A Nonlinear Growth Analysis of Integrated Device Manufacturers' Evolution to the Nanotechnology Manufacturing Outsourcing
Keywords:
semiconductor, nonlinear growth model, forecast, inventoryAbstract
With the increasing cost of setting up a semiconductor fabrication facility, coupled with significant costs of developing a leading nanotechnology process, aggressive outsourcing (asset-light business models) via working more closely with foundry companies is how semiconductor manufacturing firms are looking to strengthen their sustainable competitive advantages. This study aims to construct a market intelligence framework for developing a wafer demand forecasting model based on long-term trend detection to facilitate decision makers in capacity planning. The proposed framework modifies market variables by employing inventory factors and uses a top-down forecasting approach with nonlinear least square method to estimate the forecast parameters. The nonlinear mathematical approaches could not only be used to examine forecasting performance, but also to anticipate future growth of the semiconductor industry. The results demonstrated the practical viability of this long-term demand forecast framework.References
K. C. Ku, H. P. Kao, and C. K. Gurumurthy, “Virtual inter-firm collaborative framework—An IC foundry merger/acquisition project,” Technovation, vol. 27, pp. 388-401, Jun.-Jul. 2007.
Gartner, “Gartner, Inc. database,” http://my.gartner.com/, June 20, 2011.
S. H. Chang, P. F. Pai, K. J. Yuan, B. C. Wang, and R. K. Li, “Heuristic PAC model for hybrid MTO and MTS production environment,” International Journal of Production Economics, vol. 85, pp. 347-358, Sep. 2003.
K. H. Kang and Y. H. Lee, “Make-to-order scheduling in foundry semiconductor fabrication,” International Journal of Production Research, vol. 45, pp. 615-630, Feb. 2007.
A. Sharma and P. LaPlaca, “Marketing in the emerging era of build-to-order manufacturing,” Industrial Marketing Management, vol. 34, pp. 476-486, Jul. 2005.
C. F. Chien, Y. J. Chen, and J. T. Peng, “Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle,” International Journal of Production Economics, vol. 128, pp. 496-509, Dec. 2010.
V. Mahajan, E. Muller, and F. M. Bass, “New product diffusion models in marketing: A review and directions for research,” Journal of Marketing, vol. 54, pp. 1-26, Jan. 1990.
M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. VAN’T Riet, “Modeling of the bacterial growth curve,” Applied and Environmental Microbiology, vol. 56, pp. 1875-1881, Jun. 1990.
C. J. Kuo, C. F. Chien, and J.D. Chen, “Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time,” IEEE Transactions on Automation Science and Engineering, vol. 8, pp. 103-111, Jan. 2011.
G. Hoetker, “How much you know versus how well I know you: Selecting a supplier for a technically innovative component,” Strategic Management Journal, vol. 26, pp. 75-96, Jan. 2005.
R. C. Leachman, S. Ding, and C. F. Chien, “Economic efficiency analysis of wafer fabrication,” IEEE Transactions on Automation Science and Engineering, vol. 4, pp. 501-512, Oct. 2007.
R. Kumar, Fabless semiconductor implementation. New York: McGraw-Hill, 2008.
B. Vajpayee, and V. Dhasmana, “Standoff: Gunning for returns,” http://www.thomsonone.com/, January 13, 2011.
R. Lineback, B. McClean, B. Matas, and T. Yancey, “The McCLEAN Report 2011 Edition,” http://www.icinsights.com/, February 15, 2011.
G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: forecasting and control, 4th edition. San Francisco: Wiley, 2008.
J. A. Norton and F. M. Bass, “A diffusion theory model of adoption and substitution for successive generations of high technology products,” Management Science, vol. 33, pp. 1069-1086, Sep. 1987.
V. Mahajan and Y. Wind, “New product forecasting models: Directions for research and implementation,” International Journal of Forecasting, vol. 4, pp. 341-358, 1988.
A. A. Kurawarwala and H. Matsuo, “Forecasting and inventory management of short life-cycle products,” Operations Research, vol. 44, pp. 131-150, Jan.-Feb. 1996.
F. M. Bass, “A new product growth for model consumer durables,” Management Science, vol. 15, pp. 215-227, Jan. 1969.
N. M. Victor and J. H. Ausubel, “DRAMs as model organisms for study of technological evolution,” Technological Forecasting and Social Change, vol. 69, pp. 243-262, Apr. 2002.
L. D. Frank, “An analysis of the effect of the economic situation on modeling and forecasting the diffusion of wireless communications in Finland,” Technological Forecasting and Social Change, vol. 71, pp. 391-403, May 2004.
K. Zhu and U. W. Thonemann, “An adaptive forecasting algorithm and inventory policy for products with short life cycles,” Naval Research Logistics, vol. 51, pp. 633-653, Aug. 2004.
A. S. Blinder, “Inventories and sticky prices: More on the microfoundations of macroeconomics,” The American Economic Review, vol. 72, pp. 334-348, Jun. 1982.
J. A. Kahn, “Inventories and the volatility of production,” The American Economic Review, vol. 77, pp. 667-679, Sep. 1987.
M. Çakanyildirim and R. O. Roundy, “SeDFAM: Semiconductor demand forecast accuracy model,” IIE Transactions, vol. 34, pp. 449-465, May 2002.
J. A. Nelder, “The fitting of a generalization of the logistic curve,” Biometrics, vol. 17, pp. 89-110, Mar. 1961.
F. R. Oliver, “Methods of estimating the logistic growth function,” Applied Statistics, vol. 13, pp. 57-66, Jun. 1964.
N. R. Draper and H. Smith, Applied Regression Analysis, 2nd edition. New York: John Wiley & Sons, 1981.
A. Khamis, Z. Ismail, K. Haron, and A. T. Mohammed, “Nonlinear growth models for modeling oil palm yield growth,” Journal of Mathematics and Statistics, vol. 1, pp. 225-233, Jul. 2005.
StatSoft, “STATISTICA 9.0,” StatSoft Inc., Tulsa, OK, USA, http://www.statsoft.com/, May 2009.
C. D. Lewis, Industrial and Business Forecasting Methods. London: Butterworths, 1982.
Published
How to Cite
Issue
Section
License
Copyright Notice
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.