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

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.

Selection of Sustainable Supplier(s) in a Paint Manufacturing Company Using Hybrid Meta-Heuristic Algorithm

“…Based on the results of the ANFIS analysis, as presented on the toolbox platform, the best rating for a sustainable supplier for the paint manufacturing company is that of Supplier 4. This research finding is in line with the study conducted by Okwu and Tartibu [16], where ANFIS and TOPSIS were successfully implemented in the selection of sustainable suppliers for fast-moving consumer goods. This is a clear indication that this hybrid algorithm is highly effective and can be applied in any productbased system where raw materials are required for the production process.…”

“…Hybrid traditional algorithms and other computational techniques have previously been used by scholars [17][18][19][20][21][22][23][24][25][26][27][28]. ANFIS has demonstrated superior strength compared with techniques such as TOPSIS, ANN, and FUZZY [16,29]. A hybrid algorithm adaptive neuro-fuzzy inference system was used in this research owing to its superior and expressive power to examine the relationships between the input variables and possible suppliers.…”

Green Supplier Evaluation and Selections: A State-of-the-Art Literature Review of Models, Methods, and Applications

“…Sen et al [157] applied the methods of TOPSIS, MOORA, and GRA for evaluating sustainability performance of suppliers under intuitionistic fuzzy context, and Banaeian et al [158] used the methods of TOPSIS, VIKOR, and GRA for green supplier evaluation and selection in fuzzy environment. In [167], a hybrid model based on the adaptive neurofuzzy inference system (ANFIS) and the TOPSIS was implemented for sustainable supplier selection. In [168], an integrated method based on the fuzzy inference system (FIS) and the fuzzy TOPSIS was used to evaluate the supplier's sustainability performance.…”

“…Green image [22-24, 26, 28, 32, 35, 38, 47, 49, 60, 72, 78, 87, 94, 113, 115, 119, 120, 135, 139, 146, 151, 154, 161, 163, 179, 197] 28 Environmental management system [19, 26-29, 43, 47, 51, 57, 61, 72-78, 85, 88, 94, 98, 108, 110, 119, 120, 123, 125-127, 131-134, 136, 142, 145, 156-158, 160, 161, 165, 172, 173, 178, 180, 181, 185, 187, 188, 192, 193, 195, 196, 199, 207-209, 211] 59 Pollution control Pollution control [29,38,49,54,57,69,71,84,106,124,126,127,133,136,137,141,157,160,167,173,175,192] 22 Pollution prevention…”

“…Lead time [20, 30, 51, 57, 73, 86, 92, 93, 96, 103-105, 112, 123-125, 143, 146, 162, 169, 180, 190, 199, 204] 24 On-time delivery [17,30,32,35,61,64,67,72,86,94,108,109,116,121,124,126,136,138,140,153,167,169,170,186,198,209] 26 Service Service quality [24,27,32,35,36,42,43,72,76,79,83,89,96,122,124,145,156,…”