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Characterization of demand for short life-cycle technology products

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  • Berrin Aytac
  • S. Wu

Abstract

Most technology companies are experiencing highly volatile markets with increasingly short product life cycles due to rapid technological innovation and market competition. Current supply-demand planning systems remain ineffective in capturing short life-cycle nature of the products and high volatility in the markets. In this study, we propose an alternative demand-characterization approach that models life-cycle demand projections and incorporates advanced demand signals from leading-indicator products through a Bayesian update. The proposed approach describes life-cycle demand in scenarios and provides a means to reducing the variability in demand scenarios via leading-indicator products. Computational testing on real-world data sets from three semiconductor manufacturing companies suggests that the proposed approach is effective in capturing the life-cycle patterns of the products and the early demand signals and is capable of reducing the uncertainty in the demand forecasts by more than 20%. Copyright Springer Science+Business Media, LLC 2013

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  • 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.
    • Handle: RePEc:spr:annopr:v:203:y:2013:i:1:p:255-277:10.1007/s10479-010-0771-5
    DOI: 10.1007/s10479-010-0771-5
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    1. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    2. Roger M. Heeler & Thomas P. Hustad, 1980. "Problems in Predicting New Product Growth for Consumer Durables," Management Science, INFORMS, vol. 26(10), pages 1007-1020, October.
    3. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    4. Gary D. Eppen & Ananth. V. Iyer, 1997. "Improved Fashion Buying with Bayesian Updates," Operations Research, INFORMS, vol. 45(6), pages 805-819, December.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    7. 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.
    8. Jewel S. Bonser & S. David Wu, 2001. "Procurement Planning to Maintain Both Short-Term Adaptiveness and Long-Term Perspective," Management Science, INFORMS, vol. 47(6), pages 769-786, June.
    9. 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.
    10. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    11. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    12. Islam, Towhidul & Fiebig, Denzil G. & Meade, Nigel, 2002. "Modelling multinational telecommunications demand with limited data," International Journal of Forecasting, Elsevier, vol. 18(4), pages 605-624.
    13. 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.
    14. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    15. Quinn, Terry & Mawdsley, Andrew, 1996. "Forecasting Irish Inflation: A Composite Leading Indicator," Research Technical Papers 4/RT/96, Central Bank of Ireland.
    16. 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.
    17. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    18. Hause, John C, 1971. "Spectral Analysis and the Detection of Lead-Lag Relations," American Economic Review, American Economic Association, vol. 61(1), pages 213-217, March.
    19. 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.
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    2. Chung, Wenming & Talluri, Srinivas & Narasimhan, Ram, 2015. "Optimal pricing and inventory strategies with multiple price markdowns over time," European Journal of Operational Research, Elsevier, vol. 243(1), pages 130-141.
    3. Dennis Heffley, 2016. "Revisiting the Product Life Cycle," Working papers 2016-36, University of Connecticut, Department of Economics.
    4. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    5. 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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