A Joint Decision Model of Dynamic Pricing and Dynamic Inventory in an Apparel Supply Chain with Demand Learning

The problem of joint decision of dynamic pricing and dynamic inventory is studied for an apparel supply chain consisting of a single manufacturer and a single retailer. Assume that the final demand follow Normal distribution and the retailer could learn the parameters of demand probability distribution by applying Bayesian formula. Then an optimization model is established to decide the optimal inventory policy and retail price as well as wholesale price, which is to maximize total multi-period profits with considering expected revenues and inventory holding costs, ordering costs, production and transportation costs and stock-out costs. Finally, a numerical example is given to compare the performance of the model with and without demand learning. Computation results show that the expected profit with demand learning is much higher than the profit obtained from model without considering demand learning.