A MANUFACTURING STRATEGY FOR PRODUCT ASSEMBLY IN A SUPPLY CHAIN ENVIRONMENT

Transcription

1 A MANUFACTURING STRATEGY FOR PRODUCT ASSEMBLY IN A SUPPLY CHAIN ENVIRONMENT R. Meenakshi Sundaram and Rakesh B. Patel Department of Industrial and Manufacturing Engineering Tennessee Technological University Cookeville, TN Abstract The intense competition in manufacturing and the need to introduce new products faster have forced manufacturers to explore unconventional methods of meeting these challenges. In that endeavor, postponement as a new manufacturing strategy has evolved. The research reported in this paper is based on the postponement strategy. A multi-echelon supply chain consisting of a retailer, a warehouse and an assembly plant was considered to evaluate the postponement strategy. It was concluded that postponement is a cost-effective manufacturing strategy for the hypothetical supply chain investigated. Keywords: Postponement strategy - multi-echelon supply chain - relevant cost 1. Introduction The intense international competition in manufacturing is forcing companies to not only depend on their ability to meet customer demands, but also to react quickly to customer needs by delivering quality products, faster at the least possible cost. The developments in information technologies and communication systems are helping the emergence of borderless organization [1]. To take advantage of reliable and inexpensive sources for parts and components from different parts of the globe companies are globalizing their operations [2]. This has helped them focus on corecompetencies. As customers demand for customized products, the focus of manufacturing companies is changing from design for manufacturing to design for customization to meet the ever-changing customer demands at reduced cost. One way of achieving this is by employing a strategy such as postponement. Postponement can be defined as [2], A strategy in which the time of shipment and the location of final product processing is delayed until a customer order is received or at least until a clear picture of the demand pattern has emerged. With the evolution of global markets and increase in product awareness among customers, there has been an increase in variety of products. As a result, the average demand for an individual product is on the decrease. This also has resulted in increased demand uncertainty for each type of products. As a result, managing inventory at various echelons in the supply chain has become harder. Manufacturing firms are expected to fill customer orders faster with customized products at competitive prices. To face this challenge, many manufacturing firms are using new practices such as modular manufacturing and postponement strategy. As the product variety increases, it is not feasible to make--to-stock. Postponement strategy reduces the risk associated with holding finished goods inventory. Thus, the time lag between the customers' orders and delivery time can be reduced greatly. The objective of postponement strategy is to provide a high level of customer service at least cost [3]. Postponement strategy can also result in improving the responsiveness of the firms in meeting the customer demand. Postponement strategies are classified into three types; form postponement, time postponement and tailored postponement [2]. Postponement that involves delaying the final differentiation of product till the very end of supply chain (for example, carrying out the final product differentiation at warehouse or at retailer or even at customer s location or until accurate demand for the product is realized) is defined as form postponement. It is sometimes referred to as delayed-product differentiation. Time postponement strategy involves delaying the final shipment of finished goods to the retailers or distribution centers, till a firm order arrives or the demand pattern is clear [2]. It is also known as time based logistics. The finished products are stored at one or a few strategic locations based on their anticipated demand [4]. Tailored postponement involves producing a part of sub-assembly using lower-cost

2 production method without postponement [5]. The remaining part of the demand, which is uncertain, is produced using postponement. The profits obtained by using tailored postponement can be greater than no postponement or one hundred percent postponement. In the research reported, the effect of postponement on the total relevant cost was studied in a multi-echelon supply chain consisting of a manufacturing plant, warehouse and retailers. Costs related to ordering inventory, holding, logistics and obsolescence were considered. 2. Review of Literature on Postponement The postponement strategy is being used in industries ranging from consumer electronics to apparel to automotive manufacturing. Lately, postponement as a manufacturing strategy is receiving significant attention. The postponement strategy has been successfully implemented in companies like Dell, HP, Levi-Strauss, and Motorola. Manufacturers are finding this strategy increasingly attractive to provide better customer service at reduced cost. Lee, Billington and Carter [6] present an analysis of the postponement principle applied at HP DeskJet-Plus Printer Division. HP DeskJet printers are sold in North America, Europe and Asia. By modularizing the design of power pack and postponing the assembly of the power pack and manuals with the printer, HP has reduced the finished goods inventory from about 7 weeks to 5 weeks. The investment in finished goods inventory is reduced by 21 percent. The total inventory investment too is reduced by 18 percent. As generic printers are shipped to distribution centers, economies of scale are obtained by consolidating transportation loads. Brown, Lee and Petrakian [7] discuss the effects of wide product varieties, longer lead times and demand unpredictability in the semiconductor industry on the efficiency of manufacturing firms in meeting customer demand. The authors describe the postponement strategy adopted by Xilinx, Inc., a semiconductor-manufacturing firm, to maintain a high level of customer service. Both product and process postponement strategies are employed to reduce its investment in inventory. The application of postponement strategy has resulted in inventory reduction from 113-dollar days to 87-dollar days. Gupta and Benjaafar [8] evaluate the cost and benefits of delayed-differentiation principle. The authors compare delayed-differentiation benefits over pure inventory policies like make -to-order and make -to-stock inventory policies. Delayed differentiation is found to be more beneficial in the following circumstances: When lesser percentage of work-content is postponed. When order delay requirements are tight. When inventory-holding costs involved at lower echelons are high. Swaminathan and Tayur [9] describe the application of delayed differentiation at IBM. They explain the implementation of this principle at IBM. IBM designed its end products based on component similarities in personal computers. A two-stage integer-programming model is formulated. The authors analyze the assembly process based on vanilla boxes, which enabled customization of end products with short lead times. End products are differentiated from the vanilla boxes at a later stage based on confirmed customer orders. The authors also determine optimal configuration of vanilla boxes and the inventory levels that need to be maintained at all stages. The model developed address the issues related to product line characteristics, demand characteristics and assembly line characteristics. However, the model has its shortcomings. It fails to integrate design and manufacturing feasibility. Fisher, Ramdas and Ulrich [10] design an effective component sharing strategy. The assembly of automotive front brakes is used as a design example. The authors discuss the factors affecting the success of such a strategy. The following issues are addressed in the model: The key drivers and trade-off of component sharing decision. Variation from actual component sharing practice. Causes of this variation. Robinson and Elofson [11] investigate the effects of the addition of electronic brokers (demand variability filters) to supply chains on the value of postponement. The model developed assumes that the buyers with locally distinct

3 preferences join together to form a market that can be served by the suppliers in a cost-effective manner. The model also assumes substantial similarity in buyers preferences. It evaluates the effects of electronic broker on supply chains with postponement and without postponement. It is concluded that electronic brokered supply chain results in greater value of postponement than non-brokered supply chain. Billington and Amaral [12] make a comparison of investment in postponement to investment in information sharing to design, develop and deliver new products. It is concluded that organization must evaluate and compare the costs involved in redesigning the products for postponement and the investment required in information technology to improve information sharing with supply chain partners. Lee [13] describe an inventory model developed to support the logistic aspect of product/process design. The author discusses the importance to look beyond functionality, performance and manufacturability of a product for design engineers. The model describes the changing focus of manufacturers to issues like logistics and customer service in order to be more competitive. Garg and Tang [14] present two models to examine the effect of early and late postponement strategies on inventories. The first model is based on a centralized inventory control policy. For the first model, only finished goods inventory is assumed to be held. The second model developed is based on a decentralized control policy in which inventory was kept after each stage of production process. The authors analyze the benefits of product differentiation for the two models. The authors also derive the necessary conditions for the two models to be beneficial. Lee and Tang [15] analyze the costs and benefits associated with the redesign strategy. Different type of redesign strategies namely, standardization, modularization, and process redesign, for delayed-product-differentiation are discussed. The authors provide a quantitative evaluation of optimizing the point of delayed-product-differentiation. The authors also discuss the managerial aspects in implementing the redesign strategy. It is concluded that redesign strategy results in the reduction of complex manufacturing process and improvement in the service levels. Knolmayer [16] discusses the pros and cons of mass customization. The author has extended the classical Cournot model of determining the maximum profit in a monopolistic situation to deal with circumstances in which mass customization can be applied. Factors like reaction of market to customization offered, necessary investment to implement customized production and effects of customization on direct cost influencing customization are modeled. For optimal allocation of investments in mass customization in different parts of the plant, a mathematical programming model is formulated. A decision support model is developed to derive product groups suitable for customization. It can be inferred from the literature review that there is an increasing interest on the postponement strategy in manufacturing. Some quantitative and qualitative models have been developed to delve into the benefits gained by using the postponement strategy. The literature review showed the need to improve the synchronization of supply with demand to reap the benefits of postponement strategy. The postponement strategy has proved to be beneficial to manufacturing firms having a large variety of products demand that is independent and comparable in size [5]. Many of the models developed have focused mainly on the tradeoffs between the benefits of inventory pooling and products/process redesign. Thus, the postponement strategy has enhanced the flexibility of the supply chain by providing opportunities to increase product variety, reduce inventory levels, shorten order fulfillment times, reduce costs and improve customer service. The literature review presented coupled with the above discussion provides an adequate basis for conducting research in an area that has both academic and practical importance. 3. Problem Description and Objective The proposed supply chain for the research investigation consists of a retailer, a distribution center/warehouse and an assembly plant. The objective of the study is to develop and evaluate a heuristic method to determine simultaneously the optimal ordering quantities at the retailer, distribution center/warehouse and assembly plant such that the total relevant cost incurred in meeting the product demand is minimized.

4 The distribution center is assumed to be equipped to perform minor assembly operations. To start with, the model assumes that 100% of the assembly work is performed at the assembly plant. All the modules required for the final assembly of the product are assembled at the assembly plant. The distribution center does not perform any assembly operation. The optimal ordering quantities, (at each of the three levels) are simultaneously determined. The percentage of work carried out at distribution center/warehouse is then increased in steps of 5%. The percentage of work carried out at assembly plant is decreased proportionately. Thus, most of the modules required for the final product are assembled at the assembly plant, while the remaining modules are assembled at the distribution center/warehouse. The objective of the study also includes the determination of optimal postponement strategy for the multi-echelon supply chain under consideration such that the total relevant cost involved in meeting the product demand is minimized. The total cost equation developed for the multi-echelon supply chain under consideration is also analyzed to evaluate the effects of the following cost parameters on the optimization of postponement strategy: inventory holding cost, obsolescence cost, logistics cost, and ordering cost. 4. Methodology The analysis was done for seven levels of postponement strategies between assembly plant and warehouse as shown in Table 1. It was assumed that the assembly plant and warehouse/distribution center could operate on any of the following postponement strategy. Table 1 Postponement strategies for the heuristic method Strategy % of assembly work at Assembly Plant % of assembly work at Warehouse I % 0.00% II 95.00% 5.00% III 90.00% 10.00% IV 85.00% 15.00% V 80.00% 20.00% VI 75.00% 25.00% VII 70.00% 30.00% The percentage of assembly work done at assembly plant and at warehouse/distribution center was calculated based on number of modules assembled at each of the two echelons. The cost of assembling at assembly plant and at warehouse was assumed fixed for each of the above strategies, such that the total cost in assembling the final product increases at an increasing rate with the amount of work postponed. It was assumed that a maximum of 30% postponement was feasible. The effects of cost parameters on these strategies were evaluated. Following parameter were used to carry out a sensitivity analysis to assess their influence on postponement strategies: Percentage of annual obsolete demand Ratio of fixed transportation cost (T fp ) to variable transportation cost (T vp ) at assembly plant Ratio of fixed transportation cost (T fw ) to variable transportation cost (T vw ) at warehouse/distribution center Ratio of set-up cost (A p ) to inventory rate (R p ) at assembly plant Ratio of ordering cost (A w ) to inventory rate (R w ) at warehouse/distribution center

5 Several test problems were developed to evaluate the effects of the above parameters on the postponement strategies. These effects were investigated through the calculation of the total relevant cost (TRC) per year for all the postponement strategies described above. The postponement strategy that yields the least total relevant cost (TRC) per year was selected as optimum for the data set used. A graph showing variation of total relevant cost per year to percentage of postponement is shown in Fig 1. The above parameters were varied to study their influence on the decision of selecting an optimum postponement strategy for the given supply chain under study. Total Relevant Cost per Year ($/Year) 3,510, ,505, Total Relevant Cost ($/year) 3,500, ,495, ,490, ,485, ,480, % 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% % Postponement Figure 1 Effects of Postponment on Total Relevant Cost per year 5. Sensitivity Analysis A sensitivity analysis was made to investigate the effect of all the parameters discussed above on percentage saving in costs and on postponement strategy for the supply chain. A range was identified for each of the above parameters to carry out the test runs. Percentage savings was calculated as the ratio of difference between total relevant cost incurred for zero percent postponement and the minimum total relevant cost incurred by selecting the optimum postponement strategy to the total relevant cost incurred at zero percent postponement. The effects of changes in each of the above parameters on the percentage savings incurred and on postponement were studied keeping all other parameters constant. The details are discussed in the sections that follow. 5.1 Effect of Percentage of Annual Product Obsolescence The total relevant cost per year increases with the percentage increase in obsolete demand. As the total relevant cost per year is directly proportional to the percentage of postponement, it can be stated that the percentage of obsolescence weighs heavily on the postponement decisions. Higher the obsolescence costs, better it is to postpone

6 the final assembly. This supports the contention that higher the risk of product obsolescence, greater are the savings in postponement. 5.2 Effect of Ratio of Fixed Transportation Cost (T fp ) to Variable Transportation Cost (T vp ) at Assembly Plant Changes in variable transportation cost at assembly plant have a tremendous effect on the postponement decisions in minimizing the total relevant cost than the changes in fixed transportation cost at assembly plant. Higher the variable transportation costs at assembly plant, higher is the percentage of postponement for a specific value of fixed transportation cost. Conversely, changes in fixed transportation cost within the specific range for a particular value of variable transportation cost do not affect the percentage of postponement. Thus, high cost of transportation of finished product from assembly to warehouse/distribution center favors postponement. 5.3 Effect of Ratio of Fixed Transportation Cost (T fw ) to Variable Transportation Cost (T vw ) at Warehouse/Distribution Center Changes in variable transportation cost at warehouse/distribution center have a greater impact on percentage postponement in minimizing the total relevant cost than the changes in fixed transportation cost at warehouse/distribution center. However, the effect of variable transportation cost at warehouse/distribution center is not as significant as the effect of variable transportation cost at assembly plant. Also a change in fixed transportation cost within the given range does not affect the percentage of postponement for a chosen value of variable transportation cost at warehouse/distribution center. 5.4 Effect of Ratio of Set-up Cost (A p ) to Inventory Holding Rate (R p ) at Assembly Plant Changes in inventory holding rate have some effect on the percentage savings achieved. The changes in the setup cost at assembly plant do not seem to affect percentage savings significantly. Also, as the inventory holding cost is a very small percentage of total relevant cost, it does not significantly affect postponement decisions. 5.5 Effect of Ratio of Ordering Cost (A w ) to Inventory Holding Rate (R w ) at Warehouse/Distribution Center Changes in ordering cost as well as inventory holding rate at warehouse/distribution center do not seem to have a significant effect on percentage savings and consequently the postponement decisions. 6. Conclusions Test problems were formulated to study the influence of the cost parameters discussed on the postponement strategies. From the results obtained for the test runs, it is evident that postponement does reduce the annual total relevant cost. Decisions related to optimum postponement strategy are significantly influenced by the product obsolescence cost. As the product obsolescence cost increases, the total relevant cost also increases. The supply chain of a product with a very high obsolescence cost in consumer market can reduce its cost by delaying the product assembly till the demand materia lizes. The model provides decisions that are optimum for the entire supply chain based on the assumptions for a given percentage of obsolescence. The heuristic algorithm developed provides trade-offs between the obsolescence cost and other costs in the selection of suitable postponement strategy. The shipping costs from assembly plant to warehouse/distribution center are also a significant factor in choosing the level of postponement. As the variable transportation cost per unit increases, it is economical to opt for a higher level of postponement. This is because the cost of shipping finished goods is larger compared to the cost of shipping semi -finished goods. The variable cost of transportation particularly affects the postponement decision especially for overseas shipments. The tariffs and taxes incurred on finished products are higher than for semi -finished products. The fixed transportation cost does not have a significant effect on the cost saving and consequently on the

7 postponement decisions. However, increases in variable transportation cost at warehouse/distribution center suggest increased postponement percentages leading to reduction in total relevant cost. The percentage saving incurred is also influenced by the inventory holding rate at assembly plant. The total inventory holding cost decreases as higher percentage of work is postponed and completed at warehouse. Higher the inventory holding rate at assembly plant, higher is the percentage saving for the range of set-up cost chosen. The ordering cost and the inventory holding rate at warehouse/distribution center does not seem to affect the postponement and costs. The heuristic algorithm developed has helped to make decisions on percentage postponement. References 1. Bhatnagar, R. and S. Viswanathan, Re-engineering Global Supply Chains- Alliances between Manufacturing Firms and Global Logistics Services Providers, International Journal of Physical Distribution and Logistics Management, Vol. 30, No. 1, 2000, P Apte, U. M. and S. Viswanathan, Strategic and Technological Innovations in Supply Chain Management, International Journal of Technology Management, December, Schmidt, G., Mass Customization, The McDonough School of Business, Georgetown University, rev. June Bowe rsox D. J. and D. J. Closs, Logistical Management: The Integrated Supply Chain Process, The McGraw- Hill Companies, Inc., New York, Meindl P., and S. Chopra, Supply Chain Management: Strategy, Planning, and Operation, Prentice-Hall Inc., Lee, H. L., C. Billington and B. Carter, Hewlett-Packard Gains Control of Inventory and Service through Design for Localization, Interfaces, Vol. 23, No. 4, July-August 1993, P Brown, A. O., H. L. Lee and R. Petrakian, Xiling Improves its Semiconductor Supply Chain Using Product and Process Postponement, Interfaces, Vol. 30, No. 4, 2000, P Gupta, D. and S. Benjaafar, Make-to-order, Make-to-stock, or Delayed Product Differentiation? A Common Framework for Modeling and Analysis, University of Minnesota, Revised October Swaminathan, J. M. and S. R. Tayur, Managing Broader Product Lines through Delayed Differentiation using Vanilla Boxes, Management Science, 44(1998), S161-S Fisher, K. Ramdas and M., K. Ulrich, Component Sharing in Management of Product Variety", Management Science, 45(1999), Robinson, W. N. and G. Elofson, Electronic Broker Impacts on the Value of Postponement, August Billington, C. and J. Amaral, Investing in Product Design to Maximize Profitability Through Postponement, April Lee, H. L., Effective Inventory and Service Management through Product and Process Redesign, Operation Research, 44(1996), Garg, A. and C. S. Tang, On postponement strategies for product families with multiple points of differentiation, IIE Transactions, August 1997, Vol. 29, N8, p641-p650.

8 15. Lee H. L. and C. S.Tang, Modeling the Costs and Benefits of Delayed Product Differentiation, Management Science, 43(1997), p Knolmayer,G.F., On the Optimal Extent of Mass Customization, August Biographical sketch: Prof. Meenakshi Sundaram is Professor of Industrial and Manufacturing Engineering at Tennessee Technological University. He teaches, conducts research and consults in the are of manufacturing engineering and manufacturing systems design and operation. He holds a Ph.D. in Industrial Engineering from Texas Technological University. He is a registered professional engineer in the Commonwealth of Virginia. Mr. Rakesh Patel has an MS in industrial engineering from Tennessee Technological University, USA. He received his Bachelor's degree in Mechanical Engineering in Mumbai, India, in Then he worked for Bajaj Automotive Group Ltd, a premier two-wheeler in India for a year. He is also a student member of IIE (Institute of Industrial Engineers). His research interests include Lean Manufacturing, and Supply Chain Management.