A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

A group decision model based on quality function deployment and hesitant fuzzy for selecting... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2018.02.197
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Cleaner ProductionElsevier

Published: May 10, 2018

References

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