ABSTRACT
Nanotechnology facilitated the development and scalable commercialization of many SARS-CoV-2 vaccines. However, the supply chains underpinning vaccine manufacturing have demonstrated brittleness at various stages of development and distribution. Whereas such brittleness leaves the broader pharmacological supply chain vulnerable to significant and unacceptable disruption, strategies for supply chain resilience are being considered across government, academia, and industry. How such resilience is understood and parameterized, however, is contentious. Our review of the nanotechnology supply chain resilience literature, synthesized with the larger supply chain resilience literature, analyzes current trends in implementing and modeling resilience and recommendations for bridging the gap in the lack of quantitative models, consistent definitions, and trade-off analyses for nano supply chains.
ABSTRACT
The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007-2016, while 94 were found in 2017-2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of "unknown unknowns" remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.