Skip to main content
SpringerLink
Log in

Measuring the relative performance for leading fabless firms by using data envelopment analysis

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

IC Design (fabless) is critical for the global semi-conductor industry. The total revenue of all global fabless firms in 2003 was about US$20 billion, with the top 30 firms earning accounting for 96% of the market share. To examine the leaders in the field, this research analyzes the relative performances of those top 30 fabless firms. Fabless firms are often evaluated based on subjective judgments, and an overall scheme to measure the performance involving objective, multi-input and multi-output criteria is yet to be established. There is also a need for identifying and determining suggestions of how specific firms could improve their performance. Data Envelopment Analysis (DEA) method has been employed in this paper to satisfy the above needs. Using the input and output data of 2003, this study used the DEA method to build a model to evaluate the performance of those global top 30 fabless firms. The current research used four efficiency models: CCR, A&P, BCC, and Cross-Efficiency. To offer a comparison of efficiencies and associated discussions, an analysis of the Scale-Return is provided. Finally, the performance of various fabless firms in 2003 is analyzed. According to the CCR and A&P models, the results showed that the top ten Decision Management Units (DMUs) achieved better operation performance among the 30 leading global fabless firms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Andersen P., Petersen N.C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science 39(10): 1261–1264

    Article  Google Scholar 

  • Banker R.D., Charnes A., Cooper W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30(9): 1078–1092

    Google Scholar 

  • Brown M.G., Svenson R.A. (1998). Measuring R&D productivity. Research Technology Management 41(6): 30–35

    Google Scholar 

  • Cavalluzzo K.S., Ittner C.D. (2004). Implementing performance measurement innovations: Evidence from government. Accounting, Organizations and Society 29(3–4): 243–267

    Article  Google Scholar 

  • Chang P.-L., Tsai C.-T. (2002). Finding the niche position—competition strategy of Taiwan’s IC design industry. Technovation 22(2): 101–111

    Article  Google Scholar 

  • Charnes A., Clark T., Cooper W.W., Golany B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces. Annals of Operation Research 2(1): 95–112

    Article  Google Scholar 

  • Charnes A., Cooper W.W. (1984). The non-Archimedean CCR ratio for efficiency analysis:A rejoinder Boyd and Fare. European Journal of Operational Research 15: 333–334

    Article  Google Scholar 

  • Charnes A., Cooper W.W., Rhodes E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research 2: 429–444

    Article  Google Scholar 

  • Chen Y., Iqbal Ali A. (2002). Output–input ratio analysis and DEA frontier. European Journal of Operational Research 142(3) 1: 476–479

    Article  Google Scholar 

  • Chen Y., Morita H., Zhu J. (2003). Multiplier bounds in DEA via strong complementary slackness condition solution. International Journal of Production Economics 86(1): 11–19

    Article  Google Scholar 

  • Doyle J. (1992). MCC:Multiple correlation clustering. International Journal of Man-Machine Studies 37: 751–765

    Article  Google Scholar 

  • Doyle J., Green R. (1994). Efficiency and cross-efficiency in DEA:Derivations, meanings and uses. Journal of the Operational Research Society 45(5): 567–578

    Article  Google Scholar 

  • Everitt B. (1980). Cluster analysis (2nd ed). London, Halsted Press (Wiley)

    Google Scholar 

  • Farrell M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General 120(3): 253–281

    Article  Google Scholar 

  • Folan P., Browne J. (2005). A review of performance measurement: Towards performance management. Computers in Industry 56(7): 663–680

    Article  Google Scholar 

  • Golany B., Roll Y. (1989). An application procedure for DEA. OMEGA 17(3): 237–250

    Article  Google Scholar 

  • Hoque Z. (2004). A contingency model of the association between strategy, environmental uncertainty and performance measurement: Impact on organizational performance. International Business Review 13(4): 485–502

    Article  Google Scholar 

  • Hung S.-W., Yang C. (2003). The IC fabless industry in Taiwan: Current status and future challenges. Technology in Society 25(3): 385–402

    Article  Google Scholar 

  • Ittner C.D., Larcker D.F., Randall T. (2003). Performance implications of strategic performance measurement in financial services firms. Accounting, Organizations and Society 28(7–8): 715–741

    Article  Google Scholar 

  • Kleine A. (2004). A general model framework for DEA. Omega 32(1): 17–23

    Article  Google Scholar 

  • Lee E.S., Li R.J. (1993). Fuzzy multiple objective programming and compromise programming with pareto optimum. Fuzzy Sets and Systems 53(3): 275–288

    Article  Google Scholar 

  • Lewis H.F., Sexton T.R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers and Operations Research 31(9): 1365–1410

    Article  Google Scholar 

  • Liu, L.-C., Lee, C., & Tzeng, G.-H. (2003a). DEA for evaluating the current-period and cross-period efficiency of Taiwan’s upgraded technical institutes. In T. Tanino, T. Tanaka, & M. Inuiguchi (Eds.), Multi-objective programming and goal-programming: Theory and applications (pp. 353–360). Springer.

  • Liu, L.-C., Lee, C., & Tzeng, G.-H. (2003b). Using DEA of REM and EAM for efficiency assessment of technology institutes upgraded from junior colleges: The case in Taiwan. In T. Tanino, T. Tanaka, & M. Inuiguchi (Eds.) Multi-objective programming and goal- programming: Theory and applications (pp. 361–366). Springer.

  • Liu L.-C., Lee C., Tzeng G.-H. (2004). DEA approach of the current-period and cross-period efficiency for evaluating the vocational education. International Journal of Information Technology and Decision Making 3(2): 1–22

    Google Scholar 

  • Ohta H., Yamaguchi T. (1995). Multi-goal programming including fractional goal in consideration of fuzzy solutions. Journal of Japan Society for Fuzzy Theory and System 7(6): 1221–1228

    Google Scholar 

  • Opricovic, S., & Tzeng, G.-H. (2003). Comparing DEA and MCDM method. In T. Tanino, T. Tanaka, & M. Inuiguchi (Eds.), Multi-objective programming and goal-Programming: Theory and applications (pp. 227–232). Springer.

  • Poh K.L., Ang B.W., Bai F. (2001). A comparative analysis of R&D project evaluation. R&D Management 31(1): 63–74

    Article  Google Scholar 

  • Schmitz J., Platts K.W. (2004). Supplier logistics performance measurement: Indications from a study in the automotive industry. International Journal of Production Economics 89(2): 231–243

    Article  Google Scholar 

  • Semiconductor Yearbook of 2003. International Technology Information Services. Industrial Technology Research Institute in Taiwan, pp. 52–61..

  • Shuai J.-J., Tzeng G.-H., Li H.-L. (2004). The multi-source fabless-foundry partnership selection model. International Journal of Manufacturing Technology and Management 6(1/2): 137–154

    Article  Google Scholar 

  • Tzeng G.-H., Feng G.-M., Kang C.-C. (2001). The fuzzy set theory and DEA model for forecasting production efficiency: Case study for Taipei city bus company. Journal of Advance Computational Intelligences 5(3): 128–138

    Google Scholar 

  • Yu J.-R., Tzeng Y.-C., Tzeng G.-H., Yu T.Y., Sheu H.J. (2004). Fuzzy multiple objective programming approach to DEA with imprecise data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12(5): 591–600

    Article  Google Scholar 

  • Zimmermann H.J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems 1(1): 45–55

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Business Systems and Knowledge Modeling Laboratory, La Trobe University, Bundoora Campus, Melbourne, VIC, 3086, Australia

    Mei-Tai Chu & R. Khosla

  2. Institute of Management of Technology, National Chiao-Tung University, 1001 Ta Hsueh Rd., Hsinchu, 300, Taiwan

    Joesph Z. Shyu

Authors
  1. Mei-Tai Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joesph Z. Shyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Khosla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mei-Tai Chu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chu, MT., Shyu, J.Z. & Khosla, R. Measuring the relative performance for leading fabless firms by using data envelopment analysis. J Intell Manuf 19, 257–272 (2008). https://doi.org/10.1007/s10845-008-0079-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-008-0079-3

Keywords

Search

Navigation