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Managing Short Life-Cycle Technology Products for Agere Systems

Author

Listed:
  • S. David Wu

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Berrin Aytac

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Rosemary T. Berger

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Chris A. Armbruster

    (Agere Systems, 1110 American Parkway NE, Allentown, Pennsylvania 18109)

Abstract

Over the past decade, the high-tech industry has been rapidly innovating technology and introducing new products. Firms have moved from vertically integrated operations to horizontally integrated operations that include contract manufacturers. In September 2002, Agere Systems recognized that it needed new tools for managing the capacity in its increasingly complex, global supply chain. Agere and the Center for Value Chain Research at Lehigh University formed a team to develop new methods for characterizing the demands for short life-cycle technology products. The team developed a leading-indicator engine that identifies products that provide advanced warning of demand changes for a group of products. For a data set including 3,500 semiconductor products, the analysis identified leading indicators that predicted the demand pattern of the product group one to seven months ahead of time with correlation values ranging from 0.51 to 0.95. The leading-indicator concept provides a new perspective on demand forecasting and can be extended to other corporate planning functions, such as financial forecasting and inventory forecasting.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:36:y:2006:i:3:p:234-247
    DOI: 10.1287/inte.1050.0195
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    References listed on IDEAS

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    1. John Wells, 1999. "Seasonality, leading indicators, and alternative business cycle theories," Applied Economics, Taylor & Francis Journals, vol. 31(5), pages 531-538.
    2. Abbas A. Kurawarwala & Hirofumi Matsuo, 1996. "Forecasting and Inventory Management of Short Life-Cycle Products," Operations Research, INFORMS, vol. 44(1), pages 131-150, February.
    3. 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.
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    Citations

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    Cited by:

    1. Xu, Xiaobo & Zhang, Weiyong & Li, Ling, 2016. "The impact of technology type and life cycle on IT productivity variance: A contingency theoretical perspective," International Journal of Information Management, Elsevier, vol. 36(6), pages 1193-1204.
    2. 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.
    3. 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.
    4. Saljooghi, Saeed & Safisamghabadib, Azamdokht, 2016. "Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system," MPRA Paper 79846, University Library of Munich, Germany.
    5. Brian Tomlin & Yimin Wang, 2008. "Pricing and Operational Recourse in Coproduction Systems," Management Science, INFORMS, vol. 54(3), pages 522-537, March.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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