Required parameters for modelling heterogeneous geographically dispersed manufacturing systems

Mark A Goudswaard; Chris M Snider; Martins U Obi; Lorenzo Giunta; Kautsar Ramli; Jennifer L Johns; Ben J Hicks; James A Gopsill

doi:10.1016/j.procir.2022.05.189

Required parameters for modelling heterogeneous geographically dispersed manufacturing systems

Mark A Goudswaard, Chris M Snider, Martins U Obi, Lorenzo Giunta, Kautsar Ramli, Jennifer L Johns, Ben J Hicks, James A Gopsill

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Abstract

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.

Original language	English
Pages (from-to)	1545-1550
Number of pages	6
Journal	Procedia CIRP
Volume	107
Early online date	26 May 2022
DOIs	https://doi.org/10.1016/j.procir.2022.05.189
Publication status	E-pub ahead of print - 26 May 2022

Bibliographical note

Funding Information:
The work reported in this paper has been undertaken as part of Engineering and Physical Sciences Research Council (EPSRC) funded projects Brokering Additive Manufacturing (BAM) and ProtoTwinning (grant references EP/V05113X/1 and EP/R032696/1 respectively) and conducted at the University of Bristol in the Design and Manufacturing Futures Lab.

Publisher Copyright:
© 2022 The Authors. Published by Elsevier B.V.

Research Groups and Themes

MGMT Strategy International Management and Business and Entrepreneurship

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10.1016/j.procir.2022.05.189Licence: CC BY-NC-ND

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via Elsevier at https://doi.org/10.1016/j.procir.2022.05.189 . Please refer to any applicable terms of use of the publisher.
Final published version, 461 KBLicence: CC BY-NC-ND

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Brokering Additive Manufacturing
Gopsill, J. A. (Principal Investigator), Hicks, B. J. (Co-Investigator) & Snider, C. M. (Co-Investigator)
1/04/21 → 30/09/22
Project: Research, Parent

Cite this

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title = "Required parameters for modelling heterogeneous geographically dispersed manufacturing systems",

abstract = "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.",

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AU - Snider, Chris M

AU - Obi, Martins U

AU - Giunta, Lorenzo

AU - Ramli, Kautsar

AU - Johns, Jennifer L

AU - Hicks, Ben J

AU - Gopsill, James A

N1 - Funding Information: The work reported in this paper has been undertaken as part of Engineering and Physical Sciences Research Council (EPSRC) funded projects Brokering Additive Manufacturing (BAM) and ProtoTwinning (grant references EP/V05113X/1 and EP/R032696/1 respectively) and conducted at the University of Bristol in the Design and Manufacturing Futures Lab. Publisher Copyright: © 2022 The Authors. Published by Elsevier B.V.

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N2 - 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.

AB - 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.

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DO - 10.1016/j.procir.2022.05.189

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Required parameters for modelling heterogeneous geographically dispersed manufacturing systems

Abstract

Bibliographical note

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Projects

Brokering Additive Manufacturing

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