Implementing a Distribution-Network Decision-Support System at Pfizer/ Warner-Lambert Vijay Gupta • Emmanuel Peters • Tan Miller Pfizer Inc., 201 Tabor Road, Morris Plains, New Jersey 07950
Kelvin Blyden Lockheed Martin, 199 Borton Landing Road, Moorestown, New Jersey 08057 vijay.gupta@pfizer.com • emmanuel.peters@pfizer.com • tan.miller@pfizer.com •
[email protected] This paper was refereed.
We constructed a decision-support system (DSS) to help the distribution network of Pfizer/ Warner Lambert to plan its operations. Using OR tools, databases, programming languages, and spreadsheets, this system supports decisions concerning daily operations, annual planning, and long-run strategic planning. This DSS improved the firm’s ability to make rapid, better-informed decisions in many different areas of distribution and supply chain ranging from individual customer deliveries to long-term manufacturing location and technology. (Decision analysis: applications. Industries: pharmaceuticals.)
T
o manage a large-scale distribution network effectively, a firm must have a well-constructed and well-maintained decision support system (DSS). A strong DSS can facilitate effective short-term and longterm decision making. Recognizing the importance of distribution-network DSSs, Warner-Lambert Company (now Pfizer Inc.) developed and implemented such a system between 1998 and 2000. It uses basic OR tools, such as optimization and simulation, coupled with large-scale databases, spreadsheet tools, and programming tools, such as Microsoft Excel and Access. The system is designed to support activities ranging from long-run strategic network design to short-run day-to-day operations and customer service.
Company Background Pfizer is a Fortune-100 manufacturer and distributor of pharmaceuticals, animal health products, and consumer products. In June 2000, Pfizer Inc. and WarnerLambert Company merged to form the new Pfizer company, whose annual sales exceed $30 billion. The Interfaces, 2002 INFORMS Vol. 32, No. 4, July–August 2002, pp. 28–45
consumer sector of Pfizer consists of a number of global operating units, including the Adams, shavingproducts, and consumer-healthcare divisions. These divisions supply confectionery products, Schick and Wilkinson Sword shaving products, and over-thecounter drugs and health and beauty aids, respectively. In the United States, a single distribution organization warehouses and distributes all of the products of these three consumer businesses, as well as those of the firm’s pharmaceutical division. We originally developed the DSS to support the premerger sharedservice Warner-Lambert US distribution network. However, Pfizer continues to use this DSS to support the merged Pfizer/Warner-Lambert US distribution network. The original Warner-Lambert US distribution network was essentially a two-echelon network. The first echelon consisted of two large distribution centers in Elk Grove, Illinois and in Lititz, Pennsylvania and the second echelon included over 35 small third-party pool distribution locations dispersed throughout the US. 0092-2102/02/3204/0028$05.00 1526-551X electronic ISSN
GUPTA, PETERS, MILLER, AND BLYDEN Pfizer/Warner-Lambert
(The new Pfizer consumer and pharmaceutical distribution network includes all of these locations and three additional echelon-one distribution centers that comprised the old Pfizer pharmaceutical and consumer division network.) Warner-Lambert distributed finished goods to its customers’ receiving locations (warehouses) either directly from its two major distribution centers (DCs) or in shipments that flowed from a distribution center to a pool distributor and then to
Pfizer’s annual sales exceed $30 billion. the customer. As a general rule, large consumerproduct orders flowed as truckloads from Elk Grove or Lititz direct to the customer, while small orders typically flowed from the DC to the pool distributor to the customer. Most pharmaceutical orders were delivered by courier services.
Motivation for Developing the Decision-Support System A number of factors motivated the Warner-Lambert distribution organization to develop a DSS. Like many consumer-products companies in the 1990s, WarnerLambert felt increasing pressure from its customers to improve its supply chain and distribution services. Distribution services are more than delivering orders on time and safely. To be considered a preferred vendor by a large retailer, a supplier must also monitor, maintain, and provide information on all key elements of the supply chain between itself and its customers. The supply-chain information that requires continuous review and updating spans a broad spectrum of functional activities and processes. However, suppliers and customers focus on such key drivers of supply-chain relationships as order cycle time, on-time delivery, order and line-item fill rates, and delivery overages, shortages, and damages. (A rich literature exists on supply-chain and distribution-measurement systems: Bowersox et al. 1989, Bowersox and Closs 1996, Fawcett and Cooper 1998, Johnson and Davis 1998, Mentzer and Konrad 1991, van Amstel and D’hert 1996.) Warner-Lambert also recognized that it had to improve its ability to make informed, rapid decisions Interfaces Vol. 32, No. 4, July–August 2002
about logistics planning over varying planning horizons (Figure 1). Warner-Lambert’s planning-horizon framework was a traditional hierarchical framework (Anthony 1965, Hax and Meal 1975, Miller 2002). In this framework, logistics planning and scheduling activities fall into three horizons: (1) operational planning focusing on the short-run (one to 18 months), (2) tactical planning generally spanning a 12 to 18 month horizon, and (3) strategic planning typically covering two to five years or so into the future. Warner-Lambert wanted a planning system to address diverse problems and functions over a broad planning horizon.
Decision-Support Systems Literature A bountiful and ever-increasing collection of literature addresses decision-support systems (Table 1). Bowersox and Closs (1996) and Liberatore and Nydick (1998, 2000) review DSSs in depth.
The Decision-Support System Warner-Lambert’s (WL’s) DSS was designed to support strategic, tactical, and operational planning and management. The foundation of an effective modeling and DSS system is a comprehensive database that provides historical information (and, ideally, forecast information) on all major transactional distribution activities (for example, detailed shipment histories, sales, and freight costs). The database must also have appropriate summary fields and hierarchical relationships to facilitate its efficient use. Database elements needed to support DSSs include —Historical sales (shipments) by end item, by location, and by product family, —Transportation rates, costs, accessorials, duties, and shipments by origin-destination pair, by mode, and by weight and cubic area, —Transit times by origin-destination pair and by mode, —Inventory (actual and targets) in units and by cost by end items (sku) and by location, —Manufacturing rates and costs by production line, by plant, by end item, and by product family, —Purchasing costs and terms by vendor, by location, by end item, and by product family,
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Figure 1:
Warner-Lambert designed its DNDSS to support short-run and long-run planning and scheduling.
—Tax information, local content rules, and intracompany transfer pricing (profits) for global and international models, —Accounts receivable, deduction claims, and shortage and damage claims by customer and by location, and —A well-defined hierarchical product-line structure with data available at any level of product aggregation. WL’s information systems group developed and maintained a database, which included many of these elements.
Individual Elements of the Strategic and Tactical DSS WL’s DSS contributed to many decisions concerning distribution, customer service, and the supply chain. However, its main purpose was to support decisions
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regarding the US network for distributing finished goods, including warehousing, transportation, and delivery. To support long-run planning of warehouse capacities in the network, WL developed three linked simulation models: (1) a DC storage-capacity model, (2) a DC picking-and-shipping model, and (3) a DC facilitysizing model (Figure 2). Collectively these models evaluated the capacity requirements and projections for the DCs in WL’s network over planning horizons of two and five years, depending upon the decision in question. WL did its tactical and strategic planning of warehouse capacities as part of its overall network planning. For example, in a typical strategic-planning exercise, WL determined whether its warehouse network had enough capacity to handle the demands projected for the next three to five years. The projected demand was based on a sales forecast. Interfaces Vol. 32, No. 4, July–August 2002
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Functional Area
Authors
Organization
Distribution
Boykin and Levary (1985)
Monsanto
Distribution
Van der Heyden and Ottjes (1985) ABA Banking Journal (2000)
Description
Hazardous-waste management Health-care management
Sen et al. (2000)
Grann Elevator Meatschappij (GEM) Fannie May, Freddi Mac, Home Side Lending, and Advantor DOE and other institutions
Wong et al. (2000)
Indiana University
Marketing
Brown (1985)
Materials management
Bender et al. (1985)
Materials management
Ramani (2001)
Abbott Labs Hospital Products Division IBM (New York manufacturing facility) MP Trust Hospital
Natural-resources management
Giorgio et al. (1985)
Production planning
Liberatore and Miller (1985)
Production planning
Sullivan and Secrest (1985)
Dairyman’s Cooperative Creamery Association
Production planning
Monsanto Antwerp
Production planning
Bloemen and Maes (1992) Manjunath et al. (1997)
Workforce management
Hall (2000)
Lube Management Corporation
Financial management
Table 1:
Italian National Research Council and International Institute for Applied Resources American Olean
Mattel Toys
System determined optimal routes for effective capacity utilization. System allocated berths and equipment to loading and unloading operations. System provided decision support for mortgage organization, loan underwriting, and credit authorizations. System integrated hazardous-waste clean-up efforts. System provided comprehensive management of patient records, including monitoring medications and lab results. System integrated marketing plans for product portfolio. Optimization-based vendor selection system identified 5–10 percent savings on commodity purchases. Integrated materials (inventory) management system resulted in 12–15 percent reduction in annual purchase costs. System provided efficient management of multipurpose Lake Como reservoir.
Hierarchical production planning system integrated firm’s annual plan, short-term scheduling, and inventory control. Reduced distribution costs by $400,000 to $750,000 per year. System generated daily production plans and inventory forecasts. Allowed better coordination between line supervisors and production managers. Model provided a cost trade-off between production and inventory costs. System developed types and quantities of tools required to produce toys. System allocated daily workforce among several stores based on real-time demand data.
These selected DSSs have been implemented in private industry and the public sector.
Such sales forecasts often consisted of a series of alternative forecast scenarios, each with a projected probability of occurring. These alternative forecasts facilitated sensitivity analyses on the base case (the most likely forecast scenario). Projections of finished-goodsinventory turns also drove the planning process. Because inventory turns are important in determining storage-space requirements, WL also used projections of turn rates in its sensitivity analyses. The outputs of the individual planning models became inputs to the other models. In practice, the planning methodology was usually both sequential and iterative. First, planners input the sales and inventory Interfaces Vol. 32, No. 4, July–August 2002
projections for the planning horizon into the DC storage-capacity model and the DC picking-andshipping-capacity model. Each model projected capacity-utilization rates (surplus or deficit) over the planning horizon for its areas of warehouse operations. The outputs of these two models became inputs to the DC facility-sizing model (Table 2). The DC facility-sizing model evaluated such factors as the total networkwide warehouse square footage required to store, pick, and ship the projected sales and inventory over the planning horizon. Projections for sales and finished-goods-inventory turns were also inputs to the model. Warner-Lambert
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Figure 2: Pfizer developed these principal DSS components to facilitate both strategic and tactical warehouse and transportation planning.
developed this optimization model of its US network as part of a project to evaluate long-run warehouse capacity requirements. This model then became a key component of the firm’s strategic and tactical DSS. The optimization model was a mixed-integer programming model that produced plans for distribution of WL products over the planning horizon. The model’s results included projected product flows through the network by individual location from plants to customers. It forecast the future product volumes that each warehouse would have to handle. In some applications, planners would run this model before running the DC workload models to identify the projected portion of total demand each DC would handle. In other cases, a supply and distribution plan was in place, and planners would input the initial forecast for the plan-
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ning horizon directly into the DC workload models. In either case, an iterative planning process would typically ensue in which the network optimization model would evaluate the implications of results from the DC workload and facility-sizing models, and vice versa. Planners would also run optimization model scenarios that simultaneously considered freight, variable and fixed warehousing costs, warehouse storage and throughput capacity constraints, and so on (that is, they would employ a traditional optimization-based cost-minimization approach). Such runs also included constraints on customer-order cycle time (for example, constraints specifying that all customer locations must be served by a DC within three days transit time of the customer’s location when served by motor carriers). For evaluative purposes, however, we usually found Interfaces Vol. 32, No. 4, July–August 2002
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Input
Factor
1. 2. 3. 4. 5. 6. 7.
40,000,000 cases 15,000,000 cases 20,000 pallets 10 sq. ft. 12 sq. ft. 40 sq. ft. 1.25
8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.
Maximum throughput capacity of DC Projected increase above maximum capacity Projected increase in storage pallets above current capacity Facility space needed per increase of 1,000 cases of annual throughput (excluding inbound and staging space) Inbound and outbound staging area needed per increase of 1,000 cases of annual throughput Area needed per increase of 10 pallets in average inventory Ratio of increase in racking positions required to increase in pallet storage locations required, i.e., (racking positions)/(pallet storage locations) Cost of storage rack per pallet position Construction cost per sq. ft. of expansion at facility Sq. ft. of land required per sq. ft. of facility expansion Cost per sq. ft. of land for expansion Total land available at facility Additional supervisors to support full 2nd shift Additional nonsupervisory labor needed for 2nd shift Cost of additional guards and trailer-yard spotter for 2nd shift Cost of supervisor (salary benefits) Cost of nonsupervisory labor (salary benefits) Warehouse laborer required for every XX cases of annual throughput (above 2nd shift requirements) Warehouse supervisor required for every XX additional laborers (above 2nd shift requirements) Material handling equipment units needed to support 2nd shift Cost per material handling unit to support 2nd shift Material handling units needed for expanded facility above requirements for 2nd shift Cost per material handling unit purchased to support facility expansion above requirements for 2nd shift Increase in nonlabor and nonmaterial handling equipment costs for full 2nd shift One-time costs to upgrade computers for 2nd shift One-time costs to upgrade computer capacity for expansion beyond requirements for 2nd shift Years to depreciate all machinery and equipment Years to depreciate all building improvement costs
$50 $65 2 $20 200,000 sq. ft. 5 75 $120,000 $XX,XXX $XX,XXX 250,000 cases 20 10 $30,000 45 $30,000 25% $100,000 $100,000 10 25
Table 2: The facility-sizing model for each warehouse contained many other inputs not shown. The input values shown are illustrative and do not reflect actual costs or rates.
that an iterative or sequential modeling approach, supplemented by true optimization when appropriate, yielded the most insightful and useful planning results. The inventory investment model (Figure 2) was the final major planning element in Warner-Lambert’s strategic DSS. WL constructed several inventory models over the years, including a traditional item-level statistical safety-stock model and several high-level portfolio-effect and square-root-of-N models (Evers and Beier 1993, Maister 1976, Tallon 1993, Tyagi and Das 1998, Zinn et al. 1989). For most strategic applications, the portfolio-effect and square-root-of-N models provided sufficient accuracy. WL planners typically integrated the inventory decision-support models with Interfaces Vol. 32, No. 4, July–August 2002
the other models (Figure 2) using the iterative, scenario-planning approach. Specifically, they developed strategic scenarios, perhaps for different numbers of DCs for the network (for example, three or two) and alternate locations. For each scenario, the inventory model would project the overall inventory investment for the network. This projection would then be disaggregated to the DC level. Planners need these projections to calculate the inventory investment and carrying cost of each scenario and as validation of the DC storage-capacity-model projections for individual scenarios. Over several years, the DNDSS system provided key inputs for a number of long-run distribution studies and decisions, including the following:
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Projection $(millions)
Output 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
One-time cost for buying and installing racking One-time cost for facility-expansion construction One-time cost for land for expansion One-time cost for material handling equipment for 2nd shift One-time cost for material handling equipment for expanded facility above requirements for 2nd shift Annual cost of labor for full 2nd shift Annual cost of labor to support facility above requirements for 2nd shift One-time cost for operating a 2nd shift and expanding facility Increase in annual operating cost (excluding depreciation) associated with a full 2nd shift and expanding facility Annual depreciation of capital costs required to support a full 2nd shift and expansion of the facility Increase in annual operating cost (including depreciation) associated with a full 2nd shift and expanding facility
1.5 15.0 1.0 0.4 1.1 2.0 1.0 19.0 3.0 1.3 4.3
Table 3: The facility-sizing model for each warehouse contained many other outputs not shown. The fictitious results shown are for one warehouse in a scenario in which both expanding a second shift and expanding the total facility were under consideration.
(1) Determining whether Warner-Lambert should expand the first echelon of its two-echelon network (for example, expand from two to three regional DCs), (2) Determining a new pharmaceutical delivery network, and (3) Determining the best long-term US distribution network for the new Pfizer (that is, consolidating the Warner-Lambert and Pfizer premerger networks).
Elements of the Operational DSS At the operational level, Warner-Lambert developed a DSS that contained a toolkit of diagnostic models, analyses, and standardized reports, including the following: —A customer logistics scorecard, —An order-cycle monitoring tool, —An on-time-delivery monitoring tool, —An inventory-level-and-turns monitoring tool, —An overage, shortage, and damages monitoring tool, —A detention-and-delivery-unload monitoring tool, —Daily e-mails to transportation-load planners on opportunities to improve transportation, —Daily e-mails to planners on delivery performance, and —Daily e-mails to customers on any back-orders. Also, at this level, Warner-Lambert focused on DSS components that (1) monitored distribution and trans-
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portation operations, (2) identified opportunities for improving short-run operations, and facilitated their application, and (3) provided information to support immediate decisions. Logistics analysts used the logistics-planning tool kit, a customized point-and-click system, to monitor and analyze customer-related activities on the distribution network. One component of this tool kit is the customer logistics scorecard (Figure 3). WarnerLambert maintained scorecards at the individualcustomer level and if needed, at the customerreceiving-location level. Warner-Lambert also had tools to track critical logistics activities, for example, a report designed to monitor planned and actual freight costs and flows by transport lane (Table 4).
One-Time Model Developed from the DSS DSS modeling systems often produce benefits beyond those originally anticipated. An established and effective DSS modeling system often interests groups or departments beyond its original users. Warner-Lambert ultimately developed several optimization models that served business needs outside the scope of the DSS system. Specifically, distribution’s implementation of the US-network optimization model interested several Interfaces Vol. 32, No. 4, July–August 2002
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On-Time Delivery Customer Customer 1 Customer 2 Customer 3 Customer 4
Order Cycle Time
Actual
Goal
Mean
Std. Dev.
Goal
98% 97% 94% 98%
97% 97% 97% 97%
8.9 6.1 6.5 6.0
3.4 2.1 2.1 2.9
8.0 7.0 7.0 7.0
Line Item Fill Rate Customer Customer 1 Customer 2 Customer 3 Customer 4
Order Fill Rate
Freight Cost Per Pound
Avg.
Goal
Avg.
Goal
Actual
Goal
97.9% 97.1% 96.4% 97.5%
98% 96% 97% 98%
83% 83% 74% 87%
85% 82% 80% 79%
$0.024 $0.027 $0.036 $0.031
$0.030 $0.035 $0.049 $0.037
Percent of Cases Picked at DCs in Full Pallet and Full Layer Quantities
Inventory Turns
Customer
Actual
Goal
Actual
Goal
Customer 1 Customer 2 Customer 3 Customer 4
94.1% 80.7% 75.0% 96.8%
90% 85% 70% 99%
7 3 6 8
7 4 5 7
Customer Customer 1 Customer 2 Customer 3 Customer 4
Carrier Handling Charges
Shortage Claims
Other Carrier Charges
Total Accessorials
Unearned Cash Discounts
Unsaleables
$10,000 $2,000 $10,000 $7,000
$5,000 $0 $10,000 $15,000
$9,000 $2,000 $4,000 $7,000
$24,000 $4,000 $24,000 $29,000
$50,000 $70,000 $0 $0
$0 $0 $10,000 $20,000
Figure 3: The customer logistics scorecard monitored key measures of the supply-chain relationship between Warner-Lambert and its customers.
Adams confectionery colleagues. They decided to employ optimization in support of their global-supplychain-planning efforts. The Adams division wanted to enhance its strategic and tactical manufacturingplanning capabilities. Thus, Adams supply-chain personnel worked in conjunction with several people who had worked on the original DSS to develop a global manufacturing-and-distribution-planning model. The Adams global-manufacturing-optimization model included all its major plants and products. This represented over 12 plants scattered across the Americas, Europe, and Asia and over 70 product families. Interfaces Vol. 32, No. 4, July–August 2002
To construct this model, the team identified the capacities and costs associated with the three major production stages (bulk processing, finishing, and packaging) and then modeled these stages explicitly. Further, it identified distribution costs, locations, echelons, and major transport lanes. It incorporated this data into the model, a traditional mixed-integer formulation (Appendix). For tactical planning, the model could generate integrated production and sourcing plans for global manufacturing and distribution. For strategic planning, the model, somewhat enhanced, would support decisions concerning plant location, closing,
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(1) Pool Point
1 2 3 4 5 • • • • • • Total/Average
(3) Average Shipments per Week from Regional Warehouse to Pool Point
(2) Number of Shipments from Regional Warehouse to Pool Point During Last 10 Weeks
(4) Average Lbs. per Shipment
(5) Average Freight Cost per Lb.
Planned
Actual
Planned
Actual
Planned
5.0 5.0 3.0 3.0 4.0 • • • • • •
5.5 5.0 3.0 2.0 4.5 • • • • • •
36,000 35,000 37,000 35,000 35,000 • • • • • •
37,000 38,000 40,000 17,000 36,000 • • • • • •
$.04 $.03 $.04 $.04 $.03 • • • • • •
55 50 30 20 45 • • • • • •
Actual $.04 $.03 $.04 $.08 $.03 • • • • • •
Table 4: Monitoring reports, such as this table that tracks planned and actual freight costs and flows by transport lane, are a key component of the logistics planning toolkit.
expansion, and contraction. The Adams division next requested a more sophisticated model for evaluating strategic-technology decisions.
A Strategic Model for Planning Manufacturing Technology We developed a multiplant model to help the Adams division make optimal decisions about locating manufacturing technology. Management wanted to see whether moving its manufacturing technology among plants would reduce overall costs. The decision concerned relocation and optimizing the costs of moving equipment, closing and opening plants, manufacturing, transporting freight, and labor. The logistics-planning group and the manufacturingstrategy group of the Adams division collaborated on the model. It focused on the North Atlantic region, including Western Europe, Canada, and the United States, and considered demand for candy and sugar and sugarless pellet and stick gum and their manufacture. In the late 1990s, in the mature markets, the demand for gum products declined, and demand for new prod-
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ucts increased. As the firm introduced new products in the different markets, it used its existing plants in those markets to produce them. For example, when it introduced sugarless pellets in the United States, it began production in a midwest plant. Similarly, when it introduced this product in Canada, it added its production in a Canadian plant. This practice led to lowvolume products being produced by multiple plants. Thus, a future objective of the modeling project was to look for opportunities to consolidate the production of similar products. With these objectives and the traditional supplyand-demand requirements in mind, we developed a mathematical model (Appendix). We solved the model using a GAMS interface with a MIP solver. The results provided the manufacturing strategy group with new insights and allowed it to carry out sensitivity analyses. The firm implemented results from this study and obtained some key benefits: (1) The model identified a new manufacturing and distribution plan projected to reduce annual operating costs by $5.9 million. (2) The revised operating plan required a one-time investment of about $11 million, with a payback period therefore of less than two years. Interfaces Vol. 32, No. 4, July–August 2002
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(3) The model identified new manufacturing technologies (and their associated product families) that should be transferred into a plant facing imminent layoffs, thus avoiding human hardships and the costs of layoffs. (4) In all, the model identified three manufacturing technologies (and their associated product families) to be relocated. (5) The model also identified a plan for transferring similar manufacturing technologies to one location, thus reducing the technology spread. (6) One of the production-technology moves identified would also reduce the transit time by about four weeks for a high-demand product in a key sales region and would also reduce in-transit and safety-stock inventories.
Benefits and Enablers of the Decision-Support System Warner-Lambert’s DNDSS has generated many benefits, some quantified, some never completely quantified, and some qualitative. The important quantified benefits include —Annual savings exceeding $0.5 million in freight costs from improved transportation-scheduling support, —The elimination of customer deductions amounting to several hundred thousand dollars annually, and —A strategic manufacturing technology plan that could save $5.9 million annually. The DSS also helped distribution managers to understand the cost and service implications of proposed
such as customers’ shortage claims on deliveries and the impact of order-cycle time and on-time delivery performance on the firm’s relationship with its customers. The DNDSS raised the firm’s awareness of such issues and enhanced its ability to remediate supplychain problems and to make proactive improvements. Finally, other key enablers facilitated the success of the DNDSS: —The logistics planners who developed this system worked on a variety of operational, tactical, and strategic decision-support problems. This mix helped them to understand, for example, the operational implications of potential strategic or tactical decisions about the long term. This knowledge helped them to develop better decision-support tools than they would otherwise. —The DSS contained tools developed for and used by field operations (for example, distribution-center colleagues). The process for developing them increased logistics planners’ knowledge about field
The DNDSS continues to evolve and expand. operations and increased people’s confidence in the planners and the DSS. —The DSS monitoring functions were based on exceptions reporting. —Warner-Lambert’s activity-based-costing (ABC) system for evaluating distribution and customerservice operations provided inputs to the DSS (Liberatore and Miller 1998).
DSS modeling systems often produce benefits beyond those anticipated.
Implementation and Information Technology Notes
strategic network alternatives. The cost implications of these alternatives ranged between one and 10 million dollars annually. The greatest benefits may have been qualitative or nonquantified. The initial US distribution DSS led to optimization modeling in other parts of the organization. Further, within the US distribution organization and related organizations, the DSS raised people’s awareness and ability to act on supply-chain issues,
We implemented the DNDSS between 1998 and 2000, and it continues to evolve and expand. Within two years, by the end of 1999, the core strategic, tactical, and operational elements were functional. In implementing this DSS, Warner-Lambert built the various components on a project-by-project basis. That is, planners developed individual DSS components only to support specific objectives of actual projects and not to meet anticipated possible needs. Because Warner-Lambert had limited resources, it constructed
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the individual DSS components to support real nearterm projects as well as to contribute to the long-term DSS. By developing DSS components as part of individual projects with specific completion dates and objectives, it created a sense of urgency and immediate need for the DNDSS components. For example, Warner-Lambert developed the strategic and tactical components of its DSS (Figure 2) to evaluate what capacity it needed in its distribution network over the long term. The firm initially developed the optimization, inventory, and DC-capacity models of the current Pfizer DNDSS to study the WarnerLambert US network in the late 1990s. The firm used this approach of developing DSS components to support specific projects and then incorporating the components into the formal DNDSS repeatedly. Typically it incorporated project components into the DNDSS by appending front-end input interfaces and back-end interfaces and reporting technology. The resulting DNDSS tool readily supported a variety of projects and applications. Because the firm and the US distribution organization needed these tools for a project objective, the organization did not view them as threatening. Rather, it saw them as aides to making better, quicker decisions. In addition, people from the distribution organization who had some OR background worked with logistics and distribution colleagues to construct this system. In particular, they provided advice on what decision-support tools would enhance operations. The collective, inclusive effort of constructing the DNDSS contributed to its positive reception.
Expanded Use of the DNDSS Outside the Distribution Group As parts of the organization other than distribution adopted components of the DNDSS, the DSS developers made existing components available or added components on a project-by-project basis. When planners developed a decision support tool for a particular project, they also incorporated it into the DNDSS. As other groups became aware of the DNDSS’s contributions to the distribution group, they became interested in having similar decision support. Thus, implementing a DSS successfully in distribution
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facilitated its eventual migration and expansion to other areas of the firm.
Resources and Costs Required to Develop DNDSS Warner-Lambert developed its DNDSS with minimal resources. During the initial development between 1998 and 2000, three or four members of distribution with graduate degrees in operations research and operations management spent 50 to 70 percent of their time developing the core of the DNDSS. Most of their
Warner-Lambert’s DSS quickly became an integral planning-support component. time was devoted to DSS components to support specific projects. Only 15 to 20 percent of their time was devoted to pure DNDSS development (to append generic front ends and back ends to the DNDSS components developed for various projects). The information systems group also expended effort and resources to develop, maintain, and enhance largescale Oracle databases that indirectly supported the DNDSS. However, it started developing this database system well before the inception of the DNDSS, and these databases supported many activities. These databases were a foundation block of the DNDSS; a firm without such large-scale databases would incur much greater costs in developing a DSS than WarnerLambert did.
IT Direct Costs and IT Architecture The DNDSS required few IT components. To build its DSS, Warner-Lambert bought an optimization package (SAILS), a simulation language (EXTEND), a geographic information system and mapping package (MAPINFO), and several desktop PCs. (Planners also had GAMS and a MIP solver available and used these very occasionally, for example, to develop the strategic-technology-planning model.) In addition, the firm made some standard tools available to employees: Oracle databases, laptop PCs, internal network e-mail and related systems, Microsoft office software, Visual Interfaces Vol. 32, No. 4, July–August 2002
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Basic, Microsoft Access, and links to data in the ordermanagement system. The one-time costs to purchase the optimization, simulation, and GIS software were less than $100,000, and ongoing annual software expenditures did not exceed $30,000. Warner-Lambert primarily used Microsoft Access, Visual Basic, and Excel to develop the interfaces, links, input screens, and reporting systems. We designed the system to be used by nontechnical logistics, ordermanagement, and other employees. They typically could use input screens to select the customers, products, analyses, reports, and so forth that they wanted to use. Standard networking topologies facilated user access from laptops or from several desktop PCs. While Microsoft Access is not a large-scale database language, it can support applications of about one million records effectively. Most of the applications in the DNDSS used fewer than one million records for any particular activity. Thus, we developed many DNDSS decision tools with Visual Basic programming support. We supported the occasional applications that used millions of records with Oracle. The DNDSS produced reports as Access, Excel, or even Word documents. Analytic reports in Excel could be used as inputs to users’ own analyses or monthly reports.
Conclusion Once developed, Warner-Lambert’s DSS quickly became an integral planning-support component for operational, tactical, and strategic activities and decisions. The system relied on such standard operations research methodologies as optimization and simulation. Just as important to the success of this system was the support of people at headquarters and in the field. Colleagues ranging from senior distribution managers to entry-level employees used the information and insights the DNDSS provided.
APPENDIX Integrated Manufacturing and Distribution Planning Optimization Model We developed the Adams global manufacturing and distribution model, which included all of the division’s Interfaces Vol. 32, No. 4, July–August 2002
major plants and products, to facilitate tactical and strategic planning. Given the multiple purposes and planning horizons we expected this model to support, we formulated several variations of this basic model. These included mixed-integer and linear-programming formulations. The mixed-integer variations could address fixed costs at the tactical level (for example, annual fixed costs for plant or production line) and at the strategic level (for example, plant openings or closings). The following is a simple formulation designed to provide integrated production and distribution plans for a scenario in which the division planned to operate all plants over a tactical planning horizon. This is the model variation used most frequently. We use the following notation: p index on the plant. i index on the bulk-product family. k index on the finished (but not packaged) product family. m index on the packaged finished-goods product family. (Note: i, k, and m have a one-to-one correspondence. That is, i 1, k 1, m 1 represents the same product family in bulk, finished, and packaged forms. We use three indices to distinguish where a product family is in the production process. However, one could more concisely formulate this problem with just one index representing product families at each of the three production stages.) j index on the bulk production line. l index on the finishing production line. n index on the packaging production line. Jip the subset of all lines j 1, . . . , J that can produce product i at plant p. Lkp the subset of all lines l 1, . . . , L that can produce product k at plant p. Nmp the subset of all lines n 1, . . . , N that can produce product m at plant p. r index on the sales region (country). t index on the time periods of the defined planning horizon. epijt the variable cost per unit of producing bulkproduct family i at plant p on bulk line j during period t. fpklt the variable cost per unit of producing finished-product family k at plant p on finishing line l during period t.
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gpmnt the variable cost per unit of packaging packaged-product family m at plant p on packaging line n during period t. apmwt the cost per unit of shipping packagedproduct family m from plant p to warehouse w during period t. bwmrt the cost per unit of shipping packagedproduct family m from warehouse w to sales region r during period t. cpmrt the cost per unit of shipping packagedproduct family m from plant p to sales region r during period t. hwmt the cost per unit of carrying packagedproduct family m in inventory at warehouse w during period t. hrmt the cost per unit of carrying packagedproduct family m in inventory at sales region r during period t. dmrt the demand for packaged-product family m in sales region r during period t. iminwmt the minimum inventory level allowed (or lowest inventory target) for packaged-product family m at warehouse w at the end of period t. iminrmt the minimum inventory level allowed for packaged-product family m at sales region r at the end of period t. ypik the conversion factor that translates what 1 unit of bulk-product family i contributes to the output of finished-product family k at plant p. For example, a value of 1.0 indicates that 1 unit of bulk production yields 1 unit of finished production. (This factor does not change from period to period, and therefore, we omit the subscript t.) zpkm the conversion factor that translates what 1 unit of finished-product family k contributes to the output of packaged-product family m at plant p. opijt the total potential production capacity (in units) to produce bulk-product family i at plant p on production line j during period t. qpklt the total potential capacity (in units) to produce finished-product family k at plant p on production line l during period t. vpmnt the total potential production capacity (in units) to produce packaged-product family m at plant p on packaging line n during period t. We define the decision variables as follows:
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Xpijt the units of bulk-product family i produced at plant p on production line j during period t. Ypklt the units of finished-product family k produced at plant p on production line l during period t. Zpmnt the units of packaged-product family m produced at plant p on packaging line n during period t. Spmwt the units of packaged-product family m shipped from plant p to warehouse w during period t. Twmrt the units of packaged-product family m shipped from warehouse w to sales region r during period t. Upmrt the units of packaged-product family m shipped from plant p to sales region r during period t. Iwmt the units of packaged-product family m in inventory at warehouse w at the end of period t. Irmt the units of packaged-product family m in inventory at sales region r at the end of period t. (Each sales region or country has a local warehouse that inventories and ships its products to the customer locations within the sales region.)
The Global Manufacturing and Distribution Model Minimize:
兺p 兺i 兺j 兺t Xpijtepijt 兺p 兺k 兺l 兺t Ypklt fpklt
兺p 兺 兺n 兺t Zpmntgpmnt 兺p 兺 兺 兺t Spmwtapmwt m m w
兺w 兺 兺r 兺t Twmrtbwmrt 兺p 兺 兺r 兺t Upmrtcpmrt m m
兺w 兺 兺t Iwmthwmt 兺r 兺 兺t Irmthrmt m m
(A1)
subject to Iwmt ⱖ iminwmt for all w, m, t,
(A2)
Irmt ⱖ iminrmt for all r, m, t,
(A3)
Iwmt Iwmt1
兺p Spmwt 兺r Twmrt
for all w, m, t, Irmt Irmt1
(A4)
兺w Twmrt 兺p Upmrt dmrt
for all r, m, t,
(A5)
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兺
j僆JiP
Xpijt ypik
兺
l僆Lkp
Ypklt
for all p, i, k, t (where i k),
兺
l僆Lkp
Ypkltzpkm
兺
n僆Nmp
(A6)
Zpmnt
for all p, k, m, t (where k m),
(A7)
Zpmnt 兺w Spmwt 兺r Upmrt n 兺 Nmp 僆
for all p, m, t
(A8)
兺i (1/opijt) Xpijt ⱕ 1
for all p, j, t,
(A9)
兺k (1/qpklt) Ypklt ⱕ 1
for all p, l, t,
(A10)
(1/vpmnt) Zpmnt ⱕ 1 兺 m
for all p, n, t,
(A11)
Xpijt , Ypklt , Zpmnt , Spmwt , Twmrt , Upmrt , Iwmt ⱖ 0 for all p, i, j, t, k, l, m, n, w, r.
(A12)
Overall, this model produces an integrated global operations plan that minimizes production and distribution costs subject to satisfying production-capacity constraints and forecast customer-demand constraints. The output of the model includes a production plan for each plant by time period, by production line (for each of the three major stages of production), by product family. The model also creates a global shipping and sourcing plan that indicates plant-to-warehouseto-country assignments by product family and by time period. Constraints (A2) and (A3) specify the end-of-period inventory targets for all packaged-product families at all warehouses. Constraints (A4) ensure that the endof-period inventory quantity of each packagedproduct family at each warehouse equals the beginning-of-period inventory quantity of that family plus shipments received and minus shipments made during the period. Equations (A5) perform a similar balancing function at the sales-region (country) level. Specifically, these constraints stipulate that the end-ofperiod inventory of each packaged-product family at each region equals the region’s beginning-of-period inventory plus inbound shipments received by the region less demand satisfied during the period (that is, outbound shipments from the local warehouse to cusInterfaces Vol. 32, No. 4, July–August 2002
tomers). Inbound shipments to a sales region could come from either a warehouse or directly from a plant. Constraints (A6) and (A7) describe the relationships between the inputs and outputs of the three-stage production process. As production flows through a plant from one manufacturing stage to the next (for example, from a rolling-and-scoring process to a coating process), X units input into one stage of production yield Y units of output (for input into the next stage). Constraints (A6) translate what the total units of a bulkproduct family processed during a period on a production line at a plant will yield in units of a finished-product family. Parameter Y indicates this relationship. Similarly, Equations (A7) and parameter Z capture what the total units of a finished-product family processed during a period on a production line at a plant will generate in units of a packaged-product family ready for sale. Constraints (A8) ensure that a plant cannot ship out more of a packaged-product family m during a period t than it produces. Plants do not hold packaged-product families in inventory but immediately ship packaged products to warehouses or sales regions. In actual practice, shipping packaged products immediately to a warehouse often means simply transporting cases of a product by conveyor from a plant to an adjacent warehouse. Constraints (A9), (A10), and (A11) ensure that the model’s production plans do not exceed capacity limitations at any of the three stages of manufacturing. These constraints include multipliers at each stage of production (opijt, qpklt, and vpmnt, respectively) that adjust for the fact that the various product families that can be produced on a production line (at each stage of production) may have unique production rates. Finally, Equations (A12) represent standard nonnegativity constraints on all decision variables in the model. With straightforward enhancements to this simple formulation, one can consider the fixed costs associated with strategic decisions (such as opening or closing plants or distribution centers) and tactical decisions (such as operating a production line for only part of a planning horizon). Other manufacturing considerations, such as minimum run sizes for product families on individual production lines, can also readily be incorporated into this model. The distribution costs evaluated in a model run
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would typically include such factors as customs, duties, and other local taxes. One could also easily insert variable warehouse-handling costs by product family and by location as well as warehouse throughput capacity constraints. Further, one can easily convert this cost-minimization model into a profit-maximization model to evaluate tax strategies and intercompanytransfer pricing. One could also incorporate constraints on maximum allowable transit times between a warehouse-stocking location and a sales region into this basic model.
A Strategic ManufacturingTechnology Planning Model The Adams division wanted to see if moving existing manufacturing technology among plants would reduce overall network costs. Thus, the decision concerned relocation and optimizing costs and labor capacities. The costs considered included those of relocating existing equipment, hiring and firing, closing and opening plants, manufacturing, freight, and duties. To capture the decision to move production technology, we defined various decision variables. For plants, finished goods, and demand regions, we use the following notation: p index on the initial manufacturing plant. m index on the finished-goods product families. p index on the new manufacturing plant. r index on the sales region (country). We use the following decision variables: Xpmp binary variable representing the move of technology to produce product family m from manufacturing location p to location p. 1, if the production technology for product m is moved from p to p. 0, if it is not moved. Tpmr binary variable representing manufacturing plant p serving demand for product family m of sales region r. 1, if plant p supplies product m to sales region r. 0, if it does not. Fp ⱖ 0 head-count reduction due to reduction in production at location p.
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Hp ⱖ 0 head-count increase due to increase in production at location p. Qp ⱖ 0 variable to capture the need for existing manufacturing location p to continue to operate. 0, if plant p is open. 1, if plant p is closed. Yp ⱖ 0 variable to capture the need to open a new manufacturing plant at location p (or need to sign a new lease to keep an existing location open). 0, if the company needs to open a new plant at location p. 1, if it does not need to open a new plant at location p. Upm ⱖ 0 variable to capture the need for the labor that exists at location p to produce product m (assuming that unneeded labor will be laid off). 0, if labor at plant p is needed for producing product m. 1, if labor at plant p is not needed for producing product m. We use the following parameters: bpmp cost of moving technology to produce product family m from p to p (assuming that a partial move of technology is not allowed). lpm number of laborers required for producing product m at location p. sp cost of reducing head count by one at manufacturing location p. np cost of increasing head count by one at manufacturing location p. ip original number of technologies present at location p. cp cost of closing the plant at location p. kp new investment required to keep plant p open or to sign a new lease for p. ap number of definite layoffs required at plant p due to reduced demands. dmr demand for product family m at sales region r. vmr coverage matrix (0 and 1) indicating other demand exists for product family m at sales region r. gpm capital investment required at new location when the move of technology for producing product family m from p is moved. opm production capacity available at plant p for product family m. Interfaces Vol. 32, No. 4, July–August 2002
GUPTA, PETERS, MILLER, AND BLYDEN Pfizer/Warner-Lambert
epmr total cost of producing a unit of product family m at plant p and shipping m to sales region r. w budget available for capital investments. z budget available for expenses. Using these parameters and decision variables, we developed the objective function and constraints for the following linear mixed-integer programming (LMIP) model: Minimize cost
兺p 兺 兺r epmr • dmr • Tpmr m
cost of headcount reduction,
(B1.2)
兺p np • Hp hiring cost,
(B1.3)
兺p 兺 兺 (gpm bpmp) • Xpmp m p moving cost,
(B1.4)
兺p cp • Qp cost of plant closing,
(B1.5)
kp • (1 Yp) new investment,
(B1.6)
subject to
lpm • Xpmp Fp ⱕ 0 兺 p
∀p,
兺 兺 兺 lpm • Xpmp Hp ⱕ 0 m m p ip
∀p,
(B2)
(B3)
兺 兺 Xpmp 兺 兺 Xpmp m p m p
ⱕ (1 Qp) ∀p,
(B4)
兺p sp • Fp 兺p np • Hp 兺p 兺 兺 bpmp • Xpmp cp • Qp ⱕ z, m p
(B5)
兺p 兺 兺 Xpmp • gpm kp • (1 Yp) ⱕ w, m p
(B6)
Xpmp 兺 Tpmr ⱖ (1 Upm) 兺 p r Interfaces Vol. 32, No. 4, July–August 2002
∀ p, m
Qp ⱕ 1, ∀p, ip 1
(B9) (B10)
Xpmp ⱖ (1 Yp) 兺 兺 Xpmp 兺 m p p
(B11) (B12)
for location that needs new lease.
Model Objective Function The objective function consists of the costs of producing and delivering goods (Equation (B1.1)), the costs of layoffs caused by moving technology and reducing capacity (Equation (B1.2)), the costs of hiring new labor (Equation (B1.3)), the costs of moving machines to new locations and expanding facilities at new locations (Equation (B1.4)), the cost of closing plants (Equation (B1.5)), and investments for a new lease (Equation (B1.6)).
Model Constraints
兺 兺 lpm • Xpmp m p
兺 兺 lpm • Xpmp m p
兺r Tpmr • dmr ⱕ opm 兺 Xpmp • opm 兺 Xpmp • opm p p
(B8)
Yp ⱕ Qp (B1.1)
sp • lpm • Upm 兺p sp • Fp 兺p 兺 m
∀ m, r
for location that needs new lease,
goods supply cost,
ap
兺p Tpmr vmr
∀p, m,
(B7)
We developed constraints to meet decision makers’ requirements or to determine values for certain variables. Equation (B2) helps in assigning a severance value. To determine this value at a location p, we evaluate the labor capacity moving out and the labor capacity moving in to work on relocated technologies. We used a similar approach to determine new hires at a plant in Equation (B3). Equation (B4) determines whether any manufacturing technologies remain at a location and helps determine the value for variable Qp. A value of zero implies that a plant retains some production technologies (that is, it should supply one or more products) and thus should remain open. A value of one implies it retains no production technologies and should be closed. Equations (B5) and (B6) place budget constraints on expense-related costs and capital investments. Total expenses and total capital costs have separate constraints because capital investments are depreciable
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over years and thus are treated differently for accounting purposes than expenses, which affect profit and loss in a single year. Equation (B7) ensures a plant reduces its staff appropriately when production and product(s) are moved out of a plant or when it has excess capacity. Equation (B8) specifies that each sales region r must have a single plant source for any product family m for which it has demand. Constraint (B9) ensures that the demand a plant meets for any product family m does not exceed its capacity. The RHS of this equation represents existing capacity plus new capacity added to a plant minus any existing capacity moved out. Equation (B10) forces the value of positive variable Qp to not exceed one. Constraint (B11) determines whether a location requires a new plant or a new lease for an existing plant. We needed this equation to handle a situation one plant faced; it could not extend the existing lease and had to commit to a lengthy new lease and to make significant capital investments to maintain a facility at this same site, or it had to move. References ABA Banking Journal. 2000. Decision support for lending and other business applications. 92(10) 58. Anthony, R. N. 1965. Planning and Control Systems: A Framework for Analysis. Harvard University Press, Boston, MA. Bender, P. S., R. W. Brown, M. H. Isaac, J. F. Shapiro. 1985. Improving purchasing productivity at IBM with a normative decision support system. Interfaces 15(3) 106–116. Bloemen, R., J. Maes. 1992. A DSS for optimizing the aggregate production planning at Monsanto Antwerp. Eur. J. Oper. Res. 61(1,2) 30–40. Bowersox, D. J., D. J. Closs. 1996. Logistical Management, the Integrated Supply Chain Process. McGraw-Hill, New York. ——, P. Daugherty, C. Droge, D. Rogers, D. Wradlow. 1989. Leading Edge Logistics—Competitive Positioning for the 1990’s. Council of Logistics Management, Oakbrook, IL. Boykin, R. F., R. R. Levary. 1985. An interactive decision support system for analyzing ship voyage alternatives. Interfaces 15(2) 81–85. Brown, D. C. 1985. The anatomy of a decision support system: How Abbott Labs puts DSS to work for 2,000 products. Bus. Marketing 70(6) 80–84. Evers, P. T., F. J. Beier. 1993. The portfolio effect and multiple consolidation points: A critical assessment of the square root law, J. Bus. Logist. 14(2) 109–125. Fawcett, S. E., M. B. Cooper. 1998. Logistics performance measurement and customer success. Indust. Marketing Management 27 341–357.
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Giorgio, G., S. Rinaldi, R. Soncini-Sessa. 1985. Design support systems for water management: The Lake Como case study. Eur. J. Oper. Res. 21(2) 295–309. Hall, M. 2000. DSS leads firm to ASP business. Comput. World 34(30) 40. Hax, A. C., H. C. Meal. 1975. Hierarchical integration of production planning and scheduling. M. A. Geisler, ed. TIMS Studies in the Management Sciences, Vol. 1, Logistics. North-Holland, Amsterdam, The Netherlands. Johnson, M. E., T. Davis. 1998. Improving supply chain performance by using order fulfillment metrics. National Productivity Review/ Summer 1998. John Wiley and Sons, New York. Liberatore, M. L., T. C. Miller. 1985. A hierarchical production planning system. Interfaces 15(4) 1–11. ——, ——. 1998. A framework for integrating activity based costing and the balanced scorecard into the logistics strategy development and monitoring process. J. Bus. Logist. 19(2) 131–154. ——, R. Nydick. 1998. Decision Technology for Business Application. McGraw-Hill Companies, Primus Custom Publishing, Villanova, PA. ——, ——. 2000. Introduction to Decision Technology Modeling. Software and Applications. LN Publishing, Villanova, PA. Maister, D. H. 1976. Centralization of inventories and the square root law. Internat. J. Physical Distribution 6(3) 124–134. Manjunath, M. L., V. M. R. Tummala, K. Cox. 1997. A decision support system model in estimating budgets for standard figure tools at Mattel Toys. Comput. Indust. Engrg. 33(3,4) 617–620. Mentzer, J. T., B. P. Konrad. 1991. An efficiency/effectiveness approach to logistics performance analysis. J. Bus. Logist. 12(1) 33– 62. Miller, T. C. 2002. Hierarchical Operations and Supply Chain Planning. Springer-Verlag, London, U.K. Ramani, K. V. 2001. DSS-enabled materials management process at MP Trust Hospital. Production Inventory Management J. 42(1) 1– 11. Sen, T. K., L. J. Moore, T. J. Hess. 2000. An organizational decision support system for managing the DOE hazardous waste cleanup program. Decision Support Systems 29(1) 89–109. Sullivan, R. S., S. C. Secrest. 1985. A simple optimization DSS for production planning at Dairyman’s Cooperative Creamery Association. Interfaces 15(5) 46–54. Tallon, W. J. 1993. The impact of inventory centralization on aggregate safety stock: The variable supply lead time case. J. Bus. Logist. 14(1) 185–203. Tyagi, R., C. Das. 1998. Extension of the square root law for safety stock to demands with unequal variances. J. Bus. Logist. 19(2) 197–204. Van Amstel, R. P., G. D’hert. 1996. Performance indicators in distribution. Internat. J. Logist. Management 7(1) 73–82. Van der Heyden, W. P. A., J. A. Ottjes. 1985. A decision support system for the planning of the workload on a grain terminal. Decision Support Systems 1(4) 293–298. Wong, H. J., M. W. Legnini, H. H. Whitmore, R. S. Taylor. 2000. The diffusion of decision support systems in healthcare: Are we
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there yet?/Practitioner application. J. Healthcare Management 45(4) 240–253. Zinn, W., M. Levy, D. J. Bowersox. 1989. Measuring the effect of inventory centralization/decentralization on aggregate safety stock: The square root law revisited. J. Bus. Logist. 10(1) 1–14.
Paul Darlington, V.P. Logistics and Customer Service Operations, Warner-Lambert/Pfizer Inc., 201 Tabor Road, Morris Plains, NJ 07950, writes: “The Distribution Network Decision Support System implemented by the four authors of this paper has proven to be an extremely valuable asset to Warner-Lambert and the new Pfizer organization. The DSS is unique in that it uses the same data to perform many important functions, whether daily operations, annual planning, or long-range planning. “To support daily operations, the system generates exception reports that allow Warner-Lambert/Pfizer to improve service to its customers and lower operational cost. As an example, Warner-Lambert/Pfizer can proactively inform a customer of a back-ordered item or late delivery during the daily planning phase. “The DSS group has made significant improvements to a Transportation Planning System that has resulted in several hundred thousand dollar savings annually while at the same time reducing costs. The system has been used to analyze order-cycle times by functional area, including our customers’ processes. The data allows Warner-Lambert/Pfizer to deal with facts and not perceptions to determine how best to improve the cycle time. There are many examples where we partnered with customers to reduce cycle time and im-
Interfaces Vol. 32, No. 4, July–August 2002
prove on-time delivery performance using the data from the DSS. “For Warner-Lambert, output from the DSS demonstrated how we could reduce our transportation time to a maximum of three days for the Parke-Davis Division without having to utilize a premium package carrier for all traffic lanes. The system implemented was unique to Warner-Lambert. Where feasible, we used the tonnage from the consumer businesses to avoid premium cost while still insuring delivery within three days. “Over the years, we used the DSS to continually evaluate our Distribution Network. For example, several years ago, prior to the merger of Pfizer and Warner-Lambert, we were able to illustrate to WarnerLambert management that customer service would not be improved by adding a third distribution center at that time. The majority had the perception that an additional DC was needed to meet customer requirements. This resulted in a large cost avoidance for Warner-Lambert. “In summary, the DSS improved customer service, reduced operations cost, assisted in the annual planning, and designed the optimal Distribution Network for the U.S. and some foreign countries. The DSS group also supported the different divisions of WarnerLambert in analyzing many supply-chain activities. We consider it a definite competitive advantage. What is truly exciting is that the system continues to make improvements as knowledge is gained. The value to the company will only continue to increase.”
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