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Required parameters for modelling heterogeneous geographically dispersed manufacturing systems
COVID-19 and global crises/events are driving governments to rethink their national manufacturing strategies. The drastic change of societal conditions has exposed our reliance on a constrained set of production practices. Furthermore, the future manufacturing landscape indicates - supply chain cris...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883052/ https://www.ncbi.nlm.nih.gov/pubmed/36743208 http://dx.doi.org/10.1016/j.procir.2022.05.189 |
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author | Goudswaard, Mark Snider, Chris Obi, Martins Giunta, Lorenzo Ramli, Kautsar Johns, Jennifer Hicks, Ben Gopsill, James |
author_facet | Goudswaard, Mark Snider, Chris Obi, Martins Giunta, Lorenzo Ramli, Kautsar Johns, Jennifer Hicks, Ben Gopsill, James |
author_sort | Goudswaard, Mark |
collection | PubMed |
description | COVID-19 and global crises/events are driving governments to rethink their national manufacturing strategies. The drastic change of societal conditions has exposed our reliance on a constrained set of production practices. Furthermore, the future manufacturing landscape indicates - supply chain crises, trade agreements and natural disasters - a high level of volatility which requires a response that is far from being achieved. While these emergent challenges have called the efficacy of established practices into question, new manufacturing technologies, such as Additive Manufacturing (AM), present the capability to provide a solution. One proposal is agent-based brokering of AM which could be a method for tackling local, regional, national, and international production needs. However, to achieve the reality of brokered AM, it is imperative that the diversity of AM capability is considered. Diversity that existing homogeneous modelling of AM and manufacturing systems rarely consider or capture. This paper conceptualizes the reality of AM systems and elucidates parameters that are necessary for successful modelling and subsequent co-ordination. Having presented the required parameters the paper continues to discuss requisite levels of abstraction, suitable performance metrics and the role of humans in agent-based manufacturing systems. |
format | Online Article Text |
id | pubmed-9883052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author(s). Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98830522023-01-30 Required parameters for modelling heterogeneous geographically dispersed manufacturing systems Goudswaard, Mark Snider, Chris Obi, Martins Giunta, Lorenzo Ramli, Kautsar Johns, Jennifer Hicks, Ben Gopsill, James Procedia CIRP Article COVID-19 and global crises/events are driving governments to rethink their national manufacturing strategies. The drastic change of societal conditions has exposed our reliance on a constrained set of production practices. Furthermore, the future manufacturing landscape indicates - supply chain crises, trade agreements and natural disasters - a high level of volatility which requires a response that is far from being achieved. While these emergent challenges have called the efficacy of established practices into question, new manufacturing technologies, such as Additive Manufacturing (AM), present the capability to provide a solution. One proposal is agent-based brokering of AM which could be a method for tackling local, regional, national, and international production needs. However, to achieve the reality of brokered AM, it is imperative that the diversity of AM capability is considered. Diversity that existing homogeneous modelling of AM and manufacturing systems rarely consider or capture. This paper conceptualizes the reality of AM systems and elucidates parameters that are necessary for successful modelling and subsequent co-ordination. Having presented the required parameters the paper continues to discuss requisite levels of abstraction, suitable performance metrics and the role of humans in agent-based manufacturing systems. The Author(s). Published by Elsevier B.V. 2022 2022-05-26 /pmc/articles/PMC9883052/ /pubmed/36743208 http://dx.doi.org/10.1016/j.procir.2022.05.189 Text en © 2022 The Author(s). Published by Elsevier B.V. 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 Goudswaard, Mark Snider, Chris Obi, Martins Giunta, Lorenzo Ramli, Kautsar Johns, Jennifer Hicks, Ben Gopsill, James Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title | Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title_full | Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title_fullStr | Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title_full_unstemmed | Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title_short | Required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
title_sort | required parameters for modelling heterogeneous geographically dispersed manufacturing systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883052/ https://www.ncbi.nlm.nih.gov/pubmed/36743208 http://dx.doi.org/10.1016/j.procir.2022.05.189 |
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