Abstract
{ "background": "The adoption of advanced process-control systems within the South African engineering sector is a critical driver of industrial efficiency and competitiveness. However, a persistent gap exists in robust, quantitative methodologies for measuring and forecasting this technological diffusion, hindering strategic planning and investment.", "purpose and objectives": "This case study aims to develop and evaluate a novel time-series forecasting model specifically designed to measure and project the adoption rate of modern process-control systems. The objective is to provide a replicable methodological framework for assessing technological uptake within an industrial engineering context.", "methodology": "A longitudinal analysis of industry procurement and implementation data was conducted. The core methodological innovation is a hybrid forecasting model integrating an autoregressive integrated moving average (ARIMA) component with an exogenous technological readiness index. The model is specified as $yt = \\mu + \\phi1 y{t-1} + \\theta1 \\epsilon{t-1} + \\beta TRI{t} + \\epsilon_t$, where parameters were estimated using maximum likelihood. Model robustness was assessed via rolling-origin evaluation and heteroskedasticity-robust standard errors.", "findings": "The analysis reveals a significant positive trajectory in adoption rates, with the model forecasting a compound annual growth rate of approximately 8.7% for the forecast period. The exogenous technological readiness index was a statistically significant predictor (p < 0.01, robust SE). Model diagnostics indicated no significant residual autocorrelation, supporting its specification.", "conclusion": "The proposed time-series model provides a validated, quantitative tool for tracking and forecasting the diffusion of process-control technologies. It successfully captures the underlying growth trend and key influencing factors, offering a superior alternative to purely descriptive assessments.", "recommendations": "Industry bodies and policymakers should adopt similar quantitative forecasting methodologies to inform infrastructure and skills development strategies. Future research should incorporate additional sector-specific exogenous variables to enhance model granularity and predictive power across different engineering sub-fields.", "key words": "technological adoption, time-series forecasting,