Bayesian Information Update for Retailer’s Two-stage Inventory Model under Demand Uncertainty

This paper investigates demand forecast and order placement policy of a seasonal product for the retailers in an active business season. In a two-phase demand model, the market demand is uncertain, and decision-making for order placement is addressed for the second phase after observing the demand at the first phase during the active season. The model uses a Poisson process for customer demand and a gamma prior distribution for the demand rate variation. The method addresses the updating of the prior probability to a posterior one dictating by Bayesian process. The experts’ opinion for the peak demand is incorporated in parameter updating process. The numerical analyses are discussed and conclusion is presented.