Globalization and the quest for lean production have significantly increased vulnerability of contemporary supply chains. A disruptive event in Bangkok can stop production in Beijing, and in turn hamper product delivery in Boston. Such events materialize in various, sometimes unexpected forms: natural disasters, labor protests, utility outage, cyber-attacks, political shifts, and epidemic outbreaks, to name a few. They can lead to cascading supply chain failures and business continuity interruptions, with potentially severe near- and long-term physical, financial, and reputational consequences. The key barrier to the effective management of supply chain disruptions is the limited visibility into the interconnected supply network structure and the associated risk profiles. For example, the Department of Defense has specifically highlighted limited visibility of supply chain structures and risks as items of high strategic risks in 2015. This EArly-concept Grant for Exploratory Research (EAGER) project directly addresses this challenge. It develops predictive analytics, risk learning and mitigation strategies that can be readily deployed by firms and organizations to increase visibility and control of their supply chain risks. The data driven approach is particularly useful to organizations with complex supply chains, such as multinational firms and governmental agencies, to better measure the distribution and impact of supply network risks, and proactively manage such risks and achieve better supply chain resilience.
While existing theories assume perfect knowledge of supply network structure and the associated risk profiles, this research follows a two-pronged approach to directly account for and mitigate limited visibility. First, it develops a series of empirical models that increase visibility into supply chain risks and their driving factors, with a particular emphasis on network-driven risk interdependencies. It is the first research to combine automated textual analysis (topic coding and sentiment analysis) and high-dimensional statistical analysis, to isolate supply risk information from large scale, qualitative data. Specifically, the project will quantify the language of corporate disclosures and user generated content on social media to 1) characterize risk distributions and interdependencies, 2) quantify the impacts of these risks on both immediate and sub-tier supply chain partners, and 3) identify early-warning factors and develop predictive models for risk events. Second, leveraging insights gained from the empirical results, this project develops new quantitative models on risk learning and mitigation that address and account for limited visibility. One class of models focuses on optimal risk learning given complete knowledge of supply network but incomplete knowledge of risks. The other class of models focuses on designing optimal risk mitigation strategies with general incomplete information. The model addresses effectiveness of direct (procuring excess inventory and multi-sourcing) versus indirect (supply contracts) mitigation strategies in a game-theoretic framework.
