Seminar : Improved Inventory Targets in the Presence of Limited Historical Demand Data

Speaker:

Bahar Biller

Associate Professor of Operations Management

Tepper School of Business

Carnegie Mellon University

Date, Time, Location:

Friday, October 7, 2011

11:00am - 12:00pm

Mohler Laboratory, Room 453

Abstract:

Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. We consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives:  (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. We further consider the demand data that may be auto-correlated or intermittent as well as the data sets that may contain sales rather than demand realizations. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.

Biography:

Bahar Biller is an Associate Professor in the Tepper School of Business at Carnegie Mellon University. Her research focuses on three distinct yet related areas: (1) developing a comprehensive input modeling framework for stochastic system simulations with the ability to represent, fit, and generate multivariate time-series input processes with arbitrary marginal distributions and dependence structures; (2) accounting for the uncertainty of multivariate input-distribution parameters which are estimated from finite historical input data on simulation outputs; and (3) testing and proving the efficacy of the developed methods on a wide range of industrial applications in Operations Management and Finance. She received a National Science Foundation CAREER award in 2006 and the Presidential Early Career Award for Scientists and Engineers in 2007. She is a member of INFORMS and currently the Vice President and President-elect of the INFORMS Simulation Society.