Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Abstract

The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the `Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the `Return from Waiting’ activity to `Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.