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The case study draws on structured interviews with Rob Chase, Founder and CEO of NewGen Surgical, as well as secondary data sources to analyze the effectiveness of these solutions in mitigating the risks and enhancing the company’s competitive advantage.

This case study examines how NewGen Surgical, a small- to medium-sized medical equipment manufacturer based in the USA, navigates a supply chain crisis caused by post-pandemic (COVID-19) supply and demand distress, trade restrictions, and the US–China trade war in 2022. It outlines the journey of CEO and Founder, Robert Chase, as he started, grew and is maintaining the company and its various challenges. The case study reviews the risks and vulnerabilities of the company, which heavily relies on Chinese suppliers for most of its operations. To address the supply chain challenges, the case study explores alternative solutions such as insourcing, reshoring, diversifying the supplier base, changing safety stock and implementing new technologies. The case can be designed to teach business courses such as global business, supply chain and entrepreneurship.

This case study is intended for undergraduate and graduate students in courses such as global business, supply chain and entrepreneurship. In addition, this case study may be incorporated with modules on learning organizations, knowledge management and entrepreneurship to aid students in comprehending the principles of global sourcing, offshoring and supply chain management.

Literature has numerous debates about the relation between emerging financial environmental, social, and governance (ESG) factors and financial performance with mixed results. The authors use a unique data set generated by big data analytics (from web-based data mining) for three environmental areas (water, land, and air) to test hypothesis in the extreme events (defined as those that are over/under ±2.58 multiplied by the standard deviation) have a high chance of predicting equity price movements within an window of −3/+10 days, respectively, prior to and after the event. The authors repeat the similar robustness study for a sample of 2018 and the results still holds. The authors interpret these findings to suggest that: (1) studies using continuously AI-generated data for ESG categories can have significant predictive power for extreme events; and (2) that such high correlations can be used to confirm the materiality of some ESG data. The authors conclude with noting limitation of this initial study, and present specific areas for future research.