A novel decision-making framework based on probabilistic linguistic term set for selecting sustainable supplier considering social credit

Abstract

Sustainable supplier selection (SSS) has become an essential task for decision-makers in competitive environments. We construct a new decision-making framework for SSS. First, classical SSS usually includes fixed factors in environmental, social and economic dimensions. Differently, we adopt new social factors from credit perspective with corporate social credit system being promoted vigorously by the Chinese government. Next, we employ probabilistic linguistic term sets (PLTSs) to collect experts’ judgments about interactive influence between factors. Third, we combine PLTSs with Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify critical success factors (CSFs) for improving decision-making efficiency. And we also give definition to relative importance degree, standard relative importance degree, deviation of importance degree and influence degree to reflect the interactive influence between factors. To eliminate subjective influence, we combine entropy weighting approach and DEMATEL to compute weights. Fourthly, we redefine dominance degree and apply it into TODIM method for SSS. Finally, the proposed decision-making framework’s effectiveness is verified by using the case study of a new energy vehicle (NEV) company. Based on this, sensitivity analysis and comparison of methods are conducted. The results verify that the decision-making framework is valid and effective for SSS.