DEA models for supply chain efficiency evaluation
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An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Two hurdles are present in measuring the performance of a supply chain and its members. One is the existence of multiple measures that characterize the performance of chain members, and for which data must be acquired; the other is the existence of conflicts between the members of the chain with respect to specific measures. Conventional data envelopment analysis (DEA) cannot be employed directly to measure the performance of supply chain and its members, because of the existence of the intermediate measures connecting the supply chain members. In this paper it is shown that a supply chain can be deemed as efficient while its members may be inefficient in DEA-terms. The current study develops several DEA-based approaches for characterizing and measuring supply chain efficiency when intermediate measures are incorporated into the performance evaluation. The models are illustrated in a seller-buyer supply chain context, when the relationship between the seller and buyer is treated first as one of leader-follower, and second as one that is cooperative. In the leader-follower structure, the leader is first evaluated, and then the follower is evaluated using information related to the leader's efficiency. In the cooperative structure, the joint efficiency which is modelled as the average of the seller's and buyer's efficiency scores is maximized, and both supply chain members are evaluated simultaneously. Non-linear programming problems are developed to solve these new supply chain efficiency models. It is shown that these DEA-based non-linear programs can be treated as parametric linear programming problems, and best solutions can be obtained via a heuristic technique. The approaches are demonstrated with a numerical example.
KeywordsSupply chain Efficiency Best practice Performance Data envelopment analysis (DEA) Buyer Seller
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- Camm, J.D., T.E. Chorman, F.A. Dull, J.R. Evans, D.J. Sweeney, and G.W. Wegryn. (1997). “Blending OR/MS, judgment, and GIS: Restructuring P&G's supply chain.” Interfaces, 27(1), 128–142.Google Scholar
- Chen, Y., L. Liang, and F. Yang. (2004). “A DEA game model approach to supply chain efficiency.” Annals of Operations Research, (this issue).Google Scholar
- Cohen, M.A. and H.L. Lee. (1989). “Resource deployment analysis of global manufacturing and distribution networks.” Journal of Manufacturing and Operations Management, 2, 81–104.Google Scholar
- Golany, B., S.T. Hackman, and U. Passy. (2003). “An Efficiency Measurement Framework for Multi-Stage Production Systems.” Working Paper.Google Scholar
- Lee, H.L. and C. Billington. (1992). “Managing supply chain inventory: Pitfalls and opportunities.” Sloan Management Review, 33(3), 65–73.Google Scholar
- Lee, H.L. and C. Billington. (1993). “Material management in decentralized supply chains.” Operations Research, 41, 835–847.Google Scholar
- Porter, M.E. (1974). “Consumer behavior, retailer power and market performance in consumer goods industries.” Review of Econom. Statist. LVI, 419–436.Google Scholar
- Ross, D.F. (1998). Competing Through Supply Chain Management. Boston: Kluwer Academic Publishers.Google Scholar
- Tayur, S., R. Ganeshan, and M. Magazine. (1998). Quantitative Models for Supply Chain Management. Boston: Kluwer Academic Publishers.Google Scholar
- Zhu, J. (2002). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Boston: Kluwer Academic Publishers.Google Scholar