An interpretable machine learning approach for engineering change management decision support in automotive industry

Abstract

As an essential part of the Product Life Cycle (PLC), the time-to-market of products is influenced by Engineering Change Management (ECM) processes. An Engineering Change (EC) is part of a formal process in industry to describe, rationalize, determine, release components for final production or make changes to already released design. It includes information about shape, functionality, production location, cost, and other relevant data entries. The duration from creation to the approval of a change request can take weeks or even months, without apparent reasons for the bottleneck. In addition, changes to one component can lead to unexpected chain reactions to other components. Therefore, identifying impacts of changes is challenging for all Original Equipment Manufacturers (OEMs). To address the above challenges, the authors have developed and built a machine learning-based decision support solution in this article. Community detection and stacking algorithms were applied to build more robust models. Impacts and lead time of Engineering Change Requests (ECRs) are predicted and explained by Local Interpretable Model-agnostic Explanations (LIME). A case study was conducted on real-world data from an automotive company. After evaluation with industry experts, the solution approach was proved to have positive contributions to increasing the quality, efficiency, and transparency of the existing ECM processes.