246 The formula above defines the digger fuel ratio (O UV), expressed in liters per ton, by dividing the total fuel consumed by the digger (UW) by the total material produced or loaded (P). For diggers, production is estimated from loaded volume converted to tons, while truck operations incorporate hauling distance for a more comprehensive fuel-efficiency evaluation (Fahrul Indrajaya, 2020), as expressed in Equation (4): > @A = ; @5':& (4) These metrics allow consistent fuel‐performance comparisons across operating conditions and equipment types, enabling the identification of fuel-optimization opportunities that lower costs and reduce environmental impact(Yu et al., 2024). Fleet Match Factor: The Fleet Match Factor measures the balance between loaders and trucks to identify queuing or idle-time inefficiencies and in this study is used to evaluate how fleet configuration affects operational and fuel efficiency, (Arauzo et al., 2024; Yu et al., 2024), as shown in Equation (5). B@ = C C E % D D 8 8 % % ( ( > : (5) The formula above describes the Matching Factor (XU), which is calculated by multiplying the number of trucks (HM) by their productivity (LMN S) and dividing the result by the product of the number of diggers (H4) and their productivity (LMN O). Energy Intensity and Emission Factor CO₂ emissions per ton are derived from fuel consumption using standardized emission factors, with adjustments for biodiesel blends such as B40[50]. Energy use and cumulative emissions are aggregated for large-scale comparison, while normalization by material moved ensures consistency across fleets and operating conditions (Oluokun et al., 2024). These metrics, defined in Equations (7) and (8), enable robust assessment of environmental impacts and energy intensity reduction potential. !F = @' D !@G (6) ECO₂ = (!F D !@')/1.000 (7) Energy intensity (YZ) is calculated by multiplying total fuel consumption (UW) by the energy factor (YU[, GJ/L), while CO₂ emissions (ECO₂) are derived by applying the emission factor (YUW) to YZ and converting the result to tons of CO₂e. Based on the site’s internal energy assessment, EFe is standardized at 0.0379 GJ/L and EFc at 77.4 tCO₂/TJ (equivalent to 0.0774 tCO₂ or 77.4 kgCO₂/MJ). These constants provide a consistent baseline for reliable estimation of energy use and associated greenhouse gas emissions. This study employed an inferential statistical framework to compare FMS and non-FMS operations using Welch’s T-Tests and 95% Confidence Intervals across key indicators: truck and excavator productivity, fuel ratio, and fleet match factor (Bajany et al., 2019; Yu et al., 2024). T-Tests evaluated mean differences between independent contractor groups, while CIs quantified effect size and uncertainty, enabling assessment of both statistical significance (p < 0.05) and practical relevance, minimizing Type I and II errors (Jasrai, 2024; Ruxton, 2006). Comparative analysis between Alpha (FMS) and Bravo (non-FMS) controlled equipment class, shift duration, and haul distance heterogeneity. This approach aligns with industrial systems best practices, providing a robust basis to assess digitalization impacts in ESG-focused mining operations. 3. RESULT AND DISCUSSION Figure 3 compares truck and excavator productivity between Alpha (FMS-enabled) and Bravo (Non-FMS). Alpha consistently outperforms Bravo, with average truck productivity approximately 39% higher and exhibiting lower variability over time (Figure 3a). Excavator productivity shows a similar trend, where Alpha records an average improvement of 17.4% compared to Bravo with more stable monthly performance (Figure 3b). These results indicate that FMS adoption is associated with better productivity.
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