A Study on Predicting the Effects of Applying Automated Inspection to Urban Railway Rolling Stock using BPM and Simulation

Traditionally, railway rolling stock maintenance has relied on manpower; however, with recent technological advancements, AIS are increasingly being integrated into rolling stock maintenance, with a focus on automated visual inspection. Although research on AIS-related technologies is active, it predominantly centers on the technology itself, with limited studies predicting the potential impacts of AIS implementation. This paper aims to forecast the effects of budget investment and process modifications following AIS integration into urban railway rolling stock maintenance operations, using Business Process Modeling and Monte Carlo simulation. The BPM analysis revealed that AIS application shifts the existing inspection process to prioritize AIS-generated defect detection data, reducing human inspector involvement. When no defects are detected by AIS, the inspection process is streamlined, enhancing efficiency. A Monte Carlo simulation was conducted on five components—pantograph, coupler & jumper, axle box, and pad & shoe—using BPM result and direct site observations as simulation parameters. The simulation results quantitatively estimate reductions in working time and costs for each component. These findings support the view that AIS implementation positively influences urban railway rolling stock maintenance inspections and operations.