Performance Evaluation of Supply Chains by Bi-Level DEA

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Springer Science and Business Media Deutschland GmbH

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Today, supply chains have become one of the dominant paradigms in the field of business. Experts believe the competition between companies in the past decades has turned into a competition between chains in the present era. Thus, supply chain management (SCM) is one of the essential and critical points in business because proper supply chain management improves the activities of various components and levels of a supply chain to improve the overall situation of the supply chain system. One of the crucial tools in supply chain management is evaluating the performance of the supply chain and each component and comparing the performance of the supply chain under evaluation with other competitors. Therefore, the calculation of relative efficiency, cost, and profit efficiency are essential factors in examining the performance of supply chains. Although to calculate the relative efficiency of DMUs under evaluation, the use of DEA models seems to be suitable, using classic DEA models by considering the supply chain as a black box or, even more precisely, assessing all components of the chain as a network and using NDEA models is not the answer to all situations in the supply chain. For example, suppose the members in a supply chain do not have the same level of importance (leader–follower). In this case, another branch of DEA is called bi-level DEA, and generally, multi-level DEA is recommended. Although the leader–follower point of view is explained in Chap. 8, we will deal with this issue with a different approach in this chapter and describe the evaluation of supply chains using bi-level DEA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


Anahtar Kelimeler

Efficiency, Black Boxes, Dea Models, Leader-Follower, Management Is, Performance, Performances Evaluation, Relative Costs, Relative Efficiency, Relative Profit, Supply Chain Systems, Supply Chain Management


Studies in Big Data

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