A Note on the Relationship Among Capacity, Pricing, and Inventory in a Make‐to‐Stock System

Allon, Gad; Zeevi, Assaf

doi:10.1111/j.1937-5956.2010.01193.x

Cited by 19 publications

(15 citation statements)

References 23 publications

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“…Prior literature often models the capacity adjustment cost by Gðj À j 0 Þ ¼ Àg 1 ðj À j 0 Þ À þ g 2 ðj À j 0 Þ þ , where g 1 (g 2 ) is the return (cost) from using one less (more) unit of capacity (cf. Angelus andPorteus 2002 andAllon andZeevi 2011). We adopt this structure, as it provides a tractable and practical way to model the cost.…”

Section: Evolving Toward Decision Scenariosmentioning

confidence: 99%

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Jia

Zhao

2017

Production and Operations Management

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Drug shortages have been a major challenge facing the US pharmaceutical industry and government in recent years. Although the problem has drawn tremendous attention from the government and media, limited academic research has been devoted to this problem, and few solutions have been proposed based on rigorous research. This study addresses the drug shortage problem from a supply chain perspective, a key aspect missing in the literature, and proposes to mitigate shortages through drug purchase contracts. By modeling the drug supply chain, we capture the objectives of various supply chain parties, and investigate Pareto‐improving contracts that mitigate drug shortages, improve drug manufacturer's and group purchasing organization (GPO)'s profits, and cut government spending and healthcare providers’ costs. We explore structural properties of key supply chain decisions and the Pareto‐improving contracts, and conduct scenario analysis with realistic industry data to evaluate shortage mitigation solutions. Our analysis shows that increasing drug prices only, a solution advocated by many, is not very effective in shortage mitigation. Price increases must be paired with strengthened failure‐to‐supply clauses (called the IPS approach) to achieve consistent and significant shortage reduction as well as Pareto improvement. Across all scenarios tested, a 30% price increase under IPS can lead to a minimum, average, and maximum shortage reduction of 25%, 53%, and 70%, respectively. Our analysis also shows the impacts of IPS on different parties in the supply chain and the impacts of various model parameters on shortage mitigation. The IPS approach rewards reliability of drug supply, which is in line with the FDA's strategic plan to reward quality, but is easier to achieve in this regulation‐based industry. Interactions with the government and industry practitioners indicate that IPS also challenges the current mindset in pharmaceutical contracting.

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Section: Evolving Toward Decision Scenariosmentioning

confidence: 99%

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Jia

Zhao

2017

Production and Operations Management

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“…The simultaneous determination of pricing, resource utilization, and capacity management decisions under demand uncertainty has been broadly studied in recent years (e.g., Allon and Zeevi 2011, Chayet et al 2011, Gans and Savin 2007, Kostami et al 2017, Lee and Ward 2019, Qi et al 2019. Most of this literature uses queuing theory to model customers' behavior, except for a few papers (e.g., Kostami et al 2017, Qi et al 2019.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most of this literature uses queuing theory to model customers' behavior, except for a few papers (e.g., Kostami et al 2017, Qi et al 2019. Allon and Zeevi (2011) model the simultaneous determination of pricing, production and capacity investment decisions by a monopolistic firm in a multi-period setting under demand uncertainty. A service provider is modeled as a queue with a given service rate and the question is how to determine the optimal price and priority rule assuming that customers are sensitive to both estimated cost and waiting time.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Slack Capacity in IaaS Contract Cloud Services

Núñez

Bai

2021

Production and Operations Management

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We study capacity planning in the context of Infrastructure as a Service (IaaS) cloud services. Concretely, we propose models for optimal pricing and virtual machine allocation to maximize revenue while maintaining service reliability. Our models are based on current pricing schemes used by predominant cloud services providers in the market. These providers frequently face slack capacity and low utilization in resources, so that they require better planning in the allocation of non‐revenue‐earning resources that could significantly affect profitability. In particular, we propose two novel hybrid service models, each combining two basic types of cloud services: reserved‐instance (contract) and on‐demand, and provide conditions under which they become dominant. We compare analytical properties of different service models, derive closed‐form solutions and structural results to assess the strategic impact of the different models, and conduct numerical analysis to test how optimal solutions change with respect to critical system parameters such as service rate, market size, and service reliability.

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“…m (S, +∞) be the subspace of L 2 m (R) of functions which vanish on (−∞, S). Similarly let W 1,2 m (S, +∞) be the subspace of W 1,2 m (R) of functions which vanish on (−∞, S).…”

Section: Verification Theoremmentioning

confidence: 99%

“…In literature, it is shown that the BSLP policy is the underlying structure of the optimal strategy, even when perturbations of the general setup have been made to fit various business scenarios, see for example [1], [14] [16], [20], [24] and [25].…”

mentioning

confidence: 99%

Base stock list price policy in continuous time

Bensoussan¹,

Skaaning

2017

Discrete &Amp; Continuous Dynamical Systems - B

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We study the problem of inventory control, with simultaneous pricing optimization in continuous time. For the classical inventory control problem in continuous time, see [5], as a recent reference. We incorporate pricing decisions together with inventory decisions. We consider the situation without fixed cost for an infinite horizon. Without pricing, under very natural assumptions, the optimal ordering policy is given by a Base stock, which we review briefly. With pricing, the natural generalization is the so called "Base Stock list price" (BSLP) term coined by E. Porteus, see [26], and was shown in discrete time by A. Federgruen and A. Herching to be the optimal strategy, see [14]. We extend the concept to continuous time which not only complicates the dynamics of the problem, which has never been considered before.

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A Note on the Relationship Among Capacity, Pricing, and Inventory in a Make‐to‐Stock System

Cited by 19 publications

References 23 publications

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Leveraging Slack Capacity in IaaS Contract Cloud Services

Base stock list price policy in continuous time

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