Abstract
{ "background": "Operational risk in manufacturing systems within developing economies is a critical yet under-researched area, with a paucity of robust methodological frameworks for evaluating engineering interventions in real-world settings.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental design to quantitatively evaluate the efficacy of a structured engineering risk control programme in reducing operational incidents within manufacturing plants.", "methodology": "A pre-test/post-test control group design was implemented across four matched manufacturing facilities. The intervention comprised a systematic hazard identification and operability study combined with procedural safeguards. Operational risk was quantified via a composite metric of safety incidents and process deviations. The treatment effect was estimated using a difference-in-differences model: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, with inference based on cluster-robust standard errors.", "findings": "The intervention led to a statistically significant reduction in the composite risk metric. The estimated treatment effect, $\\beta_3$, was -0.41 (95% CI: -0.68 to -0.14), corresponding to a 34% reduction in recorded risk events relative to control plants.", "conclusion": "The quasi-experimental design provided a rigorous, field-based method for causal inference, demonstrating that the integrated engineering risk control programme substantially mitigated operational risks in the studied context.", "recommendations": "Manufacturing enterprises should adopt structured, evidence-based risk assessment protocols. Further research should validate this methodological framework across other industrial sectors and geographical regions.", "key words": "operational risk, quasi-experimental design, manufacturing systems, difference-in-differences, safety engineering, industrial engineering", "contribution statement": "This paper provides a novel application of a quasi-experimental methodology for causal evaluation of engineering safety interventions