An Analysis of Managing Sustainable Competitiveness for Semiconductor Manufacturers
Innovative capability is considered inevitable for firms to sustain their competitiveness. In the recent rapidly changing global competition environment, the traditional integrated device manufacturer (IDM) model in semiconductor industry is facing the limitation of sustaining its profitability and competitiveness. IDM’s focusing both chip design and manufacturing for various application segments disperse its resources of innovating sustainable competiveness. This study develops an analysis framework with incorporating data envelopment analysis (DEA) approach to measure the efficiency through proper input and output variables setting. This framework aims at providing guidelines for developing firm’s business and technology strategies. We conducted a DEA analysis by collecting financial data from twenty-six leading semiconductor manufacturing companies, including twenty IDMs and six foundries. The results reveal that the foundry companies have higher competitive efficiency than those of IDMs. The empirical analysis suggests that adopting the asset-light business model may provide IDMs a better resource allocation and help the increase of relative efficiency scores.
WSTS, http://www.ests.org/download/bbhist-26.xls, 2012.
R. Kumar, Fabless semiconductor implementation, McGraw Hill, New York, 2008.
Gartner, http://my.gartner.com/, 2012.
A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” European Journal of Operational Research, vol. 2, no. 6, pp. 429-444, 1978.
L. M. Seiford and J. Zhu, Manage,“Profitability and Marketability of the Top 55 U.S. Commercial Banks,” Management Science, vol. 45, no. 9, pp. 1270-1288, September, 1999.
F. H. Liu and P. H. Wang, “DEA Malmquist productivity measure: Taiwanese semiconductor companies,” International Journal of Production Economics, vol. 112, no. 1, pp. 367-379, 2008.
R. D. Banker, A. Charnes, and W. W. Cooper, “Some models for estimating technical and scale inefficiencies in data envelopment analysis,” Management Science, vol. 30, no. 9, pp. 1078-1092, 1984.
R. Färe, S. Grosskopf, and C. A. K. Lovell, Production frontiers, Cambridge University Press, Cambridge, 1994.
R. C. Leachman, S. Ding and C. F. Chien, “Economic efficiency analysis of wafer fabrication,” IEEE Transactions on Automation Science and Engineering, vol. 4, no. 4, pp. 501-512, 2007.
M. Asmild, J. C. Paradi, D. N. Reese, and F. Tam, “Measuring overall efficiency and effectiveness using DEA” European Journal of Operational Research, vol. 178, pp. 305-321, 2007.
K. Cullinane, T. Wang, D. Song, and P. Ji, “The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis,” Transportation Research Part A: Policy and Practice, vol. 40, no. 4, pp. 354-374, May 2006.
P. Hughes and M. Edwards, “Leviathan vs. Lilliputian: a data envelopment analysis of government efficiency,” Journal of Regional Science, vol. 40, pp. 649-669, 2000.
C. Kao and S. N. Hwang, “Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan,” European Journal of Operational Research, vol. 185, pp. 418-429, 2008.
W. W. Cooper, L. M. Seiford, and K. Tone, Introduction to data envelopment analysis and its uses: with dea-solver software and references, Springer, New York, 2006.
S. Thore, F. Phillips, T. W. Ruefli, and P. Yue, “DEA and the Management of the Product Cycle: the US Computer Industry,” Computer and Operations Research, vol. 23, no. 4, pp. 341-356, 1996.
G. Kozmetsky and P. Yue, “Comparative performance of global semiconductor companies,” International Journal of Management Science, vol. 26, no. 2, pp. 153-175, 1998.
Y. Chen and A. I. Ali, “DEA Malmquist productivity measure: new insights with an application to computer industry,” European Journal of Operational Research, vol. 159, no. 1, pp. 239-249 (2004)
J. K. Sengupta, “Non-parametric efficiency analysis under uncertainty using data envelopment analysis,” International Journal of Production Economics, vol. 95, no. 1, pp. 39-49, 2005.
D. D. Wu and C. B. Ho, “Productivity and efficiency analysis of Taiwan’s integrated circuit industry,” International Journal of Business Performance Management, vol. 56, pp. 715-730, 2007.
Y. S. Chen and B. Y. Chen, “Using data envelopment analysis (DEA) to evaluate the operational performance of the wafer fabrication industry in Taiwan,” Journal of Manufacturing Technology Management, vol. 20, pp. 475-488, 2009.
T. Y. Chen and L. H. Chen, “DEA performance evaluation based on BSC indicators incorporated: the case of semiconductor industry,” International Journal of Productivity and Performance, vol. 56, no. 4, pp. 335-357, 2007.
Y. C. Tang and F. M. Liou, “Does firm performance reveal its own causes? The role of Bayesian inference,” Strategic Management Journal, vol. 31, no. 1, pp. 39-57, 2010.
J. Zhu and Z. H. Shen, “A discussion of testing DMUs’ returns to scale,” European Journal of Operational Re-search, vol. 81, pp. 590-596, 1995.
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 of their article with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Oct. 01, 2015, PETI will publish new articles with Creative Commons Attribution Non-Commercial License, under The 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