Simulation platform for anticipative plant-level maintenance decision support system

Abstract

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.

Keywords:

• production performance
• throughput
• look-ahead bottleneck analysis
• maintenance prioritisation
• decision support system
• automotive industry

Acknowledgements

We thank the anonymous reviewers and an associate editor for their very helpful and constructive suggestions which helped improve the clarity and quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Body shops are the most upstream process in a typical automotive assembly plant (the other shops are the paint shop and the final assembly). Here, many stamped metal pieces are assembled through various welding and other operations to build up the body of a vehicle.

2. FIS is an information technology that monitors and archives asset operating attributes (cycling, blocking, starving and down times) and fault conditions. It is mainly used for data management, representation and report generation.

3. Despite the corrective and preventive maintenance, traditionally, times for doing OM is not scheduled beforehand. Instead, buffer contents that exceed a certain level present the opportunity to maintain the equipment with failure alarms. This type of maintenance is called OM.