Conference Agenda

Abstract- Determining the optimal replacement timing for high-CAPEX mining equipment under operational uncertainty remains a strategic challenge in capital-intensive industries. While deterministic life cycle cost (LCC) models provide baseline estimates, they fail to capture the stochastic nature of maintenance degradation patterns across different operational regimes. This research develops a multi-scenario stochastic framework integrating Monte Carlo simulation (30,000 realizations) with Equivalent Uniform Annual Cost (EUAC) optimization to quantify the economic life of ultra-class haul trucks under three operational contexts: World-Class operations (maintenance degradation rate g = 8% ± 2%), Industry Standard (g = 10% ± 2%), and Severe Conditions (g = 12% ± 2%). The methodology employs geometric gradient modeling with structural constraint g_max = 15% based on physical wear limits documented in tribological studies. Results demonstrate that the technical driver (g) exhibits dominant influence over the financial driver (TMAR): a 2-percentage-point increase in g contracts economic life by 25% (from 16 to 12 years), translating to earlier CAPEX reinvestment of $3.5M per unit. Under Severe Conditions, optimal replacement converges to 11 years. Sensitivity analysis via Power BI visual analytics reveals that maintenance strategy transitions yield higher economic leverage than financial parameter adjustments. The contribution establishes a Decision Support System (DSS) architecture that operationalizes uncertainty into actionable replacement policies.