Developing a new chance constrained NDEA model to measure performance of sustainable supply chains
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Developing a new chance constrained NDEA model to measure performance of sustainable supply chains Mohammad Izadikhah1 · Elnaz Azadi2 · Majid Azadi3 · Reza Farzipoor Saen4 Mehdi Toloo5,6
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© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Owing to the increasing importance of sustainable supply chain management (SSCM), it has received much attention from both corporate and academic over the past decade. SSCM performance evaluation plays a crucial role in organizations success. One of the practical techniques that can be used for SSCM performance assessment is network data envelopment analysis (NDEA). This paper develops a new NDEA for performance evaluation of SSCM in the presence of stochastic data. The proposed model can evaluate the efficiency of SSCM under uncertain conditions. A case study in the soft drinks industry is presented to demonstrate the efficacy of the proposed method. Keywords Performance measurement · Sustainable supply chain management (SSCM) · Data envelopment analysis (DEA) · Network DEA (NDEA) · Stochastic network DEA
1 Introduction Duo to the presence of highly competitive markets, customers’ awareness and media pressure many industries have been incorporated sustainability issues in their supply chain management (SCM). The sustainable SCM (SSCM) tries to meet economic environmental and social issues in different echelons of a supply chain. Though, SSCM will not be a competitive advantage if organizational performance is not evaluated by an appropriate framework and approach. Performance evaluation plays a crucial role in SSCM. One of the rigorous techniques to evaluate the SSCM is data envelopment analysis (DEA) (Azadi et al. 2015; Mirhedayatian et al. 2014). DEA is a nonparametric mathematical tool to assess the relative efficiency of homogeneous decision making units (DMU). Conventional DEA models consider a DMU as a whole system while ignoring the operation of individual processes within a DMU.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10479-020-0 3765-8) contains supplementary material, which is available to authorized users.
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Reza Farzipoor Saen [email protected]
Extended author information available on the last page of the article
123
Annals of Operations Research
In other words, traditional DEA models consider DMU as a black box with single-process. Nevertheless, there are a number of so-called network DEA (NDEA) approaches that consider the whole system as composed by distinct processes or stages, each one with its inputs and outputs and with intermediate flows among the stages (Tone and Tsutsui 2009). In the real-world problems, there are some situations that we deal with uncertainty in data. Stochastic programming is a framework for modeling optimization problems that involve uncertainty (Reza-Pour and Khalili-Damghani 2017; Azadi and Farzipoor Saen 2011, b). Deterministic optimization problems are formulated with known parameters. Charnes and Cooper (1961) developed chance-constrained
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