POSTPONED: Applying Machine Learning in Online Revenue Management

Tuesday, February 10, 2015 - 4:00pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

David Simchi-Levi

Affiliation

MIT

Building and Room Number

32-155

Abstract

THIS TALK HAS BEEN POSTPONED DUE TO THE SNOW EMERGENCY. THE NEW DATE WILL BE IN FALL 2015.

 

***********************************

In a dynamic pricing problem where the demand function is unknown a priori, price experimentation can be used for demand learning. In practice, however, online sellers are faced with a few business constraints, including the inability to conduct extensive experimentation, limited inventory and high demand uncertainty. In this tutorial we discuss models and algorithms that combine machine learning and price optimization and significantly improve revenue. We report results from live implementations at companies such as Rue La La, Groupon and a large European Airline carrier.

Biography

David Simchi-Levi is a Professor of Engineering Systems at MIT and Chairman of OPS Rules Management Consultants, an operations strategy consulting company.  He is considered one of the premier thought leaders in supply chain management.

His research focuses on developing and implementing robust and efficient techniques for logistics and manufacturing systems. He has published widely in professional journals on both practical and theoretical aspects of logistics and supply chain management.

Professor Simchi-Levi coauthored the books Managing the Supply Chain (McGraw-Hill, 2004), The Logic of Logistics (Springer 2005), as well as the award winning Designing and Managing the Supply Chain (McGraw-Hill, 2007). His new book Operations Rules: Delivering Customer Value through Flexible Operations was published by MIT Press in 2011.

Professor Simchi-Levi has consulted and collaborated extensively with private and public organizations. He is the founder of LogicTools which provides software solutions and professional services for supply chain planning. LogicTools is now part of IBM.

Reception information

Reception to follow.