Toward an Overall System Effectiveness Metric for Truck–Shovel Systems in Surface Mines

Abstract Truck–shovel systems are the dominant materials handling method in surface mining and typically account for more than half of total site operating costs, making their productivity central to mine performance. Despite extensive research into fleet selection, stochastic simulation, queuing theory modelling, and real time dispatch optimisation, the mining industry still lacks a commonly accepted, system wide performance metric that captures the true productivity drivers of truck–shovel systems and can be implemented using standard Fleet Management System (FMS) data. In practice, many operations rely on incomplete or misleading key performance indicators (KPIs) that distort decision making, encourage counterproductive behaviours, and obscure significant opportunities for improvement. This review synthesises insights from more than 50 foundational studies across six decades, covering stochastic discrete event simulation, finite queue models, match factor analysis, haulage network optimisation, and dispatch algorithms. Across these diverse modelling approaches, consistent themes emerge that show productivity is strongly shaped by variability in truck arrival and service times, shovel–truck interaction losses, haul road geometry and intersection delays, match factor dynamics, and the effectiveness of dispatch decisions. The review also highlights how management decisions across all planning horizons—from long term mine design and fleet configuration to medium term haul road planning and short-term execution—directly influence these productivity drivers, underscoring the need for KPIs that reflect system wide interactions rather than isolated equipment performance. The main finding of this review is that while existing KPIs provide useful building blocks, none offer a mathematically rigorous, integrated measure of truck–shovel effectiveness. The literature consistently demonstrates that equipment centred KPIs fail to capture system variability, shovel–truck interdependencies, and dispatch inefficiencies, and that speed efficiency and distance drivers and are absent leading to blind spots in operational improvement opportunities. At worst, the wrong KPIs result in adverse behavioural responses reducing productivity. This gap presents a clear opportunity to develop an Overall System Effectiveness (OSE) metric that unifies the underlying productivity drivers into a single, actionable indicator. Such a metric would enable mine management to align operational roles with system relevant KPIs, target improvements more effectively, and unlock substantial productivity and financial gains in truck–shovel operations.