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.

Publication DOI: https://doi.org/10.1016/j.procir.2022.05.189
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering
Aston University (General)
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
Additional Information: Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0).
Publication ISSN: 2212-8271
Last Modified: 05 Mar 2025 08:34
Date Deposited: 03 Jan 2025 13:44
Full Text Link:
Related URLs: https://www.sci ... 4735?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2022-05-26
Published Online Date: 2022-05-26
Accepted Date: 2022-05-01
Authors: Goudswaard, Mark
Snider, Chris
Obi, Martins (ORCID Profile 0000-0001-7668-9586)
Giunta, Lorenzo
Ramli, Kautsar
Johns, Jennifer
Hicks, Ben
Gopsill, James

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