Abstract
{ "background": "The reliability of manufacturing plant systems is critical for industrial productivity and economic development. In many developing economies, systematic evaluations of these systems are scarce, leading to inefficient maintenance strategies and production losses. There is a pressing need for robust methodological frameworks to assess system performance in such contexts.", "purpose and objectives": "This study aims to develop and apply a rigorous econometric framework to evaluate the operational reliability of manufacturing systems. The primary objective is to quantify the causal impact of a major technical intervention programme on system failure rates within a sample of industrial plants.", "methodology": "A quasi-experimental difference-in-differences (DiD) modelling approach was employed. Panel data from a sample of plants, both participating and non-participating in the intervention, were analysed. The core model is specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the monthly failure rate. Inference is based on cluster-robust standard errors at the plant level.", "findings": "The DiD estimator $\\delta$ was -0.147 (95% CI: -0.211, -0.083), indicating a statistically significant reduction in the monthly failure rate for treated plants following the intervention. This corresponds to a 14.7 percentage point decrease relative to the control group, a substantial improvement in system reliability.", "conclusion": "The applied DiD model provides a valid and powerful methodological tool for evaluating engineering system reliability in manufacturing settings, demonstrating a significant positive effect of the technical intervention.", "recommendations": "Plant managers and policymakers should adopt similar causal inference techniques for capital investment appraisals. Future research should integrate real-time sensor data into the DiD framework for more dynamic assessment.", "key words": "system reliability, manufacturing,