An Empirical Study on Demand Models for a Price-Setting Newsvendor

We consider the price-setting newsvendor model where the ordering and pricing decisions have to be made at the beginning of a selling period before demand is realized. Unsatisfied demand is lost and excess inventory has to be salvaged. The standard approach is to assume stochastic demand to be composed of deterministic functions decreasing in price and a stochastic error term. We present an empirical study which includes demand modelling as well as price and inventory optimization. Using the sales data of a retailing company, additive and multiplicative demand models are estimated and their adequacy of representing the data is assessed according to some statistical methods. Seeing the need and possibility of using a more general demand model we suggest estimating a more flexible demand distribution in a simple way. Applying the newsvendor problem formulation, the optimal policies under each of the three models as well as the policy under the sequential approach are calculated. The performance of each model is evaluated by simulating the corresponding policies using the same data set. We conclude that using a general model can increase the profits significantly