Data envelopment analysis models for supply chain efficiency evaluation

Data envelopment analysis models for supply chain efficiency evaluation

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Article ID: iaor20071786
Country: Germany
Volume: 145
Issue: 1
Start Page Number: 35
End Page Number: 49
Publication Date: Jul 2006
Journal: Annals of Operations Research
Authors: , , ,
Keywords: statistics: data envelopment analysis, performance
Abstract:

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.

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