<![CDATA[General Background: In the era of digital transformation, efficient industrial asset maintenance is critical to meeting global competitiveness demands. Specific Background: Heavy equipment in sectors like construction, plantations, and agriculture requires significant investment and continuous maintenance, yet many companies still rely on manual, unscheduled, and reactive practices. Knowledge Gap: Limited research addresses the direct integration of Total Productive Maintenance (TPM) with real-time, data-driven maintenance information systems tailored for heavy equipment operations. Aims: This study aims to develop and implement an integrated TPM-based heavy equipment maintenance information system to improve productivity, reduce downtime, and enhance decision-making. Results: Using the Overall Equipment Effectiveness (OEE) method, the system identified performance gaps, with the highest OEE at 97% (Truck) and the lowest at 14% (Roller), enabling targeted maintenance actions. The integration reduced downtime, optimized resource use, and provided predictive insights. Novelty: Unlike prior studies focusing solely on TPM theory or measurement, this work delivers a practical, field-ready, IT-enabled solution directly integrated into daily heavy equipment operations. Implications: The approach offers a scalable model for other industries, supporting sustainable asset management through predictive, data-driven maintenance strategies.Highlight : Integrating TPM with information systems reduces downtime and improves performance. OEE is used to measure the availability, performance, and quality of heavy equipment. The system facilitates real-time monitoring and maintenance decision-making. Keywords :Heavy Equipment, Efficiency, Maintenance, Productivity, Total Productive Maintenance]]>
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