Combined Pricing and Inventory Control Under Uncertainty

Federgruen, Awi; Heching, Aliza

doi:10.1287/opre.47.3.454

Cited by 572 publications

(500 citation statements)

References 29 publications

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“…Another welcome extension would be to consider postponement and competition in a dynamic, multi-period model. Useful starting points may be the multi-period monopoly models of Amihud and Mendelson (1983a, b), Federgruen and Heching (1999), Gallego and van Ryzin (1984) and Li (1988). Finally, it may be interesting to incorporate the option to ex-post buy additional capacity (at a higher cost than c K ) or sell excess capacity.…”

Section: Concluding Remarks and Extensionsmentioning

confidence: 99%

Price Versus Production Postponement: Capacity and Competition

1999

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T his article presents a comparative analysis of possible postponement strategies in a two-stage decision model where firms make three decisions: capacity investment, production (inventory) quantity, and price. Typically, investments are made while the demand curve is uncertain. The strategies differ in the timing of the operational decisions relative to the realization of uncertainty.We show how competition, uncertainty, and the timing of operational decisions influence the strategic investment decision of the firm and its value. In contrast to production postponement, price postponement makes the investment and production (inventory) decisions relatively insensitive to uncertainty. This suggests that managers can make optimal capacity decisions by deterministic reasoning if they have some price flexibility. Under price postponement, additional postponement of production has relatively small incremental value. Therefore, it may be worthwhile to consider flexible ex-post pricing before production postponement reengineering. While more postponement increases firm value, it is counterintuitive that this also makes the optimal capacity decision more sensitive to uncertainty. We highlight the different impact of more timely information, which leads to higher investment and inventories, and of reduced demand uncertainty, which decreases investment and inventories. Our analysis suggests appropriateness conditions for simple make-to-stock and make-to-order strategies. We also present technical sufficiency and uniqueness conditions. Under price postponement, these results extend to oligopolistic and perfect competition for which pure equilibria are derived. Interestingly, the relative value of operational postponement techniques seems to increase as the industry becomes more competitive.

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Section: Concluding Remarks and Extensionsmentioning

confidence: 99%

Price Versus Production Postponement: Capacity and Competition

1999

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“…In this stream of research, demand is a random variable that depends on price. Under the assumption that unsatisfied demand in each period is fully backlogged, Federgruen and Heching (1999) and Simchi-Levi (2004a, 2004b) have considered periodic review models with both finite and infinite horizons. In Federgruen and Heching (1999), the ordering cost is proportional to the order quantity, and there is no setup cost.…”

Section: Introductionmentioning

confidence: 99%

“…They generalize some structural results in Li (1998) and prove an optimal base-stock policy exists for the discounted infinite horizon Markov decision process. The base-stock policy is similar to that in Federgruen and Heching (1999).…”

Section: Introductionmentioning

confidence: 99%

Joint pricing and inventory control with a Markovian demand model

Yin

Rajaram

2007

European Journal of Operational Research

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We consider the joint pricing and inventory control problem for a single product with a finite horizon and periodic review. The demand distribution in each period is determined by an exogenous Markov chain. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. The surplus costs as well as fixed and variable costs are state dependent. We show the existence of an optimal (s, S, p)-type feedback policy for the additive demand model. We extend the model to the case of emergency orders and also incorporate capacity and service level constraints. We compute the optimal policy for a class of Markovian demand and illustrate the benefits of dynamic pricing over fixed pricing strategies through numerical examples. The results indicate that it is more beneficial to implement the dynamic pricing strategy in a Markovian demand environment with a high fixed ordering cost or with high demand uncertainty.

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“…Most of the previous approaches to address dynamic joint pricinginventory problems (for general probability distributions) rely on the joint concavity of αH v (p, y)−y in (p, y) for any concave value function v(·) (see, for instance, Federgruen and Heching (1999) …”

Section: The Modelmentioning

confidence: 99%

Resource and Revenue Management in Nonprofit Operations

Véricourt

Lobo

2009

Operations Research

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Nonprofit firms sometimes engage in for-profit activities for the purpose of generating revenue to subsidize their mission activities. The organization is then confronted with a consumption vs. investment tradeoff, where investment corresponds to providing capacity for revenue customers, and consumption corresponds to serving mission customers. Exemplary of this approach are the Aravind Eye Hospitals in India, where profitable paying hospitals are used to subsidize care at free hospitals. We model this problem as a multi-period stochastic dynamic program. In each period, the organization must decide how much of the current assets should be invested in revenue-customer service capacity, and at what price the service should be sold. We provide sufficient conditions under which the optimal capacity and pricing decisions are of threshold type. Similar results are derived when the selling price is fixed but the banking of assets from one period to the next is allowed. We compare the performance of the optimal threshold policy with heuristics that may be more appealing to managers of nonprofit organizations, and assess the value of banking and of dynamic pricing through numerical experiments. Terms of use: Documents in

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Combined Pricing and Inventory Control Under Uncertainty

Cited by 572 publications

References 29 publications

Price Versus Production Postponement: Capacity and Competition

Price Versus Production Postponement: Capacity and Competition

Joint pricing and inventory control with a Markovian demand model

Resource and Revenue Management in Nonprofit Operations

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