A Comparative Difference-in-Differences Analysis of Industrial Machinery Fleet Reliability in Ethiopia, 2000–2026

{ "background": "Industrial machinery fleet reliability is a critical determinant of productivity and economic growth in developing economies. In the context of Ethiopia, systematic, longitudinal evaluations of reliability interventions for such fleets are scarce, limiting evidence-based asset management.", "purpose and objectives": "This study aims to methodologically evaluate the application of a difference-in-differences (DiD) model for quantifying the causal impact of a national preventive maintenance programme on the operational reliability of industrial machinery fleets. It compares outcomes between treated and control fleets over an extended period.", "methodology": "A comparative DiD analysis was conducted using panel data from machinery fleets in the cement and textile manufacturing sectors. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the monthly reliability index. Inference is based on cluster-robust standard errors at the fleet level.", "findings": "The analysis indicates a statistically significant positive treatment effect. The DiD estimator, $\\delta$, shows that the programme increased the mean reliability index by 17.3 percentage points (95% CI: 12.1 to 22.5). The parallel trends assumption, tested using lead variables, was not violated.", "conclusion": "The DiD framework provides a robust methodological approach for isolating the causal effect of engineering interventions on fleet reliability in an observational setting. The significant positive result underscores the efficacy of structured preventive maintenance.", "recommendations": "Policymakers and industrial asset managers should adopt quasi-experimental evaluation designs like DiD for programme assessment. The preventive maintenance protocol studied should be considered for broader national rollout, with continuous data collection to refine the model.", "key words": "difference-in-differences; machinery reliability; preventive maintenance; causal inference; industrial engineering