What question did this study set out to answer?

The aim is to evaluate the implementation of process-control systems and forecast their adoption rates in Ethiopia.

March 13, 2026Open Access

Methodological Evaluation and Time-Series Forecasting of Process-Control System Adoption in Ethiopia (2000–2026)

Key Points

The aim is to evaluate the implementation of process-control systems and forecast their adoption rates in Ethiopia.
Conducted a comparative analysis using national industrial survey data.
Developed a time-series forecasting model based on autoregressive integrated moving average (ARIMA).
Estimated model parameters using maximum likelihood with robust standard errors.
Significant positive trend in process-control system adoption was observed.
Forecasts predict a 40% increase in adoption prevalence over the next forecast period.
The 95% confidence interval for projections ranged from 32% to 48%.

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 \ᵈ yt = c + =1^{p\ \ᵈ yt-i + =1^q\ -j + \, where \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",

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