"background": "Industrial machinery fleets in developing economies face significant operational risks, yet systematic, data-driven methodologies for quantifying and mitigating these risks are scarce. Current practices often rely on reactive maintenance and anecdotal evidence, lacking robust statistical frameworks for proactive risk management. ", "purpose and objectives": "This study aims to develop and evaluate a methodological framework for assessing machinery fleet risk. Its objectives are to construct a panel-data model for measuring risk reduction and to empirically test this model using operational data from Ugandan industrial sites. ", "methodology": "A longitudinal dataset was compiled from maintenance logs, incident reports, and operational records for a fleet of heavy machinery. A fixed-effects panel regression model was employed to isolate the impact of systematic interventions. The core model is Risk{it = \ + \1 Interventionit + \2 Ageit + \3 Utilisationit +, where \ captures unobserved machine-specific heterogeneity. Inference is based on robust standard errors clustered at the machine level. ", "findings": "The implementation of a structured preventive maintenance protocol was associated with a 22. 5% reduction in major incident risk. This effect was statistically significant (p < 0. 01, 95% CI: 17. 1% to 27. 9%). Machine age and utilisation intensity were also significant positive predictors of risk. ", "conclusion": "The panel-data estimation provides a robust methodological tool for quantifying risk reduction in industrial machinery fleets. The results demonstrate that structured maintenance interventions can substantially mitigate operational risks in this context. ", "recommendations": "Fleet managers should adopt panel-data methodologies for continuous risk monitoring. Investment in data capture systems to support such analyses is critical. Policy should encourage the standardisation of maintenance reporting to facilitate industry-wide benchmarking. ", "key words": "risk assessment, panel data, fixed-effects model, preventive maintenance, asset management, industrial engineering", "contribution statement": "This paper provides
Nalwoga et al. (Fri,) studied this question.