A Time-Series Forecasting Model for Yield Improvement in Ugandan Manufacturing Systems: A Methodological Evaluation
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2023
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| _version_ | 1866901565772660736 |
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| author | Kigozi, Joseph Nalwoga, Aisha Mbabazi, Patricia |
| author_facet | Kigozi, Joseph Nalwoga, Aisha Mbabazi, Patricia |
| contents | <p>{ "background": "Manufacturing systems in developing economies often lack robust, data-driven methodologies for continuous yield improvement. Existing forecasting approaches are frequently ill-suited to the high-variability, low-data-density environments typical of such settings, leading to suboptimal production planning and resource allocation.", "purpose and objectives": "This article presents a methodological evaluation of a novel time-series forecasting model designed specifically to measure and predict yield improvements in manufacturing systems. The objective is to provide a formalised, adaptable framework for plant engineers to enhance production efficiency through more accurate yield projections.", "methodology": "The proposed methodology integrates a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model with exogenous variables (SARIMAX) to account for operational factors. The core model is defined as $\\Phip(B)\\PhiP(B^s)\ abla^d\ ablas^D yt = \\delta + \\Thetaq(B)\\ThetaQ(B^s)\\epsilont + \\sum{i=1}^k \\betai x{i,t}$, where $yt$ is the yield series and $x{i,t}$ are exogenous regressors. Model parameters were estimated using maximum likelihood, with inference based on robust standard errors to mitigate heteroscedasticity.", "findings": "The methodological evaluation, applied to a case study, demonstrated that the integrated SARIMAX model reduced one-step-ahead forecast error by approximately 18% compared to a standard ARIMA benchmark. Diagnostic checks confirmed model adequacy, with residual autocorrelation plots showing no significant structure.", "conclusion": "The evaluated methodology provides a statistically sound and operationally relevant framework for yield forecasting in resource-constrained manufacturing environments. It offers a substantial improvement over conventional, less adaptive time-series models.", "recommendations": "Manufacturing plant engineers should adopt this integrated modelling approach, incorporating both temporal patterns and contextual operational data. Further research should focus on automating model selection and integrating real-time data streams for dynamic updating.", "key words": "Time-series forecasting,</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18972840 |
| institution | Zenodo |
| language | eng |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Time-Series Forecasting Model for Yield Improvement in Ugandan Manufacturing Systems: A Methodological Evaluation Kigozi, Joseph Nalwoga, Aisha Mbabazi, Patricia Time-series forecasting Yield improvement Manufacturing systems Sub-Saharan Africa Methodological evaluation Data-driven methodology <p>{ "background": "Manufacturing systems in developing economies often lack robust, data-driven methodologies for continuous yield improvement. Existing forecasting approaches are frequently ill-suited to the high-variability, low-data-density environments typical of such settings, leading to suboptimal production planning and resource allocation.", "purpose and objectives": "This article presents a methodological evaluation of a novel time-series forecasting model designed specifically to measure and predict yield improvements in manufacturing systems. The objective is to provide a formalised, adaptable framework for plant engineers to enhance production efficiency through more accurate yield projections.", "methodology": "The proposed methodology integrates a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model with exogenous variables (SARIMAX) to account for operational factors. The core model is defined as $\\Phip(B)\\PhiP(B^s)\ abla^d\ ablas^D yt = \\delta + \\Thetaq(B)\\ThetaQ(B^s)\\epsilont + \\sum{i=1}^k \\betai x{i,t}$, where $yt$ is the yield series and $x{i,t}$ are exogenous regressors. Model parameters were estimated using maximum likelihood, with inference based on robust standard errors to mitigate heteroscedasticity.", "findings": "The methodological evaluation, applied to a case study, demonstrated that the integrated SARIMAX model reduced one-step-ahead forecast error by approximately 18% compared to a standard ARIMA benchmark. Diagnostic checks confirmed model adequacy, with residual autocorrelation plots showing no significant structure.", "conclusion": "The evaluated methodology provides a statistically sound and operationally relevant framework for yield forecasting in resource-constrained manufacturing environments. It offers a substantial improvement over conventional, less adaptive time-series models.", "recommendations": "Manufacturing plant engineers should adopt this integrated modelling approach, incorporating both temporal patterns and contextual operational data. Further research should focus on automating model selection and integrating real-time data streams for dynamic updating.", "key words": "Time-series forecasting,</p> |
| title | A Time-Series Forecasting Model for Yield Improvement in Ugandan Manufacturing Systems: A Methodological Evaluation |
| topic | Time-series forecasting Yield improvement Manufacturing systems Sub-Saharan Africa Methodological evaluation Data-driven methodology |
| url | https://doi.org/10.5281/zenodo.18972840 |