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Sustainable supplier selection in the retail industry: A TOPSIS- and ANFIS-based evaluating methodology

M. Okwu,L. Tartibu

Published 2020 in International Journal of Engineering Business Management

ABSTRACT

In this study, a hybrid model based on ANFIS (Adaptive Neuro-Fuzzy Inference Systems), a predictive intelligent-based technique, and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) was implemented for sustainable supplier selection. Selection of supplier is a crucial task for companies to achieve the objectives of inbound and outbound supply chain system. This selection process may be complex due to the inclusion of diverse subjective and objective factors. There is, therefore, the need for a reliable methodology to provide higher accuracy in predictions of anticipated supplier performance, and this article makes contributions in that direction. The model was applied in the retail end of a fast-moving consumer goods (FMCG) industry to select the best possible suppliers using the sustainability criteria of the triple-bottom-line. Available data from the FMCG retail sector was fed into the TOPSIS and ANFIS model to rank the suppliers. Results indicated that the most dominant sustainability factors in the Nigerian FMCG retail sector are advanced technology, cost, reliability, on-time delivery, and environmental competencies. The finding should encourage companies in the retail sector to explore sustainability opportunities in order to improve their competitiveness for selection during bidding processes. The novelty of this study is the application of ANFIS to sustainable supplier selection problem in the context of a developing economy like Nigeria. It should also assist managers in the FMCG retail sector to highlight areas of possible sustainability improvements.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    International Journal of Engineering Business Management

  • Publication date

    2020-01-22

  • Fields of study

    Business, Engineering, Environmental Science, Computer Science

  • Identifiers
    DOI 10.1177/1847979019899542
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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