Supply chain design and analysis:: Models and methods

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Abstract

For years, researchers and practitioners have primarily investigated the various processes within manufacturing supply chains individually. Recently, however, there has been increasing attention placed on the performance, design, and analysis of the supply chain as a whole. This attention is largely a result of the rising costs of manufacturing, the shrinking resources of manufacturing bases, shortened product life cycles, the leveling of the playing field within manufacturing, and the globalization of market economies. The objectives of this paper are to: (1) provide a focused review of literature in multi-stage supply chain modeling and (2) define a research agenda for future research in this area.

Introduction

A supply chain may be defined as an integrated process wherein a number of various business entities (i.e., suppliers, manufacturers, distributors, and retailers) work together in an effort to: (1) acquire raw materials, (2) convert these raw materials into specified final products, and (3) deliver these final products to retailers. This chain is traditionally characterized by a forward flow of materials and a backward flow of information. For years, researchers and practitioners have primarily investigated the various processes of the supply chain individually. Recently, however, there has been an increasing attention placed on the performance, design, and analysis of the supply chain as a whole. From a practical standpoint, the supply chain concept arose from a number of changes in the manufacturing environment, including the rising costs of manufacturing, the shrinking resources of manufacturing bases, shortened product life cycles, the leveling of the playing field within manufacturing, and the globalization of market economies. The current interest has sought to extend the traditional supply chain to include “reverse logistics”, to include product recovery for the purposes of recycling, re-manufacturing, and re-use. Within manufacturing research, the supply chain concept grew largely out of two-stage multi-echelon inventory models, and it is important to note that considerable progress has been made in the design and analysis of two-echelon systems. Most of the research in this area is based on the classic work of Clark and Scarf 1, 2. The interested reader is referred to Federgruen [3]and Bhatnagar et al. [4]for comprehensive reviews of models of this type. More recent discussions of two-echelon models may be found in Diks et al. [5]and van Houtum et al. [6]. The objectives of this paper are to: (1) provide a focused review of literature in the area of multi-stage supply chain design and analysis, and (2) develop a research agenda that may serve as a basis for future supply chain research.

Section snippets

The supply chain defined

As mentioned above, a supply chain is an integrated manufacturing process wherein raw materials are converted into final products, then delivered to customers. At its highest level, a supply chain is comprised of two basic, integrated processes: (1) the Production Planning and Inventory Control Process, and (2) the Distribution and Logistics Process. These processes, illustrated below in Fig. 1, provide the basic framework for the conversion and movement of raw materials into final products.

The

Literature review

The supply chain in Fig. 1 consists of five stages. Generally, multi-stage models for supply chain design and analysis can be divided into four categories, by the modeling approach. In the cases included here, the modeling approach is driven by the nature of the inputs and the objective of the study. The four categories are: (1) deterministic analytical models, in which the variables are known and specified, (2) stochastic analytical models, where at least one of the variables is unknown, and

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

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,

Research agenda

The models reviewed here, and summarized above in Table 1, utilize a number of the performance measures identified in Section 4.1Section 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).

The approach and scope of existing research in the design and analysis of supply chains

Summary

A supply chain is defined as a set of relationships among suppliers, manufacturers, distributors, and retailers that facilitates the transformation of raw materials into final products. Although the supply chain is comprised of a number of business components, the chain itself is viewed as a single entity. Traditionally, practitioners and researchers have limited their analyses and scope to individual stages within the larger chain, but have recently identified a need for a more integrated

Acknowledgements

The author wishes to thank the editor and anonymous referees for their helpful comments and suggested improvements.

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