Risk management plays a critical role in designing and operating effective and resilient supply chains. This paper focuses on bankruptcy as a measure for assessing the financial risk of companies within supply chain networks. While numerous bankruptcy models for public companies exist in literature, there is a lack of predictive bankruptcy models tailored for private firms, which serve as key entities within many supply chain networks. Existing models for private firms either depend on data that would require insider knowledge or focus on countries where private firms must disclose financials publicly. It is notably difficult to predict bankruptcy of private firms in the United States and Canada where such companies are not required by law to publicly disclose financials. This paper introduces an innovative quantitative bankruptcy prediction model tailored for private U.S. companies, leveraging publicly available information including but not limited to sentiment analysis, geographic location, firm age, and economic indicators. The methodology integrates the data of these diverse sources through a logistic regression model which outputs a bankruptcy classification that can be subsequently utilized in the design and planning of resilient supply chains. Our framework provides purchasers and investors with a simple way to assess bankruptcy risk using only publicly available information. The model can also be used in conjunction with other predictive metrics in a holistic risk assessment model.
Jackovic et al. (2024) studied this question.