Performance Modelling of Manufacturing Equipment: Results from an Industrial Case Study

Abstract

The ability to realize maximum returns from manufacturing equipment is affected by various interrelated business and technical factors that affect equipment performance. Among the key factors are operating and maintenance practices that significantly affect equipment performance. Understanding how these factors interact and impact manufacturing performance is essential in ensuring that the equipment is operated in a manner that provides desired performance and enables informed management decisions on performance prediction and improvement. However, performance analysis in practice is driven by past events (lagging indicators) and little has been done to model the various cause and effect relationships that determine performance (the leading indicators). There lacks therefore an approach of conducting predictive performance analysis for manufacturing systems. In this research, a performance modelling approach is developed that integrates process knowledge and corresponding dynamics that determine equipment performance. The approach consists of; first identification and quantification of the key interactions and factors (technical and operation factors) affecting manufacturing equipment performance. Secondly, a simulation model is developed (in ARENA software) to model the relationships and interactions among the various factors and their impact on performance. The approach is tested with an industrial case study in a processing plant and results are presented in the paper. The model is used in predictive performance analysis and screening of improvement scenarios for decision support.