Abstract
{ "background": "The operational efficiency of heavy machinery fleets is a critical determinant of productivity and cost in industrial sectors. In many developing economies, systematic evaluations of interventions aimed at improving fleet management are lacking, leading to investment decisions based on anecdotal evidence.", "purpose and objectives": "This study aims to develop and apply a robust quasi-experimental methodology to quantify the causal impact of a structured maintenance and telematics intervention on the operational efficiency of industrial machinery fleets.", "methodology": "A difference-in-differences (DiD) model was employed, analysing panel data from treatment and control groups of machinery across multiple sites. The core econometric specification is $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the efficiency metric. Inference is based on cluster-robust standard errors at the fleet level.", "findings": "The intervention generated a statistically significant average treatment effect, increasing fleet availability by 17.3 percentage points (95% CI: 12.1, 22.5). This gain was primarily driven by reductions in unplanned downtime and improved scheduling accuracy.", "conclusion": "The applied DiD framework provides a rigorous method for evaluating engineering management interventions, confirming that targeted telematics and maintenance protocols can substantially enhance machinery utilisation in an industrial context.", "recommendations": "Industry practitioners should adopt similar causal inference techniques for capital investment appraisals. Policymakers are advised to support the integration of real-time monitoring systems, coupled with structured data collection protocols, to enable evidence-based fleet management.", "key words": "difference-in-differences, fleet management, operational efficiency, quasi-experimental design, telematics, maintenance engineering", "contribution statement": "This paper provides the first application of a difference-in-differences model to isolate the causal effect of a management intervention on heavy machinery efficiency in an African industrial setting, establishing a methodological benchmark for