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List pricing versus dynamic pricing: Impact on the revenue risk

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  • Koenig, Matthias
  • Meissner, Joern

Abstract

We consider the problem of a firm selling multiple products that consume a single resource over a finite time period. The amount of the resource is exogenously fixed. We analyze the difference between a dynamic pricing policy and a list-price capacity control policy. The dynamic pricing policy adjusts prices steadily resolving the underlying problem every time step, whereas the list pricing policy sets static prices once but controls the capacity by allowing or preventing product sales. As steady price changes are often costly or unachievable in practice, we investigate the question of how much riskier it is to apply a list pricing policy rather than a dynamic pricing policy. We conduct several numerical experiments and compare expected revenue, standard deviation, and conditional-value-at-risk between the pricing policies. The differences between the policies show that list pricing can be a useful strategy when dynamic pricing is costly or impractical.

Suggested Citation

  • Koenig, Matthias & Meissner, Joern, 2010. "List pricing versus dynamic pricing: Impact on the revenue risk," European Journal of Operational Research, Elsevier, vol. 204(3), pages 505-512, August.
  • Handle: RePEc:eee:ejores:v:204:y:2010:i:3:p:505-512
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    Cited by:

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    2. Koenig, Matthias & Meissner, Joern, 2015. "Value-at-risk optimal policies for revenue management problems," International Journal of Production Economics, Elsevier, vol. 166(C), pages 11-19.
    3. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    4. Kuo, Chia-Wei & Huang, Kwei-Long, 2012. "Dynamic pricing of limited inventories for multi-generation products," European Journal of Operational Research, Elsevier, vol. 217(2), pages 394-403.
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    6. Franz Wirl, 2010. "Optimal Pricing of Nondurables when Demand is Dynamic and Stochastic," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 17(2), pages 187-206.
    7. R. Schlosser, 2021. "Scalable relaxation techniques to solve stochastic dynamic multi-product pricing problems with substitution effects," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(1), pages 54-65, February.
    8. Bernardo Bertoldi & Chiara Giachino & Alberto Pastore, 2016. "Strategic pricing management in the omnichannel era," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2016(4), pages 131-152.
    9. Basu, Sanjay, 2011. "Comparing simulation models for market risk stress testing," European Journal of Operational Research, Elsevier, vol. 213(1), pages 329-339, August.
    10. Schur, Rouven & Gönsch, Jochen & Hassler, Michael, 2019. "Time-consistent, risk-averse dynamic pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 587-603.
    11. Sun, Shuxiao & Zheng, Xiaona & Sun, Luping, 2020. "Multi-period pricing in the presence of competition and social influence," International Journal of Production Economics, Elsevier, vol. 227(C).
    12. Sato, Kimitoshi & Sawaki, Katsushige, 2013. "A continuous-time dynamic pricing model knowing the competitor’s pricing strategy," European Journal of Operational Research, Elsevier, vol. 229(1), pages 223-229.
    13. Gönsch, Jochen, 2017. "A survey on risk-averse and robust revenue management," European Journal of Operational Research, Elsevier, vol. 263(2), pages 337-348.
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    More about this item

    Keywords

    Revenue management Pricing Risk analysis Dynamic programming Capacity control;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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