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Pavithra Harsha

Pavithra Harsha

Research Staff Member at IBM Research

Dr. Pavithra Harsha is a Research Staff Member in the IBM’s Thomas J. Watson Research Center. She received her Ph.D in Operations Research from the Massachusetts Institute of Technology (MIT) in 2008. Prior to joining IBM in 2011, she worked as a scientist at Oracle Retail for a year and was a postdoctoral associate at MIT for two years. Dr. Harsha’s research focuses on problems in the pricing, revenue management, supply chain and optimization with applications to retail and travel and transportation. Her awards include the 2017 Best Service Science Paper Award, 2017 INFORMS Revenue Management and Pricing Practice Award, Honorable Mention in the 2017 MSOM Best Practice Paper Competition and the 2015 IBM Research Outstanding Technical Achievement Award. Dr. Harsha’s doctoral work also received honorable mentions in two dissertation award categories at INFORMS 2009: Aviation’s Application Dissertation Prize, and Transportation Science and Logistics Dissertation Prize.

Track: Revenue Management & Pricing

Monday, April 15, 11:30am–12:20pm

Dynamic Pricing of Omni-Channel Inventories

Omnichannel retail refers to a seamless integration of an e-commerce channel and a network of brick-and-mortar stores. An example is cross-channel fulfillment, which allows a store to fulfill online orders in any location. Another is price transparency, which allows customers to compare the online price with store prices. This paper studies a new and widespread problem resulting from omnichannel retail: price optimization in the presence of cross-channel interactions in demand and supply, where cross-channel fulfillment is exogenous. We propose two pricing policies that are based on the idea of “partitions”to the store inventory that approximate how this shared resource will be utilized. These policies are practical because they rely on solving computationally tractable mixed integer programs that can accept various business and pricing rules. In extensive simulation experiments, they achieve a small optimality gap relative to theoretical upper bounds on the optimal expected profit. The good observed performance of our pricing policies results from managing substitutive channel demands in accordance with partitions that rebalance inventory in the network. A proprietary implementation of the analytics that also includes demand estimation is commercially available as part of the IBM Commerce markdown price solution. The system results in an estimated 13.7% increase in clearance-period revenue based on causal model analysis of the data from a pilot implementation for clearance pricing at a large U.S. retailer.