{ "background": "The reliability of manufacturing systems is critical for industrial productivity and economic development. In many developing economies, however, rigorous, data-driven frameworks for assessing system reliability are lacking, leading to reactive maintenance and unplanned downtime.", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental methodological framework to evaluate the operational reliability of electromechanical systems within manufacturing plants. The primary objective was to quantify reliability metrics and identify key factors influencing system failure.", "methodology": "A quasi-experimental design with a non-equivalent control group was employed across multiple sites. System performance data, including time-to-failure and maintenance logs, were collected. Reliability was modelled using a Weibull survival analysis, specified as $R(t) = \\exp^{-(t/\\eta)^\\beta}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Inference was based on robust standard errors to account for plant-level clustering.", "findings": "The analysis revealed a significant positive effect of the implemented predictive maintenance protocol on system reliability. The mean time between failures (MTBF) increased by approximately 32% in the intervention group compared to the control. The Weibull shape parameter $\\beta$ was estimated at 1.85 (95% CI: 1.62, 2.11), indicating a wear-out failure characteristic.", "conclusion": "The proposed framework provides a valid and practical approach for reliability evaluation in resource-constrained industrial settings. The findings confirm that structured, data-informed maintenance strategies can substantially enhance system uptime.", "recommendations": "Manufacturing managers should adopt quasi-experimental designs for evaluating operational interventions. Investment in foundational reliability data collection and analysis is essential to support evidence-based maintenance policy.", "key words": "system reliability, quasi-experimental design, manufacturing, survival analysis, predictive maintenance, industrial engineering", "contribution statement": "This paper presents a novel application of quasi-experimental methodology combined with survival analysis to rigorously