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Growing Science » Decision Science Letters » A fuzzy MADM approach for project selection: a six sigma case study

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print)
Quarterly Publication
Volume 5 Issue 2 pp. 255-268 , 2016

A fuzzy MADM approach for project selection: a six sigma case study Pages 255-268 Right click to download the paper Download PDF

Authors: Rajeev Rathi, Dinesh Khanduja, S.K. Sharma

DOI: 10.5267/j.dsl.2015.11.002

Keywords: Fuzzy logic, MADM, Modified digital logic, Project selection, Six Sigma, TOPSIS, VIKOR

Abstract: Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.

How to cite this paper
Rathi, R., Khanduja, D & Sharma, S. (2016). A fuzzy MADM approach for project selection: a six sigma case study.Decision Science Letters , 5(2), 255-268.

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Journal: Decision Science Letters | Year: 2016 | Volume: 5 | Issue: 2 | Views: 2785 | Reviews: 0

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