Data quantity generated in supply chains expands as supply chain processes get more complex. Compa-nies can leverage such data and gain valuable insight into their processes with Supply Chain Analytics (SCA) tools. The selection of the most appropriate SCA tool directly affects companies' productivity since they allow enhancing visibility, obtaining informed decision-making and developing well-planned strategies for companies. A simple, practical, efficient and robust decision-making method can help select the most suitable SCA tool. Such a selection problem can be solved with multi-criteria decision-making (MCDM) methods that consider different criteria. This paper proposes an SCA tool evaluation model which combines hesitant fuzzy linguistic term set (HFLTS), analytic hierarchy process (AHP), multi-objective optimization by ratio analysis, and the full multiplicative (MULTIMOORA). The HFLTS technique is applied for handling the uncertainty and hesitancy of experts' views in the evaluation process. The weights of the six main selection criteria and their thirty sub-criteria are computed via the hesitant fuzzy linguistic (HFL) AHP method. Then, the HFL MULTIMOORA method is combined with the fuzzy envelope technique for the first time in the literature to rank the eight SCA tool alternatives from different companies. A case study for a logistics firm illustrates the potential of the proposed evaluation model, which underlines the most important criteria to be the statistical power, product quality improvement, organizational performance enhancement, and service cooperation. It also ranks PeopleSoft as the most appropriate SCA tool for the case company. A comparative analysis with the HFL VIKOR method demonstrated that the proposed method is robust and consistent. (C) 2021 Elsevier B.V. All rights reserved.