Maintenance Performance Optimization for Critical Subsystems in Cement Pre-Grinding Section: A Case Study Approach

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.

Article history: Received (December 3, 2022); Revised (May 30, 2023); Accepted (June 13, 2023); Published online (July 10, 2023)