Development of a Maintenance Management Model to Enhance Machine Workshop Availability Performance at the Center for Agricultural Mechanization and Rural Technology (CAMARTEC): A Case Study of Machine Workshop Operations

This study aimed to develop a comprehensive maintenance management model to enhance the availability performance of machine workshops at the Center for Agricultural Mechanization and Rural Technology (CAMARTEC). Data were systematically collected from 100 maintenance professionals, including engineers, technicians, artisans, and operators across CAMARTEC's workshop operations. SPSS software was employed for statistical analysis using multiple regression techniques and Relative Importance Index (RII) analysis to ensure data accuracy and reliability. The RII analysis identified seven critical factors affecting machine workshop availability performance. Machine Age emerged as the most influential factor (RII = 0.872; 87.2%), followed by Hydraulic System maintenance (RII = 0.864; 86.4%), Lubrication System management (RII = 0.848; 84.8%), Gearbox Issues (RII = 0.840; 84.0%), Machine Overheating (RII = 0.824; 82.4%), Preventive Maintenance Scheduling (RII = 0.800; 80.0%), and Spare Parts Management and Availability (RII = 0.792; 79.2%). Conversely, factors such as Equipment Condition Monitoring Systems, CMMS Implementation, and Equipment Reliability Analysis were found to have lower impacts, with RII values below 42.4%. The developed multiple regression model demonstrated exceptional statistical performance, with a correlation coefficient (R = 0.990) and coefficient of determination (R² = 0.980), indicating that the model explains 98% of the variance in machine availability performance. The model maintains robustness with an adjusted R² of 0.970 and demonstrates high significance (F = 120.508, p < 0.001). Validation testing over an 11-month period showed the model predicted 95% availability compared to the actual performance of 98%, demonstrating a conservative 3% prediction bias suitable for maintenance planning. This research provides a data-driven maintenance management framework specifically tailored to agricultural machinery workshops in developing countries. CAMARTEC can significantly improve machine availability from the current 45% to optimal levels by prioritising critical factors such as machine age management, hydraulic system maintenance, and systematic lubrication programs. The findings offer practical guidance for maintenance managers seeking to implement targeted strategies that ensure continuous and reliable workshop operations.