IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v90y2019i3d10.1007_s00186-019-00682-w.html
   My bibliography  Save this article

Order and exit decisions under non-increasing price curves for products with short life cycles

Author

Listed:
  • J. B. G. Frenk

    (Sabancı University)

  • Canan Pehlivan

    (Yeditepe University)

  • Semih O. Sezer

    (Sabancı University)

Abstract

We consider a supplier selling a product with a relatively short life cycle and following a non-increasing price curve. Because of the short cycle, there is a single procurement opportunity at the beginning of the cycle. The objective of the supplier is to determine the initial order quantity and the time to remove the product from the market in order to maximize her profits. We study this problem in a continuous-time framework where the demand is modeled with a non-homogeneous Poisson process having a general intensity function and the pricing strategy is given by an arbitrary non-increasing function. We give a rigorous mathematical analysis for the problem and show how it can be solved in two stages. We also consider the special case with piecewise constant intensity and price functions. For this case, we show that the optimal exit time is included in the set of break points of these functions. This brings a fast method to obtain the optimal solution for this special case.

Suggested Citation

  • J. B. G. Frenk & Canan Pehlivan & Semih O. Sezer, 2019. "Order and exit decisions under non-increasing price curves for products with short life cycles," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 90(3), pages 365-397, December.
  • Handle: RePEc:spr:mathme:v:90:y:2019:i:3:d:10.1007_s00186-019-00682-w
    DOI: 10.1007/s00186-019-00682-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00186-019-00682-w
    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/s00186-019-00682-w?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. John R. Moore, Jr., 1971. "Forecasting and Scheduling for Past-Model Replacement Parts," Management Science, INFORMS, vol. 18(4-Part-I), pages 200-213, December.
    2. Ritchie, E. & Wilcox, P., 1977. "Renewal theory forecasting for stock control," European Journal of Operational Research, Elsevier, vol. 1(2), pages 90-93, March.
    3. M. Pourakbar & E. Laan & R. Dekker, 2014. "End-of-Life Inventory Problem with Phaseout Returns," Production and Operations Management, Production and Operations Management Society, vol. 23(9), pages 1561-1576, September.
    4. Peter M. Noble & Thomas S. Gruca, 1999. "Response to the Comments on “Industrial Pricing: Theory and Managerial Practice”," Marketing Science, INFORMS, vol. 18(3), pages 458-459.
    5. Youyi Feng & Guillermo Gallego, 2000. "Perishable Asset Revenue Management with Markovian Time Dependent Demand Intensities," Management Science, INFORMS, vol. 46(7), pages 941-956, July.
    6. Ayşegül Toptal & Sıla Çetinkaya, 2015. "The impact of price skimming on supply and exit decisions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 551-574, July.
    7. S. David Wu & Berrin Aytac & Rosemary T. Berger & Chris A. Armbruster, 2006. "Managing Short Life-Cycle Technology Products for Agere Systems," Interfaces, INFORMS, vol. 36(3), pages 234-247, June.
    8. Dasu, Sriram & Tong, Chunyang, 2010. "Dynamic pricing when consumers are strategic: Analysis of posted and contingent pricing schemes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 662-671, August.
    9. B Aytac & S D Wu, 2011. "Modelling high-tech product life cycles with short-term demand information: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 425-432, March.
    10. Felipe Caro & Jérémie Gallien, 2010. "Inventory Management of a Fast-Fashion Retail Network," Operations Research, INFORMS, vol. 58(2), pages 257-273, April.
    11. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    12. J P J van Kooten & T Tan, 2009. "The final order problem for repairable spare parts under condemnation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1449-1461, October.
    13. Peter M. Noble & Thomas S. Gruca, 1999. "Industrial Pricing: Theory and Managerial Practice," Marketing Science, INFORMS, vol. 18(3), pages 435-454.
    14. Frenk, J.B.G. & Javadi, S. & Pourakbar, M. & Sezer, S.O., 2019. "An exact static solution approach for the service parts end-of-life inventory problem," European Journal of Operational Research, Elsevier, vol. 272(2), pages 496-504.
    15. Yossi Aviv & Amit Pazgal, 2008. "Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 339-359, December.
    16. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    17. Wedad Elmaghraby & Altan Gülcü & P{i}nar Keskinocak, 2008. "Designing Optimal Preannounced Markdowns in the Presence of Rational Customers with Multiunit Demands," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 126-148, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ozyoruk, Emin & Erkip, Nesim Kohen & Ararat, Çağın, 2022. "End-of-life inventory management problem: Results and insights," International Journal of Production Economics, Elsevier, vol. 243(C).

    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. Khouja, Moutaz & Liu, Xin & Zhou, Jing, 2020. "To sell or not to sell to an off-price retailer in the presence of strategic consumers," Omega, Elsevier, vol. 90(C).
    2. 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.
    3. Ken Moon & Kostas Bimpikis & Haim Mendelson, 2018. "Randomized Markdowns and Online Monitoring," Management Science, INFORMS, vol. 64(3), pages 1271-1290, March.
    4. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    5. Behfard, S. & Al Hanbali, A. & van der Heijden, M.C. & Zijm, W.H.M., 2018. "Last Time Buy and repair decisions for fast moving parts," International Journal of Production Economics, Elsevier, vol. 197(C), pages 158-173.
    6. Christian Borgs & Ozan Candogan & Jennifer Chayes & Ilan Lobel & Hamid Nazerzadeh, 2014. "Optimal Multiperiod Pricing with Service Guarantees," Management Science, INFORMS, vol. 60(7), pages 1792-1811, July.
    7. J. B. G. Frenk & Sonya Javadi & Semih O. Sezer, 2019. "An optimal stopping approach for the end-of-life inventory problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 90(3), pages 329-363, December.
    8. Pourakbar, Morteza & Dekker, Rommert, 2012. "Customer differentiated end-of-life inventory problem," European Journal of Operational Research, Elsevier, vol. 222(1), pages 44-53.
    9. Tatsiana Levina & Yuri Levin & Jeff McGill & Mikhail Nediak, 2009. "Dynamic Pricing with Online Learning and Strategic Consumers: An Application of the Aggregating Algorithm," Operations Research, INFORMS, vol. 57(2), pages 327-341, April.
    10. Negin Golrezaei & Hamid Nazerzadeh & Ramandeep Randhawa, 2020. "Dynamic Pricing for Heterogeneous Time-Sensitive Customers," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 562-581, May.
    11. Arenoe, Bjorn & van der Rest, Jean-Pierre I. & Kattuman, Paul, 2015. "Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach," Tourism Management, Elsevier, vol. 51(C), pages 96-102.
    12. Chia-Wei Kuo & Hyun-Soo Ahn & Göker Aydın, 2011. "Dynamic Pricing of Limited Inventories When Customers Negotiate," Operations Research, INFORMS, vol. 59(4), pages 882-897, August.
    13. Hou, Lihua & Nie, Tengfei & Zhang, Jianghua, 2024. "Pricing and inventory strategies for perishable products in a competitive market considering strategic consumers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    14. Behfard, S. & van der Heijden, M.C. & Al Hanbali, A. & Zijm, W.H.M., 2015. "Last time buy and repair decisions for spare parts," European Journal of Operational Research, Elsevier, vol. 244(2), pages 498-510.
    15. Yuri Levin & Jeff McGill & Mikhail Nediak, 2009. "Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition," Management Science, INFORMS, vol. 55(1), pages 32-46, January.
    16. Gökgür, Burak & Karabatı, Selçuk, 2019. "Dynamic and targeted bundle pricing of two independently valued products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 184-198.
    17. Guillermo Gallego & Özge Şahin, 2010. "Revenue Management with Partially Refundable Fares," Operations Research, INFORMS, vol. 58(4-part-1), pages 817-833, August.
    18. Goker Aydin & Serhan Ziya, 2009. "Technical Note---Personalized Dynamic Pricing of Limited Inventories," Operations Research, INFORMS, vol. 57(6), pages 1523-1531, December.
    19. Vincent Mak & Amnon Rapoport & Eyran J. Gisches & Jiaojie Han, 2014. "Purchasing Scarce Products Under Dynamic Pricing: An Experimental Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 425-438, July.
    20. Vincent Mak & Amnon Rapoport & Eyran J. Gisches, 2018. "Dynamic Pricing Decisions and Seller-Buyer Interactions under Capacity Constraints," Games, MDPI, vol. 9(1), pages 1-23, February.

    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:mathme:v:90:y:2019:i:3:d:10.1007_s00186-019-00682-w. 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.