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Business Process Improvement through Data Mining Techniques: An Experimental Approach
Abstract
The chapter proposes a general methodology on how to use data mining techniques to support total quality management especially related to the quality tools. The effectiveness of the proposed general methodology is demonstrated through their application. The goal of this chapter is to build the 7 new quality tools based on the rules that are “hidden” in the raw data of a database and to propose solutions and actions that will lead the organization under study to improve its business processes by evaluating the results. Four popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) were applied on a set of 12.477 case records concerning vehicles damages. The set of rules and patterns that was produced by each algorithm was used as input in order to dynamically form each of the quality tools. This would enable the creation of the quality tools starting from the raw data and passing through the stage of data mining, using automatic software was employed.
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