Abstract
{ "background": "The management of industrial machinery fleets in resource-constrained environments requires robust, evidence-based strategies to optimise maintenance expenditure and operational availability. Current evaluations often lack rigorous counterfactual analysis, making true cost-effectiveness difficult to ascertain.", "purpose and objectives": "This case study aimed to develop and apply a quasi-experimental design to measure the cost-effectiveness of a novel predictive diagnostics system implemented for a large haul truck fleet in the mining sector. The primary objective was to quantify the net financial impact against a comparable control group.", "methodology": "A difference-in-differences (DiD) framework was employed, comparing maintenance cost trajectories between a treatment fleet (\(n=42\) vehicles) fitted with advanced telematics and a matched control fleet (\(n=38)\) using legacy scheduled maintenance. The core statistical model was $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ captures the causal effect. Inference was based on cluster-robust standard errors at the fleet level.", "findings": "The diagnostic intervention yielded a statistically significant reduction in mean monthly maintenance costs per vehicle. The DiD estimator, $\\delta$, was -ZAR 18,750 (95% CI: -ZAR 22,100, -ZAR 15,400), representing a 14.2% cost reduction relative to the control group's post-intervention mean. The analysis showed a high likelihood (p < 0.01) that the observed savings are attributable to the new system.", "conclusion": "The quasi-experimental design provided a rigorous, defensible method for isolating the causal effect of the diagnostics system on maintenance costs. The results demonstrate that targeted predictive technologies can be cost-effective in heavy industrial applications within the regional context.", "recommendations": "Fleet