Manufacturing systems in developing economies face significant operational and safety risks, yet rigorous methodological frameworks for evaluating systemic interventions are scarce. This gap hinders evidence-based engineering management and policy formulation. This paper aims to develop and apply a robust quasi-experimental methodology to quantify the causal effect of a standardised safety and process intervention programme on risk reduction within industrial plants. A difference-in-differences (DiD) model is employed, leveraging panel data from treatment and control groups of plants. The core estimating equation is $Y_{it} = \beta_0 + \beta_1 \text{Treat}_i + \beta_2 \text{Post}_t + \delta (\text{Treat}_i \times \text{Post}_t) + \epsilon_{it}$, where $Y_{it}$ is a composite risk index. Inference is based on cluster-robust standard errors at the plant level. The intervention yielded a statistically significant average treatment effect, reducing the composite risk index by 18.2 percentage points (95% CI: 12.5 to 23.9). The parallel trends assumption, tested via event-study analysis, held for the pre-intervention period. The DiD approach provides a credible and transferable methodological framework for evaluating engineering system interventions in settings where randomised controlled trials are impractical. The results demonstrate the programme's substantial efficacy in mitigating systemic risk. Engineering managers and policymakers should adopt quasi-experimental evaluation designs for capital projects. Future programmes should incorporate phased roll-outs to facilitate robust impact evaluation. Difference-in-differences, causal inference, risk management, manufacturing systems, industrial safety, programme evaluation This paper provides the first application of a difference-in-differences model to isolate the causal impact of a systemic engineering intervention on operational risk in a sub-Saharan African manufacturing context.