Lean Six Sigma project selection in a manufacturing environment using hybrid methodology based on intuitionistic fuzzy MADM approach
Affiliation
Lovely Professional University, Phagwara, IndiaHeriot-Watt University, Edinburgh
University of Derby
Issue Date
2021-02-08
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Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multi-attribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.Citation
Singh, M., Rathi, R., Antony, J., Garza-Reyes, J.A. (2020). 'Lean Six Sigma project selection in a manufacturing environment using hybrid methodology based on intuitionistic fuzzy MADM approach'. IEEE Transactions on Engineering Management, pp. 1-15.Publisher
IEEEJournal
IEEE Transactions on Engineering ManagementDOI
10.1109/TEM.2021.3049877Additional Links
https://ieeexplore.ieee.org/document/9349623Type
ArticleLanguage
enEISSN
1558-0040ae974a485f413a2113503eed53cd6c53
10.1109/TEM.2021.3049877
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