Combining fuzzy MCDM with Kano model and FMEA: A novel 3-phase MCDM method for reliable assessment

Jia Shao, Shuya Zhong*, Mi Tian, Yuanyuan Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In the circular economy era, this study addresses sustainable business management for high-investment and long-life cycle projects, where accurate and reliable assessments are crucial to ensuring successful outcomes. The objective is to elevate the reliability of assessments by introducing a novel decision-making method that, for the first time, integrates time-based satisfaction and risk factors simultaneously. We propose a 3-phase multi-criteria decision-making (MCDM) method, which combines fuzzy MCDM comprising fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal situation (TOPSIS), Kano model, and failure mode and effects analysis (FMEA) techniques, to handle reliable assessments effectively. Our method is distinct in its incorporation of time-based satisfaction weights derived from Kano model, emphasising decision-makers’ criteria preferences in short, medium, and long terms. Furthermore, we introduce risk-discounted weights by using FMEA to tune criteria scores. The method is validated via a numerical example case, assessing and selecting the most appropriate hydrogen storage method for lightweight vehicles. The results suggest that cryo-compressed hydrogen tank with 250–350 bar and at cryogenic temperature is the most suitable storage method. Health & safety with a weight of 0.5318 emerges as the most important main criterion, and permeation & leakage with a weight of 0.4008 is the most important sub-criterion. To bridge the gap between theoretical research and practical application, we transform the new method into a user-friendly web application with graphical user interface (GUI). End-users can conduct reliable assessments and foster sustainable business management through informed decision-making.
Original languageEnglish
Number of pages41
JournalAnnals of Operations Research
Early online date21 Feb 2024
DOIs
Publication statusE-pub ahead of print - 21 Feb 2024

Bibliographical note

Acknowledgements
We acknowledge Jack Kirby for the co-development of the MCDM web application.

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