Abstract
{ "background": "The reliability of manufacturing systems is a critical determinant of industrial productivity and economic development. In many developing economies, systematic evaluations of production system performance are scarce, leading to data-driven decision-making challenges. Rwanda's manufacturing sector, while growing, lacks rigorous methodological frameworks for assessing the impact of system interventions on operational reliability.", "purpose and objectives": "This study aims to develop and apply a robust quasi-experimental methodology to evaluate the causal effect of systematic interventions on the reliability of manufacturing plants. The primary objective is to quantify the change in system reliability following targeted engineering improvements, controlling for external temporal trends.", "methodology": "A difference-in-differences (DiD) model was employed, leveraging panel data from a sample of plants that implemented standardised system upgrades (treatment group) and a control group that did not. The core econometric specification is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the reliability metric. Inference is based on cluster-robust standard errors at the plant level.", "findings": "The DiD estimator ($\\delta$) indicated a statistically significant positive effect of the system interventions. Plants in the treatment group exhibited a 17.5% improvement in mean time between failures (MTBF) relative to the control group, with a 95% confidence interval of [12.2%, 22.8%]. The parallel trends assumption, validated by pre-intervention data, supports a causal interpretation.", "conclusion": "The applied DiD framework provides a valid and powerful methodological tool for isolating the impact of engineering system changes on reliability within an industrialising context. The results confirm that structured interventions can substantially enhance manufacturing system robustness.", "recommendations": "Manufacturing plant managers and policymakers should adopt quasi-experimental evaluation designs for