Events

Prof. Di (Andrew) WU
Associate Professor of Technology and Operations
Stephen M. Ross School of Business
University of Michigan

I present two novel use cases of natural language processing (NLP) models in Operations. In the first study, using a new technique of “seeded word embedding”, I develop a firm-level measure of supply chain risk exposure from the audio-recorded discussion between managers and equity analysts on supply chain–related topics. I validate the measure by showing that the measure exhibits intuitive variations over time and across firms, and it significantly correlates with realized and options-implied stock return volatility. I then demonstrate that, consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, particularly in raw materials and intermediate inputs, increased cash holdings in lieu of investments, and significantly lower trade credit received from suppliers. Moreover, during unexpected risk episodes such as the Tohoku earthquake, firms with higher ex-ante risk exposure have worse operating and financial performance. These results indicate that the text-based measure provides a credible quantification of firm-level exposure to supply chain risks, and can thus be reliably utilized as outcome or explanatory variables in empirical supply chain research.

The second project develops and trains a generative large language model (LLM) as an “AI tutor” in higher education. In collaboration with the University of Michigan’s IT team, we developed a GPT-powered AI—“Maizey”—that is trained using individual faculty’s course content to provide immediate, accurate responses to student inquiries. The AI automatically integrates into course Canvas sites, then creates a knowledge repository for each course by absorbing all text, audio and video contents from the Canvas course. The model automatically retrieves from the repository the most relevant course content for each student query, then employs GPT’s generative capabilities to deliver direct and relevant answers in natural language. With implementation across courses involving thousands of students, we aim to enhance user satisfaction, optimize the utilization of teaching staff time, and improve student learning outcome.

Andrew is the Associate Professor of Technology and Operations (with tenure) at the Ross School of Business, University of Michigan, and the Faculty Co-Director for the Michigan Ross Fintech Initiative. He is also the Michael R. and Mary Kay Hallman Faculty Fellow. His research centers on unlocking new business insights from human language and other unstructured data. He develops innovative natural language processing (NLP) and computer vision methodologies to extract operational intelligence, strategic insights, and trading signals from diverse sources such as corporate communications, news and social media, investor and executive interviews, etc.

His work received numerous research and practice awards, including the BlackRock Applied Research Award, seven Best Paper Awards from INFORMS, M&SOM, POMS, and finance societies, and the finalist for the Financial Times‘ Research Impact Award. His methodologies have been adopted by a variety of industry practitioners, nonprofit and government organizations, and investment managers. He is recognized as Poets & Quants Best 40 Under 40 Business School Professors.