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Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

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  • Quante, R.
  • Meyr, H.
  • Fleischmann, M.

Abstract

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.

Suggested Citation

  • 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.
    • Handle: RePEc:ems:eureri:10464
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    Cited by:

    1. 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.
    2. Andrade, Xavier & Guimarães, Luís & Figueira, Gonçalo, 2021. "Product line selection of fast-moving consumer goods," Omega, Elsevier, vol. 102(C).
    3. 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.
    4. Staeblein, Thomas & Aoki, Katsuki, 2015. "Planning and scheduling in the automotive industry: A comparison of industrial practice at German and Japanese makers," International Journal of Production Economics, Elsevier, vol. 162(C), pages 258-272.
    5. Fleischmann, Moritz & Kloos, Konstantin & Nouri, Maryam & Pibernik, Richard, 2020. "Single-period stochastic demand fulfillment in customer hierarchies," European Journal of Operational Research, Elsevier, vol. 286(1), pages 250-266.
    6. Martin Albrecht, 2021. "Component Allocation in Make-to-stock Assembly Systems," SN Operations Research Forum, Springer, vol. 2(2), pages 1-19, June.
    7. Catherine Cleophas & Jan Ehmke, 2014. "When Are Deliveries Profitable?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 153-163, June.
    8. 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.
    9. Alım, Muzaffer & Beullens, Patrick, 2020. "Joint inventory and distribution strategy for online sales with a flexible delivery option," International Journal of Production Economics, Elsevier, vol. 222(C).
    10. Lang, Magdalena A.K. & Cleophas, Catherine & Ehmke, Jan Fabian, 2021. "Multi-criteria decision making in dynamic slotting for attended home deliveries," Omega, Elsevier, vol. 102(C).
    11. 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.
    12. 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.
    13. Marketa Kubickova, 2022. "Revenue management in manufacturing: systematic review of literature," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 147-152, April.

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    More about this item

    Keywords

    Advanced Planning Systems; Demand Fulfillment; Manufacturing; Revenue Management; Software;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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