Application of Total Productive Maintenance in Heavy Equipment Maintenance Information Systems
Year: 2025
Type: article
Abstract: 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
Source: Academia Open
Authors: Diva Aulia, Raissa Amanda Putri
Institution: Universitas Islam Negeri Sumatera Utara
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414339159