Abstract:
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.
