"background": "Process-control systems are critical for industrial efficiency, yet their long-term cost-effectiveness in developing economies is poorly understood. In many such contexts, including Ghana, there is a lack of robust methodological frameworks for evaluating and forecasting the financial performance of these engineering investments over time. ", "purpose and objectives": "This case study aims to develop and apply a novel time-series forecasting model to evaluate the historical and projected cost-effectiveness of process-control systems. The objective is to provide a quantitative methodology for asset management decision-making within the industrial sector. ", "methodology": "A case study methodology was employed, utilising longitudinal operational and financial data from multiple industrial sites. The core analytical tool was an Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model, specified as Yt = \ + =1^{p\ Yt-i + =1^q\ -j + =1^r\ Xt, k + \, where Xt represents maintenance expenditure. Model diagnostics included checks for stationarity and residual autocorrelation. ", "findings": "The forecasting model indicates a significant positive relationship between scheduled maintenance spending and long-term cost-effectiveness, with a coefficient of 0. 85 (95% CI: 0. 72, 0. 98). Projections suggest that a strategic increase in preventative maintenance could improve the cost-benefit ratio of these systems by approximately 18% over a five-year forecast horizon. ", "conclusion": "The developed ARIMAX model provides a validated, data-driven tool for forecasting the financial performance of process-control systems. It demonstrates that systematic evaluation and proactive maintenance planning are crucial for maximising the return on such engineering investments. ", "recommendations": "Industrial operators should adopt similar time-series forecasting techniques for capital equipment lifecycle analysis. Policymakers should consider guidelines that encourage the collection of standardised operational data to facilitate such analyses across the sector
Boateng et al. (Fri,) studied this question.