Abstract:
This paper aims to develop a simulation-based framework to identify critical equipment,
critical maintenance, and operational factors 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’s availability and maintenance cost. The framework is
applied to a real case study of a manufacturing firm, where a simulation model is
developed based on 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 costs) as interaction effects. The study
provides a pragmatic reference model framework for practitioners to enhance
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 and considers various maintenance strategies simultaneously.
Additionally, the study discovers the stochasticity of spare parts availability and
replenishment and the interactions for decision support.
