The adoption of advanced process-control systems (PCS) is critical for enhancing industrial efficiency and productivity in developing economies. However, rigorous methodological frameworks for quantifying and forecasting this adoption, particularly in sub-Saharan Africa, are lacking. This study aims to methodologically evaluate the determinants of PCS adoption and to provide robust panel-data estimates for adoption rates, projecting future trajectories within the engineering sector. A novel panel-data econometric model was developed, integrating firm-level survey data with national industrial statistics. The core specification is a fixed-effects model: $Adoption_{it} = \alpha_i + \beta_1TechCap_{it} + \beta_2Reg_{t} + \beta_3Inv_{it} + \epsilon_{it}$, where $\alpha_i$ denotes entity-specific effects. Inference is based on heteroskedasticity-robust standard errors. Technological capacity and targeted regulatory interventions were statistically significant positive drivers of adoption (p < 0.01). The model forecasts a substantial increase in adoption rates, with a projected penetration of advanced systems exceeding 60% in the engineering sector by the end of the forecast period. The methodological approach confirms that sustained investment in technological capability, coupled with supportive policy, is fundamental for accelerating the integration of modern process-control technologies. Policymakers should prioritise initiatives that build long-term technical skills and provide fiscal incentives for capital investment in automation. Industry associations must facilitate knowledge transfer on system implementation. process-control systems, adoption modelling, panel data, econometric analysis, engineering technology, industrial automation This paper provides the first longitudinal, firm-level econometric analysis and forecast model for process-control system adoption in the region, introducing a novel methodology that isolates the effects of technological capacity from regulatory and investment variables.