Technical Note—Data-Driven Profit Estimation Error in the Newsvendor Model

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.