Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

No abstract

The Coronavirus Disease 2019 outbreak has had alarming effects on human lives and the economies of affected countries. With the world's manufacturing hubs experiencing a period of extended factory closures, the economic impact transcends territorial borders via global supply chains. This paper provides a roadmap on how to evaluate the vulnerability that cascades through the supply chain due to a disease outbreak at the firm level, national level, and global scale. The final extent of losses is not yet known, but the development of economic models combined with epidemiological models and network analysis techniques can yield more realistic estimates to select appropriate strategies in a timely manner.

No abstract

The COVID-19 pandemic has forced governments around the world to implement unprecedented lockdowns, mandating businesses to shut down for extended periods of time. Previous studies have modeled the impact of disruptions to the economy at static and dynamic settings. This study develops a model to fulfil the need to account for the sustained disruption resulting from the extended shutdown of business operations. Using a persistent inoperability input-output model (PIIM), we are able to show that (1) sectors that suffer higher levels of inoperability during quarantine period may recover faster depending on their resilience; (2) initially unaffected sectors can suffer inoperability levels higher than directly affected sectors over time; (3) the economic impact on other regions not under lockdown is also significant.