Christy and Grout (1994) develop an economic, game-theoretic framework for modeling the buyer-supplier relationship in a supply chain. The basis of this work is a 2 x 2 supply chain relationship matrix, which may be used to identify conditions under which each type of relationship is desired. These conditions range from high to low process specificity, and from high to low product specificity. Thus, the relative risks assumed by the buyer and the supplier are captured within the matrix. For example, if the process specificity is low, then the buyer assumes the risk; if the product specificity is low, then the supplier assumes the risk. For each of the four quadrants (and therefore, each of the four risk categories), the authors go on to assign appropriate techniques for modeling the buyer-supplier relationship. For the two-echelon case, the interested reader is referred to Cachon and Zipkin (1997).
3.4 Simulation Models
Towill (1991) and Towill, et. al. (1992) use simulation techniques to evaluate the effects of various supply chain strategies on demand amplification. The strategies investigated are as follows [38]:
(1) Eliminating the distribution echelon of the supply chain, by including the distribution function in the manufacturing echelon.
(2) Integrating the flow of information throughout the chain.
(3) Implementing a Just-In-Time (JIT) inventory policy to reduce time delays.
(4) Improving the movement of intermediate products and materials by modifying the order quantity procedures.
(5) Modifying the parameters of the existing order quantity procedures.
The objective of the simulation model is to determine which strategies are the most effective in smoothing the variations in the demand pattern. The just-in-time strategy ( strategy (3) above) and the echelon removal strategy (strategy (1) above) were observed to be the most effective in smoothing demand variations.
Wikner, et. al. (1991) examine five supply chain improvement strategies, then implement these strategies on a three-stage reference supply chain model. The five strategies are [42]:
(1) Fine-tuning the existing decision rules.
(2) Reducing time delays at and within each stage of the supply chain.
(3) Eliminating the distribution stage from the supply chain.
(4) Improving the decision rules at each stage of the supply chain.
(5) Integrating the flow of information, and separating demands into real orders, which are true market demands, and cover orders, which are orders that bolster safety stocks.
Their reference model includes a single factory (with an on-site warehouse), distribution facilities, and retailers. Thus, it is assumed that every facility within the chain houses some inventory. The implementation of each of the five different strategies is carried out using simulation, the results of which are then used to determine the effects of the various strategies on minimizing demand fluctuations. The authors conclude that the most effective improvement strategy is strategy (5), improving the flow of information at all levels throughout the chain, and separating orders.
4 Supply Chain Performance Measures
An important component in supply chain design and analysis is the establishment of appropriate performance measures. A performance measure, or a set of performance measures, is used to determine the efficiency and/or effectiveness of an existing system, or to compare competing alternative systems. Performance measures are also used to design proposed systems, by determining the values of the decision variables that yield the most desirable level(s) of performance. Available literature identifies a number of performance measures as important in the evaluation of supply chain effectiveness and efficiency. These measures, described in this Section, may be categorized as either qualitative or quantitative.
4.1 Qualitative Performance Measures
Qualitative performance measures are those measures for which there is no single direct numerical measurement, although some aspects of them may be quantified. These objectives have been identified as important, but are not used in the models reviewed here:
• Customer Satisfaction: The degree to which customers are satisfied with the product and/or service received, and may apply to internal customers or external customers. Customer satisfaction is comprised of three elements [8]:
(1) Pre-Transaction Satisfaction: satisfaction associated with service elements occurring prior to product purchase.
(2) Transaction Satisfaction: satisfaction associated with service elements directly involved in the physical distribution of products.
(3) Post-Transaction Satisfaction: satisfaction associated with support provided for products while in use.
• Flexibility: The degree to which the supply chain can respond to random fluctuations in the demand pattern.
• Information and Material Flow Integration [31]: The extent to which all functions within the supply chain communicate information and transport materials.
• Effective Risk Management [22]: All of the relationships within the supply chain contain inherent risk. Effective risk management describes the degree to which the effects of these risks is minimized.
• Supplier Performance: With what consistency suppliers deliver raw materials to production facilities on time and in good condition.
4.2 Quantitative Performance Measures
Quantitative performance measures are those measures that may be directly described numerically. Quantitative supply chain performance measures may be categorized by:
(1) objectives that are based directly on cost or profit and (2) objectives that are based on some measure of customer responsiveness.
4.2.1 Measures Based on Cost
• Cost Minimization: The most widely used objective. Cost is typically minimized for an entire supply chain (total cost), or is minimized for particular business units or stages.
• Sales Maximization [19]: Maximize the amount of sales dollars or units sold.
• Profit Maximization: Maximize revenues less costs.
• Inventory Investment Minimization [24]: Minimize the amount of inventory costs ( including product costs and holding costs )
• Return on Investment Maximization [8]: Maximize the ratio of net profit to capital that was employed to produce that profit.
4.2.2 Measures Based on Customer Responsiveness
• Fill Rate Maximization: Maximize the fraction of customer orders filled on time.
• Product Lateness Minimization: Minimize the amount of time between the promised product delivery date and the actual product delivery date.
• Customer Response Time Minimization: Minimize the amount of time required from the time an order is placed until the time the order is received by the customer. Usually refers to external customers only.
• Lead Time Minimization: Minimize the amount of time required from the time a product has begun its manufacture until the time it is completely processed.
• Function Duplication Minimization [31]: Minimize the number of business functions that are provided by more than one business entity.
4.3 Performance Measures Used in Supply Chain Modeling
As mentioned above, an important element in supply chain modeling is the establishment of appropriate performance measures. Each of the models reviewed in Section 3 sought to optimize one or more measures of supply chain performance, given a set of physical or operational system constraints. Table 1 below summarizes the performance measures used in the reviewed research.
Basis Performance Measure Author(s)
Cost Minimize cost Camm, et. al. (1997)
Lee, et. al. (1997)
Lee and Feitzinger (1995)
Tzafestas and Kapsiotis (1994)
Pyke and Cohen (1994)
Pyke and Cohen (1993)
Lee, et. al. (1993)
Svoronos and Zipkin (1991)
Cohen and Moon (1990)
Cohen and Lee (1988)
Ishii, et. al. (1988) Williams (1983)
Williams (1981)
Minimize average inventory levels Altiok and Ranjan (1995) Towill and Del Vecchio (1994)
Maximize profit Cohen and Lee (1989)
Minimize amount of obsolete inventory Ishii, et. al. (1988)
Customer
Responsiveness Achieve target service level (fill rate) Lee and Billington (1993)
Lee, et. al. (1993)
Towill and Del Vecchio (1994)
Minimize stockout probability Altiok and Ranjan (1995) Ishii, et. al. (1988)
Cost and
Customer
Responsiveness Minimize product demand variance or demand amplification Newhart, et. al. (1993)
Towill, et. al. (1992) Towill (1991)
Wikner, et. al. (1991)
Maximize buyer-supplier benefit Christy and Grout (1994)
Cost and Activity Time Minimize the number of activity days and total cost Arntzen, et. al. (1995)
Flexibility Maximize available system capacity Voudouris (1996)
Table 1. Performance Measures in Supply Chain Modeling
5 Decision Variables in Supply Chain Modeling
In supply chain modeling, the performance measures (such as those described in Section 4) are expressed as functions of one or more decision variables. These decision variables are then chosen in such a way as to optimize one or more performance measures. The decision variables used in the reviewed models are described below. • Production/Distribution Scheduling: Scheduling the manufacturing and/or distribution.
• Inventory Levels: Determining the amount and location of every raw material, subassembly, and final assembly storage.
• Number of Stages (Echelons): Determining the number of stages (or echelons) that will comprise the supply chain. This involves either increasing or decreasing the chains level of vertical integration by combining (or eliminating) stages or separating ( or adding) stages, respectively.
• Distribution Center (DC) - Customer Assignment: Determining which DC(s) will serve which customer(s).
• Plant - Product Assignment: Determining which plant(s) will manufacture which product(s).
• Buyer - Supplier Relationships: Determining and developing critical aspects of the buyer-supplier relationship.
• Product Differentiation Step Specification: Determining the step within the process of product manufacturing at which the product should be differentiated (or specialized).
• Number of Product Types Held in Inventory: Determining the number of different product types that will be held in finished goods inventory.
6 Research Agenda
The models reviewed here, and summarized above in Table 1, utilize a number of the performance measures identified in Sections 4.1 and 4.2. Table 2 summarizes the reviewed research. For each of the models studied, the table illustrates: (1) the type(s) of modeling methodology used, (2) the performance measure(s) used, and (3) the decision variable(s) used to optimize the associated performance measure(s).

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The approach and scope of existing research in the design and analysis of supply chains illustrates a number of issues that have not yet been addressed in the literature. This section suggests a research agenda for supply chain design and analysis in: (1) the evaluation and development of supply chain performance measures, (2) the development of models and procedures to relate decision variables to the performance measures, (3) consideration of issues affecting supply chain modeling, and (4) the classification of supply chain systems to allow for the development of rules-of-thumb or general techniques to aid in the design and analysis of manufacturing supply chains.
6.1 Supply Chain Performance Measures
Table 1 identifies the performance measures that have been used in the literature. These measures, and others, may be appropriate for supply chain design and analysis. Available research has not specifically addressed the adequacy or appropriateness of existing supply chain performance measures.
More specifically, the research questions that may be answered are:
(1) Are the existing performance measures appropriate for supply chains? It is unlikely that a single performance measure will be adequate for an entire supply chain (the interested reader is referred to Beamon (1996) for an evaluation of supply chain performance measures). It is more likely that a system or function of performance measures will be necessary for the accurate and inclusive measurement of supply chain systems.
(2) What are the appropriate performance measures for supply chains? That is, what types of performance measures or performance measurement systems are appropriate for supply chain performance analysis, and why?
6.2 Supply Chain Optimization
An important component in supply chain design is determining how an effective supply chain design is achieved, given a performance measure, or a set of performance measures. Research in supply chain modeling has only scratched the surface of how supply chain strategies (or decision variables) may affect a given performance measure, or a set of performance measures. Lee and Whang (1993) and Chen (1997) are examples of such research. Lee and Whang (1993) develop a performance measurement system that attempts to match the performance metric of individual supply chain managers with those of the entire supply chain, in an attempt to minimize the total loss associated with conflicting goals. Similarly, Chen (1997) also investigates the relationship between individual supply chain managers and the supply chain as a whole, but does so on the basis of inventory costs. In this work, Chen (1997) seeks to develop optimal inventory decision rules for managers (who have only local information) that result in the minimum long-run average holding and backorder costs for the entire system.
Table 2 indicates that the majority of the models use inventory level as a decision variable and cost as a performance measure. However, as also indicated in Table 2, there are a number of other decision variables (and perhaps others that have not yet been studied) that may be appropriately linked to a system of performance measures comprised of measures listed in Table 2 and perhaps others that have not yet been studied. Thus, research is needed that associates appropriate performance measurement systems to critical supply chain decision variables.
6.3 Supply Chain Modeling Issues
In supply chain modeling, there are a number of issues that are receiving increasing attention, as evidenced by their prevalent consideration in the work reviewed here. These issues are: (1) product postponement, (2) global versus single-nation supply chain modeling, and (3) demand distortion and variance amplification.
6.3.1 Product Postponement
Product postponement is the practice of delaying one or more operations to a later point in the supply chain, thus delaying the point of product differentiation. There are numerous potential benefits to be realized from postponement, one of the most compelling of which is the reduction in the value and amount of held inventory, resulting in lower holding costs. There are two primary considerations in developing a postponement strategy for a particular end-item: (1) determining how many steps to postpone and (2) determining which steps to postpone. Current research addressing postponement strategy includes Lee and Feitzinger (1995) and Johnson and Davis (1995).
6.3.2 Global vs. Single-Nation Supply Chain Modeling
Global Supply Chains (GSC) are supply chains that operate (i.e., contain facilities) in multiple nations. When modeling GSCs, there are additional considerations affecting SC performance that are not present in supply chains operating in a single nation. Export regulations, duty rates, and exchange rates are a few of the additional necessary considerations when modeling GSCs. Kouvelis and Gutierrez (1997), Arntzen, et. al. (1995) and Cohen and Lee (1989) address modeling issues associated with GSCs.
6.3.3 Demand Distortion and Variance Amplification
Demand distortion is the phenomenon in which orders to the supplier have larger variance than sales to the buyer and variance amplification occurs when the distortion of the demand propagates upstream in amplified form [28]. These phenomena (also known collectively as the bullwhip effect or whiplash effect) are common in supply chain systems and were observed as early as Forrester (1961). The consequences of the bullwhip effect on the supply chain may be severe, the most serious of which is excess inventory costs. As a result, a number of strategies have been developed to counteract the effects of demand distortion and variance amplification. A detailed discussion of the issues and strategies associated with the bullwhip effect may be found in Lee, et. al. (1997) , Towill (1996), Newhart, et. al. (1993), Towill, et. al. (1992), Towill (1991), Wikner, et. al. (1991), and Houlihan (1987).