Developing an integrated model for the evaluation and selection of six sigma projects based on ANFIS and fuzzy goal programming

Abstract Six sigma is one of the most popular tools to eliminate waste in organizations, reduce the cost and improve quality. The process of creating and evaluating projects is an initial activity in implementing six sigma. This paper aims at proposing a comprehensive methodology for the evaluation and selection of the six sigma projects. For the evaluation of projects, reviewing the literature and decision team’s opinion, we identified three main categories of criteria including business criteria, technological & process criteria and financial criteria which contain eight sub-criteria. For deriving the overall utility of projects, we designed an adaptive neuro fuzzy inference system which is capable to consider interrelations among criteria. Then, applying a fuzzy weighted additive goal programming model, we obtained the optimal portfolio of projects which should be implemented. Finally, we applied the proposed model in a leading company in Iran to illustrate the applicability of the model.

[1]  U. D. Kumar,et al.  On the optimal selection of process alternatives in a Six Sigma implementation , 2008 .

[2]  Bill Robinson,et al.  Build a Management System Based on Six Sigma , 2005 .

[3]  Roger G. Schroeder,et al.  Six Sigma: Definition and underlying theory , 2008 .

[4]  Frank M. Gryna,et al.  Juran's Quality Planning and Analysis for Enterprise Quality , 2005 .

[5]  Haritha Saranga,et al.  Six sigma project selection using data envelopment analysis , 2007 .

[6]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[7]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[8]  Praveen Gupta,et al.  Six Sigma Deployment , 2003 .

[9]  R. Tiwari,et al.  Fuzzy goal programming- an additive model , 1987 .

[10]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[11]  Chao-Ton Su,et al.  A systematic methodology for the creation of Six Sigma projects: A case study of semiconductor foundry , 2008, Expert Syst. Appl..

[12]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[13]  Sally Lanyon At Raytheon Six Sigma works, too, to improve HR management processes , 2003 .

[14]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Gülçin Büyüközkan,et al.  A fuzzy optimization model for QFD planning process using analytic network approach , 2006, Eur. J. Oper. Res..

[16]  Taho Yang,et al.  Six-Sigma Project Selection Using National Quality Award Criteria and Fuzzy Multiple Criteria Decision-Making Method , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[17]  R. Yager,et al.  Approximate Clustering Via the Mountain Method , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[18]  Jiju Antony,et al.  An Application of Six Sigma to Reduce Waste , 2005 .

[19]  Gülçin Büyüközkan,et al.  A Combined Fuzzy AHP and Fuzzy Goal Programming Approach for Effective Six-Sigma Project Selection , 2008, J. Multiple Valued Log. Soft Comput..