COMPARING SEMICONDUCTOR SUPPLY CHAIN STRATEGIES UNDER DEMAND UNCERTAINTY AND PROCESS VARIABILITY. Yang Sun

Transcription

1 COMPARING SEMICONDUCTOR SUPPLY CHAIN STRATEGIES UNDER DEMAND UNCERTAINTY AND PROCESS VARIABILITY by Yang Sun Copyright 2003 by Yang Sun All rights reserved. No part of this work covered by the copyright hereon may be reproduced or used in any form or by any means graphic, electronic, or mechanical, including photocopying, recording, taping, or information storage and retrieval systems without the written permission of the copyright holder. Yang Sun ( ; Department of Industrial Engineering Arizona State University Tempe, AZ ARIZONA STATE UNIVERSITY vi

2 ABSTRACT A fundamental issue in designing and managing a semiconductor supply chain is to identify its supply chain strategy. Supply chain strategies can be generally categorized as push, push-pull and pull. In the push strategy, semiconductors are built-to-stock to final products. In the push-pull strategy, wafers with generic parent dies are produced in the front-end and pushed into the die-bank inventory. When demand occurs the parent dies are pulled from the die-bank inventory and assembled-to-order in the back-end to create different final products. In the pull strategy, production is not started until real demand occurs. In this paper, simulation models and designed experiments are used to compare the three strategies under different patterns of demand and process dynamics. The results indicate that identifying an appropriate strategy is a consequence of understanding the nature of the demand as well as the systemic behavior of the process. A conceptual decision support framework is provided following the analysis that can be used in the selection from push, push-pull and pull semiconductor supply chain strategies that seeks to optimize the overall production cost and on-time delivery service under demand uncertainty and process variability. vii

3 TABLE OF CONTENTS Page LIST OF TABLES...viii LIST OF FIGURES... ix CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW Introduction Literature Review Organization of the Paper COMPARING SEMICONDUCTOR SUPPLY CHAIN STRATEGIES UNDER DEMAND UNCERTAINTY AND PROCESS VARIABILITY Abstract Introduction Literature Review Modeling and Analysis Modeling Considerations and Assumptions Performance Criteria Description of Simulation Model Experimental Design and Analysis Validation and Verification CONCLUSIONS AND FUTURE RESEARCH Conclusions vi

4 CHAPTER...Page 3.2. Future Research REFERENCES APPENDIX A SAMPLE CODE FOR PUSH-PULL MODEL vii

5 LIST OF TABLES Table...Page 1. Literature that Addresses the Comparison of Push and Pull Xilinx s Supply Chain Strategies (Brown et al. 2000) The Key Issues, Adapted from Shunk et al. (2003) High Variability Cycle Time Distribution in the Semiconductor Supply Chain Cost Structure Example Service Penalty vs. Due-Date Lead Time The DOE Factors Layer Two Framework viii

6 LIST OF FIGURES Figure...Page 1. The push, push-pull and pull strategies Overlapping responsibilities across product, process, and supply chain characteristics, Adapted from Fine (1998) Demand patterns The "black-box" processes Effects of the global experiment Strategy vs. due-date tightness when service penalty is light Strategy vs. due-date tightness when service penalty is heavy Effects of the step-down experiments Strategy vs. aggregate demand pattern The push effects product A Interactive effect of demand pattern and back-end variability for push The push-pull effects The curvature effect of demand on push-pull Cycle time variability effects on pull Demand vs. process variability with medium due-date and light penalty Demand vs. process variability with loose due-date and heavy penalty The first layer of the decision support framework ix

7 Chapter 1 Introduction and Literature Review 1.1. Introduction Supply chain concerns are now on the semiconductor executive s radar screen (Maltz et al. 2001). The semiconductor supply chain contains sequential stages of wafer fabrication, probe, assembly and test. A fundamental issue in designing and managing a semiconductor supply chain is to identify an appropriate supply chain strategy. Semiconductor companies now have multiple supply chain strategy choices due to technology availability. Such a choice is a strategic decision that will be implemented throughout the entire product lifecycle. Fisher (1997) provided a well-known framework to answer the question what is the right supply chain for your product? The nature of the demand of the product, generally categorized as being either primarily functional or primarily innovative, drives the decision. Semiconductors are perfect examples of innovative products with unpredictable demand and short product lifecycles (Maltz et al. 2001). Thus, a market-responsive supply chain is suggested to be generally more appropriate than a physically efficient one according to Fisher (1997). The semiconductor industry is highly capital intensive and is characterized by high customer expectations, short product life cycles, proliferating product variety, unpredictable demand, long and variable manufacturing cycle times, globally distributed logistics, and considerable supply chain complexity. On one hand, companies try to maximize the utilization of the facilities under multi-million dollar weekly depreciation;

8 2 on the other hand, companies try to build in more responsiveness to the market. Not only the nature of the product demand but also the underlying system behavior of the entire semiconductor processes matters. Eventually it is important for operation executives to understand the overlapping responsibility of product, process and supply chain (Fine 1998) to answer the question what is the right supply chain for your semiconductor? Notwithstanding many approaches to naming supply chain strategies, integrated supply chain strategies can be categorized simply as push, pull and hybrid push-pull systems (Figure 1). In the push strategy, semiconductors are built-to-stock to final products. In the push-pull strategy, wafers with generic parent dies are produced in the front-end and pushed into die-bank inventory. When demand occurs the parent dies are pulled from die-bank inventory and assembled-to-order in the back-end to create different final products. In the pull strategy, production is not started until real demand occurs, thus semiconductor devices are built-to-order. Material Fab Probe Die Assembly Test Bank Final Goods Delivery Push Push Pull Pull Figure 1 The push, push-pull and pull strategies

9 3 Our research addresses the comparison of these three generic semiconductor supply chain strategies under demand uncertainty and process variability aiming at lower cost and better service. The comparative analysis leads to a conceptual decision support framework which attempts to guide the selection of the semiconductor supply chain strategy that optimizes cost and service under demand and process dynamics. An overview of the problem structure is given below: Decision Alternatives: Push strategy Die-bank push-pull strategy This state-of-the-art solution is used to represent many push-pull approaches. Pull Strategy Key Variables: Demand uncertainty exists due to semiconductor s upstream position in the electronics supply chain (Brown et al. 2000). Also, many forms of process variability exist throughout the entire semiconductor supply chain that affect the supply chain performance. Thus, our comparison is carried out under different patterns of the following: Demand Uncertainty Process Variability Criteria: Lee et al. (2002) indicated that the goal of supply chain performance management is to have increased customer service and reduced costs. Companies

10 4 generally need to perform well on the following two key dimensions from an operations perspective: Cost Service Top management may have other competition concerns such as R&D speed and assets. However, performing better in the two major supply chain performance metrics mentioned above will lead to operational excellence and ultimately to competitive advantage. Note that quality is absent here; in modern Supply Chain Management thinking, quality is always taken as a given (Hausman 2002). Time Horizon: Majority of the product lifecycle of one product family (typically 18 months) Literature Review In this section we start with a general discussion of the terms push and pull, followed by an overview of literature that addresses the comparison of push and pull in production systems. Some state-of-the-art approaches to the push-pull semiconductor supply chain strategy, as well as the latest empirical analysis in the semiconductor supply chain, are also discussed.

11 5 A push supply chain makes production and distribution decisions based on forecasts and a pull supply chain drives production and distribution by customer demand (Simchi- Levi et al., 2003). However, we need to understand the connection between the push/pull supply chain strategies and the order release strategies in production systems. Formal definitions for push and pull production systems at the conceptual level are provided by Hopp and Spearman (2000). A push system schedules the release of work based on demand, while a pull system authorizes the release of work based on system status. Note that the demand placed on the factories is not always the true customer demand. In many cases, companies make the production plan based on forecasts and place either an internal order to the enterprise s own factory or an external order to a third party manufacturer/foundry so that products are built-to-stock and pushed into the inventory in the push portion of the supply chain. Planned lots may or may not be released immediately to the factory s shop floor control domain at the scheduled time. The system status is a real-time signal that drives the release of the work if the factory runs a mainly pull philosophy. In the pull supply chain portion, true customer orders (which drive the production and distribution) are real-time signals. In essence, the push/pull supply chain system and the push/pull production system share the same philosophy. In production systems push approaches are driven by what one desires to produce and pull approaches are driven by what one is capable of producing (Fowler et al. 2002). In supply chains the companies push what they desire to sell and pull what they are capable to sell. The essential context is to match the demand with the supply.

12 6 One produces what are to be sold. One cannot sell what one is not capable of producing nor can one sell to nonexistent demand. In the context of matching the demand with the supply, demand uncertainty causes major problems in the company s supply chain operations. This uncertainty is amplified as it moves upstream in the supply chain; this is the bullwhip effect described by Lee et al. (1997). Also the variability within the system is detrimental to system performance. For example, the fuzzy line between the production plan domain and the shop floor control domain, discussed in the last paragraph, sometimes is a major cause of the difficulties in production control (Fowler et al. 2002). There are many forms of variability, but increasing variability always degrades the performance of a production system. To reduce its impact, variability is buffered by some combination of inventory, capacity and time (Hopp and Spearman 2000). Companies have been making efforts to transition from push to pull for more than 20 years. The transition has, in general, been focused on reducing inventory buffers but increasing capacity buffers (Schwarz 2003). By separating the concepts of push and pull from their specific implementations, it is observed that most real-world systems are actually hybrids or mixtures of push and pull (Hopp and Spearman 2000). The hybrid push-pull supply chain strategy pushes the goods into an inventory buffer somewhere in the middle of the entire supply chain awaiting real demands to drive the pull processes. Ultimately, any supply chain system can be considered a push-pull system; it just depends on where the push-pull boundary is. If the boundary is at the beginning of the total process, it is a pull system; at the end, push.

13 7 No significant body of published research appears to exist addressing push/pull semiconductor supply chains. However, numerous articles had been published focusing on push/pull production systems in semiconductor manufacturing and most of them focused on the wafer fabrication process. Manufacturers implementing a push strategy simply release all the orders into the factory with MRP methods. Some of them limit daily release to a fixed quantity based on production goals to avoid excessive WIP (still a push philosophy). In the 1980s companies realized the lack of intelligent control and attempted to move to pull philosophies such as J-I-T (Monden 1983) or Kanban (Kimura and Treda 1981, Mitra and Mitrani 1990, Sugimori et al. 1977). Such efforts were followed by increased interest in bottleneck methods based on the theory of constraints (Jacobs 1991). The CONWIP method (Spearman et al. 1990), which targeted the control of WIP rather than the control of throughput, was also given much attention much recently. (Fowler et al. 2002) Are push and pull really so different (Bonney et al., 1999)? In the literature, there has been considerable interest in the comparison of push and pull systems. Many studies have been conducted with a variety of environmental considerations to compare and analyze push and pull production systems. Table 1 summarizes the area of application, the method used and the conclusion drawn in selected papers.

14 8 Table 1 Literature that Addresses the Comparison of Push and Pull Article Applied Area Method Conclusion Bonney et al., 1999 Generic Simulation It may be possible to obtain similar performance improvement in push systems as in pull systems with particular control information. Ragatz and Mabert, Semiconductor Analytical Pull performed better with due date 1988 Model Hoshino, 1996 Generic Analytical Model Pandey and Khokhajaikiat, 1996 Commodity Analytical Model Hirakawa, 1996 Generic Analytical Model Hurley and Whybark, 1999 Engine assembly in a manufacturing cell Simulation Ou and Jiang, 1997 Generic Markov Chain Dengiz and Akbay, 2000 Savsar, 1997 Kelle and Peak, 1996 tightness. Pull performed better in reducing safety stock when the variance of forecast error was large relative to the variance of demand. Otherwise, push was more effective. It was impossible to specify a policy that dominates the other when material supply was constrained and demand had large variability. Suggested shorter processing cycle time for each of the multiple stages to achieve J-I-T delivery without having excessive inventory. Indicated the trade-off between inventory buffers and capacity buffers and concluded that variance reduction and protective capacity was a good alternative to inventory. Pull achieved higher yield than push with equal throughput in a production system with unreliable machines. PCB production Simulation Implementation of a pull system increased productivity by 12% Electronic Simulation Assembly Line Indicated that random fluctuation of operations reduced the on-time delivery and suggested future research addressing trade-offs between inventory holding costs and additional shipping costs. Chemical Simulation Switch from a fixed schedule to an adaptive schedule decreased inventory holding costs and increased customer service while maintaining approximately the same annual setup costs. Fowler et al., 2002 Semiconductor Literature Survey Push is still the most widely employed strategy in semiconductor manufacturing

15 9 Despite the extensive literature that discusses the advantages of a pull strategy in achieving better operations with simulation and analysis results, this knowledge does not appear to be common in the boardroom in the semiconductor industry. It is still the push strategy that semiconductor companies usually implement. The push approach and the seeming protection of large WIP have not been easily overcome in this industry, even in companies purporting to adopt J-I-T philosophies (Fowler et al. 2002). What the industry learned from the most recent downturn is that time and availability rather than technology are becoming the first priority driver (Shunk et al. 2002). In the recent major downtown, on-time delivery appears to become more and more important for semiconductor companies to survive, while the industry s delivery performance today is not good. A good example is that Gateway punished Intel by shifting business to AMD in response to Intel s poor delivery service (Read 2002). Considering the trade-off between inventory and service, there is no dominating conclusion that can be cited directly for identifying a semiconductor supply chain strategy to perform better on cost saving and service improvement especially under the reality of unpredictable demand and unstable processes. A hybrid push-pull semiconductor supply chain with a die-bank was recommended by i2 (2002) for the next generation of production for high-margin, high-volume semiconductor products to take advantage of both push and pull strategies. The die-bank sits between the front-end, which contains the wafer fabrication and the probe steps, and the back-end, which contains the assembly and the test steps, as buffer inventory. Lee (2001) built optimization models for a die-bank push-pull strategy based on different order release strategies to maximize throughput and minimize process cycle time in front-

16 10 end, and to maximize on-time delivery and revenue through the back-end. But the model was not tested due to the lack of data. Brown et al. (2000) provided a case study regarding different postponement supply chain strategies implemented at Xilinx. In a product postponement strategy, Xilinx pushes programmable chips into final product inventory and customers can customize the chips with specific software after they get them. In a partial process postponement strategy (still a push strategy), Xilinx produces generic dies in the front-end based on the aggregate forecast and afterwards decides the back-end production of final products based on revised demand forecasts. In a die-bank push-pull strategy, the company pushes the generic parent dies into the die-bank inventory and the parent dies are customized to create final product chips when demand occurs. Xilinx also implements a combined strategy with a mix of push and push-pull. Table 2 summarizes the different decisionmaking points and inventory buffer points for such strategies. Using a push-pull strategy allows Xilinx to hold less finished goods, yet still be responsive to its customers. Xilinx improved their financial performance based on reduced inventory costs. Table 2 Xilinx s Supply Chain Strategies (Brown et al. 2000) Strategy Postponement of decision Inventory at diebank No postponement (Push) Partial postponement (Push) O O Die-bank push-pull O O Hybrid O O O Inventory at finished goods O

17 11 Die-bank is a very typical push-pull boundary in semiconductor supply chains. However, there are other possible choices. Some companies hold inventory before the interconnection process in the wafer fab so that logic portions on the die can be connected to program specific functions when further demand information is updated. The parent die must be designed specifically for being able to be programmed. Such a design always leads to a larger die area and consequently leads to a higher cost. Furthermore, companies can implement a pure pull strategy if the long lead time is acceptable for customers. As for product postponement, the test stage may be the postponement point so that chips with different estimated performance can be marked as different final products. The semiconductor industry is a hundred-billion-dollar (plus) industry and plays a fundamental role for today s global economy. Almost all products are becoming more and more electronic today than they were yesterday. Lately, empirical analysis indicates that the semiconductor industry is always under stress: either in a lack for capacity or a lack for sales position (Shunk et al. 2002). Also, many technological constraints may be reached in the foreseeable future. How long Moore s Law (Moore 1965) is going to be applicable is unknown at present. The technology-driven semiconductor industry has realized the importance of putting more intelligent control into the supply chain and manufacturing. The stress is compelling the industry to change. Companies are beginning to restructure their inventory and capacity buffers and to accelerate their transition from push to pull. Such a strategic transition requires tremendous organizational support and cultural transformation (Kempf 2003). However, before the development of detailed

18 12 control mechanisms, identifying the appropriate supply chain strategy is a more fundamental issue. This strategic decision will likely be in place throughout the entire product life cycle, typically 1.5 to 2 years, because a transition in the middle of the cycle would be costly involving joint work of marketing, production and technology. An appropriate strategic decision will lead the tactical and operational decisions to achieve operational excellence. Huge demand uncertainty always exists in the semiconductor industry. Customer demand is dependent on product variety, technical specifications, order quantity, required lead time and final delivery destination. Abundant forms of process variability also exist. Among them, manufacturing cycle time is a key issue since the semiconductor industry is very much driven by cycle time (Fowler et al. 1992). Maltz et al. (2001) also indicated that global logistics, thought not on the A-list, has enormous impact on today s semiconductor business. A US company can possibly process the wafer in a European fab, ship them cross the ocean back to the US for probe, have them assembled in a subcontracted Asian assembly house and ship them all the way back to the US for final test since the test process has many technical secrets and a company may not want to outsource it. It could be difficult to achieve on-time delivery service not only for customers on the other side of the world but also for domestic ones. In summary, semiconductor supply chain strategies need to be compared under different scenarios of demand and process dynamics. We cannot handle such a strategic decision without understanding the demand nature of the products, nor can we do so without truly understanding the underlying system behavior of the process (Figure 2).

19 The question is not only what is the right supply chain for your product?, but what is the right supply chain for your process? 13 Product Process Supply Chain Figure 2 Overlapping responsibilities across product, process, and supply chain characteristics, Adapted from Fine (1998) 1.3. Organization of the Paper This paper is organized into three chapters. Chapter 1 introduces the problem and reviews literature that addresses push and pull strategies in semiconductor manufacturing and supply chains. In Chapter 2, the three semiconductor supply chain strategies are modeled using discrete event simulation for playing what-if games. Structured simulation experiments are designed to capture different scenarios of input uncertainty

20 14 and system variability. Impacts of these variables are measured. Finally, summary of the results leads to a conceptual decision support framework in Chapter 3 that attempts to guide the choice of semiconductor supply chain strategy with the goal of optimizing both overall production cost and on-time delivery service. Possible future research is also suggested. Please note that the organization of this paper leads to some redundant content between chapters.

21 Chapter 2 Comparing Semiconductor Supply Chain Strategies under Demand Uncertainty and Process Variability 2.1. Abstract A fundamental issue in designing a semiconductor supply chain is to identify the strategy under which it will operate. Supply chain strategies can be generally categorized as push, push-pull and pull. In this research we use simulation models and designed experiments to compare the three strategies under different patterns of demand and process dynamics. The results indicate that identifying an appropriate strategy is a consequence of understanding the nature of the demand as well as the systemic behavior of the process. A conceptual decision support framework is provided following the analysis that can be used in the selection from push, push-pull and pull semiconductor supply chain strategies that seeks to optimize the overall production cost and on-time delivery service under demand uncertainty and process variability. Keywords: Semiconductor Supply Chain; Push/Pull Strategy; Discrete Event Simulation; Design of Experiments; Decision Support 2.2. Introduction This research is driven by the problem of identifying appropriate supply chain strategies for certain semiconductor products. As supply chain concerns are now on the

22 16 executive s radar screen (Maltz et al. 2001), such problem has become a fundamental issue in the hundred-billion-dollar (plus) semiconductor industry, which provides building blocks for today s global information economy. Regardless of the embarrassment of answering the question what is the right supply chain for your product, we consider the well-known, concise framework provided by Fisher (1997) as the starting point. The nature of the demand of the product, generally categorized as being either primarily functional or primarily innovative, drives the decision. Semiconductors are perfect examples of innovative products with unpredictable demand and short product lifecycles (Maltz et al. 2001). Thus, a market-responsive supply chain is suggested to be generally more appropriate than a physically efficient one according to Fisher (1997). The semiconductor industry is highly capital intensive and is characterized by high customer expectations, short product life cycles, proliferating product variety, unpredictable demand, long and variable manufacturing cycle times, globally distributed logistics, and considerable supply chain complexity. On one hand, companies try to maximize the utilization of the facilities under multi-million dollar weekly depreciation; on the other hand, companies try to build in more responsiveness to the market. Not only the nature of the product demand but also the underlying system behavior of the entire semiconductor processes matters. Eventually it is important for operation executives to understand the overlapping responsibility of product, process and supply chain (Figure 2) to answer the question what is the right supply chain for your semiconductor?

23 17 Lee et al. (2002) indicated that the goal of supply chain performance management is to have increased customer service and reduced costs. Thus, a right supply chain is intended to perform well on both cost and service from an operations perspective. Today, chip customers are so insisting on better service, especially on-time delivery performance (Maltz et al. 2001). Having the latest, fastest chip is not the only key to competition. Performing better in the two major supply chain performance metrics will lead to operational excellence and ultimately to competitive advantage. Note that quality is absent here; in modern Supply Chain Management thinking, quality is always taken as a given (Hausman 2002). Notwithstanding many approaches to naming supply chain strategies, integrated supply chain strategies can be categorized simply as push, pull and hybrid push-pull systems (Figure 1). The semiconductor supply chain contains sequential manufacturing stages of wafer fabrication, probe, assembly and test. The die-bank sits between the frontend (wafer fabrication and probe) and the back-end (assembly and test) to store the silicon wafers with produced semiconductor dies on them. In the push strategy, semiconductors are built-to-stock to final products. In the push-pull strategy, wafers with generic parent dies are produced in the front-end and pushed into die-bank inventory. When demand occurs the parent dies are pulled from die-bank inventory and assembledto-order in the back-end to create different final products. There are many forms of hybrid push-pull strategies in today s semiconductor businesses. However, we consider the state-of-the-art die-bank push-pull approach as the delegate. In the pull strategy,

24 18 production is not started until real demand occurs, thus semiconductor devices are builtto-order. The challenge is to establish generic procedures for identifying appropriate supply chain strategy transferable across semiconductor businesses. As Aitken et al. (2003) commented on such problems, we can see how conceptual frameworks, such as the one of Fisher (1997), work in practice, but figuring out how they work in theory is still of critical importance because without a suitable model, establishing generic properties in the dynamic semiconductor businesses becomes extremely difficult. In this research we attempt an analytic model to capture the basic elements of demands and processes in a supply chain context to compare these three generic semiconductor supply chain strategies. The comparative analysis leads to a conceptual decision support framework which attempts to guide the selection of the semiconductor supply chain strategy with the goal of optimizing overall production cost and on-time delivery service under demand and process dynamics Literature Review In this section we start with a general discussion of the terms push and pull, followed by an overview of literature that addresses the comparison of push and pull in production systems. Some state-of-the-art approaches to the push-pull semiconductor supply chain strategy, as well as the latest empirical analysis in the semiconductor supply chain, are also discussed.

25 19 A push supply chain makes production and distribution decisions based on forecasts and a pull supply chain drives production and distribution by customer demand (Simchi- Levi et al., 2003). However, we need to understand the connection between the push/pull supply chain strategies and the order release strategies in production systems. Formal definitions for push and pull production systems at the conceptual level are provided by Hopp and Spearman (2000). A push system schedules the release of work based on demand, while a pull system authorizes the release of work based on system status. Note that the demand placed on the factories is not always the true customer demand. In many cases, companies make the production plan based on forecasts and place either an internal order to the enterprise s own factory or an external order to a third party manufacturer/foundry so that products are built-to-stock and pushed into the inventory in the push portion of the supply chain. Planned lots may or may not be released immediately to the factory s shop floor control domain at the scheduled time. The system status is a real-time signal that drives the release of the work if the factory runs a mainly pull philosophy. In the pull supply chain portion, true customer orders (which drive the production and distribution) are real-time signals. In essence, the push/pull supply chain system and the push/pull production system share the same philosophy. In production systems push approaches are driven by what one desires to produce and pull approaches are driven by what one is capable of producing (Fowler et al. 2002). In supply chains the companies push what they desire to sell and pull what they are capable to sell. The essential context is to match the demand with the supply.

26 20 One produces what are to be sold. One cannot sell what one is not capable of producing nor can one sell to nonexistent demand. In the context of matching the demand with the supply, demand uncertainty causes major problems in the company s supply chain operations. This uncertainty is amplified as it moves upstream in the supply chain; this is the bullwhip effect described by Lee et al. (1997). Also the variability within the system is detrimental to system performance. For example, the fuzzy line between the production plan domain and the shop floor control domain, discussed in the last paragraph, sometimes is a major cause of the difficulties in production control (Fowler et al. 2002). There are many forms of variability, but increasing variability always degrades the performance of a production system. To reduce its impact, variability is buffered by some combination of inventory, capacity and time (Hopp and Spearman 2000). Companies have been making efforts to transition from push to pull for more than 20 years. The transition has, in general, been focused on reducing inventory buffers but increasing capacity buffers (Schwarz 2003). By separating the concepts of push and pull from their specific implementations, it is observed that most real-world systems are actually hybrids or mixtures of push and pull (Hopp and Spearman 2000). The hybrid push-pull supply chain strategy pushes the goods into an inventory buffer somewhere in the middle of the entire supply chain awaiting real demands to drive the pull processes. Ultimately, any supply chain system can be considered a push-pull system; it just depends on where the push-pull boundary is. If the boundary is at the beginning of the total process, it is a pull system; at the end, push.

27 21 No significant body of published research appears to exist addressing push/pull semiconductor supply chains. However, numerous articles had been published focusing on push/pull production systems in semiconductor manufacturing and most of them focused on the wafer fabrication process. Manufacturers implementing a push strategy simply release all the orders into the factory with MRP methods. Some of them limit daily release to a fixed quantity based on production goals to avoid excessive WIP (still a push philosophy). In the 1980s companies realized the lack of intelligent control and attempted to move to pull philosophies such as J-I-T (Monden 1983) or Kanban (Kimura and Treda 1981, Mitra and Mitrani 1990, Sugimori et al. 1977). Such efforts were followed by increased interest in bottleneck methods based on the theory of constraints (Jacobs 1991). The CONWIP method (Spearman et al. 1990), which targeted the control of WIP rather than the control of throughput, was also given much attention much recently. (Fowler et al. 2002) Are push and pull really so different (Bonney et al., 1999)? In the literature, there has been considerable interest in the comparison of push and pull systems. Many studies have been conducted with a variety of environmental considerations to compare and analyze push and pull production systems. Table 1 summarizes the area of application, the method used and the conclusion drawn in selected papers. Despite the extensive literature that discusses the advantages of a pull strategy in achieving better operations with simulation and analysis results, this knowledge does not appear to be common in the boardroom in the semiconductor industry. It is still the push strategy that semiconductor companies usually implement. The push approach and the

28 22 seeming protection of large WIP have not been easily overcome in this industry, even in companies purporting to adopt J-I-T philosophies (Fowler et al. 2002). What the industry learned from the most recent downturn is that time and availability rather than technology are becoming the first priority driver (Shunk et al. 2002). In the recent major downtown, on-time delivery appears to become more and more important for semiconductor companies to survive, while the industry s delivery performance today is not good. A good example is that Gateway punished Intel by shifting business to AMD in response to Intel s poor delivery service (Read 2002). Considering the trade-off between inventory and service, there is no dominating conclusion that can be cited directly for identifying a semiconductor supply chain strategy to perform better on cost saving and service improvement especially under the reality of unpredictable demand and unstable processes. A hybrid push-pull semiconductor supply chain with a die-bank was recommended by i2 (2002) for the next generation of production for high-margin, high-volume semiconductor products to take advantage of both push and pull strategies. The die-bank sits between the front-end, which contains the wafer fabrication and the probe steps, and the back-end, which contains the assembly and the test steps, as buffer inventory. Lee (2001) built optimization models for a die-bank push-pull strategy based on different order release strategies to maximize throughput and minimize process cycle time in frontend, and to maximize on-time delivery and revenue through the back-end. But the model was not tested due to the lack of data. Brown et al. (2000) provided a case study regarding different postponement supply chain strategies implemented at Xilinx. In a product postponement strategy, Xilinx

29 23 pushes programmable chips into final product inventory and customers can customize the chips with specific software after they get them. In a partial process postponement strategy (still a push strategy), Xilinx produces generic dies in the front-end based on the aggregate forecast and afterwards decides the back-end production of final products based on revised demand forecasts. In a die-bank push-pull strategy, the company pushes the generic parent dies into the die-bank inventory and the parent dies are customized to create final product chips when demand occurs. Xilinx also implements a combined strategy with a mix of push and push-pull. Table 2 summarizes the different decisionmaking points and inventory buffer points for such strategies. Using a push-pull strategy allows Xilinx to hold less finished goods, yet still be responsive to its customers. Xilinx improved their financial performance based on reduced inventory costs. Die-bank is a very typical push-pull boundary in semiconductor supply chains. However, there are other possible choices. Some companies hold inventory before the interconnection process in the wafer fab so that logic portions on the die can be connected to program specific functions when further demand information is updated. The parent die must be designed specifically for being able to be programmed. Such a design always leads to a larger die area and consequently leads to a higher cost. Furthermore, companies can implement a pure pull strategy if the long lead time is acceptable for customers. As for product postponement, the test stage may be the postponement point so that chips with different estimated performance can be marked as different final products.

30 24 The semiconductor industry is a hundred-billion-dollar (plus) industry and plays a fundamental role for today s global economy. Almost all products are becoming more and more electronic today than they were yesterday. Lately, empirical analysis indicates that the semiconductor industry is always under stress: either in a lack for capacity or a lack for sales position (Shunk et al. 2002). Also, many technological constraints may be reached in the foreseeable future. How long Moore s Law (Moore 1965) is going to be applicable is unknown at present. The technology-driven semiconductor industry has realized the importance of putting more intelligent control into the supply chain and manufacturing. The stress is compelling the industry to change. Companies are beginning to restructure their inventory and capacity buffers and to accelerate their transition from push to pull. Such a strategic transition requires tremendous organizational support and cultural transformation (Kempf 2003). However, before the development of detailed control mechanisms, identifying the appropriate supply chain strategy is a more fundamental issue. This strategic decision will likely be in place throughout the entire product life cycle, typically 1.5 to 2 years, because a transition in the middle of the cycle would be costly involving joint work of marketing, production and technology. An appropriate strategic decision will lead the tactical and operational decisions to achieve operational excellence. Huge demand uncertainty always exists in the semiconductor industry. Customer demand is dependent on product variety, technical specifications, order quantity, required lead time and final delivery destination. Abundant forms of process variability also exist. Among them, manufacturing cycle time is a key issue since the semiconductor industry is

31 25 very much driven by cycle time (Fowler et al. 1992). Maltz et al. (2001) also indicated that global logistics, thought not on the A-list, has enormous impact on today s semiconductor business. A US company can possibly process the wafer in a European fab, ship them cross the ocean back to the US for probe, have them assembled in a subcontracted Asian assembly house and ship them all the way back to the US for final test since the test process has many technical secrets and a company may not want to outsource it. It could be difficult to achieve on-time delivery service not only for customers on the other side of the world but also for domestic ones. In summary, semiconductor supply chain strategies need to be compared under different scenarios of demand and process dynamics. We cannot handle such a strategic decision without understanding the demand nature of the products, nor can we do so without truly understanding the underlying system behavior of the process (Figure 2). The question is not only what is the right supply chain for your product?, but what is the right supply chain for your process?

32 Modeling and Analysis Modeling Considerations and Assumptions Supply chain management is an integrated term that binds demand management, supply management and production. Shunk et al. (2003) listed value-proposition functions and cross-functional processes in a taxonomy context that addresses the entire supply chain spectrum. Table 3 summarizes the relevant issues from this taxonomy that directly affect the comparison of the three generic supply chain strategies. All these issues are discussed in a semiconductor supply chain context. Table 3 The Key Issues, Adapted from Shunk et al. (2003) Supply Management Production Demand Management Materials Procurement Equipment Procurement Outsourcing/Insourcing etc. Capacity Cycle Time Order release Batching Cost etc. Lifecycle Forecasting of Demand Product Mix Lead Time Logistics

33 27 Everything starts with a forecast. There is sometimes confusion between two kinds of forecasting: what can be sold (WCBS) and what will be sold (WWBS) (Montgomery et al. 1990). The former represents possible market trends and unrestricted sales. The latter always represents the company s capacity, budget constraints and sales target. Since capacity utilization is extremely important in the semiconductor industry, it is always the WWBS forecast that triggers production. In this research two general demand patterns are modeled: either lack-for-sales (WCBS < WWBS) or lack-for-capacity (WCBS > WWBS) (Figure 3.). Quantity WCBS (Lack-for-capacity) WWBS WCBS (Lack-for-sales) t Figure 3 Demand patterns Other demand factors are due-date lead time requirements and the mix of products. Customers can possibly require a tight due-date lead time of several days as well as a loose one of several weeks. As for product mix, in this research we consider a general situation with two final products made from one single parent die. It is very common that companies build parent dies in the front-end and assemble them in different packages to fit different environmental requirements such as temperature, humidity and pressure. For

34 28 example, the same microprocessor used for laptop and for desktop computers are put into different packages. Similar products for commodity and for military usage may have differently required packages. The packaging costs for each final product are assumed to be different. We also assume that each final product has an independent demand. As is often the case, we assume that the cheaper product has higher demand volume and the more expensive product has lower demand volume. It is likely that all future semiconductor manufacturing facilities will be 300mm production lines (TSMC 2002). One 300mm wafer can generate more than twice the chips that a 200mm wafer produces. Our research focuses on 300mm wafer manufacturing. In our research, wafers are released in a lot size of 13, which is feasible for 300mm wafer production (Seligson 1998). With laser technology, today s wafers can be marked with wafer identification numbers (wafer-id s) rather than lot-id s, therefore customer orders can be satisfied with units of wafers rather than units of lots. We do not include the lot-to-order matching issue (Fowler et al. 2000) for simplicity. Investment in semiconductor manufacturing is beyond the reach of many companies. More and more companies are running fabless today, e.g., Broadcom, NVIDIA, QUALCOMM, VIA and Xilinx. Nevertheless, a contracted third party manufacturer or foundry may not perform much different than a company s own factory from a supply chain perspective. Companies can freely choose to insource or outsource its fab, probe, assemble and test processes and the overall supply chain performance should be measured using many of the same metrics.

35 29 Without respect to whether the processes are insourced or outsourced, we model the supply chain as three integrated processes: front-end, back-end and final product delivery (Figure 4). Two key components of each process are its capacity and its cycle time. A semiconductor factory always tries to fully utilize its capacity. There are often a large number of different products running in the factory and they actually compete with each other for factory resources. Thus, it is difficult to determine the true capacity for a certain product in such a dynamic environment. Cycle time is a function of capacity and is becoming a major part of the game. Besides capacity, there are many issues that can affect the variance of cycle times, e.g., shortage of material, priority in lot release, priority in scheduling, priority in dispatching, machine breakdown frequency, operator error, etc. All these factors can also have interaction effects on cycle time. It is almost impossible for a supply chain executive to have full knowledge of all the details within the factory even though the company owns the factory or great collaboration is established between the company and the third-party manufacturer. Therefore we model the three processes as three black-boxes with a single integrated cycle time variability characteristic. Material Fab Probe Die Assembly Test Bank Final Goods Delivery Front-end Back-end Delivery Figure 4 The black-box processes

36 30 In the front-end and the back-end processes, cycle time is the time between releasing a lot to the factory and releasing the produced wafers to inventory. It is a sum of queueing time, processing time, moving time and holding time. Note that transportation can also cause variability since the processes could be globally distributed. Such effects are modeled in the black-box as total cycle time variability. The final product delivery is almost always performed by a third-party logistics company (3PL) such as FedEx or UPS (Read 2002). The 3PL will pick up the goods regularly and deliver to the customer quickly via air. The company may expect a longer delivery time for specific customer locations. Also regional transportation methods and traffic conditions can affect the delivery time since the airplanes cannot park at the front door. A worse situation is that products could be held at customs for several days due to international trade issues Performance Criteria We measure supply chain performance as a combination of production costs, inventory costs and service related costs. The key service issue in today s semiconductor supply chain is on-time delivery as described previously. Logistics costs are ignored since semiconductor devices are very small products and the transportation costs are relatively small. The production costs can be estimated for certain products (either a per wafer price charged by the third party foundry or a per wafer cost estimated for the wafers produced

37 31 in the company s own factory). The inventory holding costs mainly come from the opportunity cost of bank interest. The service performance is put into a penalty cost. Each order has a cited due-date lead time. If the order misses the cited due date, a penalty is incurred with the amount based on how long the order is delayed. The penalty per unit time of delay, in essence, is the weight on service performance that represents the importance of service. A reasonable example for the penalty is a rebate given back to the customer when the order delays. In summery, the following factors need to be analyzed so that we can understand the impact of demand uncertainty and process variability on semiconductor supply chain performance under different strategies: Demand Factors: o Demand Pattern (Lack-for-sales or lack-for-capacity) o Lead Time Requirement o Importance of On-time Delivery Service Process Factors: o Cycle-time Variability o Process Costs

38 Description of Simulation Model Several research studies were conducted with simulation experiments as shown in Table 1. Simulation modeling facilitates describing the overall supply chain processes, helps capture the system dynamics with probability distributions, and helps compare alternatives with what-if games in a cost-effective way (Chang and Makatsoris 2001). The three strategies (push, pull and push-pull) are coded as discrete event simulation models with Matlab 6.5 R13. Appendix A demonstrates a sample code for a push-pull scenario. In our model, forecasts are modeled with ramp-up, steady state, and drop-down stages. Each stage lasts for 6 months, thus the product lifecycle is 18 months. This is a very typical semiconductor product lifecycle. On a Pentium GHz personal computer with 1G RAM, the run-time for each replicate (18 months simulation time) varies from 1 second to 60 seconds depending on how many orders waited in queue for available inventory. Large uncertainty of demand exists due to upstream position of semiconductor devices in the electronics supply chain. The simulation generates random time-betweenarrival (TBA) signals based on the market trends (WCBS forecasting). Order events are generated based on the TBA signals. Orders are placed in units of wafers. Each order contains a random quantity of wafers. The aggregate demand per unit time follows a Poisson distribution. See Figure 3 for the two demand patterns discussed previously. The low demand, or lack-for-sales scenario, is in essence the situation that demand is below

39 33 the capacity since the WWBS forecasting mostly represents the capacity. The high demand, or lack-for-capacity scenario, is the situation that demand surpasses the capacity. The essential difference between the models is where the inventory is held (i.e., the location of the push-pull boundary). In the push portion of the system, the simulation model simply releases works based on WWBS forecasting and generates factory output events to build inventory after a process delay. (The pull strategy does not have this portion. The replenishment of the material inventory is the factory s business). The inventory is checked when an order event occurs and if the inventory is available, the pull processes will be activated to fulfill the order (for the push strategy this is the final product delivery process) and the total lead time is measured. Otherwise, the order is held in queue awaiting available inventory. In the push portion, we simply assume that monthly production quantities are planned based on WWBS forecasting. We also assume that factories operate 24 hours per day, seven days per week, and 9 lots of the product are released every 8 hours until the planned monthly quantity is satisfied. Note that the distinction between production plan and shop floor control is fuzzy. Thus, the factory may or may not release the jobs immediately as they are scheduled. Cycle time variability affects when each wafer comes out of the factory and reaches the inventory. A triangular distribution with most likely, upper bound and lower bound values is suggested to model the semiconductor manufacturing cycle time characteristics (Duarte 2002). However we model the three integrated process cycle times with larger ranges of

40 variability than that was given in Duarte (2002) (see Table 4). The typical process has a cycle time in between no variability and such big variability. 34 Table 4 High Variability Cycle Time Distribution in the Semiconductor Supply Chain Process Min Most Likely Max Mean Front-end Back-end Final Product Delivery Note: time is in days The current 300mm raw wafer purchase price is about $400 per wafer. This contributes to about 12% of $3200 front-end wafer processing cost (Seligson 1998). The cost varies considerably for different IC designs and process technologies. It could be a per-wafer production price charged by the outsourced foundry if the company runs fabless or a per-wafer cost estimated for the company s own factory. The back-end cost varies based on the packaging technology. For instance, the assembly and test cost for Intel s FCPGA Pentium 4 microprocessor is around $8 (IC Knowledge 2003). One 300mm wafer can generate about 400 dies of such product. Thus, total back-end cost per wafer is about $3200. This is quantitatively in the same scale as the front-end cost. In this research, we consider a range of cost as shown in Table 5 following the illustrated cost model provided by IC Knowledge (2003). Note that the delivery cost is ignored.

41 35 Table 5 Cost Structure Example Portion Low Cost High Cost Front-end Back-end for Commodity Goods Back-end for Military Goods Note: cost is in US dollar per wafer model. Table 6 shows the service penalties based on different cited due-date lead times in our Table 6 Service Penalty vs. Due-Date Lead Time Due-date Lead Time or Level 1: Tight Level 2: Medium Level 3: Loose Cited Lead Time 5 days 30 days 55 days Light Penalty $55 $30 $5 Penalty per delayed hour Heavy Penalty $275 $150 $25 The total inventory cost throughout the product life cycle is: InventoryC ost = Interest WaferCost InventoryLevel( t) dt t The penalty cost for each order is calculated as a weighted tardiness: Tardiness = max( 0, LeadTime CitedLeadTime) P = Penalty per unit time of delay PenaltyCost = P Tardiness

42 36 The total penalty cost and the inventory cost are shared by each of the sold wafers. In the push model, the total manufacturing cost is shared by all the sold wafers. Thus, the overall cost per wafer sold in the push model is calculated as: C = ( FrontendCost + BackendCost) + InventoryCost + wafer _ produced Number _ wafers _ sold order PenaltyCost In the push-pull model Product A and B share the same generic parents die in the diebank inventory. Only the total front-end manufacturing cost is shared by all the sold wafers. The back-end cost is added only for each sold wafer. The overall cost per wafer sold in the push-pull model is calculated as: C = wafer _ produced FrontendCost + InventoryCost + Number _ wafers _ sold order PenaltyCost + BackendCost In the pull model the manufacturing costs is added for each sold wafer. The overall cost per wafer sold in the pull model is calculated as: C = order PenaltyCost Number _ wafers _ sold + FrontendCost + BackendCost Experimental Design and Analysis The goal of the Design of Experiments (DOE) is to determine the impact of different factors so that desired information may be gained cost-effectively. Table 7 provides an overview of the DOE factors used to access the performance of the three strategies under

43 various operating conditions. The experimental design is implemented using Design- Expert 6 (Montgomery 2001). 37 Table 7 The DOE Factors Factors Level 1 Level 2 Level 3 Strategy Pull Push-Pull Push Due-Date Lead Time Tight * Medium * Loose * Penalty Weight Light * Heavy * Demand of Product A Demand of Product B Lack-for-sales (Low)** Lack-for-capacity (High)** Lack-for-sales (Low)** Lack-for-capacity (High)** Front-end Cycle Time Variability Zero Variability *** High Variability *** Back-end Cycle Time Variability Zero *** High *** Delivery Time Variability Zero *** High *** Front-end Mfg Cost Low **** High **** Back-end Cost Product A Low **** High **** Back-end Cost Product B Low **** High **** Note: Product A and Product B are two final products packaged from the same parent die. * Use the data in Table 6 ** See Figure 3 for demand patterns. *** Use the triangular distribution in Table 4 as high variability. Use the mean value as Zero variability **** Use the data in Table 5 Note that we have both 3-level factors and 2-level factors. We use two 2-level factors to represent one 3-level factor. Thus, there are 13 factors and a fractional factorial design of experiments is conducted. This is a Resolution V Design so that no main effect or two-factor interaction is aliased with any other main effect or two-factor interaction (Montgomery 2001). Figure 5 is a Half Normal Plot for the results of this experiment for

44 38 Product A. The factors and interactions that are significant are the ones that are not on the line. There are many significant terms. Wan et al. (2003) suggested a sequential bifurcation analysis for simulation experiments since in simulation most of the factors have some effects. Factors are grouped as either being important or being unimportant. In each step only one group of factors is analyzed for importance leaving the other factors for further step-down analysis. The significant main effects of front-end and back-end costs are so obvious that we do not need to measure them at this step. Thus, in this global experiment only the effects of strategy, service penalty, due-date lead time and their interactions are captured for examination. Total cost per wafer sold -Product A Important Group Unimportant Group Too obvious Figure 5 Effects of the global experiment

45 39 Therefore, the responses of total cost per wafer sold of Product A are examined under different patterns of due-date requirement and service penalty. Figure 6 and Figure 7 show the results of the three strategies under different due-date tightness. Figure 6 is the results when service penalty is light; Figure 7, heavy. Total Cost per wafer sold in $ Further Step-Down Analysis Pull Pushpull Push Tight 1 Medium 2 Loose 3 Due Time Figure 6 Strategy vs. due-date tightness when service penalty is light

46 40 Total Cost per wafer sold in $ Further Step-Down Analysis Pull Pushpull Push Tight 1 Medium 2 Loose 3 Due Time Figure 7 Strategy vs. due-date tightness when service penalty is heavy When on-time delivery service is less important (Figure 6), the experimental results indicate that the push strategy achieves the lowest cost when the due-date requirement is tight and the pull strategy works well when the due-date requirement is loose. Thus, the appropriate decision is to choose push when due-date lead time is tight and to choose pull when due-date lead time is loose. Note that there is a cross-over on the performance of the three strategies. Since demand uncertainty and process variability can have impacts on the performance (the unimportant group in Figure 5), thus, moving the curves, we further consider the area around the cross-over. When on-time delivery service becomes very important (Figure 7, as is the industrial trend these days), the push strategy dominates unless the due-date lead time is very loose. Note that the cross-over shifts right when a heavier service penalty is added. The area around the cross-over is where step-down experiments and analysis should be performed.

47 41 Two sets of step-down experiments are conducted. Set 1: Penalty = Light; Lead Time = 4 weeks Set 2: Penalty = Heavy; Lead Time = 8 weeks All other factors remain the same. Two experiments (Resolution VI Design) are designed and implemented, still having two 2-level factors representing each 3-level factor. Figure 8 clearly shows the factorial effects of Set 1 experiments. Total Cost per Wafer Sold Product A Strategy Figure 8 Effects of the step-down experiments The factorial effects of Set 2 experiments are very similar as those of Set 1 (Handling in Figure 8). The most significant effects are main effects of costs. Again, we do not need

48 42 to analyze them since they are obvious. Demand pattern (2-factor-interaction of EF in Figure 8) and cycle time variability (main effect of C as well as interaction of CDJ in Figure 8) appear to be important. Note that the 2-factor-interaction of the demand pattern of both products is actually the effect of aggregate demand. Figure 9 shows the different performance of the strategies under aggregate demand patterns $ Total Cost per wafer sold Product A Pull Push-Pull Push Low Demand Medium Demand High Demand Figure 9 Strategy vs. aggregate demand pattern Demand has no effect under the pull strategy. The hockey stick shape of the push strategy is because of the larger overhead cost of unsold wafers and high inventory under low demand. The V shape of the push-pull strategy is mostly due to the risk pooling mechanism of the inventory of generic parent dies so that the uncertainty of one product s

49 43 demand can be reduced by another s. Note that the service weight and due-date are the dominate drivers. The exact positions of the cross-over can be shifted based on how the service penalty and due-date requirements are picked. The large impact of cycle time variability (main effect of C as well as the interaction of CDJ) cannot be deciphered clearly in this step of analysis. Thus, we establish further step-down analysis to gain more detailed information. Further step-down experiments are implemented to address the effects for the push, push-pull and pull strategies separately. Figure 10 shows the effects of the push strategy when it is a dominating or possible strategy.

50 44 Push Effects Product A Tight Due-Date Lead Time Medium Due- Date Lead Time Loose Due-Date Lead Time Factors: A: FTime B: BTime C: Dmd A D: Dmd B E: Deliv T F: FCost G: BCostA H: BCostB Light Penalty n/a Push is not chosen here Designs Heavy Penalty Figure 10 The push effects product A Demand pattern (main effect of C in Figure 10) has a negative impact on overall cost (makes performance worse) when service requirements are tougher (tighter due-date, heavier penalty). Many penalties are added when the demand is high. Such impact turns out to be positive (makes performance better), discussed following Figure 9, when service requirements become easier (around the cross-overs of Figures 6 and 7) and most of the orders can be caught up without delays under the push strategy. Demand

51 45 pattern and back-end variability can have an interaction effect under tougher service requirements (refer to the interaction of BC in Figure 10). The back-end variability has a positive impact on cost performance when demand is high (Figure 11). Such impact is overcome by the main effect of the demand pattern when service requirements become easier and the positive impact of demand pattern becomes more important Demand is Low Demand is High Low Variability HIghVariability Figure 11 Interactive effect of demand pattern and back-end variability for push The push-pull strategy is also tested with a medium due-date lead time and a light penalty as well as with a loose due-date and a heavy penalty.

52 46 Pushpull Effects Product A/B Tight Due-Date Lead Time Medium Due- Date Lead Time Loose Due-Date Lead Time Factors: A: FTime B: BTime C: Dmd A D: Dmd B E: Deliv T F: FCost Light Penalty 6 Full 2 Designs n/a Heavy Penalty n/a n/a Figure 12 The push-pull effects Front-end variability can have a positive impact when due-dates are medium. Such effect becomes less important when due-dates are loose. The aggregate demand, rather than the independent demand, has a significant curvature effect. The risk-pooling effect of the push-pull strategy is dramatic as shown. The trade-off between inventory and service affected by demand is distinctly shown in Figure 13.

53 47... Figure 13 The curvature effect of demand on push-pull What is interesting is that variability can be good in a supply chain context when it is buffered by the inventory. For push, the positive impact of back-end variability on performance exists when server requirements are tougher (tight due-date, high service penalty) and when demand is high (demand surpasses capacity). The reason is that when demand is low, the inventory level is high. Almost all the orders are satisfied soon and demand drives the performance. When demand is high, orders are going to be delayed anyway. Orders sit in the queue waiting for available inventory. Refilled inventory is taken away by the orders very soon. Zero process variability results in units that come out of the factory and reach the inventory every 8 hours and then are taken away by the orders. A sawtooth inventory level shape can be formed. With high process variability, units come out of the factory and reach the inventory at anytime and are taken away by