National Taipei University of Technology, Department of Business Management; National Taipei University of Technology, Department of Business Management; National Kaohsiung First University of Science and Technology, Department of Marketing & Distribu
台灣半導體產業在國家經濟發展過程中,扮演著極重要角色,如何有效運用有限資源達到最大的效率,已成為各家廠商極力追求的目標。本文旨在以半導體產業中型封測領域廠商為例,運用獨立成份分析法(independent component analysis, ICA)與資料包絡分析法(data envelopment analysis, DEA),探討其在2007至2010年間之經營效率。為了驗證所提方法的有效性,我們除了使用蒙地卡羅模擬法進行模擬分析外,也使用傳統的DEA技術進行比較。此外,我們也針對台灣半導體產業上市櫃封測領域廠商在2007年至2010年的財務相關資料進行實證研究,更以麥氏生產力指數探討封測領域廠商在2007年至2010年間生產力及跨期效率的平均變動情形,希望能藉此瞭解各廠商在各年度效率與生產力的成長及衰退。本文主要貢獻:(1)利用模擬方式驗證ICA方法可以有效解決DEA模式中變項資料間存在高度相關性的問題;(2)根據實際之台灣半導體產業上市櫃公司財務資料,有效分析出各公司的專業技術能力與生產效率,藉以協助效率較低的企業找出應調整之投入量。實證結果發現,部分廠商無效率的主要原因為技術效率的退步及規模報酬遞減,這樣的結果顯示廠商在經營上應更注意市場的實際情況與企業內部資源的配適性。
(120_M57e1eff126c59_Abs.pdf(檔案不存在))半導體產業、經營績效、績效評估、獨立成份分析、資料包絡法
In the development of Taiwan’s economy, the semiconductor industry plays a very important role. To keep the competitive advantage, almost all the semiconductor companies are dedicating in efficiency improvement. That is, how to achieve maximum efficiency based on limited resources has become their primary target. In this study, the dataset provided by several medium-size semiconductor packaging and testing companies in Taiwan is used for analysis. In additions, two approaches, independent component analysis (ICA) and data envelopment analysis (DEA) are proposed to evaluate the efficiency of each Decision Making Unit (DMU) from 2007 to 2010. To demonstrate the performance of the proposed method, we are not only comparing the discrimination capability of ICA-DEA with traditional DEA approach but also applying Malmquist productivity index (MPI) to investigate the productivity change over time. This study contributes to the DEA literature and the semiconductor industry in two aspects. First, independent component analysis (ICA) can be applied to eliminate multicollinearity between explanatory variables in DEA model. Second, this study provides the practical contribution that the selected medium-size semiconductor packaging and testing companies will have their optimal input and output resources setups so that they can enhance their competitive advantage. The result shows that the causes of inefficiency for some companies are the reduction of their technical efficiencies and the decreasing of their returns to scale, which means that their resource allocation problems should be considered more carefully.
(120_M57e1eff126c59_Abs.pdf(檔案不存在))Semiconductor Industry, Business Performance, Efficiency Evaluation, Independent Component Analysis, Data Envelopment Analysis
台灣半導體產業在國家經濟發展過程中,扮演著極重要角色,其經營之良窳與國家經濟穩定的成長,存在著密不可分的關係。而近年來,由於產業競爭及相關資源日趨有限的情況下,國內半導體業之經營也越來越競爭,因此如何有效運用有限的資源以達到最大的效率,保持在市場中的競爭優勢,已成為各家廠商極力追求的目標。 本文以半導體產業封測領域之中型廠商為例,運用獨立成份分析(independent component analysis, ICA)與資料包絡分析法(data envelopment analysis, DEA)探討其在2007年至2010年間之經營效率。論文中,我們除了使用蒙地卡羅模擬法(Monte Carlo Simulation)進行模擬分析,以說明所提方法的有效性外,更以12家台灣半導體產業上市櫃封測領域廠商之財務相關資料進行實證研究。本文之實證研究發現如下:(1)各廠總技術效率不佳,除了是因為市場之規模報酬已呈現逐漸遞減的情形外,多家廠商純粹技術效率的退步也是其中的主因。各廠商除了應更注意市場的實際情況,切勿盲目擴大生產使效率更趨下降,也應重新檢討企業內部資源的配適性;(2)在設定最適生產規模為目標的情況下,計算出各廠無效率單位應有的投入量,相關的數據結果將可做為決策單位於規劃資源配置時的參考;(3)依據效率變動與技術變動關係情形,分析出總要素生產力衰退之主要原因。若整體效率不佳的原因,來自公司管理階層的決策錯誤或管理不善,造成投入資源的浪費,顯示半導體封測領域業者在經營策略及管理決策上仍有需要改善的空間。 由於半導體廠商經營效率尚未有一套完善的評估準則,本文針對封測業各中型廠績效進行經營效率評估,依據績效指標表現衡量其總技術效率、技術效率、規模效率及規模報酬,分析各廠經營現況之優勢及劣勢,提供管理者強化優勢與改善弱勢的方向,並具體提出管理意涵及建議,讓管理者能瞭解各廠資源運用的效果及妥適性,進一步協助效率較低落的廠商找出改善的參考標竿,並有效提出改善對策以提升各廠的效率、增加生產力及整體競爭力,進而引導企業未來完善資源之調配方向,具實際執行面重要參考價值。
李明德,2008,以資料包絡分析法分析半導體封裝測試廠經營績效,逢甲大學工業工程與系統管理學研究所碩士論文。(Lee, M. T., 2008, A study of operational
efficiency of DEA for assembly and testing house, Master Thesis, Feng-Chia
University.)
陳玲君,2012,2012 半導體工業年鑑,初版,新竹:工業技術研究院產業經濟與趨勢
研究中心。(Chen, L. C., 2012, 2012 Semiconductor industry yearbook, 1st
, Hsinchu, TW: Industrial Economics and Knowledge Center, Industrial Technology
Research Institute of Taiwan.)
Adler, N. and Yazhemsky, E., 2010, “Improving discrimination in data envelopment
analysis: PCA-DEA or variable reduction,” European Journal of Operational
Research, Vol. 202, No. 1, 273-284.
Andersen, P. and Petersen, N. C., 1993, “A procedure for ranking efficient units in data envelopment analysis,” Management Science, Vol. 39, No. 10, 1261-1264.
Banker, R. D., Charnes, R. F., and Cooper, W. W., 1984, “Some Models for Estimating
Technical and Scale Inefficiencies in Data Envelopment Analysis,” Management
Science, Vol. 30, No. 9, 1078-1092.
Bartlett, M. S., Movellan, J. R., and Sejnowski, T. J., 2002, “Face recognition by
independent component analysis,” IEEE Transactions Neural Networks, Vol. 13, No. 6, 1450-1464.
Beasley, J. E., 1990, “Comparing university departments,” Omega International Journal
of Management Science, Vol. 18, No. 2, 171-183.
Beckmann, C. F. and Smith, S. M., 2004, “Probabilistic Independent Component Analysis
for Functional Magnetic Resonance Imaging,” IEEE Transactions on Medical Imaging, Vol. 24, No. 2, 137-152.
Beckmann, C. F., 2012, “Modeling with independent components,” Neuroimage, Vol. 62,
No. 2, 891-901.
Carbone, T. A. and Semicond, F., 2000, “Measuring efficiency of semiconductor
manufacturing operations using data envelopment analysis. ”, IEEE 2000 Advanced
Semiconductor Manufacturing Conference and Workshop, Boston, USA.
Charnes, A. and Cooper, W. W., 1962, “Programming with linear fractional functions,” Naval Research Logistics Quarterly, Vol. 9, No. 3-4, 181-186.
Charnes, A., Cooper, W. W., and Rhodes, E., 1978, “Measuring the efficiency of decision making units,” European Journal of Operational Research, Vol. 2, No. 6, 429-444.
Chen, W. C., Chien, C. F., and Chou, M. H., 2008, “Economic efficiency analysis of wafer fabrication facilities. ”, Proceedings of the 2009 Winter Simulation Conference, Miami, USA.
Chueh, H. E. and Jheng, J. Y., 2012, “Applying data envelopment analysis to evaluation of
Taiwanese solar cell industry operational performance,” International Journal of Computer Science & Information Technology, Vol. 4, No. 4, 1-8.
Cichocki, A. and Amari, S., 2002, Adaptive Blind Signal and Image Processing:
Learning Algorithms and Applications, 1st, New York: John Wiley & Sons, Inc.
Comon, P., 1994, “Independent component analysis: a new concept?” Signal Processing, Vol. 36, No. 3, 287-314.
Cover, T. M. and Thomas, J. A., 1991, Elements of Information Theory, 2nd, New York:
John Wiley & Sons, Inc.
David, V. and Sanchez, A., 2002, “Frontiers of research in BSS/ICA,” Neruocomputing,
Vol. 49, No. 1-4, 7-23.
Déniz, O., Castrillón, M., and Hernández, M., 2003, “Face recognition using independent
component analysis and support vector machines,” Pattern Recognition Letters,
Vol. 24, No. 13, 2153-2157.
Dulá, J. H. and Hickman, B. L., 1997, “Effects of excluding the column being scored from the DEA envelopment LP technology matrix,” Journal of the Operational
Research Society, Vol. 48, No. 10, 1001-1012.
Dyson, R. G. and Thanassoulis, E., 1988, “Reducing weight flexibility in data envelopment
analysis,” Journal of the Operational Research Society, Vol. 39, No. 6, 563-576.
Eloyan, A. and Ghosh, S. K., 2013, “A semi-parametric approach to source separation using
independent component analysis,” Computational Statistics & Data Analysis, Vol. 58, No. 1, 383-396.
Farrar, D. E. and Glauber, R. R., 1967, “Multicollinearity in regression analysis: the problem revisited,” Review of Economics and Statistics, Vol. 49, No. 1, 92-107.
Farrell, M. J., 1957, “The measurement of productive efficiency,” Journal of the Royal
Statistical Society, Vol. 120, No. 3, 253-281.
Fried, H. O., Lovell, C. A. K., Schmidt, S. S., and Yaisawarng, S., 2002, “Accounting for
environmental effects and statistical noise in data envelopment analysis,” Journal
of Productivity Analysis, Vol. 17, No. 1-2, 157-174.
Fried, H. O., Schmidt, S. S., and Yaisawarng, S., 1999, “Incorporating the operating
environment into a nonparametric measure of technical efficiency,” Journal of
Productivity Analysis, Vol. 12, No. 32, 49-267.
Guo, Y., 2011, “A general probabilistic model for group independent component analysis
and its estimation methods,” Biometrics, Vol. 67, No. 4, 1532-1542.
Huang, M. Y. and Huang, S. Y., 2010, “Productivity evaluation of Taiwanese semiconductor
companies using a three-stage Malmquist DEA approach,” Journal of Applied
Economics, Special Issue, 31-57.
Hyvärinen, A. and Oja, E., 2000, “Independent component analysis: Algorithms and applications,” Neural Networks, Vol. 13, No. 4, 411-430.
Hyvärinen, A., 1999, “Fast and robust fixed-point algorithms for independent component
analysis,” IEEE Transactions on Neural Networks, Vol. 10, No. 3, 626-634.
Hyvärinen, A., Karhunen, J., and Oja, E., 2001, Independent Component Analysis, 1st, New York: John Wiley & Sons, Inc.
Kurowicka, D. and Cooke, R. M., 2006, Uncertainty Analysis and High Dimensional
Dependence Modeling, 1st, New York: John Wiley & Sons, Inc.
Leach, R. C. and Hodges, D. A., 1996, “Benchmarking semiconductor manufacturing,”
IEEE Transactions on Semiconductor Manufacturing, Vol. 9, No. 2, 158-169.
Lee, T. W., 1998, Independent Component Analysis: Theory and Application, 1st, Boston: Kluwer Academic Publishers.
Liu, W. F. and Wang, P. H., 2008, “DEA Malmquist productivity measure: Taiwanese
semiconductor companies,” International Journal of Production Economics, Vol.
112, No. 1, 367-379.
Roll, Y. W. and Golany, B., 1991, “Controlling factor weights in DEA,” IIE Transactions,
Vol. 23, No. 1, 2-9.
Shen, C. W., Cheng, M. J., and Chi, M. C., 2009, “Measurement of Production Efficiency in Semiconductor Assembly House: Approach of Data Envelopment Analysis” in
Soomro, S. (ed.), Engineering the Computer Science and IT, First Edition, New
Delhi, IN: In-Teh, 465-476.
Silva, E. and Lansink, O. A., 2013, “Dynamic efficiency measurement: a directional
distance function approach.” Unpublished Manuscript, Wageningen University.
Silva, E. and Stefanou, S., 2003, “Nonparametric Dynamic Production Analysis and the
Theory of Cost,” Journal of Productivity Analysis, Vol. 19, No. 2, 5-32.
Silvey, S. D., 1969, “Multicollinearity and imprecise estimation,” Journal of the Royal
Statistical Society, Vol. 31, No. 3, 539-552.
Thrall, R. M., 1996, “Duality, classification and slacks in DEA,” The Annals of
Operations Research, Vol. 66, No. 1, 109-138.
Tone, K., 2001, “A slacks-based measure of efficiency in data envelopment analysis,”
European Journal of Operational Research, Vol. 130, No. 3, 498-509.
Tone, K., 2002, “A slacks-based measure of super-efficiency in data envelopment analysis,”
European Journal of Operational Research, Vol. 143, No. 1, 32-41.
Vigario, R., Sarela, J., Jousmaki, V., Hamalainen, M., and Oja, E., 2000, “Independent
component approach to the analysis of EEG and MEG recordings,” IEEE
Transactions on Biomedical Engineering, Vol. 47, No. 5, 589-593.
Weber, C., 2004, “Yield learning and the sources of profitability in semiconductor
manufacturing and process development,” IEEE Transactions on Semiconductor
Manufacturing, Vol. 17, No. 4, 590-596.
Wena, H. C., Huanga, J. H., and Cheng, Y. L., 2012, “What Japanese semiconductor enterprises can learn from the asset-light business model for sustainable competitive advantage,” Asian Business & Management, Vol. 11, No. 5, 615-649.
Zhu, J., 2001, “Super-efficiency and DEA sensitivity analysis,” European Journal of
Operational Research, Vol. 129, No. 2, 443-455.