Notice of RetractionCustomer requirements analysis method in lean six sigma project selection based on RAHP

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

By applying lean six sigma (LSS) project management method, customer satisfactory level can be promoted and organization cost can be reduced. But firstly we need understand veritable customer requirements. When making decision aiming at complex products of various requirements, it is necessary to resolve sub-requirements that are easy to be mastered by policymakers according to the level of customer requirements, so as to help them handle a better understanding and to make the most reasonable decision. Because of natural defects of fuzzy AHP and ANP method, we should research more efficient multi-level and multi-semantic analytical methods of customer requirements to provide analytic basis for LSS projects selection. Because rough number and rough border area can handle the order problem of importance ratings on ambiguous and uncertain decision attribution, this paper introduces rough sets theory into the process of AHP, and draws support from the rough sets to feature the fuzzy information on customer requirements. Firstly, this paper establishes the analytic framework of customer requirements based on rough sets theory, and then designs importance ratings determining algorithm of customer requirements based on rough AHP (RAHP). An applying example and simulation computation results show that this method efficiently solves the problem without any more information beyond what has been gained. Better reflecting customer real experience and the surveyed customer groups´ entire recognition, this method provides mor- objective analytic basis for the choice of LSS projects.