Optimal online markdown and markup pricing policies with demand uncertainty

Ni, Guanqun; Luo, Li; Xu, Yinfeng; Xu, Jiuping

doi:10.1016/j.ipl.2015.06.006

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…That is, the work of [19] is more general than that of [18] by assuming the availability of limited demand information. The competitive analysis of online algorithms has also been applied for other single-leg RM problem under various situations in the literature [21][22][23]. All of these studies assume that the resource is non-hierarchical, which is sometimes out of line with reality.…”

Section: Introductionmentioning

confidence: 99%

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes

2022

PLoS ONE

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Resource allocation problem is one of key issues in the field of revenue management. The traditional models usually rely on some restrictive assumptions about demand information or arrival process, which is sometimes out of line with reality. To overcome this shortcoming, the method of competitive analysis of online algorithms, which eliminates the need for the assumptions on demand and arrivals, is adopted to deal with the quantity-based revenue management problem. The current model in this paper considers two downgrade compatible levels of resources. Given the capacities and fares of both levels of resources, the objective is to accept appropriate customers and assign them to appropriate resources so as to maximize revenues. Compared with the existing literature, this paper generalizes the concerned resource allocation problem by considering multiple fares for each level of resources. From the perspective of online algorithms and competitive analysis, both an upper bound and an optimal online strategy are derived in this paper.

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Section: Introductionmentioning

confidence: 99%

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes

2022

PLoS ONE

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“…It is widely known both in industry and academic domain, that many sellers including airline industry follow markdown and markup price optimization policy (Cosgun et al , 2017; Nagare and Dutta, 2018; Chung et al , 2011) for revenue management problem with uncertain customer demand. In particular, Ni et al (2015) developed novel markdown and markup dynamic pricing policies based on competitive analysis of demand uncertainty. Considering seasonality in demand forecasting models significantly improves inventory management of seasonal products (Ehrenthal et al , 2014).…”

Section: Introductionmentioning

confidence: 99%

Mitigating demand risk of durable goods in online retailing

Ghadge¹,

Bag²,

Goswami³

et al. 2020

IJRDM

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PurposeAn uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.Design/methodology/approachFour e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.FindingsMixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.Research limitations/implicationsLimited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.Practical implicationsThe analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.Originality/valueThe study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.

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Markdown Optimization—A Taxonomy and Realization of the Economic Decision Problem

Weber

Schütte

2019

Intelligent Systems Applications in Software Engineering

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Optimal online markdown and markup pricing policies with demand uncertainty

Cited by 5 publications

References 26 publications

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes

Mitigating demand risk of durable goods in online retailing

Markdown Optimization—A Taxonomy and Realization of the Economic Decision Problem

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