Speaker: Michael Ketzenberg
Title: Managing Perishables Subject to Freshness Dependent Demand: with and without Backroom Storage
Time: November 24th (Friday), 13:30 – 14:30
We investigate the impact of retail backroom storage on the management of perishables in which waste and ongoing sustainablity are significant concerns. We consider a retailer that sells a single perishable product with freshness dependent, stochastic demand. Product decays at a constant rate on the shelf, but the backroom provides temperature and environmental control that preserves product quality. However, inventory in the backroom is not immediately available for sale, and storing product in Multiple locations increases operational complexity and handling costs. We develop a stylized model to capture the key features of the above setting and derive analytical properties regarding system behavior, both with and without the availability of backroom storage. We partially characterize the optimal ordering policy and prove that under certain conditions a simple bang-bang policy is optimal: either keep all the units or dispose of all the units on the shelf at the start of each replenishment cycle. We also employ an extensive numerical study thatprovides exact analysis of system behavior on a more generalized model. We find that while the backroom offers an additional lever to help preserve product quality, this added flexibility is in general not Pareto improving across performance measures that include quality, waste, and service. Surprisingly, we find that backroom storage may worsen product quality or waste, relative to the case without backroom storage. Nevertheless, the backroom offers greatest value when the salvage value is low, and demand sensitivity to freshness, demand uncertainty, and decay rates are all high.
Michael Ketzenberg is a Professor of Information and Operations Management in the Mays Business School at Texas A&M University. His research falls under the umbrella of retail operations and focuses on omnichannel services, consumer returns, and perishable inventory management. A key integrating theme throughout his work addresses the value of information for inventory replenishment. Michael also has broad interest in data analytics, with particular emphasis on machine learning with retail applications using large scale retail transaction data sets. He has also applies machine learning methods in the context of algorithmic stock trading. Dr. Ketzenberg’s research work has been published in several academic and practitioner journals, among them, Harvard Business Review, Sloan Management Review, Production and Operations Management, Management Science, European Journal of Operational Research, and Journal of Operations Management.
Prior to joining the faculty at Texas A&M University, Michael worked at Colorado State University and George Mason University. He received his doctorate at University of North Carolina at Chapel Hill. He also earned a master in business administration from Vanderbilt University and a bachelor degree from Carnegie Mellon University.