Optimizing the Formation of the Quality Improvement Teams through a Data Mining–Based Methodology

Abstract

A data mining–based methodology is proposed for optimizing the process of designing and allocating the quality improvement teams to investigate and eliminate the quality problems (defects) in manufacturing enterprises. A methodology based on grouping the related quality problems using a data mining technique is suggested as a first stage to assign the correct types and numbers of quality problems to the appropriate quality improvement teams. The resulting groups of quality problems are then refined in the second stage using a cost minimization model that scrutinizes the expected quality costs associated with the quality improvement process. A heuristic algorithm and mathematical programming are used to solve for the optimal decisions in the refining stage. Furthermore, quality problems of the Electrical Discharge Machining for fast hole drilling process are presented as a case study that demonstrates the procedure for implementation of the proposed methodology.

Keywords:

• Data mining
• Defects elimination
• Quality improvement team
• Set covering problem

Notes

∗In thousands.