Affiliation:
1. Information Systems and Operations Management, Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195
Abstract
An unbiased forecast of profit is important in most business environments. Typically, forecasts are generated from data. However, in “Technical Note—Data-Driven Profit Estimation Error in the newsvendor model,” Siegel and Wagner identify a strictly positive bias in a natural estimation of expected profit in a data-driven newsvendor model, where managers will expect more profit than will actually be realized, on average. This bias can reach significant proportions (in some cases 50%+) of the true expected profit and could therefore have undesired and damaging effects in the real world. Siegel and Wagner then design a data-driven adjustment that results in an unbiased estimator of expected profit, so that managers may have an accurate forecast of future profit that is free of systematic bias.
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Subject
Management Science and Operations Research,Computer Science Applications