Selecting Six Sigma projects: MCDM or DEA?
Abstract
Purpose
Nowadays, most of the organizations have focused through the world on Six Sigma to reduce the costs, improve the productivity and enhance concerned individuals’ satisfaction, especially customers’ satisfaction. Annually, these organizations define and execute thousands of Six Sigma projects which involve a great deal of investments. But are all of these projects successful and do the organizations benefit from the above advantages? How can we reduce the risk of failure in Six Sigma projects? The first step to reduce the risk of failure in Six Sigma projects is selecting optimal ones which have the most profits and the least expected risks.
Design/methodology/approach
In this paper, the effective criteria are recognized and defined in selecting Six Sigma projects. Then, the analytic hierarchy process (AHP) is used to rank the results. Then, a real example is resolved by two important techniques in decision-making process, that is the AHP and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), as well as data envelopment analysis (DEA). The results from the above three methods are compared.
Findings
The results of this paper show that by using fewer criteria, the results from AHP and TOPSIS are very similar. Also, the results from these techniques vary from DEA’s ones in many aspects. So regarding the different results and the importance of criteria in selecting the Six Sigma projects, multi-criteria decision-making (MCDM) techniques are more reliable in comparison with DEA, because decision-maker’s point of view is more effective in MCDM techniques.
Originality/value
The paper, using a real case study, provides important new tools to enhance decision quality in Six Sigma project selection.
Keywords
Acknowledgements
The authors thank two anonymous referees for their helpful comments and suggestions, which helped to improve this paper.
Citation
Yousefi, A. and Hadi-Vencheh, A. (2016), "Selecting Six Sigma projects: MCDM or DEA?", Journal of Modelling in Management, Vol. 11 No. 1, pp. 309-325. https://doi.org/10.1108/JM2-05-2014-0036
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited