An Integrated Hesitant Fuzzy MCDM Methodology for Supply Chain Analytics Tool Selection

The complex, extended, connected and global supply chains produces a huge amount of data over various phases. Companies have to manage this data for executing their daily works. Supply chain analytics (SCA) allows more informed decisions to companies by giving insight from their data. SCA can be described as the utilization of analytical methods for deriving insights from data in order to obtain a deeper comprehension. With the utilization of SCA, various profits can be provided. However, companies need to decide on the most appropriate SCA tool for their company. For this reason, in this paper, the aim is to present a research methodology for selecting the most appropriate SCA tool. The criteria and alternatives in the evaluation model are specified by conducting the literature and industrial reports, and by collecting experts' views. The criteria weights are computed using Hesitant Fuzzy Linguistic (HFL) Analytic Hierarchy Process (AHP) method. The alternatives are ranked using HFL VIKOR (VIseKriterijumska optimizacija I Kompromisno Resenje, meaning Multi-Criteria optimization and Compromise Solution) method. Hesitant Fuzzy Linguistic Term Set (HFLTS) technique is applied to overcome the uncertainty in decision-making and the hesitancy in experts' evaluations. At the end of the study, an implementation of the research methodology is presented, and the suggestions for future studies are provided.