IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v62y2011i3d10.1057_jors.2010.89.html
   My bibliography  Save this article

Modelling high-tech product life cycles with short-term demand information: a case study

Author

Listed:
  • B Aytac

    (Lehigh University)

  • S D Wu

    (Lehigh University)

Abstract

Increasing competition and volatile conditions in high-tech markets result in shortening product life cycles with non-cyclic demand patterns. This study illustrates the use of a demand-characterisation approach that models the underlying shape of product demands in these markets. In the approach, a Bayesian-update procedure combines the demand projections obtained from historical data with the short-term demand information provided from demand leading indicators. The goal of the Bayesian procedure is to improve the accuracy and reduce the variation of historical data-based demand projections. This paper discusses the implementation experience of the proposed approach at a semiconductor-manufacturing company; the key test results are presented using product families introduced over the last few years with a comparison to real-world benchmark demand forecasts.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.89
    DOI: 10.1057/jors.2010.89
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2010.89
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/jors.2010.89?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. 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.
    2. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    3. Hag‐Soo Kim, 2003. "A Bayesian analysis on the effect of multiple supply options in a quick response environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(8), pages 937-952, December.
    4. Bewley, Ronald & Griffiths, William E, 2001. "A Forecasting Comparison of Classical and Bayesian Methods for Modelling Logistic Diffusion," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 231-247, July.
    5. Quinn, Terry & Mawdsley, Andrew, 1996. "Forecasting Irish Inflation: A Composite Leading Indicator," Research Technical Papers 4/RT/96, Central Bank of Ireland.
    6. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    7. Gary D. Eppen & Ananth. V. Iyer, 1997. "Improved Fashion Buying with Bayesian Updates," Operations Research, INFORMS, vol. 45(6), pages 805-819, December.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Marshall Fisher & Ananth Raman, 1996. "Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales," Operations Research, INFORMS, vol. 44(1), pages 87-99, February.
    10. Kaijie Zhu & Ulrich W. Thonemann, 2004. "An adaptive forecasting algorithm and inventory policy for products with short life cycles," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(5), pages 633-653, August.
    11. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    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. 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.
    2. Dennis Heffley, 2016. "Revisiting the Product Life Cycle," Working papers 2016-36, University of Connecticut, Department of Economics.
    3. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.
    4. Chihyun Jung & Dae-Eun Lim, 2016. "Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea," Sustainability, MDPI, vol. 8(3), pages 1-12, March.

    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. Berrin Aytac & S. Wu, 2013. "Characterization of demand for short life-cycle technology products," Annals of Operations Research, Springer, vol. 203(1), pages 255-277, March.
    2. S. David Wu & Karl G. Kempf & Mehmet O. Atan & Berrin Aytac & Shamin A. Shirodkar & Asima Mishra, 2010. "Improving New-Product Forecasting at Intel Corporation," Interfaces, INFORMS, vol. 40(5), pages 385-396, October.
    3. Kejia Hu & Jason Acimovic & Francisco Erize & Douglas J. Thomas & Jan A. Van Mieghem, 2019. "Forecasting New Product Life Cycle Curves: Practical Approach and Empirical Analysis," Service Science, INFORMS, vol. 21(1), pages 66-85, January.
    4. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    5. Karakaya, Emrah, 2016. "Finite Element Method for forecasting the diffusion of photovoltaic systems: Why and how?," Applied Energy, Elsevier, vol. 163(C), pages 464-475.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    8. Choi, Tsan-Ming & Chen, Yue & Chung, Sai Ho, 2019. "Online-offline fashion franchising supply chains without channel conflicts: Choices on postponement and contracts," International Journal of Production Economics, Elsevier, vol. 215(C), pages 174-184.
    9. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    10. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    11. Karakaya, Emrah, 2014. "Finite Element Model of the Innovation Diffusion: An Application to Photovoltaic Systems," INDEK Working Paper Series 2014/6, Royal Institute of Technology, Department of Industrial Economics and Management.
    12. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    13. Wu, Feng-Shang & Chu, Wen-Lin, 2010. "Diffusion models of mobile telephony," Journal of Business Research, Elsevier, vol. 63(5), pages 497-501, May.
    14. Kumar, Rajeev Ranjan & Guha, Pritha & Chakraborty, Abhishek, 2022. "Comparative assessment and selection of electric vehicle diffusion models: A global outlook," Energy, Elsevier, vol. 238(PC).
    15. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    16. Bewley, Ronald & Griffiths, William E., 2003. "The penetration of CDs in the sound recording market: issues in specification, model selection and forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 111-121.
    17. Yue Chen & Sai-Ho Chung & Shu Guo, 2020. "Franchising contracts in fashion supply chain operations: models, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 83-128, August.
    18. Ashish Sood & Gareth M. James & Gerard J. Tellis & Ji Zhu, 2012. "Predicting the Path of Technological Innovation: SAW vs. Moore, Bass, Gompertz, and Kryder," Marketing Science, INFORMS, vol. 31(6), pages 964-979, November.
    19. Sohn, So Young & Lim, Michael, 2008. "The effect of forecasting and information sharing in SCM for multi-generation products," European Journal of Operational Research, Elsevier, vol. 186(1), pages 276-287, April.
    20. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.

    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:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.89. 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.palgrave-journals.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.