The purpose of this paper is to enhance the understanding of sustainable supply chain management (SSCM) and provide a comprehensive and quantitative method to assess performance.
The study applied interval-valued triangular fuzzy numbers associated with grey relational analysis to improve the insufficient information and overcome the incomplete system under uncertainty.
The findings support the argument that the triple bottom line is insufficient to cover the entire concept of SSCM; in particular, the aspects of operations, stakeholders and resilience have not been addressed in previous studies.
The results reveal that the triple bottom line concept is insufficient to illustrate the principles of SSCM and to provide an extensive basis for theory development. The aspects and criteria considered in the study only relate to the studied company and may need to be reviewed when applied to other industries.
The methodology and findings of the study demonstrate the core applications of criteria ranking and identify priority areas that utilize less investment but that may maintain the studied company’s current performance. Suggestions for the prioritization of criteria to enhance SSCM performance are provided.
The present study provides three valuable contributions. First, it adopts collaboration theory to furnish a theoretical foundation for SSCM. Second, the proposed hybrid method is able to overcome uncertainty and subsequently evaluate SSCM while utilizing incomplete and imprecise information. Third, the evaluation provides significant results for consideration in decision making by the studied company.
This study was presented at the 9th International Conference of Operations and Supply Chain Management, Ningbo, China, July 12-15, 2015.
Wu, K., Liao, C., Tseng, M. and Chiu, K. (2016), "Multi-attribute approach to sustainable supply chain management under uncertainty", Industrial Management & Data Systems, Vol. 116 No. 4, pp. 777-800. https://doi.org/10.1108/IMDS-08-2015-0327Download as .RIS
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