Optimal Ordering Policies for Supermarkets under Static Pricing and Stochastic Demand: A Case Study of Milk Powder Product

Abstract

Demand uncertainty affects a significant portion of sales revenue in supermarkets. In this paper, we develop a new approach to formulate and optimize the single-item, finite horizon, periodic review revenue problem of milk powder product under stochastic demand. A special case of our model is where sales price and scheduled inventory replenishment periods are uniformly fixed at all supermarkets. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for milk powder product. The decision of whether or not to order additional units is made using dynamic programming over a finite period planning horizon. Numerical results demonstrate the existence of an optimal state-dependent ordering policy and the corresponding sales revenue of milk powder product in supermarkets.