Optimal inventory allocation and pricing policies under a two-tier customer market and random demand

Abstract

Along with the shortening of product life cycles, the segmentation in customer markets become more distinct. In the face of segmentation, firms determine varied pricing policies to appeal to each customer markets. Also, with a strong matching ability of supply and demand, firms enhance their profits. Hence, from the point of firms, handling the decisions of pricing and inventory jointly gains prominence. In this thesis, we analyze a model that considers joint ordering, pricing, and inventory allocation decisions of a national distributor for a technology intensive product under two-tier customer market, which consisting of non-overlapping consecutive time intervals. The demand is price dependent and random where the randomness is provided by an additive error term. For the secondary period, the demand is also considered reference price dependent where the price of the primary period is taken as reference price. At the beginning of the primary period, the distributor makes the decision of pricing and order quantity. The distributor, at the beginning of the secondary period, determines the price and any additional items to order, and how many stocks to allocate to the retailers. We employ two-stage dynamic programming algorithm for the problem and conclude that a base-stock list-price policy is optimal providing that the demand functions and transformed expected revenue functions are concave in their variables. Near-explicit expressions are presented for the optimal decisions. We further conduct a computational study to examine the effect of the several system parameters on the optimal decisions.