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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  );
  • 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.


  • 주제어

    Product Line Selection .   Data Envelopment Analysis.  

  • 참고문헌 (15)

    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 
    2. 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 
    3. Cooper, W. W., Seiford L. M., and Tone, K. (2000), Data Envelopment Analysis: Theory, Methodology and Application, Kluwer Academic, Boston, United States of America 
    4. 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 
    5. Simpson, T. W., Siddique, S., and Jiao, J. Eds. (2005), Product Platform and Product Family Design: Methods and Applications, Springer, New York 
    6. 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 
    7. Kota, S., Sethuraman, K. and Miller, R. (2000), A MetricV for Evaluating Design Commonality in Product Families, Journal of Mechanical Design, 122, 403-410 
    8. 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 
    9. 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 
    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 
    11. Spearman, C. (1904), The Proof and Measurement of Association Between Two Things, American Journal of Psychology, 15, 72-101 
    12. 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 
    13. 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 
    14. Miyashita, T. and Yamakawa H. (2002), A Study on the Collaborative Design Using Supervisor System, JSME International Journal, 45(1), 333-341 
    15. 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 
  • 이 논문을 인용한 문헌 (1)

    1. 2014. "" Industrial engineering & management systems : an international journal, 13(2): 210~220     

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