Evaluation of supply chain analytics maturity level with a hesitant fuzzy MCDM technique

Abstract

Supply chains become more complex, more extended, more connected and more global every day. The amount of data produced over different stages of the supply chain is significantly increasing, thereby making it difficult for companies to execute their daily works. By big data business analytics (BDBA), companies gain insight and perspective from their data, which allows more informed decisions and provides better strategies. Supply chain analytics (SCA) is the application of BDBA in supply chain management. The definition of the SCA refers to the advanced mathematical techniques applied to discover essential knowledge in supply chain processes. SCA brings various benefits to companies. However, companies need to measure the maturity of their use of SCA. Therefore, in this study, the aim is to present a generic model to help companies determining their SCA maturity levels. The maturity model’s main and sub-factors are determined by a literature review, industry reports, and experts’ opinions. The weights of these factors are calculated by the Hesitant Fuzzy Linguistic Simple Additive Weighting (SAW) technique. Hesitant Fuzzy Linguistic Term Set (HFLTS) technique is used to handle uncertainty and hesitancy in experts’ judgments. Finally, an application is provided to calculate ABC Company’s SCA maturity score, and the future perspectives are presented.