efficiency, and CO₂ emission intensity an integrated assessment rarely applied in prior FMS research. By isolating the operational impact of FMS on energy use and emissions, the study provides evidence-based guidance for mining companies pursuing fleet digitalization strategies (Shvedina, 2020). Moreover, by contextualizing mining-sector findings within the broader evolution of FMS applications in manufacturing and robotics, this research extends the understanding of digital fleet systems across resource-intensive industries (Gago et al., 2024). 2. METHOD This study analyzes three gold-mine contractors in Sulawesi: Alpha (FMS), Bravo (manual), and Charlie (pre/post-FMS) operating under comparable conditions with different digital maturity. Their distinct fleet configurations provide a basis for assessing FMS impacts on productivity, fuel efficiency, and fleet coordination (Table 1). Table 1- Distribution of Equipment Units by Class and Contractor Digger Class Truck Class 120 Ton 90 Ton 80 Ton 60 Ton 20 Ton 100 Ton 40 Ton 30 Ton Alpha 4 4 29 30 Bravo 1 1 20 Charlie 2 10 Total 4 1 4 1 2 29 50 10 2.1. Comparison Method As shown in Figure 1, the study applies a structured comparative approach comprising four stages: Source Data, Data Collected, Analysis, and Result. Source data were drawn from three operational entities Contractors Alpha, Bravo, and Charlie and organized into two comparison schemes. Figure 1 - Flow Chart Comparison Methods Figure 1 presents the structured comparison framework used to evaluate productivity, fuel efficiency, fleet matching, and energy–emission performance across FMS and non-FMS operations. Figure 2 complements this framework by illustrating the FMS architecture and dispatch control system that enables real-time data acquisition and optimization, linking the analytical approach with practical digital implementation in mining operations.
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