Efficiency evaluation and ranking of supply chains based on stochastic multicriteria acceptability analysis and data envelopment analysis. (12th August 2019)

• Record Type: Journal Article
• Authors: Ang, Sheng; Zhu, Yunxia; Yang, Feng
• Is Part Of: International transactions in operational research. Volume 28:Number 6(2021)
• Page Start: 3190
• Page End: 3219
• Publication Date: 2019-08-12
• Subjects: data envelopment analysis -- acceptability analysis -- supply chains -- efficiency -- ranking -- stochastic criteria; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12707
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: Evaluating performance of supply chains has been an important topic in supply chain management for researchers and practitioners. In this study, we integrate stochastic multicriteria acceptability analysis (SMAA) technique and data envelopment analysis (DEA) methodology and propose a model, named "two‐stage SMAA‐DEA, " for efficiency evaluation and ranking of two‐stage supply chains (e.g., supplier–manufacturer) with stochastic criteria values. Two stochastic efficiency measures are defined for supply chain efficiency evaluation in the model. The maximum efficiency is the best efficiency score based on the optimistic criterion. The average efficiency is the expected efficiency score based on the average criterion. In addition, the model provides rank acceptability and holistic acceptability index for the supply chain ranking. The developed two‐stage SMAA‐DEA model has several advantages. First, it extends two‐stage DEA models to handle uncertain or imprecise inputs, intermediate measures, and outputs using stochastic distributions. Second, it allows for variable process weights and does not need any prior preference information on processes. Our study extends network DEA to address uncertain or stochastic measures, and the model can be considered as a multicriteria decision‐making method with a two‐stage additive DEA value function. An empirical study evaluating 27 supply chains is presented to illustrate the proposed models.