Responding to rapidly changing product demand through a coordinated additive manufacturing production system: a COVID-19 case study. Issue 1 (October 2021)

• Record Type: Journal Article
• Authors: Goudswaard, M; Gopsill, J; Ma, A; Nassehi, A; Hicks, B
• Is Part Of: IOP conference series. Volume 1193:Issue 1(2021)
• Publication Date: 2021-10
• Subjects: Responsive manufacturing -- Additive manufacturing -- Agent based modelling -- Anarchic manufacturing -- COVID-19; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1193/1/012119
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: COVID-19's lockdown policies saw Modern Manufacturing Practice (MMP) – batch/mass/just-in-time supply chains – severed and societal demands rapidly change from products such as vehicles and clothing to Personal Protection Equipment (PPE), ventilators and equipment for remote working. The critical and, in many cases, life preserving, need for responsive manufacturing resulted in government and frontline services turning to society's Additive Manufacturing (AM) capability in homes, schools, universities, and industry to provide essential products and product replacements. While AM managed to respond and support some government and frontline services, the highly distributed and diverse nature of the nation's AM resources resulted in potentially avoidable production inefficiencies and delays. This paper develops and evaluates a series of strategies for coordinating AM for rapidly changing product demand to further enhance the responsiveness and productivity of AM. The strategies presented in the paper employ a host-client agent-based architecture that enable local governance of production thereby enabling distributed AM resource to come together to tackle society's production needs without the need for centralised coordination. To enable and support local governance, it is necessary to understand how the combination of production logics impact the overall performance of the production system. Correspondingly, the contribution of this paper lies in the characterisation and quantification of the impacts of production logic through the metrics of Time in System, Lateness and Jobs in Queue and the consequences for responsive AM production systems.