Manufacturing systems in developing economies face persistent operational and safety risks, yet longitudinal analyses of risk reduction strategies in these contexts are scarce. This gap limits evidence-based engineering management and policy formulation. This working paper aims to methodologically evaluate panel-data estimation techniques for quantifying risk reduction within manufacturing plants. The objective is to identify a robust model for measuring the efficacy of systemic engineering interventions over time. We construct a balanced panel dataset from plant-level audits. The core econometric specification is a two-way fixed effects model: $Risk_{it} = \beta_0 + \beta_1 Intervention_{it} + \mu_i + \lambda_t + \epsilon_{it}$, where $\mu_i$ and $\lambda_t$ denote plant and year fixed effects. Inference is based on cluster-robust standard errors to account for serial correlation. The methodological evaluation indicates that fixed effects estimation is superior to pooled OLS for isolating intervention effects. Preliminary model estimates suggest a negative coefficient for targeted engineering interventions, with the point estimate indicating a potential 15–20% reduction in composite risk scores, though the confidence intervals remain wide at this stage. Panel-data methods provide a rigorous framework for analysing risk dynamics in manufacturing systems. The fixed effects approach effectively controls for unobserved, time-invariant plant heterogeneity. Future data collection should prioritise consistent metrics for capital investment in safety engineering. Researchers should employ panel models to strengthen causal inference in engineering management studies. panel data, fixed effects, risk reduction, manufacturing systems, engineering management, operational safety This paper provides a novel methodological framework and preliminary evidence for quantifying the impact of engineering interventions on systemic risk in an industrialising context, using a uniquely constructed longitudinal dataset.