{ "background": "Effective management of industrial machinery fleets is a critical engineering challenge in developing economies, where capital constraints and maintenance inefficiencies significantly impact operational costs and productivity. There is a paucity of rigorous, field-based evaluations comparing the cost-effectiveness of different fleet management systems in such contexts.", "purpose and objectives": "This case study aims to provide a methodological evaluation of the cost-effectiveness of two predominant industrial machinery fleet management systems utilised in Rwanda: a centralised, technology-aided system and a decentralised, manual-logbook system. The primary objective is to quantify the difference in total operational cost per machine-hour between these systems.", "methodology": "A quasi-experimental design was employed, using a difference-in-differences approach to compare cost trajectories before and after the implementation of the centralised system in a treatment group, against a matched control group using the decentralised system. The core statistical model is $C{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $C{it}$ is total cost per machine-hour. Robust standard errors were clustered at the fleet level.", "findings": "The centralised management system was associated with a statistically significant reduction in total operational cost. The estimated treatment effect, $\\beta3$, was a cost saving of 18.7% per machine-hour (95% CI: 12.3% to 25.1%). This reduction was primarily driven by lower expenditure on reactive maintenance and reduced machinery downtime.", "conclusion": The quasi-experimental analysis demonstrates that a structured, technology-aided centralised fleet management system can yield substantial cost advantages over decentralised practices in this context. The findings underscore the importance of integrated data systems for engineering asset management.", "recommendations": "Industrial operators should consider investing in centralised fleet management architectures