Abstract
{ "background": "The adoption of advanced process-control systems in developing economies is a critical yet understudied factor in industrial modernisation and structural engineering efficiency. A systematic assessment of adoption trends and their drivers is required to inform infrastructure development policy.", "purpose and objectives": "This study aims to methodologically evaluate the implementation of process-control systems and to develop a robust time-series forecasting model for their adoption rates. The objective is to quantify historical trends and project future trajectories to identify key inflection points.", "methodology": "A comparative analysis of adoption pathways was conducted using national industrial survey data. The core forecasting model is an autoregressive integrated moving average (ARIMA) formulation, specified as $\\nabla^d yt = c + \\sum{i=1}^{p}\\phii \\nabla^d y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\epsilont$, where $\\nabla^d$ denotes the differencing operator. Model parameters were estimated using maximum likelihood, with robust standard errors reported to account for heteroskedasticity.", "findings": "The analysis indicates a significant positive trend in adoption, with the rate accelerating markedly post-. The fitted model forecasts a continued increase, with adoption prevalence projected to rise by approximately 40% over the next forecast horizon. The 95% confidence interval for this projection spans 32% to 48%, indicating substantial but quantifiable uncertainty.", "conclusion": "The adoption of process-control technology is progressing at a non-linear rate, driven by increasing integration within large-scale structural engineering projects. The developed model provides a validated tool for tracking this technological transition.", "recommendations": "Policymakers should prioritise technical skills development and targeted investment in digital infrastructure to sustain the forecasted growth. Industry stakeholders are advised to align capital planning cycles with the projected adoption curve.", "key words": "process control, forecasting, time-series analysis, technology adoption, industrial automation, structural engineering",