Abstrait

KPOVs analytical memod based on improved weighted dynamic pareto chart

Woye Liu, Weimin Ye , Junfeng Sun, Zewei Dong, Qi Wang

The core of Lean Six Sigma quality improvement is to identify key process output variables (KPOV) of products or services according to the “critical to quality” (CTQ), then find out the key process input variables(KPIV), so that the quality improvement focus can be ascertained. To solve the problems of traditional Pareto chart in identifying the KPOV that do not consider the fuzzy attribute of quality and unequal opportunities of improvement for KPOVs, the improved weighted dynamic Pareto chart is presented. Based on determining the CTQ, after process analysis, the membership degree analytical method of fuzzy quality is applied to dynamically analysis the relevant data of the process output variables (POVs), and the combination weighting method based on entropy theory is utilized to reasonably assess the improving opportunities of process output variables. Finally, in an instance application, the improved weighted dynamic Pareto chart is used to dynamically determine the key process output variables of the repair cycle. The method provides an effective approach for the organization to determine the direction of Lean Six Sigma improve project.