Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Ethiopia (2000–2026)
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2002
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| _version_ | 1866901065802186752 |
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| author | Tesfaye, Meklit |
| author_facet | Tesfaye, Meklit |
| contents | <p>Process-control systems in industrial and infrastructure sectors are critical for operational efficiency, yet robust methodologies for evaluating their long-term performance gains in developing economies are lacking. This gap hinders evidence-based investment and optimisation. This case study aims to develop and apply a novel time-series forecasting model to quantify efficiency gains from process-control system implementations. The objective is to provide a replicable methodological framework for performance evaluation. A comparative case-study analysis was conducted using longitudinal operational data from multiple sites. The core methodological innovation is a hybrid forecasting model integrating an ARIMA component with an intervention analysis term, formalised as $Y_t = \mu + \phi Y_{t-1} + \theta \epsilon_{t-1} + \omega I_t + \epsilon_t$, where $I_t$ is a step function for system implementation. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals. The model forecasts a sustained 18.5% aggregate improvement in system throughput efficiency over the forecast horizon post-intervention. Statistical inference indicates this gain is significant (p < 0.01), with model diagnostics confirming stationarity in the forecast residuals. The proposed time-series model provides a statistically rigorous framework for attributing efficiency improvements to process-control interventions, moving beyond descriptive assessment. Adopt the hybrid forecasting model for baseline efficiency measurement and post-implementation audits. Engineers and planners should integrate such models into the project lifecycle to validate control-system ROI. process control, time-series analysis, forecasting, efficiency measurement, intervention analysis, infrastructure systems This paper introduces a novel hybrid time-series model for quantitatively isolating and forecasting the efficiency gains attributable to process-control system upgrades, demonstrated with longitudinal data.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18973376 |
| institution | Zenodo |
| language | eng |
| publishDate | 2002 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Ethiopia (2000–2026) Tesfaye, Meklit Process-control systems Time-series forecasting Operational efficiency Sub-Saharan Africa Developing economies Methodological evaluation <p>Process-control systems in industrial and infrastructure sectors are critical for operational efficiency, yet robust methodologies for evaluating their long-term performance gains in developing economies are lacking. This gap hinders evidence-based investment and optimisation. This case study aims to develop and apply a novel time-series forecasting model to quantify efficiency gains from process-control system implementations. The objective is to provide a replicable methodological framework for performance evaluation. A comparative case-study analysis was conducted using longitudinal operational data from multiple sites. The core methodological innovation is a hybrid forecasting model integrating an ARIMA component with an intervention analysis term, formalised as $Y_t = \mu + \phi Y_{t-1} + \theta \epsilon_{t-1} + \omega I_t + \epsilon_t$, where $I_t$ is a step function for system implementation. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals. The model forecasts a sustained 18.5% aggregate improvement in system throughput efficiency over the forecast horizon post-intervention. Statistical inference indicates this gain is significant (p < 0.01), with model diagnostics confirming stationarity in the forecast residuals. The proposed time-series model provides a statistically rigorous framework for attributing efficiency improvements to process-control interventions, moving beyond descriptive assessment. Adopt the hybrid forecasting model for baseline efficiency measurement and post-implementation audits. Engineers and planners should integrate such models into the project lifecycle to validate control-system ROI. process control, time-series analysis, forecasting, efficiency measurement, intervention analysis, infrastructure systems This paper introduces a novel hybrid time-series model for quantitatively isolating and forecasting the efficiency gains attributable to process-control system upgrades, demonstrated with longitudinal data.</p> |
| title | Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Ethiopia (2000–2026) |
| topic | Process-control systems Time-series forecasting Operational efficiency Sub-Saharan Africa Developing economies Methodological evaluation |
| url | https://doi.org/10.5281/zenodo.18973376 |