Lectures & Seminars

Topic: Augmenting the Operations Manager with a Prediction Machine

Speaker: LU Tao, University of Connecticut

Time: January 8, 2026, 9:00

Venue: EMS 319

Abstract:

Firms increasingly use Artificial Intelligence (AI) enabled forecasting engines (“prediction machines”) to augment their managers' own forecasting capabilities and thus improve sales-and-operations planning outcomes. Deployment of a prediction machine may cause an unintended reduction in a manager's own forecasting effort which in turn diminishes the value of machine adoption. We model a firm facing uncertain demand that delegates a procurement quantity decision to a human manager who can exert effort to generate a demand prediction. The firm deploys a machine that provides the manager with a demand-prediction signal. We establish the conditions under which managerial effort reduction occurs and thus reduces the machine's potential value. Adopting a Bayesian persuasion approach, we show that partially disclosing the machine's prediction, either downplaying high predictions or exaggerating low predictions, can be optimal, depending on the product's cost-to-revenue ratio. A strategy of minimal obfuscation (to achieve effort) is optimal if the machine is more accurate than the human; however, maximal obfuscation (while maintaining effort) can be optimal if the human is more accurate. Our results imply that the firm may be better off tuning a machine to be less informative than its maximum capability.

Speaker Profile:

LU Tao, Associate Professor at the University of Connecticut School of Business, previously taught at the Rotterdam School of Management, Erasmus University in the Netherlands. His research interests broadly cover supply chain management, sustainable operations, and gig economy platforms. His work has been published in top-tier journals such as Management Science, Manufacturing & Service Operations Management, Operations Research, Information Systems Research, Production and Operations Management, and Transportation Science. He currently serves as an Associate Editor for Management Science and Service Science.Associate Professor at the University of Connecticut School of Business, previously taught at the Rotterdam School of Management, Erasmus University in the Netherlands. His research interests broadly cover supply chain management, sustainable operations, and gig economy platforms. His work has been published in top-tier journals such as Management Science, Manufacturing & Service Operations Management, Operations Research, Information Systems Research, Production and Operations Management, and Transportation Science. He currently serves as an Associate Editor for Management Science and Service Science.