Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions

Elmaghraby, Wedad; Keskinocak, Pınar

doi:10.1109/emr.2003.24939

Cited by 246 publications

(331 citation statements)

References 56 publications

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“…Early works include that of (Varian, 1980) and (Salop and Stiglitz, 1982). (Elmaghraby and Keskinocak, 2002) and (Swann, 1999) provide a comprehensive review of models of traditional retail markets where inventories are a major consideration. (Elmaghraby and Keskinocak, 2002) discuss two types of markets: markets with no inventory replenishment and markets with inventory replenishment.…”

Section: A Review Of Relevant Workmentioning

confidence: 99%

“…Determining the right prices to charge a customer for a product or a service is a complex task. It requires that a company knows not only its own operating costs and availability of supply but also how much the customer values the product and what the future demand would be Elmaghraby and Keskinocak (2002). A company therefore needs a wealth of information about its customers and also be able to adjust its prices at minimal cost.…”

Section: Introductionmentioning

confidence: 98%

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Learning dynamic prices in electronic retail markets with customer segmentation

Raju¹,

Narahari²,

Ravikumar

2006

Ann Oper Res

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In this paper, we use reinforcement learning (RL) techniques to determine dynamic prices in an electronic monopolistic retail market. The market that we consider consists of two natural segments of customers, captives and shoppers. Captives are mature, loyal buyers whereas the shoppers are more price sensitive and are attracted by sales promotions and volume discounts. The seller is the learning agent in the system and uses RL to learn from the environment. Under (reasonable) assumptions about the arrival process of customers, inventory replenishment policy, and replenishment lead time distribution, the system becomes a Markov decision process thus enabling the use of a wide spectrum of learning algorithms. In this paper, we use the Q-learning algorithm for RL to arrive at optimal dynamic prices that optimize the seller's performance metric (either long term discounted profit or long run average profit per unit time). Our model and methodology can also be used to compute optimal reorder quantity and optimal reorder point for the inventory policy followed by the seller and to compute the optimal volume discounts to be offered to the shoppers. Notation:(q, r ) Inventory policy, q = reorder quantity and r = reorder point λ Rate of Poisson arrival process of customers fFraction of customers who are captives pPosted price per unit item N Maximum number of items requested by backlogged customers

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Section: A Review Of Relevant Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Learning dynamic prices in electronic retail markets with customer segmentation

Raju¹,

Narahari²,

Ravikumar

2006

Ann Oper Res

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“…For a discussion of dynamic pricing models for non-renewable resources, see Bitran and Caldentey (2003) and for a review of pricing in the production environment, see Yano and Gilbert (2005). Elmaghraby and Keskinocak (2003) makes a taxonomy of the dynamic pricing studies with inventory considerations.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic lead time quotation under responsive inventory and multiple customer classes

Savaşaneril

Sayin

2016

OR Spectrum

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We address the lead time quotation problem of a manufacturer serving multiple customer classes. Customers are sensitive to the quoted lead times and the manufacturer has the flexibility to keep inventory to improve responsiveness. We model the problem as a Markov decision process and characterize the optimal lead time quotation, rationing, and production policies. We then define internal and external service level measures and analyze the impact of inventory keeping decision on these measures. Before analyzing the impact, we first derive the relation between inventory level and lead time quotes. We then show that the effect of inventory level on the service levels may not follow the intuition. We also study alternative lead time quotation and production schemes. We contrast the performance of these alternative policies with that of the optimal policy. Specifically, through a numerical study, we quantify the value of controlled arrivals and customer rejection, the value of information on customer status, and the value of a far-sighted policy. Through the numerical study, we also identify the effect of revenue mix and demand mix on the inventory keeping decisions and the performance measures. Finally, we measure the impact of lead time quotation on resource pooling (i.e., inventory and capacity pooling). We show that the value of resource pooling is limited under the optimal policy, since lead time quotation is already effective in balancing the demand with capacity, and in allocating the resources among different customer classes.

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“…We refer the readers to two recent survey papers, Yano and Gilbert [25] , and Elmaghraby and Keskinocak [7] . Our review below will focus on those works that relate closely to our study.…”

Section: Introductionmentioning

confidence: 98%

Coordinating pricing and inventory control in a fluctuating environment

Zhang

Chen

Zhang

2009

Acta Math. Appl. Sin. Engl. Ser.

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This paper addresses the simultaneous determination of pricing and inventory replenishment strategies under a fluctuating environment. Specifically, we analyze the single item, periodic review model. The demand consists of two parts: the deterministic component, which is influenced by the price, and the stochastic component (perturbation). The distribution of the stochastic component is determined by the current state of an exogenous Markov chain. The price that is charged in any given period can be specified dynamically. A replenishment order may be placed at the beginning of some or all of the periods, and stockouts are fully backlogged. Ordering costs that are lower semicontinuous, and inventory/backlog (or surplus) costs that are continuous with polynomial growth. Finite-horizon and infinite-horizon problems are addressed. Existence of optimal policies is established. Furthermore, optimality of (s,S,p)-type policies is proved when the ordering cost consists of fixed and proportional cost components and the surplus cost (these costs are all state-dependent) is convex.

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Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions

Cited by 246 publications

References 56 publications

Learning dynamic prices in electronic retail markets with customer segmentation

Learning dynamic prices in electronic retail markets with customer segmentation

Dynamic lead time quotation under responsive inventory and multiple customer classes

Coordinating pricing and inventory control in a fluctuating environment

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