Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.Singapore-MIT Alliance (SMA

Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field.supply chain management;inventory;capacity;dynamic pricing;operations-marketing interface

Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field

On the Benefit of Inventory-Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price-sensitive demand, focusing on the benefit of the inventory-based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78685/1/j.1937-5956.2009.01099.x.pd

The effectiveness of a simple policy for coordinating inventory control and pricing strategies

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 51-54).We investigate the effectiveness of an (s, S, p) policy relative to an (s, S, A, p) policy in a single product, periodic review, finite horizon model with stochastic multiplicative demand and fixed ordering cost, in which an (s, S, A, p) policy is optimal. An extensive numerical study shows that empirically an (s, S, p) policy is highly effective relative to an (s, S, A, p) policy. We also formulate two alternative benchmark policies and find that the (s, S, p) policy is superior in terms of profit. In addition, we propose an efficient algorithm with simulated annealing and modified binary search to determine the (s, S, p) policy for the model.by Zhibo Sun.S.M

Dynamic Pricing and Inventory Management with Regular and Expedited Supplies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102647/1/poms12047.pd

Combined Sales Effort and Inventory Control under Demand Uncertainty

We study the joint inventory and sales effort management problems of a retailer in a broad context and investigate the optimal policies for a single item, periodic-review system. In each period, the demand is uncertain depending on the sales effort level exerted by the retailer, which incurs an associated cost. The retailer’s objective is to find a joint optimal inventory replenishment and sales effort policy to maximize the discounted profit over a finite horizon. We first consider a basic setting with zero setup cost and no batch ordering, under which the base stock list sales effort policy is optimal. Two extensions are then investigated: (1) the case with nonzero setup cost, under which we show that (s,S,e) policy is optimal; and (2) the case with batch ordering, under which we prove the optimality of the (r,Nq,e) policy. Finally, we conduct numerical studies to provide additional managerial insights