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Supply chain management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance. Since most of these problems involve uncertainty and hesitation on the part of decision makers (DMs), various studies have emerged recently that present SCM applications of techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and HFLTS extensions. Given the relevance of this subject and the lack of literature review studies, this study presents a systematic review of HFLTS and HFLTS extension applications to SCM decision-making problems. In order to answer a set of research questions, the selected papers were classified in accordance with a group of factors that are pertinent to the origins of these studies, SCM, HFLTSs, and decision making. The results demonstrated that the Source and Enable processes have been studied with greater frequency, while the most common problems have to do with supplier selection, failure evaluation, and performance evaluation. The companies of the automotive sector predominated in the analyzed studies. Even though most of the studies used techniques based on HFLTSs, we identified applications of seven distinct HFLTS extensions. The main contribution of this study consists of presenting an overview of the use of HFLTSs and their extensions in practical examples of SCM, highlighting trends and research opportunities. It is the first study to analyze applications of decision-making techniques that deal with hesitation in SCM. Therefore, the results can help researchers and practitioners develop new studies that involve the use of HFLTSs and HFLTS extensions in decision-making problems, given that this study systematizes elements that should be considered in the modeling, application, and validation of these methods. [ABSTRACT FROM AUTHOR]

Supplier selection is a complex decision-making process which is influenced by uncertainty and DM hesitation. To deal with this problem, various studies have proposed the use of methods based on Fuzzy Set Theory and its extensions. There are several varieties of Hesitant Fuzzy Linguistic VIKOR (HFLVIKOR) methods in the literature which can use two or more linguistic terms in each evaluation and can generate solutions based on the group's utility or individual regret. Despite these benefits, we have not found comparative studies involving HFLVIKOR methods to supplier selection. Given this gap, this study applies and compares Extended Hesitant Fuzzy Linguistic VIKOR (EHFLVIKOR) methods and Hesitant Fuzzy Linguistic Term Set VIKOR (PDHFLVIKOR) methods with Possibility Distributions within the supplier selection context. This application was realized using a service company. We also performed 8 scenario simulations to analyze the effect of including other criteria and alternatives. Despite the identified limitations of each method, we conclude that both are appropriate for dealing with group decision making under uncertainty and require little effort in their data collection. The PDHFLVIKOR results were more consistent in maintaining the ranking and the compromise solution in the real application as well as simulated scenarios. Unlike PDHFLVIKOR, EHFLVIKOR loses information when there is a consensus. On the other hand, because it makes it possible to create subgroups of DMs, EHFLVIKOR appears to be more appropriate for companies in which there is more than one DM involved in supplier selection for each functional area. This study's results will help managers and academics in selecting the most appropriate method for supplier selection. • Compares EHFLVIKOR and PDHFLVIKOR methods for supplier selection. • Analyzes the characteristics and points out the benefits and limitations of each method. • Both methods are consistent when new alternatives or criteria are included. • EHFLVIKOR is more appropriate when there is no consensus among decision maker subgroups. • PDHFLVIKOR does not lose information when there is a consensus and deals better with uncertainty. [ABSTRACT FROM AUTHOR]

Supply chain sustainability management is gaining increasing importance. Several studies propose quantitative evaluation approaches to manage sustainable supply chains. However, none of the studies focus on the selection and weighting of the metrics as a group decision process and in a way that considers the degree of difficulty of collecting data for measuring performance on a particular metric. Therefore, this paper proposes a group decision model for selecting metrics for supply chain sustainability management. The proposal is based on the combination of Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the prioritization procedure of the house of quality of the Quality Function Deployment (QFD) method. HFLTS are used to represent judgments of different decision makers about the importance of supply chain sustainable performance requirements and the relationship between selected metrics and requirements. Prioritization of requirements and metrics is based on the method of distance measures between HFLTSs. The degree of difficulty of data collection is also estimated based on judgments using linguistic expressions and on distance measures of HFLTSs. An illustrative application is presented based on a first tier automobile manufacturing company. Through this illustrative example it is possible to see the benefit of using hesitant fuzzy sets to aggregate the judgments of different decision makers. It is also evident the importance of considering the degree of difficulty of data collecting as an additional argument to select and prioritize metrics. The proposed decision model can also be applied to other decision problems such as selecting criteria for sustainable supplier selection and evaluation. [ABSTRACT FROM AUTHOR]