Optimal Inventory And Pricing Strategy With Capacity Constraint Under Customer-Driven Vs. Retailer-Driven Search

As suppliers concentrate on competition via time and service differentiation, it is awise choice for them to make their inventory close to customers, which gives rise tomulti-location stores and warehouses. In multi-location systems, imbalance of inventorydistribution is inevitable, and one possible solution is to transship between stores or searchfrom other stores by customers. Base on the above background, this thesis discusses jointdecisions on inventory and pricing control considering supplier’s capacity constraint intwo types of market search, Customer-Driven Search (CDS) and Retailer-Driven Search(RDS), due to lack or imbalance of inventory. By comparison between our results andthose of existing literature without supplier’s capacity and inventory uncertainty, weconcluded that when the variation of random capacity is large enough, the retailer willincrease order quantity instead of changing pricing decision. Otherwise, to reduce retailprice is a better choice for the retailer. Furthermore, the equilibrium safety stock and retailprice in CDS are always higher than those in RDS. Then, through analyzing the sensitivityof search intensity, we demonstrate that the equilibrium safety stock, retail price and salesare increasing in search intensity in CDS, however this is not always true in RDS. Finally,penalty cost of stockout and transportation cost are considered to extend our model, andthrough comparing the optimal equilibrium inventory and pricing decisions of base modelwith those of extended model we get the following conclusions: when considering theabove two costs, retailers’ optimal decisions can be either higher or lower compared withthose of base model in RDS; while in CDS, the retailers’ optimal inventory and pricingdecisions will always be higher than those of base model.