Abstract
A two-stage methodology is presented for enhancing the process of assigning quality problems to quality improvement teams in industrial firms. The method advances the decision support system of the quality improvement process by grouping the related quality problems in two steps:. First, a soft grouping is performed using association rules as a data mining technique, and then, resulted groups are finalized by employing a costs minimization model. Moreover, to find the optimal groups, a mathematical programming language is used. Results show that this methodology is beneficial and attractive in making the quality improvement process more efficient and in providing support to managerial decisions for creating quality improvement teams. As a practical illustration, the implementation of this methodology is investigated for an EDM fast hole drilling process.
Recommended Citation
Al-Salim, Bashar and Abdoli, Mansour, "Data Mining for Decision Support of the Quality Improvement Process" (2005). AMCIS 2005 Proceedings. 115.
https://aisel.aisnet.org/amcis2005/115