Incorporating an Unsupervised Text Mining Approach into Studying Logistics Risk Management: Insights from Corporate Annual Reports and Topic Modeling

Abstract

This study examined the Security and Exchange Commission (SEC) annual reports of selected logistics firms over the period from 2006 through 2021 for risk management terms. The purpose was to identify which risks are considered most important in supply chain logistics operations. Section 1A of the SEC reports includes risk factors. The COVID-19 pandemic has had a heavy impact on global supply chains. We also know that trucking firms have long had difficulties recruiting drivers. Fuel price has always been a major risk for airlines but also can impact shipping, trucking, and railroads. We were especially interested in pandemic, personnel, and fuel risks. We applied topic modeling, enabling us to identify some of the capabilities of unsupervised text mining as applied to SEC reports. We demonstrate the identification of terms, the time dimension, and correlation across topics by the topic model. Our analysis confirmed expectations about COVID-19’s impact, personnel shortages, and fuel. It also revealed common themes regarding the risks involved in international trade and perceived regulatory risks. We conclude with the supply chain management risks identified and discuss means of mitigation.

Keywords

• unsupervised text mining
• topic modeling
• supply chains
• risk management
• logistics