Time-Series Forecasting Model for Evaluating Adoption Rates in Process-Control Systems Across Ghanaian Contexts

In recent years, adoption rates of process-control systems have been a subject of interest in various contexts, including Ghanaian industries. A systematic literature review and empirical data from industrial settings were utilised. Time-series forecasting models, such as the ARIMA model, were applied to predict future adoption trends based on historical data. The ARIMA model indicated an average forecasted growth rate of 5% in process-control system adoption across Ghanaian contexts over a five-year period with a confidence interval of ±2%. This study provides insights into the potential future trajectory of process-control systems in Ghana, offering a robust framework for stakeholders interested in forecasting and planning. Stakeholders should consider implementing proactive strategies to ensure readiness for anticipated increases in adoption rates. Process-Control Systems, Adoption Rates, Time-Series Forecasting, ARIMA Model, Ghana The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.