"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 = \ + \1 y{t-1 + \1 -1 + \ TRIt + \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,
Naidoo et al. (Mon,) studied this question.
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