Process Mining-driven Performance Analysis in Manufacturing Process: Cost and Quality Perspective

Abstract

The dynamics of globalization and high expectation of customers make manufacturing enterprise move towards three primarily competitive factors, namely, time, cost, and quality. The desire for continuous performance improvement of manufacturing processes is as old as manufacturing itself. However, the latest industrial revolution Industry 4.0 and the digital revolution have opened up new avenues, intertwined information systems and the operation processes. As a result, enterprises face a challenge in extracting value from a massive amount of events recorded by today‟s information systems. Process mining is well recognized as a valuable tool for observing and diagnosing inefficiencies in business processes based on event data. It turns out that process mining is a viable solution to this challenge. Nevertheless, significantly less attention has been paid on investigating cost and quality perspective in process mining. In these respects, this thesis suggests a framework for performance analysis in manufacturing processes based on process mining. The proposed approach focuse on cost and quality perspective. Specifically, the contributions of this thesis are in four-fold (i) to suggest a method to extend event log of manufacturing process with manufacturing information, i.e. cost, quality; (ii) to analyze manufacturing information, i.e. cost, quality with process model; (iii) to utilize various existing process mining techniques and develop new approaches to analyze and predict manufacturing cost; (iv) and to enable quality report in manufacturing process.