Abstract

"background": "Manufacturing systems in developing economies face unique operational risks, including equipment failure and supply chain disruption, which impede productivity and safety. Systematic evaluation of engineering interventions to mitigate these risks remains methodologically underdeveloped in the regional literature. ", "purpose and objectives": "This case study aims to methodologically evaluate the efficacy of a structured engineering risk management programme implemented across multiple plants. Its objective is to quantify the programme's causal impact on operational downtime using a robust quasi-experimental design. ", "methodology": "A difference-in-differences model is applied to panel data from treatment and control groups of manufacturing plants. The core model is Y{it = \0 + \1 + \2 + \ (\) +, where Yit is downtime percentage. Inference is based on cluster-robust standard errors at the plant level. ", "findings": "The intervention caused a statistically significant reduction in average operational downtime. The estimated treatment effect, \, was -5. 2 percentage points (95% CI: -7. 1 to -3. 3), indicating a substantial improvement in system reliability post-implementation. ", "conclusion": The structured engineering programme proved effective in significantly reducing operational risk within the studied context. The difference-in-differences approach provided a credible causal estimate of the intervention's impact. ", "recommendations": "Manufacturing firms should adopt formal, data-driven risk assessment protocols. Future studies should incorporate longer-term data to assess sustainability and explore heterogeneous effects across different system types. ", "key words": "risk reduction, difference-in-differences, manufacturing systems, operational downtime, causal inference, industrial engineering", "contribution statement": "This study provides a novel application of the difference-in-differences econometric model to isolate the causal effect of an engineering risk management intervention within an African industrial

Key Points