Abstract:
This paper aims to develop a simulation-based framework to identify critical equipment,
critical maintenance and operational factors (e.g., maintenance actions, spare sourcing lead
times and fill rate) affecting plant performance (availability and maintenance cost). The study
develops a framework that utilizes empirical maintenance data. Pareto analysis is employed
to identify critical subsystems, while expert input is incorporated to derive model variables.
A full factorial Design of Experiment (DOE) is employed to establish the variables with
significant main and interaction effects on the plant availability and maintenance cost. The
framework is applied to a real case study of a cement-manufacturing firm, where a simulation model is developed based on the empirical maintenance and operational data while
considering the availability and maintenance cost as the performance measures. Simulation
results highlight the bucket elevator as the critical subsystem. At the same time, spare parts
importation probability, among other parameters like the preventive maintenance interval
and utilization of adjust maintenance action, significantly affects the performance (availability
and maintenance cost) as main and interaction effects. The research was applied to only one
case study, in this case, a cement grinding plant. The study provides a pragmatic reference
model framework to practitioners that enhances maintenance decision-making by identifying
critical equipment, maintenance and operational parameters and disclosing their effect (main
and interaction) on the plant performance (availability and maintenance cost). This study is
one of the first to (i) investigate the maintenance and operational factors’ main and interaction effects on maintenance cost and (ii) integrate the spare parts importation probability
as a factor affecting plant performance. The developed framework assists in determining
critical systems to be optimized, considers various maintenance strategies simultaneously,
the stochasticity of spare parts availability and replenishment and ultimately discovers the
interactions for decision support.
