Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry. (March 2017)

• Record Type: Journal Article
• Authors: Ghadimi, Pezhman; Dargi, Ahmad; Heavey, Cathal
• Is Part Of: Computers & industrial engineering. Volume 105(2017)
• Page Start: 12
• Page End: 27
• Publication Date: 2017-03
• Subjects: Supply chain management -- Sustainability -- Supplier selection -- Fuzzy inference system -- Social sustainability -- Supplier audition; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.01.002
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We propose an audition check-list based fuzzy inference system approach. The approach has been tested by a real-world case study in automotive spare part industry. Sustainability triple bottom line context is incorporated in supply chain practices. The approach helps to perform a fast and efficient sustainable supplier assessment. The approach provides precise decision making assistance regarding sustainability integration. Abstract: With the global awareness of sustainability issues, sustainable development is being increasingly recognized by governments and industries. In addressing these issues, organizations worldwide have taken initiatives in adopting sustainability practices in their supply chain transferring it to sustainable supply chain management. In order to establish a responsible sustainable supply chain management, an effective way would be to make sure that the potential suppliers for procuring required components are precisely assessed and evaluated based on sustainable criteria. Therefore, this paper proposes a practical decision making approach to evaluate and select the most sustainable suppliers for an automotive spare part manufacturer licensed under a France-based automotive organization. Firstly, a requirement gathering approach, the audition check-list approach, is designed to facilitate the process of data gathering for supplier evaluation based on three pillars of sustainability. Next, the gathered data are processed using a proposed fuzzy inference system to remove impreciseness and vagueness in the gathered sustainability related data. The strength of this model falls into its applicability in data gathering phase which helps decision makers in manufacturing company to perform a fast audition of a typical supplier. Secondly, the final sustainable ranking of suppliers using the proposed fuzzy inference system provide a precise and less uncertain sustainability performance scoring which makes the developed approach a reliable system for making sustainable sourcing decisions. Comparison and sensitivity analysis are performed to evaluate the proficiency of the developed approach. Finally, theoretical and managerial implications together with conclusions of the study are presented.