IDEAS home Printed from https://ideas.repec.org/a/spr/orspec/v46y2024i2d10.1007_s00291-023-00714-2.html
   My bibliography  Save this article

Outlier detection in network revenue management

Author

Listed:
  • Nicola Rennie

    (Lancaster University)

  • Catherine Cleophas

    (Kiel University)

  • Adam M. Sykulski

    (Imperial College London)

  • Florian Dost

    (Brandenburg University of Technology)

Abstract

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn.

Suggested Citation

  • Nicola Rennie & Catherine Cleophas & Adam M. Sykulski & Florian Dost, 2024. "Outlier detection in network revenue management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(2), pages 445-511, June.
  • Handle: RePEc:spr:orspec:v:46:y:2024:i:2:d:10.1007_s00291-023-00714-2
    DOI: 10.1007/s00291-023-00714-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00291-023-00714-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00291-023-00714-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rainer Quante & Herbert Meyr & Moritz Fleischmann, 2009. "Revenue management and demand fulfillment: matching applications, models and software," Springer Books, in: Herbert Meyr & Hans-Otto Günther (ed.), Supply Chain Planning, pages 57-88, Springer.
    2. Larry Weatherford, 2016. "The history of forecasting models in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 212-221, July.
    3. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    4. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    5. Dubin, Joel A. & Muller, Hans-Georg, 2005. "Dynamical Correlation for Multivariate Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 872-881, September.
    6. Quante, R. & Meyr, H. & Fleischmann, M., 2007. "Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software," ERIM Report Series Research in Management ERS-2007-050-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    8. Sh. Sharif Azadeh & R. Labib & G. Savard, 2013. "Railway demand forecasting in revenue management using neural networks," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 7(1), pages 18-36.
    9. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    10. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    11. De Baets, Shari & Harvey, Nigel, 2020. "Using judgment to select and adjust forecasts from statistical models," European Journal of Operational Research, Elsevier, vol. 284(3), pages 882-895.
    12. Claudia Schütze & Catherine Cleophas & Monideepa Tarafdar, 2020. "Revenue management systems as symbiotic analytics systems: insights from a field study," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 1007-1031, November.
    13. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    14. He, Guozhong & Müller, Hans-Georg & Wang, Jane-Ling, 2003. "Functional canonical analysis for square integrable stochastic processes," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 54-77, April.
    15. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    16. L R Weatherford & P P Belobaba, 2002. "Revenue impacts of fare input and demand forecast accuracy in airline yield management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(8), pages 811-821, August.
    17. Thomas Fiig & Larry R. Weatherford & Michael D. Wittman, 2019. "Can demand forecast accuracy be linked to airline revenue?," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(4), pages 291-305, August.
    18. Lawrence R. Weatherford & Samuel E. Bodily, 1992. "A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing," Operations Research, INFORMS, vol. 40(5), pages 831-844, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    2. Ernst Ahlberg & Irina Mirkina & Alfred Olsson & Christian Söyland & Lars Carlsson, 2023. "On the selection of relevant historical demand data for revenue management applied to transportation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(4), pages 266-275, August.
    3. Resul Aydemir & Mehmet Melih Değirmenci & Abdullah Bilgin, 2023. "Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 501-513, December.
    4. Kavitha Balaiyan & R. K. Amit & Atul Kumar Malik & Xiaodong Luo & Amit Agarwal, 2019. "Joint forecasting for airline pricing and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 465-482, December.
    5. Timothy Webb, 2022. "Forecasting at capacity: the bias of unconstrained forecasts in model evaluation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 645-656, December.
    6. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    7. Davina Rauhaus & Jochen Gönsch & Claudius Steinhardt, 2025. "On the value of booking data for upsell decision-making in revenue management," Flexible Services and Manufacturing Journal, Springer, vol. 37(2), pages 409-442, June.
    8. Marlin W. Ulmer & Alan Erera & Martin Savelsbergh, 2022. "Dynamic service area sizing in urban delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 763-793, September.
    9. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    10. Mihai Banciu & Fredrik Ødegaard & Alia Stanciu, 2019. "Distribution-free bounds for the expected marginal seat revenue heuristic with dependent demands," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(2), pages 155-163, April.
    11. Cedric A. Lehmann & Christiane B. Haubitz & Andreas Fügener & Ulrich W. Thonemann, 2022. "The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3419-3434, September.
    12. Syed Asif Raza, 2020. "Price Differentiation and Inventory Decisions in a Socially Responsible Dual-Channel Supply Chain with Partial Information Stochastic Demand and Cannibalization," Sustainability, MDPI, vol. 12(22), pages 1-42, November.
    13. Konstantin Kloos & Richard Pibernik & Benedikt Schulte, 2019. "Allocation planning in sales hierarchies with stochastic demand and service-level targets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 981-1024, December.
    14. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    15. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    16. Tingsong Wang & Jiawei Liu & Yadong Wang & Yong Jin & Shuaian Wang, 2024. "Dynamic Flexible Allocation of Slots in Container Line Transport," Sustainability, MDPI, vol. 16(21), pages 1-23, October.
    17. Jean-François Cordeau & Manuel Iori & Dario Vezzali, 2024. "An updated survey of attended home delivery and service problems with a focus on applications," Annals of Operations Research, Springer, vol. 343(2), pages 885-922, December.
    18. Haque, Md Tabish & Hamid, Faiz, 2023. "Social distancing and revenue management—A post-pandemic adaptation for railways," Omega, Elsevier, vol. 114(C).
    19. Quante, R. & Fleischmann, M. & Meyr, H., 2009. "A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System," ERIM Report Series Research in Management ERS-2009-015-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. Reinaldo Gomes & Ruxanda Godina Silva & Pedro Amorim, 2025. "Solving Logistical Challenges in Raw Material Reception: An Optimization and Heuristic Approach Combining Revenue Management Principles with Scheduling Techniques," Mathematics, MDPI, vol. 13(6), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:orspec:v:46:y:2024:i:2:d:10.1007_s00291-023-00714-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.