Operational efficiency and sustainable asset management of heavy equipment in industry: a data-driven framework

Heavy equipment management is a critical component in industries such as plantations, construction, mining, and logistics, as it significantly impacts productivity, operational costs, and long-term business sustainability. This study proposes a data-driven framework that integrates equipment performance, equipment age, work location, and business clustering to enhance the efficiency of heavy equipment asset management in Indonesia. An empirical analysis was conducted to examine the relationships among these factors and their effects on revenue and ownership costs. The findings reveal that equipment performance significantly influences both revenue and ownership costs; higher performance can reduce ownership costs but may also lead to lower revenue due to over-maintenance or underutilization. Equipment age is positively associated with ownership costs, in line with the asset life cycle theory. Work location demonstrates a strong impact on revenue, whereas business clustering, while not showing a direct effect, indicates potential benefits for long-term operational efficiency through strategic collaboration. This study contributes to the literature by offering a practical, empirical framework that supports improved asset utilization and operational decision-making. It advances the theoretical discourse in machine asset management by integrating performance metrics, life cycle considerations, spatial factors, and organizational clustering to promote operational sustainability and business effectiveness.