Maintenance Optimization in Cold Rolling Mills: A Case Study of LISCO

This paper presents a comprehensive study on maintenance optimization for the cold rolling mill stands at the Libyan Iron & Steel Company (LISCO). While predictive maintenance and simulation are well-established in global steel industry literature, a significant gap exists in applying these methodologies to the specific context of aging mills in North Africa, which face compounded challenges of legacy infrastructure, spare part shortages, and supply chain instability. This study aims to bridge this gap by developing a context-specific, data-driven framework. Building on a comprehensive analysis of historical production data, internal, external, and planned stoppage records, and key reliability metrics, the study identifies major causes of downtime and evaluates their impact on mill availability and productivity. Statistical reliability analysis and discrete-event simulation modeling are employed to support data-driven maintenance planning and resource allocation. The findings highlight critical areas for targeted maintenance interventions that can significantly reduce unscheduled stoppages and enhance overall equipment effectiveness. The primary contribution lies in the integrated application of Pareto analysis, OEE assessment, and simulation to formulate a prioritized action plan tailored to LISCO's operational realities.