Industrial Engineering and Management Systems

  • 제8권1호
  • /
  • Pages.47-53
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  • 2009
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  • 1598-7248(pISSN)
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  • 2234-6473(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지

An Exploration on the Use of Data Envelopment Analysis for Product Line Selection

  • Lin, Chun-Yu (Department of Industrial and Manufacturing Engineering, The Pennsylvania State University) ;
  • Okudan, Gul E. (School of Engineering Design, The Pennsylvania State University)
  • 투고 : 2008.01.22
  • 심사 : 2008.09.14
  • 발행 : 2009.03.31
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초록

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

키워드

참고문헌

  1. Adler, N., Friedman, L., and Sinuany-Stern, Z. (2002), Review of Ranking Methods in the Data Envelopment Analysis Context, European Journal of Operation Research, 140, 249-265 https://doi.org/10.1016/S0377-2217(02)00068-1
  2. Banker, R. D., Charnes, A., and Cooper W. W. (1984), Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9), 1078-1092 https://doi.org/10.1287/mnsc.30.9.1078
  3. Charnes, A., Cooper, W. W., and Rhodes, E. (1978), Measuring the Efficiency of Decision Making Units, European Journal of Operation Research, 2(6), 429-444 https://doi.org/10.1016/0377-2217(78)90138-8
  4. Charnes, A., Cooper, W. W., Lewin, A. Y., and Seiford L. M. (1994), Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic, Boston, United States of America
  5. Cooper, W. W., Seiford L. M., and Tone, K. (2000), Data Envelopment Analysis: Theory, Methodology and Application, Kluwer Academic, Boston, United States of America
  6. Dyson, R. G., Allen, R., Camanho, A., Podinovski, V. V., Sarrico, C. S., and Shale. E. A. (2001), Pitfalls and Protocols in DEA, European Journal of Operation Research, 132(2), 245-259 https://doi.org/10.1016/S0377-2217(00)00149-1
  7. Farris, J. A., Groesbeck, R. L., Van Aken, E. M., and Letens, G. (2006), Evaluating the Relative Performance of Engineering Design Project: A Case Study Using Data Envelopment Analysis, IEEE Transactions on Engineering Management, 53(3): 471-482 https://doi.org/10.1109/TEM.2006.878100
  8. Kota, S., Sethuraman, K. and Miller, R. (2000), A MetricV for Evaluating Design Commonality in Product Families, Journal of Mechanical Design, 122, 403-410 https://doi.org/10.1115/1.1320820
  9. Miyashita, T. and Yamakawa H. (2002), A Study on the Collaborative Design Using Supervisor System, JSME International Journal, 45(1), 333-341 https://doi.org/10.1299/jsmec.45.333
  10. Paradi, J. C., Smith, S., and Schaffnit-Chatterjee, C. (2002), Knowledge Worker Performance Analysis Using DEA: An Application to Engineering Design Team at Bell Canada, IEEE Transactions on Engineering Management, 49(1), 161-172 https://doi.org/10.1109/TEM.2002.1010884
  11. Sexson, T. R., Silkman, R. H., and Hogan, A. J. (1986), Data Envelopment Analysis: Critique and Extensions, In Measuring Efficiency: An Assessment of Data Envelopment Analysis (Ed.: Silkman, R.H.), Jossey-Bass, San Francisco, 73-105
  12. Simpson, T. W., Siddique, S., and Jiao, J. Eds. (2005), Product Platform and Product Family Design: Methods and Applications, Springer, New York
  13. Thevenot, H. J., Steva, E. D., Okudan, G. E., and Simpson, T. W. (2006), A Multi-Attribute Utility Theory-Based Approach for Product Line Consolidation and Selection, 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Philadelphia, PA, ASME, Paper No. DETC2006-DTM99506 https://doi.org/10.1115/DETC2006-99506
  14. Thevenot, H. J., Steva, E. D., Okudan, G. and Simpson, T. W. (2007), A Multi-Attribute Utility Theory-Based Approach to Product Line Selection, Journal of Mechanical Design, 129(11), 1179-1184 https://doi.org/10.1115/1.2771574
  15. Spearman, C. (1904), The Proof and Measurement of Association Between Two Things, American Journal of Psychology, 15, 72-101 https://doi.org/10.2307/1412159