Methodological Evaluation and Time-Series Forecasting for Manufacturing Plant Efficiency Gains in Ghana, 2000–2026
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2006
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901337949601792 |
|---|---|
| author | Boateng, Ama Serwaa Ankrah, Kofi Mensah Asante, Kwame |
| author_facet | Boateng, Ama Serwaa Ankrah, Kofi Mensah Asante, Kwame |
| contents | <p>{ "background": "The manufacturing sector's productivity is a critical determinant of industrial growth and economic development. In many developing economies, systematic evaluation of plant-level efficiency and robust forecasting of gains remain underdeveloped, hindering evidence-based industrial policy formulation.", "purpose and objectives": "This policy analysis aims to methodologically evaluate systems within the manufacturing sector and develop a predictive model to forecast efficiency gains. The objective is to provide a quantitative tool for policymakers to assess the impact of interventions and plan resource allocation.", "methodology": "A time-series forecasting model was developed using panel data from plant-level operational metrics. The core model is an autoregressive integrated moving average (ARIMA) specification: $yt = \\mu + \\phi1 y{t-1} + \\theta1 \\epsilon{t-1} + \\epsilont$, where $y_t$ represents the efficiency index. Model parameters were estimated using maximum likelihood, with robust standard errors to account for heteroskedasticity.", "findings": "The analysis projects a positive but decelerating trend in aggregate manufacturing efficiency, with forecast gains averaging 1.2% per annum over the forecast horizon. A key finding is that the 95% confidence interval for this annual gain is relatively wide (0.7% to 1.8%), indicating significant uncertainty influenced by external macroeconomic factors.", "conclusion": "The methodological framework provides a viable tool for projecting efficiency trends, though forecasts are sensitive to exogenous shocks. This underscores the need for adaptive policy frameworks that can respond to volatile economic conditions.", "recommendations": "Policymakers should integrate such forecasting models into regular industrial performance reviews. Investment should be prioritised towards digital monitoring systems to improve data quality for model inputs, and policies should build in contingency mechanisms for the lower bound of forecast gains.", "key words": "industrial policy, productivity forecasting, time-series analysis, manufacturing systems, operational efficiency", "contribution statement": "This paper provides a novel application of ARIMA modelling to forecast</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18971045 |
| institution | Zenodo |
| language | eng |
| publishDate | 2006 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methodological Evaluation and Time-Series Forecasting for Manufacturing Plant Efficiency Gains in Ghana, 2000–2026 Boateng, Ama Serwaa Ankrah, Kofi Mensah Asante, Kwame Manufacturing productivity Industrial policy Sub-Saharan Africa Time-series analysis Plant-level efficiency Process optimisation Developing economies <p>{ "background": "The manufacturing sector's productivity is a critical determinant of industrial growth and economic development. In many developing economies, systematic evaluation of plant-level efficiency and robust forecasting of gains remain underdeveloped, hindering evidence-based industrial policy formulation.", "purpose and objectives": "This policy analysis aims to methodologically evaluate systems within the manufacturing sector and develop a predictive model to forecast efficiency gains. The objective is to provide a quantitative tool for policymakers to assess the impact of interventions and plan resource allocation.", "methodology": "A time-series forecasting model was developed using panel data from plant-level operational metrics. The core model is an autoregressive integrated moving average (ARIMA) specification: $yt = \\mu + \\phi1 y{t-1} + \\theta1 \\epsilon{t-1} + \\epsilont$, where $y_t$ represents the efficiency index. Model parameters were estimated using maximum likelihood, with robust standard errors to account for heteroskedasticity.", "findings": "The analysis projects a positive but decelerating trend in aggregate manufacturing efficiency, with forecast gains averaging 1.2% per annum over the forecast horizon. A key finding is that the 95% confidence interval for this annual gain is relatively wide (0.7% to 1.8%), indicating significant uncertainty influenced by external macroeconomic factors.", "conclusion": "The methodological framework provides a viable tool for projecting efficiency trends, though forecasts are sensitive to exogenous shocks. This underscores the need for adaptive policy frameworks that can respond to volatile economic conditions.", "recommendations": "Policymakers should integrate such forecasting models into regular industrial performance reviews. Investment should be prioritised towards digital monitoring systems to improve data quality for model inputs, and policies should build in contingency mechanisms for the lower bound of forecast gains.", "key words": "industrial policy, productivity forecasting, time-series analysis, manufacturing systems, operational efficiency", "contribution statement": "This paper provides a novel application of ARIMA modelling to forecast</p> |
| title | Methodological Evaluation and Time-Series Forecasting for Manufacturing Plant Efficiency Gains in Ghana, 2000–2026 |
| topic | Manufacturing productivity Industrial policy Sub-Saharan Africa Time-series analysis Plant-level efficiency Process optimisation Developing economies |
| url | https://doi.org/10.5281/zenodo.18971045 |