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Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand
The covid-19 pandemic has created problems in every manufacturing sector and has posed considerable challenges to pharmaceutical, healthcare, and sanitation companies. The challenges faced are particularly daunting for pharmaceutical companies producing vaccines with ever-growing demand and shorter...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767907/ https://www.ncbi.nlm.nih.gov/pubmed/35070729 http://dx.doi.org/10.1016/j.matpr.2021.12.531 |
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author | Mishra, R.S. Kumar, Rakesh Dhingra, Siddhant Sengupta, Suryansu Sharma, Tushar Gautam, Girish Dutt |
author_facet | Mishra, R.S. Kumar, Rakesh Dhingra, Siddhant Sengupta, Suryansu Sharma, Tushar Gautam, Girish Dutt |
author_sort | Mishra, R.S. |
collection | PubMed |
description | The covid-19 pandemic has created problems in every manufacturing sector and has posed considerable challenges to pharmaceutical, healthcare, and sanitation companies. The challenges faced are particularly daunting for pharmaceutical companies producing vaccines with ever-growing demand and shorter and shorter deadlines to fulfill them. Further, due to the vaccine's novelty and unprecedented demand, there is a lack of any available data on which traditional forecasting methods can be used. In this paper, we attempt to propose a solution by utilizing the Grey Systems Theory, particularly the AGM (1, 1) model, which has been used to significant effect for problems involving uncertain / lack of data to forecast the demand for vaccines. The experimental results obtained showed that our proposed model successfully generated accurate forecasts with a small dataset and minimal error. Additionally, judgmental forecasting has been used to qualitatively assess the future scope of vaccine manufacturing as well as the use cases of the model. We can thus effectively say proposed AGM (1,1) model is a lucid method to forecast the demand for vaccines. |
format | Online Article Text |
id | pubmed-8767907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87679072022-01-19 Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand Mishra, R.S. Kumar, Rakesh Dhingra, Siddhant Sengupta, Suryansu Sharma, Tushar Gautam, Girish Dutt Mater Today Proc Article The covid-19 pandemic has created problems in every manufacturing sector and has posed considerable challenges to pharmaceutical, healthcare, and sanitation companies. The challenges faced are particularly daunting for pharmaceutical companies producing vaccines with ever-growing demand and shorter and shorter deadlines to fulfill them. Further, due to the vaccine's novelty and unprecedented demand, there is a lack of any available data on which traditional forecasting methods can be used. In this paper, we attempt to propose a solution by utilizing the Grey Systems Theory, particularly the AGM (1, 1) model, which has been used to significant effect for problems involving uncertain / lack of data to forecast the demand for vaccines. The experimental results obtained showed that our proposed model successfully generated accurate forecasts with a small dataset and minimal error. Additionally, judgmental forecasting has been used to qualitatively assess the future scope of vaccine manufacturing as well as the use cases of the model. We can thus effectively say proposed AGM (1,1) model is a lucid method to forecast the demand for vaccines. Elsevier Ltd. 2022 2022-01-19 /pmc/articles/PMC8767907/ /pubmed/35070729 http://dx.doi.org/10.1016/j.matpr.2021.12.531 Text en Copyright © 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the First International Conference on Design and Materials (ICDM)-2021. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Mishra, R.S. Kumar, Rakesh Dhingra, Siddhant Sengupta, Suryansu Sharma, Tushar Gautam, Girish Dutt Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title | Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title_full | Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title_fullStr | Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title_full_unstemmed | Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title_short | Adaptive grey model (AGM) approach for judgemental forecasting in short-term manufacturing demand |
title_sort | adaptive grey model (agm) approach for judgemental forecasting in short-term manufacturing demand |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767907/ https://www.ncbi.nlm.nih.gov/pubmed/35070729 http://dx.doi.org/10.1016/j.matpr.2021.12.531 |
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