A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. (20th January 2017) - Hatami-Marbini, Adel; Agrell, Per J.; Tavana, Madjid; Khoshnevis, Pegah
Abstract: Sustainable sourcing is a recent priority for firms considering customer behavior and societal norms with respect to the supply chain. Customer attitudes, particularly in the developed countries, are affected by the perceived sustainability of products or services regarding environmental, social and economic aspects. Seeking to maximize their market shares, firms frequently require an effective sourcing approach in supply chain management (SCM) by selecting sustainable suppliers (sourcing) and by enforcing standards through continuous supplier evaluations (monitoring) as well as by contract adjustments (retention). Most existing sourcing methodologies are either cost-oriented or ad hoc, without the tools and techniques necessary to deal with sustainability. In this paper, we propose a product-based framework for sustainable supplier sourcing considering different sustainability, operational and organizational criteria based on the type of outsourced products in the evaluation process. We develop a flexible cross-efficiency evaluation methodology based on data envelopment analysis (DEA) for identifying supplier performance. This research also uses fuzzy set theory to tackle the vagueness of information that is often present in the information-gathering step. We present a case study from the semiconductor industry to demonstrate the applicability of the proposed model and the efficacy of the procedures and algorithms. Graphical abstract: Highlights: Most sourcing methodologies are cost-oriented and do not consider sustainability criteria. We propose a comprehensive cross-efficiency evaluation method for supplier evaluation. Fuzzy set theory is used to represent the vagueness and ambiguity in the evaluation data. Sustainability, operational and organizational criteria are uniquely combined in the model. A case study demonstrates the applicability of the model and the efficacy of the procedures.