processing schedules with a predetermined processing shop cycle. It was also shown that design capacities computed in the usual way from assembly shop demands will be inadequate for JIT schedules under batch manufacturing. This approach was however presented only for constant demand rates, but they suggest it may be implemented in a more general environment of regimes of assembly plans to accommodate seasonal and long term variations in demand.
Gupta and Turki (1996) introduced a system, which they refer to as the flexible kanban system (FKS), to cope with uncertainties and planned/unplanned interruptions. They suggest that FKS outperforms the Traditional Kanban System (TKS) when different types of variations are considered. It was found that the FKS results in lower backlog and order completion time without significantly increasing the average WIP, when compared to the TKS under varying processing times. The greatest benefits for the FKS are achieved when the volume is high and the individual processing times are low. Under demand variations
16
the FKS resulted in no backlog and a lower order completion time, and a slight increase in WIP and time in system compared to the TKS was resulted.
Balakrishnan (2005) has presented a flexible framework for modelling cellular manufacturing when product demand changes during the planning horizon. He suggests that his framework can incorporate various types of problem specific situations including qualitative considerations. It has been conceptually compared to Virtual Cell Manufacturing System (VCMS) which is appropriate under uncertain demands. Balakrishnan suggests that as cell rearrangement costs increase, jobs shops may be preferred to cellular manufacturing but if one can reduce cell rearrangement costs, then cellular manufacturing will be more rewarding for the organization. He illustrates that in order to make cellular manufacturing viable in a changing demand environment, reduction of layout rearrangement costs as seen in JIT organizations may be important.
Benjaafar (2002) suggested that a distributed layout might help in virtual manufacturing. McLean was one of the first proposed VCMS approach back in 1982 (Balakrishnan, 2005). In a virtual cell, machines are dedicated to a product or a product family as in a regular cell, but the machines are not physically relocated close to each other. In a VCMS machines in a functionally organized facility would be temporarily dedicated to a part family. When a job is to be done it is routed to those machines dedicated to the part family. Thus as in physical cells, dominant flow patterns arise. Machines in the virtual cell are set up for that product family. If the demand pattern changes, the machines in any virtual cell can be reassigned to another part family. Since no machines have to be moved, there is really no rearrangement cost. This is an important advantage since using physical cells in the face of uncertain demand might result in cells having to be rearranged frequently on an ad hoc basis
Balakrishnan (2005) suggests that VCMS combines the advantages of both process layouts and cellular manufacturing. For example, one major disadvantage of traditional cellular manufacturing is that once cells are formed, the machines in a cell may not be available for parts not dedicated to that cell. Thus the machine utilization may suffer when compared to functional layouts, where machines can be assigned to any part at any time. VCMS avoid this drawback as the machine allocations are only temporary and can be reallocated easily (Prince and Kay, 2003). In addition in a VCMS, a family could have access to multiple machines of the same type. Subsequently if the need arises, some of these multiple 17
machines can be reassigned to a part that needs it in order to ensure equitable sharing of machines (Kannan and Ghosh, 1996).
Kannan and Ghosh (1996), compare VCMS to Cellular Manufacturing (CM) and process layouts by using simulation studies. The results showed that the VCMS outperformed both the process layout and CM over a wide range of conditions. When there was less demand uncertainty, the cellular advantages of VCMS were utilized, while when demand uncertainty was high, the VCMS’ ability to reconfigure the cells quickly was utilized. The simulation showed that VCMS allowed jobs to spend less time in queues and setup as compared to process layouts due to dedicated routings and shared family setup. While the VCMS expectedly outperformed cellular manufacturing when setup time was low and demand uncertainty was high, the VCMS also outperformed CM when setup time was high and demand uncertainty was low. This was due to the fact that the VCMS had the ability to exploit production similarities while not giving up any flexibility (Kannan, Ghosh 1996). It was also shown that some of the VCM rules performed poorer than the others.
Prince and Kay (2003) discuss the use of virtual groups (VG) to enhance agility and leanness in production. Both VCMS and VG use the concept that machines in a cell need not be physically located close to one another. However, while VCMS focuses on managing the process, VG focuses on the management of products. Group managers would be assigned a team of operators and all the machines required to make complete products or major subassemblies. Thus these groups are likely to be longer lasting than in VCMS. This would make it easier to implement lean and agile concepts in the different stages of production. (Prince and Kay, 2003).
Though VCMS has been presented to have advantages over CM, it can result in not being able to use the human related factors such as team building, learning, and problem solving as stated by Subash Babu et al (2000). These human factors have immense importance in Lean when it is considered as a philosophy. One other important aspect that VCMS has overlooked is the advantage of physical cells in the amount of travel. In a virtual cell, the layout remains functional and the part may have to travel large distances within the virtual cell. VCMS and VG may not improve travel times compared to a process layout since the machines in a cell may be located far away from each other. Whenever the batch quantities are small the frequency of travel will also increase. In order to address the problem of 18
material travelling Balakrishnan (2005) suggests dynamic cellular manufacturing would be an alternative that allows the physical grouping and rearrangement periodically in appropriate situations. Under such situation it is advantageous to consider grouping cells physically together and then rearranging them to gain the complete advantage of CM as well as retain the flexibility. (Balakrishnan, 2005).
The dynamic cell suggestion also has several limitations. The frequency of rearrangement has direct relevance to equipment uptime. In order to reduce the time loss for equipment rearranging, presence of additional equipment would be necessary. The apparel industry in Sri Lanka has taken initiatives for cellular manufacturing under dynamic demand conditions where they utilize additional equipment that is rearranged off-the line. The benefit of having additional equipment has to be justified comparing the time loss during layout changeover time and the excess capacity of the additional machines that is not been utilized. The dynamic cell suggestion becomes less feasible if the equipment sizes are large and cannot be moved frequently. Hendry et al (1998) highlighted the need for further research based on action research or empirical research to develop and fully justify the applicability of WCM concepts under different demand conditions.
Previous chapters reviewed the literature on applicability of Lean concepts under different demand circumstances. It will be further important to refer to any concepts that has been established to illustrate the significance of WIP to the operations performance.
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2.5 Little’s Law John D.C. Little, in 1961 presents a fundamental law on relations of WIP and the lead time. This fundamental relationship for any process is known as Little’s Law. The average waiting time and the average number of items waiting for a service in a service system are important measurements for a manager. Little's Law relates these two metrics through the average rate of arrivals to the system. This fundamental law has found numerous uses in operations management and managerial decision making.
Little's Law says that, under steady state conditions, the average number of items in a queuing system equals the average rate at which items arrive multiplied by the average time that an item spends in the system. Little (1961)
WIP = Exit rate x Process lead time
Work in Process is the number of items in process at a given point in time. Exit Rate is the amount of work completed over a given period of time. Process Lead Time is the time from the release of work into a process until its completion.
Little comments that this relationship is remarkably simple and general. It is required to have assumptions about any stochastic processes, but this law does not require such assumptions. The law does not mention how many exit points are there, whether each exit point has its own queue or a single queue feeds all processes, what the service time distributions are, or what the distribution of inter-arrival times is, or what is the order of service of items, etc. Though it is recognised for simplicity and generality, the equation is extremely useful in operations management. The reason is that exit rates and process lead time in the equation may be easy to estimate and not the WIP (Little and Graves, 2008).
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Chapter 3:
Methodology
Hypothetical data have been employed by the majority of the previous research, and not enough efforts have been made to justify the appropriateness. One potential avenue is to identify a distinct manufacturing environment that reflects the customised environment, and then proceed to use actual data under lean application. The empirical part of this research is derived from utilizing the case study methodology, with active participation of the researcher in formulating and observing a lean transformation process. As a customised product manufacturer, lean transformation efforts at Avery Dennison Lanka, well align with the theme of this research. Therefore, the lean transformation efforts at Avery Dennison Lanka is analysed as a case study in this research.
3.1 Data Collection Three different methods were used to collect data: direct observation, discussions, and content analysis of documents. Direct observation gave access to processes and could reveal the discrepancies between what was expected and what was actually resulted by applying the lean tools. Practical implications in the customised nature were observed at different stages in the transformation process. Observations were also useful in executing the Value Stream Mapping activity that gave the foundation for identifying need of lean applications. With active participation in the formulation process, the whole transformation process was observed for a period of 5 months at the site of the case study.
Discussions provided depth, subtlety, and personal or group feeling about the suitability of standard lean applications and causes of any failures. The machine operators who directly involve in the manufacturing process engage in Managing for Daily Improvement meetings that are held for 15 minutes daily. The daily discussions give facts about tactical matters and improvement needs to meet the expected outputs. Researcher participating the MDI meetings, referred to the ground level experience of the outcomes of the lean applications.
Documents provided facts, in the form of quantitative Key Performance Indicators (KPI) both on the past performance and the performance after lean efforts. Raw data are collected
21
through production dockets; KPIs are calculated on daily basis and stored in computer database by the Industrial Engineering team. The KPIs and raw data of 8 months were used to analyse the past performance in absence of lean applications. Same KPIs were analysed after the lean applications. The major data referred through the operations documents were on time delivery performance, number of orders handled, down time, asset utilisation and Daily production out put. Daily order quantities and item specifications were gathered from the computer based order management system.
3.2 Research Design and analytical procedure The value stream mapping method was used to identify the opportunities to reduce inventory and identify needs of lean tools to manage the problems exposed by reduced inventory. The target state with Standard lean tools was mapped again as the ideal State Value Stream. The requirements for standard lean applications were referred from the literature and compared with the conditions prevailing in the customised environment considered in the case study.
Studies were made empirically for the impact of standard lean applications in the customised product environment. Actual results after lean applications were mapped again as a Value Stream and compared against the original Value Stream and the ideal state Value Stream Map. Both disadvantages and any unique advantages were analysed refereeing to Operations Key Performance Indicators with and without the application of lean tools. Some standard lean tools could not be empirically tested in the commercial product manufacturing environment, due to high risk of operations failures. Results of such applications were estimated using mathematical models.
SWOT analysis was used to classify weaknesses and threats of standard lean applications. The same model was used to investigate strengths and opportunities to develop extended applications that will support the lean inventory in the customised product manufacturing environment. The research design is presented as a schematic diagram in figure 3.1.
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Literature Review
Data gathering through Case Study
Composing and Analysis of Data
Specify Lean KPIs
Gather raw data on operations performance
Calculate Before state Lean KPIs
Refer to VSM methodology
Observations, time studies and cycle times
Generate Before State VSM
Deliverable
Identify WIP reduction opportunities
Generate Ideal State VSM
Identify required Standard Lean Tools for Ideal State
Refer to Standard Lean Tools
Refer to requirements for Standard Lean Tools
Prevailing conditions via observations and records
Identify difficulties for Standard Lean Tools
Verify Difficulties of Standard Lean Tools in customized environment
Risk Assessment of Standard Lean Tools in the Ideal State
Why is it difficult to implement Standard Lean Tools in the customized environment
Implementation of After State with selected Standard Lean Tools
Identify Standard Lean Tools to be Empirically Tested
Observations, gathering Raw Data and discussion with Operations members
Calculate After state Lean KPIs and compare against before state and Ideal state
Generate After State VSM Refer to proven benefits of Standard Lean Tools Compare After State VSM with Before state VSM and Ideal State VSM
Disadvantages of Lean Inventory in Customized Environment Identify Unique Benefits in the customized environment
Root cause analysis of variations against the Ideal State
Identify opportunities for improvements
Conduct SWOT analysis
Extended Applications
Figure 3-1: Schematic diagram of the research design
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3.3 Introduction to the case at Avery Dennison Avery Dennison Lanka (Pvt) Ltd is a part of the Information and Brand Management Division of Avery Dennison Corporation. It offers a wide range of information management and brand identification products and solutions for retail and apparel manufacturing industry including Woven Labels.
The Information and Brand management trade nature is that, the production is based on customized requirements by the customer. Presently the demands for apparel products are moving for small order quantities and rapid style changeovers. Coping to the apparel sector, brand identification manufacturing also experiences dynamics in the customer order profiles. In average, about 10% of the orders received per day, at Avery Dennison are initial orders where new designs are incorporated.
The weaving production section has being the most poorly performing section in-terms of operational performance such as on time delivery percentage and average delivery lead time. It was also discovered that the average WIP level at the weaving section varies from 4 to 40 days. Therefore the research is focused on analysing the efforts made to reduce the inventory levels at weaving section.
Key specification of woven products is the number of picks and the width of the label. Number of picks defines the density of a label. As number of picks increases the time required to produce the label increases. As width increases the capacity will be reduced because number of repeats that is woven horizontally on the machine becomes less. The number of woven labels produced per day on an automated weaving machine varies from 2,500 to 15,000 due to variations in product nature.
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Chapter 4:
Results and Discussion
This chapter presents the analysis and findings of the research. First the situation before lean initiatives (Before State) is analysed using KPIs and VSM. Rational behind the design of value stream with lean applications (Ideal State) is then discussed. The challenges faced with the lean applications (After State) are critically reviewed, laying the foundation for the recommendations of extended applications.
4.1 Before Lean initiatives - Process and Performance analysis The swimming lane flowchart in figure 4.1 presents the process flow at the weaving section. The Key sections involved are customer service (not shown in the flowchart), production planning, Designing, Production, Raw material stores, Quality assurance and Dispatch. The doted line shows the information sharing through the order management system (Texas). Customer service accepts the orders, generate factory sheet that caries order information and passes the factory sheets to planning section. Planers submit the factory sheets to design section; after raw materials been issued, the production section start production. Proof readers inspect a sample for information accuracy and the order continued to be produced. Weaving product involves three main finishing activities namely slitting, calendaring and cut and fold (C&F). 100% checking is conducted after slitting.
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Production Planning
Factory Sheet
Dotted line indicates electronic information. Texas system is updated as each stage is completed TEXAS Calculate Raw material consumption
Planning Process
Designing Process
Diskette Out
Production
Designing
Receive Factory Sheet from CS
Raw material Stores
Machine Setup
Weaving Process
Proof reading Pass
Finishing process Yes (Stilling, Starching, Calendaring)
Cut & Fold and Packing
Raw material issuing
Dispatch
No
Dispatch
Figure 4-1 : Process Flow Chart of weaving process
In order to have a high level view on the entire process while understanding the time factors involved, the before status is mapped as a value stream map (VSM). The guidelines in the text book Value Stream Management: Eight Steps to Planning, Mapping and Sustaining Lean Improvements (Tapping, Luyster and Shuker, 2002) were followed to draw the Value Stream Map. The present state VSM is shown in figure 4.2.
On the before state the total lead time was 10.6 Days while WIP waiting time was 59% out of the total lead time. The average WIP in number of pieces was 705,000. A description of the before status is as follows.
26
Figure 4-2 : Before State Value Stream Map
27
The customer Takt time is 0.49 seconds, which means that the average demand by the customer is 1 piece at every 0.49 seconds. In other terms, daily demand by the customer averages to 146,000 pieces. Customer purchase order is received by the customer service department through email. Customer service verifies the sales prices and enters data in to Texas system (Texas is the order processing system at Avery Dennison). Then the data are verified and once confirmed as correct, the factory-sheet is printed. Factory-sheet is the document that caries item specifications and the order information such as delivery date and order quantities. Factory-sheets are kept on the CS tray for the production administrator to collect. The collection frequency is every 2 hours; therefore it occurs the first waiting of factory-sheets as information inventory on the CS tray. Once a pile of 2-4 hrs is accumulated, then the administrator submits the factory-sheets to the planner, which creates the second waiting as information inventory.
The production planner enters order information to the planning spread sheet and hands the factory-sheets over to the designer who is seated beside. The designer executes the necessary design activities and forwards the design file to the diskette rack in the production department, copied to a floppy diskette. Designer gives the factory-sheet back to the planner with the design information for material calculations. The factory sheet with the material calculation waits with the planner about 4 hours and then they are submitted to the raw material stores.
Stores collect the factory-sheet of a whole day and issues material for a period of 24 hours together. At this stage, it occurs the longest waiting time as material inventory on the raw material issuing rack which averages to 150,000 pieces which is the average daily demand.
The production machines are provided a priority plan on daily basis, so the operators collect required material from the rack and start setting up the machines. The changing over of a weaving machine takes up to 2 hrs depending on the colour and the number of slitters to be adjusted. The changeover time goes up to 8 hrs if the changeover requires a beam changeover. Once the machine is setup, the bulk production is started and a sample is separated for proof reading. The sample is pasted on the standard proof reading sheet and the operator takes it over to the proof reading department which is a part of the quality assurance department. The proof reading process takes average 30 minutes depending on the
28
queue at this stage. Until the proof reading is done the machine is stopped. Once the proof reading is completed, the machine is restarted and execution of bulk order is continued.
The woven labels are generated in reel form, that latter required to be cut and fold as per the shapes and lengths required by the customer. The process sequentially involves slitting, inspection and sorting, calendaring (to improve stiffness) and cutting and folding. Sonic cutting, sorting and calendaring workstations did not get a priority plan in the before state. The supervisor refers to pilled up orders in the production floor and assigns priority as per the orders progressed. This is termed and denoted as “Go See Scheduling” in the before state value stream map. The orders are pilled up for 3-4 hours between each process stage. After the product is sent through the calendaring process they are kept as inventory for 24 hrs because the next section is fed daily basis. The cut and folding section is located about 100 meters away from the calendaring section, so the inventory is piled up and bulk is sent to cut and fold daily. Changeover time of cut and fold machines varies from 10 minutes – 60 minutes. For 70% of the machines, cut and fold type is tightly fixed. Changing the cut and fold type between Centre fold, End fold and Mitre fold takes up to 3 hrs on this machines. The maximum length that can be cut is also a factor of inflexibility on the cut and fold machines.
The goods delivery times are scheduled at 4 P.M. and 4 A.M. at this factory. Therefore, though the products are packed and ready for delivery, they will wait at the delivery point accumulating maximum 12 hours of inventory as finished goods.
Set of KPIs that reflect the operations performance have been identified referring to the Literature by Bhasin (2008) who has presented extensive research work on Lean and performance measurement and the text book Value Stream Management : Eight Steps to Planning, Mapping and Sustaining Lean Improvements by Tapping, Luyster, and Shuker (2002).
The Before status KPIs are as follows
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KPI
Before value
Total WIP Inventory (Pieces)
705,000
Average WIP Waiting Time (Hours)
127
Total Process Lead Time (Days)
10.6
Total distance materials travelled (Meters)
140
Setup time as a % of total available time
6%
Delivery Reliability (%)
73%
Table 4-1: Before state KPIs
The significance of the before status KPIs is that the delivery lead time is 10.6 days making delivery reliability 73% whereas the target of the company is to meet 95% of the deliveries on time with an average lead time of 6 days. In order to understand past performance, further detail on delivery reliability is important to consider.
Figure 4.3 shows the weekly variation of on time delivery performance and the number of orders delivered during each week. The Period concerned is 15 weeks. 120%
500 450
90% 88%
89% 87%
89%
92% 86%
Ontime delivery
80%
96% 93%
99% 99%
98% 96%
99% 96%
98% 98%
89% 88%
100% 99% 400 87%
89% 350
77%
75% 71%
71%
72%
300
60%
250 55% 48%
200
Number of Orders
100%
40% 150 100 20% 50 0%
0 23w
24w
25w
Number of Deliveries
26w
27w
28w
29w
30w
31w
On time Delivery - Promised date
32W
33W
34W
35W
36W
37W
On Time Del. Std lead time
Tgt
Figure 4-3 : Delivery reliability comparison against number of orders Delivered
The on-time delivery performance against the standard lead time (6 days) has gradually increased and remains around 96%. The performance against the promised date remains
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around 99% during the end of the period. However, when compared with number of orders handled, the delivery performance does not indicate a meaningful relationship. Ideally, in a manufacturing environment, the delivery performance should be increased as number of orders handled is reduced because capacity availability will be more with the number of orders becoming less. The behaviour is questionable since gaining more capacity does not show a relation in improving the delivery reliability. Further analysis has been made to understand the situation.
The scatter plot in figure 4.4 shows the delivery performance compared with the availability of idling capacity. The idling capacity represents the capacity availability as a percentage of the total capacity regardless of the technology types. Weekly on time delivery performance varies from 50% to 100% while the range of idling capacity is 7% to 42%. Though the common expectation is improved delivery performance with the capacity becoming more available, the relationship results only a very slight positive correlation (+0.148). 120%
100%
y = 0.1495x + 0.809
Weekly OTD
80%
60%
40%
20%
0% 0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Weekly Ideling Capacity
Figure 4-4 : Weekly on Time Delivery (OTD) performance against idling capacity
Figure 4.5 shows the reasons for downtimes and contribution of each downtime in the same period. The percentage downtimes have been calculated against the total available time. The major downtime reason is “having no planned work”. The idling capacity due to no planned work averages to 28% out of the total time available. However, the findings are questionable because the delivery performance does not show a positive relation with the
31
increase of capacity availability. Though the capacity is available, the demanding technologies by the customer would not be matching with the internal technology mix. Therefore a further investigation is needed to compare the demands against the technology types. The investigation is continued when the difficulties are determined.
No plan work, 28%
Uptime, 45%
Machine breakdown, 13% Sampling, 3%
Other , 11%
Figure 4-5 : Reasons for Downtimes
4.2 Design of Ideal State with standard lean tools The before state value stream map visualised three key area; the information flow, material flow with a very clear picture of the WIP and the time factors involving. The observations on the before state value stream map can be very effectively used to define the Ideal Lean status applying the standard lean tools. Following key WIP reduction opportunities were identified through the before status VSM. 1. Planning for a whole day inevitably results in accumulation of day’s inventory at the planning stage. RM issuing follows the same accumulation thus making a physical WIP equivalent to at least on day. Releasing the production plan in increased frequencies will reduce the inventory levels. 2. On the before state it is noted that the different production sections are planned independently. Same production plan goes to different sections but the sections do not have any interdependency. The independent planning of different sections has been the cause of accumulation of uncontrollable inventories building up. It was also
32
noted that sonic cutting, sorting and calendaring does not get any plan at all. Priorities are decided at these sections on ad hoc basis, any interdependency is not considered. The material flow could be improved if interdependency is considered in planning the sections. 3. The customer demand is 15,000 pieces for every 2 hours (one piece in every 0.49 seconds). The cycle times of individual sections on the present state are far away from the actual customer demand. (Weaving 9.2 seconds, Slitting 0.9 seconds, Sorting 4.5 seconds, Calendaring 0.8 seconds and C&F 3.8 seconds). Different cycle times result in WIP accumulation and it is required to align the independent cycle times with the actual customer demand. This has to be achieved through improved capacity balancing. 4. The before state involves 5 production stages namely weaving, slitting, sorting, calendaring and C&F. Operating 5 production stages independently means allowing WIP accumulation between these stages. In the ideal stage it will consider some interdependency so the uncontrollable WIP accumulation will be limited. It will also consider to combine some stages together so there will be no WIP accumulation among those stages. 5. In addition, opportunities for two major redundancy reductions in the information flow are identified. The F/S submission from Customer Service to Planner would be directly done eliminating the intermediate production administrator. The F/S that does not have design activities would be directly sent to the RM stores without an interaction of the designer.
Considering the 5 key areas described above the ideal state value stream was drawn as in the figure 4.6
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Figure 4-6 : Ideal State Value Stream Map
34
In the ideal state the total process lead time is 2.2 days. The WIP is 209,150 pieces. The main consideration in the ideal state is to reduce WIP by progressing smaller batches among the different process stages. Progressing small batches leads to some new problems, and suggestions have been given to address them.
On the before state VSM, the first information WIP occurred between customer service order entering and planning. It is suggested that instead of generating factory-sheets at the customer service, the factory sheet printing can be directly done on the planners’ printer through the computer network. On the Ideal state the factory-sheets are printed directly on the planners’ printer so the redundant work is eliminated.
In the future state the stores will share the raw material inventory records with the production planner. The planner him self will look for material availability at the planning stage. Designers will involve only for the size breakdown designs, therefore the orders that do not have size breakdowns will be directly submitted to the stores person who will be located close to the planning and design team.
Earlier the production plan was released on daily basis. It essentially created a waiting of 24 hours at the planner. In the Ideal state the plan will be sent every 2 hours. 2 hours is selected because the customer demand for the average order size (15,000 pieces) is 2 hours (“Paced withdrawal”). This will generate a “levelled” work load on the floor since the orders injected will be equivalent to 2 hrs work. By injecting the short term plan, the team will have a very concrete plan for the 2 hrs period and any unexpected changes will be accommodated only within the next 2 hrs.
In the ideal state the plan for 2 hrs will be communicated only to the weaving operation. Weaving section will become the “phase maker” of the process flow. The material issuing will be executed only when the weaving section requires the material through the “supermarket”. This operation will be facilitated with an issuing rack, where the material is issued for next 2 hours in baskets. Whenever a basket is taken to the machine for production, the empty space means that it requires material for the next order to come.
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In the ideal state an inline proof reading will be executed by a fulltime proof reader allocated for the weaving section, so stopping the machine is not required until the proof reading is done. The elementary times taken for setting up the weaving machines were measured and setup time elements were critically reviewed. The changeover time can be theoretically reduced to 70 minutes from 145 minutes by executing, inline proofing, removing waste activities, identifying external activities and assigning them offline, identifying parallel activities and assigning them to multiple operators and assuring material availability through the supermarket.
The operations after the weaving process will follow a First in First out (FIFO) sequence; therefore communicating the plan is not required for the operations followed by weaving. In the ideal state the cut and fold section is located within the finishing work place (Slitting, Sorting and Calendaring). Sorting is a manual operation that has equal capacity of calendaring. Sorting and calendaring are combined in the ideal state. Sorting has to be executed while the label strip is fed to calendaring machine. Machines are located closer accordance with the process sequence. Therefore small batches could be processed in FIFO sequence limiting the WIP to 1 hour (or 7,500 pieces). The relocation and re-layout of cut and fold section and finishing section is graphically presented in figure 4.7.
36
Figure 4-7 : Work place diagram with inventory controls
37
The analysis also involved a Spaghetti diagram. The physical material flow of before state was mapped on a factory layout and the unnecessary transportations were identified. The suggested ideal state was also mapped using the Spaghetti diagram method. Figure 4.8 presents comparison of the Spaghetti Diagram. Before
After Sorting
Stores Weaving
Slitting
Stores
Calendaring
Weaving
Sorting Calendaring
Slitting Distribution
C&F Planning
Planning
Designing
Designing Dispatch
Dispatch
C&F
CS Tray CS
CS
Stopping points
Figure 4-8 : Spaghetti diagram for before state and after state
It requires having a proper capacity balance to maintain a FIFO sequence. The cycle times are bought inline with the Takt (customer demand) in order to minimise WIP and promote FIFO. Figure 4.9 compares total capacities of different sections with the customer demand 200,000
Daily Capacity (Pcs)
180,000 160,000 140,000
Daily Demand (Pcs)
120,000 100,000 80,000 60,000 40,000 20,000 Weaving
Sonic
Sorting
Calendaring
C&F
Figure 4-9 : Comparison of daily demand with daily capacities
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It is noted that all 5 sections are capable of meeting the daily demand. However, the before state cycle times were far away from the Takt time. The reason is that the sections were utilising only one unit (machine) to produce the order despite the actual demand. In the ideal state it is suggested to utilise multiple units (machines) as per the demand so the Takt would be met. When multiple machines are used, there will be multiple setups and the uptime will be reduced. Since the setup time of weaving machine is reduced by 50%, it allows having two times more setups with smaller batches. It was also noted that 28% of the time was not utilised due to no planned work. Therefore the order of 15,000 pieces would be confidently split on to maximum 4 machines without any impact on available capacity by increased setup times. Then the batch size on one machine becomes 3,500 pieces. Doing the same exercise it is intended to use multiple machines to produce the same order in parallel in the ideal state. This approach improves the alignment of the cycle times with the Takt time. Table 4.2 shows the cycle time calculation of before and ideal states.
Before State
Weaving
Number of m/cs (or manual operators)
Sonic
Sorting
Calendaring
C&F
19
2
10
2
9
Customer demand per m/c per day (Pcs)
7,684
73,000
14,600
73,000
16,222
Capacity of a m/c per day with present performance (Pcs)
7,800
80,000
16,000
90,000
19,000
Order quantity (Pcs)
15,000
15,000
15,000
15,000
15,000
Batch quantity per machine (Pcs)
15,000
15,000
15,000
15,000
15,000
1
1
1
1
1
9.23
0.90
4.50
0.80
3.79
Number of m/cs to produce split order C/T (Sec) Ideal State
Weaving
Number of m/cs (or manual operators) Customer demand per m/c per day (Pcs) Capacity of a m/c per day with present performance (Pcs) Order quantity (Pcs) Batch quantity per machine (Pcs) Number of m/cs to produce split order C/T (Sec)
Sonic
Sorting Calendaring
C&F
19
2
10
2
9
7,684
73,000
14,600
73,000
16,222
7,800
80,000
16,000
90,000
19,000
15,000
7,500
7,500
7,500
7,500
3,750
3,750
750
3,750
833
4
2
10
2
9
2.31
0.45
0.45
0.40
0.42
Table 4-2 : Calculation of cycle times for before state and ideal state
39
Summarising the suggestions, standard lean applications listed below have been applied in the ideal state to reduce the WIP.
Production levelling (Heijunka)
- Planner releases levelled demand
Paced withdrawal
- Plan released in every two hours
Supermarket
- Raw materials issued on demand
Pace maker
- Weaving operation makes the “beat”
FIFO
- Finishing operation with less WIP
Capacity levelling
- Multiple machines to meet Takt time
Setup time reduction
- Weaving setup time reduced by 50%
Work cells
- Finishing operations relocated together
Expectations of the ideal state are presented below comparing with the before states KPIs
KPI
Before
Ideal
Value
value
705,000
209,150
Average WIP Waiting Time (Hours)
127
28
Total Process Lead Time (Days)
10.6
2.2
Total distance materials travelled (Meters)
140
80
Setup time as a % of total available time
6%
11%
Delivery Reliability (%)
73%
100%
Total WIP Inventory (Pieces)
Table 4-3 : Comparison of KPIs before and ideal states
40
4.3 Identification of difficulties of standard lean tools In this section the standard lean tools suggested for the ideal state are reviewed to identify their unique requirements and to compare them against the prevailing conditions. At the end of this section it is intended to verify the difficulties of standard lean tools in customised product environment.
4.3.1 Difficulties in identifying detailed product families The VSM exercise itself requires a product family analysis and the VSM is advised to be drawn for product family. The product family identified in this exercise is the rapier woven product that has a monthly demand of about 3,650,000 pieces. The rapier woven products consist of different variations based on picks per label, label width, number of colours, warp yarn colour and type etcetera. Due to the customised nature of the products the variations does not reflect meaningful patterns that can be grouped in to sub families under rapier woven products. Thus a high level family has been considered in the VSM.
Though the items are grouped in to multiple families, in a number of instances same equipment is required for multiple families. However, the company possessed insufficient numbers of specific type of equipment to allocate units to each group requiring them. Depending on the dynamic volumes of work involved, obtaining the best and fixed capacity mix becomes difficult.
If the product families are known, machine groups can be arranged to undertake all the products that fall within the family. The product family analysis is based on technology requirements and the demand patterns by the customer. As it is noted in the daily demand analysis (in section 4.3.2), it is difficult to identify any meaningful pattern of the capacity requirements. The item specification database that contains 1,600 items shows wide variation in width and picks (in section 4.3.3) so the capacity allocation under different groups of machines becomes a complex task.
41
4.3.2 Difficulties in levelling by product type and quantity Following analysis is conducted to compare the daily capacities of different technologies against the daily customer demand for each technology. The period concerned is from Jan to Aug 2009. Tables 4.4 presents the production capabilities based on different technologies. Rapier technology is again divided to Satin and Taffeta based on the warp material type.
Material type Sattin Sattin Taffata Taffata Taffata Taffata
MC type MBJ2 MBJ2 MBJ3 MBJ3 MBJ2 MBJ2
Head width Max Number (mm) of heads 75 12 100 10 200 5 200 5 75 9 200 4
Number Speed of mc (Picks/min) Capacity per minute 2 540 777,600 1 540 432,000 10 620 4,960,000 3 620 1,488,000 1 540 291,600 1 540 345,600
Daily Capacity at 60% OEE
Monthly Capacity (PicksXWidth)
513,216,000
11,290,752,000
285,120,000
6,272,640,000
3,273,600,000
72,019,200,000
982,080,000
21,605,760,000
192,456,000
4,234,032,000
228,096,000
5,018,112,000
Table 4-4 : In-house capability matrix
The time taken to produce a woven label depends on the number of picks and the number of labels the weft yarn goes through during a pick cycle. In order to compare the capacities with the customer demand, the demands and the capacity should be presented using the same metric. Therefore the capacity availability for Rapier machine has been calculated based on the machine speed and the total width of the machine head. The customer demand has also been converted to the same metric so a meaningful comparison can be done. The total capacity has been compensated with 60% Overall Equipment Effectiveness (OEE) considering the downtime losses, Speed Losses and time possibly taken for reworks.
42
(Picks X Width) Millions
Taffeta Daily Dmd 35,000
Taffeta Avg Dmd Taffeta Daily Cap
30,000
25,000
20,000
15,000
10,000
5,000
8/28/2009
8/21/2009
8/7/2009
8/14/2009
7/31/2009
7/24/2009
7/17/2009
7/3/2009
7/10/2009
6/26/2009
6/19/2009
6/5/2009
6/12/2009
5/29/2009
5/22/2009
5/8/2009
5/15/2009
5/1/2009
4/24/2009
4/17/2009
4/3/2009
4/10/2009
3/27/2009
3/20/2009
3/6/2009
3/13/2009
2/27/2009
2/20/2009
2/6/2009
2/13/2009
1/30/2009
1/23/2009
1/9/2009
1/16/2009
1/2/2009
-
Figure 4-10 : Order profiles against Capacities for Rapier machines for Taffeta items
Figure 4.10 shows the order profile and the available capacity for Taffeta items on rapier machines from January to August 2009. Order nature for Taffeta machines shows a large variation with compared to the daily capacity availability. However, when levelled out the average capacity demand is about 50% of the capacity available. The demand has exceeded the available capacity 18 occasions during the period considered.
43
(Picks X Width) Millions
Satin Daily Dmd 4,000
Satin Avg Dmd Satin Daily Cap
3,500
3,000
2,500
2,000
1,500
1,000
500
Figure 4-11 : Order profiles against Capacities for Rapier machines for Satin items
Figure 4.11 shows the order profile and the available capacity for Satin products on rapier machines from January to August 2009. Demand has exceeded the Satin capacity only for 3 times during the period concerned. The capacity availability is about 4 times more than the average daily demand.
The analysis shows a drastic variation in demands under different technologies. The objective of the organisation is to meet 6 days lead time 95% of the time. Determining the levelling frequency becomes questionable under the drastic demand changing condition. Levelling might lead to loose production targets and meeting the lead times of 6 days would not be achieved.
44
8/28/2009
8/21/2009
8/7/2009
8/14/2009
7/31/2009
7/24/2009
7/17/2009
7/3/2009
7/10/2009
6/26/2009
6/19/2009
6/5/2009
6/12/2009
5/29/2009
5/22/2009
5/8/2009
5/15/2009
5/1/2009
4/24/2009
4/17/2009
4/3/2009
4/10/2009
3/27/2009
3/20/2009
3/6/2009
3/13/2009
2/27/2009
2/20/2009
2/6/2009
2/13/2009
1/30/2009
1/23/2009
1/16/2009
1/9/2009
1/2/2009
-
4.3.3 Difficulties of phased withdrawal Customer demand comes in volumes based on number of pieces. However, under a customised environment the content of a unit product will vary in a wide range. The histograms in figure show the product nature of the current products profile that contains about 1,700 items.
Figure 4.12.A. Picks variation
Figure 4.121.B. Width Variation
Figure 4-12 : Variations of product Specifications
The average number of picks of a label is 682. The standard deviation in number of picks is 448. The profile is slightly skewed towards left (less number of picks). The average width of a label appears to be 25.7 mm, while the standard deviation is 13.8. As per the spread of the width of an item, the range from 10 mm-40 mm could frequently occur.
The capacity of the manufacturing facility is defined by the number of picks that can be run for a certain period where the basic measure is picks per minute (PPM). The output in pieces is therefore based on the number of picks and width of a label. Having a possibility of getting widths between 10 mm-40 mm frequently, the capacity allocation will vary in a wide range. This becomes even more complicated as the nature of number of picks is of standard deviation 448, averaging to 682.
The analysis shows the variation in item specifications, the actual production output would also need to be considered to understand the impact of the variations. As picks and width of items are different, the production capacity also varies in terms of daily output. The scatted plot on figure 4.13 shows the daily variation of individual machine output at the weaving
45
production department. The average daily production quantity of a machine is 7,733 labels, while the average picks generated is equivalent to 221,122. The histograms present the same information with the occurrences of ranges. The standard deviation of the out put per machine is 9,781 labels, while that is 118,959 in picks. Variation of total quantity produced per machine is skewed towards left. The most occurring range has been 0-2,500 labels with an occurrence of about 30%. This observation reveals the fact that the production has been on high density labels about 30% of the time where the output is low compared to the average daily output. However, this proportion varies with time.
Figure 4-13 : Daily production quantity & picks variation before lean implementation
In order to accomplish phased withdrawal the quantities released to production should be aligned with the customer demand for the same duration. The difficulty occurs when it is attempted to align the customer demand that comes in pieces to the capacity which is defined in picks. Determination of the frequency in phased withdrawal becomes very
46
difficult since the capacity needed for the periodic demand could vary in a wide range due to the customised nature of the product.
Customised products need very clear and precise specifications that would be circulated within the raw material. The levelling by product mix and phased withdrawal will inject the same order in split quantities to the production floor in multiple occasions. This will require the specification to be communicated multiple times together with the split quantities. This can be identified as an administrative difficulty in the customised environment.
4.3.4 Difficulties of establishing the Supermarket The raw material submission will be based on the supermarket demand in the ideal state. By doing this exercise it is targeted to reduce inventory waiting by a very significant amount. With the supermarket concept the RM will be issued only when the real demand is signalled prior to starting the production. This means that raw material would be issued multiple times for the same order. The order quantity analysis would be useful in justifying the supermarket. 1800 1600 1400
Frequency
1200 1000 800 600 400 200
48001
0
Order Quantity Category
Figure 4-14 : Order Quantity Analysis
The histogram on figure 4.14 shows the order quantity variation from January to August 2009. The minimum quantity has been a single piece and the maximum is 800,000 pieces. The average order quantity is 14,700 pieces. The most occurring range has been below 1,000 pieces. With the paced withdrawal the order that run more than two hours will require
47
to be issued with raw material multiple times. This has an impact on the machine setups as well.
The varying products with different picks densities, width and colours would require deferent amount of material for the same production period. Allocating fixed space on the supermarket becomes difficult under this circumstance. Keeping raw material available for 1,600 items (each item has at least 5 materials) that does not show a clear demand forecast is very challenging for the supply chain operation.
4.3.5 Difficulties of implementing a FIFO It is targeted in the ideal state to flow one hour’s work on the FIFO basis throughout the finishing operation and cut and fold operation. With the customised order profiles the capacity requirement for the same quantity at different stage will be varying. At weaving the time taken for 1,000 labels could vary from 1 hour to 10 hours depending on the picks densities. But the cut and fold operation would consume the same period. Hence the FIFO would be interrupted and re prioritising would be needed as the time consumed at different stages. The FIFO will work substantially well within the slitting, sorting and calendaring operations, where it does not require specific technology as per the product. However, when it comes to cut and fold section a capacity problem will arise due to very inflexible capacity in the cut and fold section. The cut lengths and cut types are fixed for most of the equipment. The difficulty occurs with the inflexible capacities for different technologies such as maximum length and the capacity ratio between different types of cutting methods. However, the technology used in 30% of the cut and fold machines are comparatively faster in changeovers and can be used for different items flexibly.
4.3.6 Difficulties in levelling the capacities A key to have a smooth material flow with lesser WIP is to have a proper balance within the capacities of different process stages. It has been attempted to align the cycle times of different stages with the Takt time. The total capacities at different stages are sufficient to meet the demands, but the total capacities comprise of several units. In order to align the cycle times with the Takt the parallel utilisation of multiple units is required. Using multiple units means that each unit needs to be setup and same set of raw material is needed to be
48
fed. With the customised nature, filling up multiple units with same set of raw material will be difficult. The changeover of multiple units for the same order will reduce the uptime available for production.
Diverse nature of order profiles that needs different technologies does not lead to a clear or stable capacity demand throughout the year. It does not indicate any meaningful pattern with the time. High density labels require lengthy production time on weaving operation but it is not a factor to consider in defining the cut and fold capacity. Capacity balance becomes more challenging with the inflexibility for changing the colours.
In order to meet the standard delivery lead times, in house capacities should be well aligned with the customer demands. The stochastic nature of demands for different technologies provides a very vague input for deciding the capacity mix. The needle and rapier machines are based on distinguished technologies so there is no room for switching the technologies as per the changes of the customer demand.
4.3.7 Risk assessment of standard lean tools In the levelling concept it is targeted to release same amount of work in similar frequencies so the work floor will be working in the same phase. Releasing schedules every 2 hours supports the levelling concept. However, the 2-hour scheduling process become complicated since the order flow from the customer is not regular. Levelling by product type is challenged with longer changeover times as it is always the case in product levelling by type. The situation becomes more challenging since the item database contains 1,600 items that has different colours and different yarn densities. With the varying demands, levelling might results in loose loading during the periods with lesser demand. The risk of levelling is the incapability of meeting the standard lead times.
The capacity levelling requires aligning the cycle times with the Takt time. It was shown that this requires usage of multiple units for the same production order. This will shorten the cycle time and contribute to reduction in total lead time. The risk of running on multiple units is that the time needed for multiple setups, which will trim down the total time availability. The risk is high in a customised environment because the demands are fluctuating having the need of increased capacities during peak periods.
49
4.4 Empirical examination of after state Having analysed the risks of ideal state it was identified that following adjustments are needed to apply during the implementation. All other initiatives identified in the ideal stage will be implemented and empirically tested.
Frequency of demand levelling and plan withdrawal has to be reduced to 2 times a day (every 10 hours) instead of 10 times a day suggested in the ideal state. In the before state it was only one time a day; empirical examination would reveal any benefits of increased frequency and need for further improvements would be justified. Along with the reduced frequency in plan withdrawal, the material issuing frequency will also made two times a day. The VSM after 1 month of implementation is shown in figure 4.15
The KPIs are also measured and compared with the before state and ideal state.
KPI
Before
Ideal
After
value
value
value
705,000
209,150
401,100
Average WIP Waiting Time (Hours)
127
28
47
Total Process Lead Time (Days)
10.6
2.2
4.1
Total distance materials travelled (Meters)
140
80
80
Setup time as a % of total available time
6%
11%
13%
Delivery Reliability (%)
73%
100%
90%
Total WIP Inventory (Pieces)
Table 4-5 : Comparison of KPIs before, ideal and after states
50
Figure 4-15 : After State Value Stream Map
51
After state shows WIP reduction by 43%, total lead time reduction by 61% and delivery reliability improvement by 17% compared to status prior to lean applications. In addition to that further more improvements can be observed in the after state. The total WIP waiting time has been reduced to 47 hours from 127 hours and total distance material travelled has reduced to 80 metres from 140 meters approximately.
During the risk assessment stage some disadvantages were estimated, and the ideal state was adjusted to reduce the risks before implementation. As the KPIs are compared one disadvantage after the lean implementation is highlighted. It is observed in the KPIs that the time taken for setups has increased by two times compared to the before status. However, the customer has not been affected due to the increased setup times. Before and after status have undertaken almost the same amount of production volumes while delivery performance has been significantly improved with more than 50% reduction in order lead time.
Comparison of Value streams at three states reveals differences in terms of method of information flow, material flow, amount of WIP accumulation and time factors involving. Constructive comparison of the time factors can be done through the summary time factors presented below. Flow type
VA/ Station NVA
Activity Factory Sheet generation Factory Sheet Distribution
1 Info
NVA CS
2 Info
NVA Admin
3 Info
NVA Planning
M/C allocation
4 Info
VA
Design for sizes/ Specs sheet attach
5 Info
NVA Planning
Material calculation
6 Mat'l
NVA Stores
Material issue
Design
Before Inventory Waiting Setup Time (Mins) Time (Mins)
Process Time (Mins)
Ideal Inventory Waiting Setup Time (Mins) Time (Mins)
Process Time (Mins)
Aftter Inventory Waiting Setup Time (Mins) Time (Mins)
Process Time (Mins)
60
25
5
60
25
5
60
25
5
120
5
5
0
0
0
0
0
0
1440
5
5
120
0
20
0
0
20
5
0
30
5
0
30
5
0
30
5
0
10
0
0
0
0
0
0
240
10
30
30
10
20
30
10
20
1440
145
2308
120
70
577
600
80
538
600
30
56
600
30
113
120
10
125
7 Mat'l
VA
proof reading and Production Weaving
8 Mat'l
VA
Finishing
Sonic Cutting
1440
30
225
9 Mat'l
NVA Finishing
Sorting
400
10
1125
10 Mat'l
VA
Finishing
Calendaring
400
5
200
60
5
50
200
5
100
11 Mat'l
VA
C&F
C&F & Packing
1440
40
947
60
30
53
600
30
947
12 Mat'l
VA
Dispatch
Ready for dispatch
600
0
15
600
0
15
600
0
15
Total % times Total LT in days
7590
275
4905
1655
170
826
2815
190
1913
59%
2%
38%
62%
6%
31%
57%
4%
39%
10.6
2.2
4.1
Table 4-6 : Comparison of time factors before, ideal and after states
52
As the value streams and the time factors are compared following variations can be observed in the after status with reference to the ideal state that was implemented.
1. It was expected to split the order at weaving stage to 4 machines to improve the cycle time alignment. But the split has been done only on to two machines. 2. Production plan has still been communicated to slitting, calendaring and cut and fold. 3. Sorting and calendaring operates as two distinct stages. 4. Inventory between slitting and calendaring is about 5 times more than the planned. 5. Inventory between calendaring and C&F is 10 times more than the planned. 6. Manual sorting operation is executed by 3 people while the plan was to utilise more people. 7. C&F has utilised only 1 machine though the paln was to utilise 9 machine for the split order. 8. The setup time of the weaving machine is 80 minutes compared to 70 minutes planed.
4.5 Root cause analysis of variations compared to ideal state Though it was planned to utilise 4 machines for split orders, in the reality it has utilised only two machines. Using multiple machines has been restricted by the technical limitations of the machines. The warp colour is one of the factors that limit the flexibility of machines. It is possible to change the warp colour, but the problem exists with the changeover time. It consumes about 5-8 hrs for the warp colour changeover. Warp yarn comes in a form of a beam which is about 250kg heavy. Each beam contains about 10,000 ends that need to be connected with the remaining yarns of the previous beam on the machine. Physical movements of the heavy beams and connecting 10,000 ends precisely consume 5-8 hrs during a beam changeover. Long changeover time limits the flexibility of the warp yarn colour. Figure 4.16 presents the demand for warp yarn colour of rapier machines from January (1) to August (8) 2009.
53
Millions
Total
80000
Sum of Cap need
70000 60000 50000 40000
Total
30000 20000 10000
Rappier
Rappier
Rappier
Rappier
Rappier
Rappier
1
2
3
4
5
6
Black Taffeta
White Taffeta
White Taffeta
Black Taffeta
White Taffeta
White Satin
Black Taffeta
Black Satin
White Taffeta
White Satin
Black Taffeta
Black Satin
White Taffeta
White Satin
Black Satin
Black Taffeta
White Satin
White Taffeta
Black Satin
Black Taffeta
White Satin
White Taffeta
Black Taffeta
Black Satin
White Taffeta
White Satin
Black Taffeta
Black Satin
0
Rappier Rappier 7
8
Month Technology Warp Material
Figure 4-16 : Demand variation for different warp colours
White taffeta has been the most demanded warp material from January to July. Second demanded colour has been black taffeta except in May where black satin has been the second. Demand for white satin and black satin changes alternatively and diminishes after June. The significance is that the demand ratio for different beam colours has shown a high variation through the period considered. The variation in warp yarn colour results in difficulties to meet the customer demands within the standard lead times committed to the customer. Having this inflexibility and non predictable demand changes the machines are not available to execute orders in parallel. The setup time planed needed to be extended by up to 20 minutes in order to accommodate increased number of colours for some labels.
The histogram in figure 4.17 shows the variation of daily output from the cut and fold department. The average output has been 14,901 labels while the standard deviation is 9,534. Out put per machine of cut and fold machine appears as twice the weaving machine output. Since the number of cut and fold machines is half of the number of weaving machines the capacity mix for average output appear to be suitable. The total number of orders received for different type of cuts and folds are shown in two bar charts in figure 4.18. As per the chart, hot cut with centre fold has been the most occurring case.
54
Variation in daily output reflects the uncertainty of demands for deferent cut and fold machine. Demand for different cut and fold method does not always tally with the capacity mix. The WIP has to wait until the limited number of machines becomes available to execute the required type of operation.
Figure 4-17 : Variation of Cut and Fold daily output
Figure 4.18.A. Cut type usage
Figure 4.18.B. Fold type usage
Figure 4-18 : Demand for different cut and fold methods
55
Sorting and calendaring was planned to execute together in the ideal state. However, as implemented, the sorting team found it difficult to check the strip of labels at the same speed the calendaring is done. Due to the customised nature some labels required longer inspection time including the time to identify the unique types of defects.
With the practical difficulties of having the suitable machines available, the stages in the finishing section could not maintain the FIFO as planned. Thus the production plan is now communicated to each section separately. However, the physical relocation of machines has helped the finishing section to process smaller batches. The WIP at the finishing section in the after state is 240,000 pieces compared to 460,000 in the before state.
4.6 Unique benefits of lean inventory in the customised environment The reduced inventory levels have been verified to have the benefits of improved lead times, reduced travel distances and space savings. In addition to the common benefits, the empirical results show two unique benefits in the customised product manufacturing environment.
The levelling and frequent order withdrawal improves the flexibility of the production plan. In the dynamic environment, new orders are received frequently and existing orders might need to be re prioritised as per the changing demands by the customer. Though the before state plan was released on daily basis, frequent updates have to be made due to the changing demands by the customer. Improved frequency of plan withdrawal helps to accommodate any dynamic demands while sticking to the plan that was made for the intended period.
In a customised environment the possibility for incorrect order specifications is relatively higher than in a standard product environment. Progressing smaller batches improves the opportunity to verify the specifications including the raw material consumption and the operations methods specified. Any deviation against the samples would be identified and adjusted with smaller raw material submission and small batch sizes. It will even improve the opportunity to produce the first time sample along with the commercial order (as it is already done in the organisation) utilising the first batch processed.
56
Chapter 5:
Conclusions and Further Recommendations
Out of the standard lean applications empirically tested, production levelling, paced withdrawal and capacity levelling have limitations in the customised product manufacturing environment. The product family concept is less probable but modified adaptation of cellular arrangement generates significant improvements in terms of reduced transport distances of raw material, reduced WIP and improved lead times.
Findings of this study verify that reduced inventory levels stimulate the need for continuous improvements. Enabling inventory be physically reduced forced the manufacturing problems to the surface. It can be expected that inventory levels will be further reduced, providing greater flexibility in terms of design changes and enabling the manufacturing system to provide faster response possibly with low cost production.
The study identified the challenges and some of the possible applications of common lean tools in the customised environment. A SWOT analysis has been carried out to identify the internal weaknesses and strengths along with the external factors that relate to threats and opportunities. Below appear the SWOT analysis and the discussion of further applications that would be exercised to reduce the inventory levels further. SWOT analysis is conducted referring to the environment in this empirical study but would be adapted by any customised products manufacturer.
57
Strengths
Weaknesses
Computer network available in the facility to implement
Factory machine layout as functional
electronic factory sheet transfer from CS to planning. Moving the locations of staff is possible to align with
basis Staff is seated on functional basis Jobs defined as functional basis
the process flow. IT enhancement is possible to improve planning process and the company is already in to a project in implementing a MRP System. Cross functional improvement projects are possible and encouraged by the corporate. Non-value adders can be trained and utilized for value adding functions without producing unemployment. Internal design changes is technically possible Some equipment can be shared by multiple cells since
Removing non value adding operations may create people idling Withdrawal of plan in fixed intervals requires additional activities at planning and might need IT support Limited number of equipment Uncertain availability of machines due to machine breakdowns Inflexible technology
they are movable. Techniques applied to strengthen preventive maintenance. Computer based time calculations for planning improves the planning reliability and already in use.
Opportunities
Threats
The customers would be negotiated for design changes
Too many products with variations
if the benefit to them is justified Sister sites are available to absorb varying demands Technical service providers available to transform the
make it difficult to create families Customer demands does not tally with in house capacity mix Order volumes are varying rapidly
technologies Flexible equipment available in the market and have been identified
Customer demand for customized items
MRP solutions are available in the market that would support optimum capacity allocations
Tight financial situation does not motivate capital investments
Marketing team available in the field to search for demands that aligns with the capacity availabilities
Figure 5-1 : SWOT Analysis to identify further extension
58
Key opportunities for extended applications identified through the SWOT analysis are explained below.
5.1 Customized WIP controllers It was noted that processing fixed amount of WIP among different sections is challenged by the dynamics of the capacity mix required. Maintaining a FIFO is also challenged by the limited availability of machines to accommodate changing demands. Designated areas would be implemented on the production floor to accommodate the WIP that is clearly known for destination and time of relief. Each batch received into the designated area would have a predetermined destination and a time when it is expected to be issued. Time of WIP issues would be defined as per the predicted times of capacity availability. Obtaining the time prediction might need to be based on an algorithm that models and updated as per the capacity availabilities in the dynamic environment.
5.2 IT assisted planning solution Because of the variety of components being processed and the irregularity of their volumes, an MRP solution would be needed to plan and control production together with lean applications. Within the system, material movement would be controlled using supermarket and FIFO that would prevent excess work-in-progress building up. The MRP system would ensure that the correct WIP were being “pushed” through successive operation. The planning activity would be automated through MRP system, so the allocation of correct machine and correct number of machines will be decided rationally. Capacities and capabilities of equipment, costs involved in machine changeovers would be predefined in the MRP system to consider optimum batching options to minimise waste, down time and to shorten lead times.
More accurate production time calculations are now done using a spreadsheet prepared to calculate the time requiring for each order. The benefit of using accurate processing times to allocate orders on equipment enables the production plan to be more realistic so the levelling process would become more practical.
59
5.3 Communicating capacity availabilities to customers A major problem has been the mismatch of diverse customer requirements with the in-house capability mix. The production department would communicate the capacity allocations and availabilities of different technologies to the sales and marketing department in a regular basis. Knowing the idling technologies, the sales team might put more effort to align the demands with the capabilities available. This effort will be an external demand levelling exercise.
5.4 Search for flexible technologies It was noted that the Satin technology has not been significantly utilised, and the daily demand does not exceed the capacity except in few occasions. A solution would be to investigate the feasibility of converting the Satin technologies to Taffeta technology so a better utilisation would be achieved. It is an opportunity that one of the sister sites is already in to a trial on transferring a machine. Whenever an unusual demand comes for Satin, the organisation would utilise subcontracting via sister facilities, so the customer will still be retained.
While the weaving technologies would be balanced and adjusted as per the customer demands, the cut and fold technology would be developed with high flexibility. Few machines are available in-house which are capable of switching between cut methods within about 1 hour. The company might need to invest on the type of machines that has more flexibility in order to serve the diverse and unpredictable demands by the customer.
5.5 Product Redesigning It was highlighted that determining product families is challenged by the amount of different items and dynamic demands. Efforts would be made together with the customers to re design the products. There have been some efforts made to redesign the labels with less number of picks using alternative yarns, so the high densities would fall within the lesser number family that will make the categorisation effort easy. Once a simplified categorisation is achieved, it will be easy to establish product families and relatively fixed capacities to produce them.
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5.6 Further Research opportunities This study paves the way to two main further research areas. It was found that in a customised environment it is important to have the optimum number of units in addition to the total capacity balance. The total capacity depends on the speed and number of units; the optimum mix of units and speed will generate the best alignment with the customer demand. Unique type of faster machines will generate more capacity with few changeovers that reduces the loss due to changeover time if assigned to multiple units. But multiple units will be better in terms of having more flexibility. Obtaining the optimum capacity mix in terms of number of units and their speeds in a customised environment will be an important research area.
Though levelling was identified as challenging due to varying demands, it was also noted that frequent plan withdrawal comprises the benefit of accommodating the dynamic demands in a customised environment. Frequent withdrawals become complicated with changing demand volumes, while it carries the benefit of frequent re-prioritisation that will useful in a customised environment. Optimising the frequencies of levelling and plan withdrawal in a customised environment is an important area of research.
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