Covid-19: Additive Manufacturing Response in the Uk

Abstract

Severe acute respiratory syndrome coronavirus 2, a novel coronavirus, caused global disruption specifically in linear supply chains. Increased demand for already disrupted services led to a global shortage of medical equipment and personal protective equipment. Use of additive manufacturing (AM) processes by the manufacturing community has shown great innovation, agility and flexibility to fill supply chain gaps and meet shortfalls. In the context of contingency reaction to a global healthcare emergency, decisions have had to be made quickly, in some cases bypassing device safety regulations. This concentrated and spontaneous use of AM has highlighted the challenges and risks of such innovation, which we discuss in relation to the UK’s current regulatory landscape. We have discussed lessons learned and the potential future impact upon wider use of AM in healthcare.

Additive manufacturing technology, process parameters influencing product quality and its applications

“…Despite the advantages mentioned, the use of AM in this case and this public health situation raises several questions regarding safety and regulation [109]. The study questioned the viability and safety of printed N95-type respiratory protective masks from many open-source respirator designs in the absence of adequate quality assurance processes [110].…”

Evaluation of 3D-Printer Settings for Producing Personal Protective Equipment

“…Members of the 3D-printing and healthcare communities have begun initiatives to print the headband components themselves for distribution in the community to circumvent the scarcity of conventionally manufactured supplies due to disruptions and shortages [28][29][30]. Such initiatives have been recognized at the institutional, local and national scale in countries like the UK [31][32][33].…”

Digital transformation of supply chain: a study on additive manufacturing practice in medical device in Australia

“…However, existing studies fall short of offering any theoretically grounded and empirically tested decision support model to determine the most suitable configuration of capabilities to mitigate the challenges of digital transformation of health care supply chain in general and SMD supply chain in particular to the enhance performance. A few studies offer a decision model for AM in health care, but those are mostly fragmented and deal with either challenges (Choudhary et al, 2021), importance (Parry and Banks, 2020;Patel and Gohil, 2021), impact (Muir and Haddud, 2017;€ Ozceylan et al, 2018) or feasibility of implementing the technology (Emelogu et al, 2016). None of the studies offers any strategic direction for the health care decision-makers to mitigate the challenges of adopting AM in the health supply chain and enhance performance.…”

“…A few studies offer a decision model for AM in health care, but those are mostly fragmented and deal with either challenges (Choudhary et al. , 2021), importance (Parry and Banks, 2020; Patel and Gohil, 2021), impact (Muir and Haddud, 2017; Özceylan et al. , 2018) or feasibility of implementing the technology (Emelogu et al.…”