{ "background": "The adoption of advanced industrial machinery fleet management systems (FMS) in developing economies is a critical engineering challenge, yet rigorous methodological frameworks for evaluating their uptake are scarce. This creates a significant evidence gap for policymakers and industry stakeholders.", "purpose and objectives": "This case study presents and applies a quasi-experimental design to methodologically evaluate the adoption rate of a new GPS-enabled FMS within the Rwandan construction and mining sectors. The primary objective is to demonstrate a robust evaluation framework suitable for resource-constrained settings.", "methodology": "A difference-in-differences (DiD) design was implemented, comparing adoption metrics between an intervention group of 42 firms receiving targeted technical support and a matched control group of 42 firms. Adoption was measured via a composite index of system utilisation. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ is the DiD estimator. Inference was based on cluster-robust standard errors.", "findings": "The analysis indicates a positive and statistically significant treatment effect. The estimated DiD coefficient ($\\delta = 0.18$, 95% CI [0.07, 0.29]) suggests that the intervention increased the average adoption index by 18 percentage points. Thematic analysis of implementation barriers highlighted the predominance of technical skills shortages over financial constraints.", "conclusion": The quasi-experimental design proved viable and generated credible estimates of causal impact on adoption, offering a transferable methodological blueprint for similar engineering technology evaluations in developing contexts.", "recommendations": "Future evaluations of industrial technology adoption should incorporate quasi-experimental designs to strengthen causal inference. Programme implementers should prioritise embedded technical training alongside system deployment to mitigate skills gaps.", "key words": "machinery management,