DOI QR코드

DOI QR Code

Prediction of Product Life Cycle Using Data Mining Algorithms : A Case Study of Clothing Industry

데이터마이닝 알고리즘을 이용한 제품수명주기 예측 : 의류산업 적용사례

  • Lee, Seulki (School of Industrial Management Engineering, Korea University) ;
  • Kang, Ji Hoon (School of Industrial Management Engineering, Korea University) ;
  • Lee, Hankyu (School of Industrial Management Engineering, Korea University) ;
  • Joo, Tae Woo (School of Industrial Management Engineering, Korea University) ;
  • Oh, Shawn (Fashion Business of Samsung Everland Inc.) ;
  • Park, Sungwook (Fashion Business of Samsung Everland Inc.) ;
  • Kim, Seoung Bum (School of Industrial Management Engineering, Korea University)
  • 이슬기 (고려대학교 산업경영공학과) ;
  • 강지훈 (고려대학교 산업경영공학과) ;
  • 이한규 (고려대학교 산업경영공학과) ;
  • 주태우 (고려대학교 산업경영공학과) ;
  • 오시연 (삼성에버랜드 패션사업부) ;
  • 박성욱 (삼성에버랜드 패션사업부) ;
  • 김성범 (고려대학교 산업경영공학과)
  • Received : 2014.01.06
  • Accepted : 2014.05.17
  • Published : 2014.06.15

Abstract

Demand forecasting plays a key role in overall business activities such as production planning, distribution management, and inventory management. Especially, for a fast-changing environment of the clothing industry, logical forecasting techniques are required. In this study, we propose a procedure to predict product life cycle using data mining algorithms. The proposed procedure involves three steps : extracting key variables from profiles, clustering, and classification. The effectiveness and applicability of the proposed procedure were demonstrated through a real data from a leading clothing company in Korea.

Keywords

References

  1. Bowerman, B. L., O'Connell, R. T. and Koehler, A. B. (2005), Forecasting, time series, and regression : an applied approach, South-Western Pub.
  2. Buzzell, R. D. (1966), Competitive behavior and product life cycles, New ideas for successful marketing, 46-68.
  3. Choi, J. H. and Kang, H. C. (2001), A Study on the Demand Forecasting using Diffusion Models and Growth Curve Models, The Korean journal of applied statistics, 14(2), 233-243.
  4. Curram, S. P. and Mingers, J. (1994), Neural networks, decision tree induction and discriminant analysis : an empirical comparison, Journal of the Operational Research Society, 440-450.
  5. Gorden, A. D. (1999), Classification, Chapman and Hall/CRC, New York, USA.
  6. Hastie et al. (2001), The Element of Statistical Learning, Springer.
  7. Hong, J. S. and Koo, H. Y. (2013), Constrained NLS Method for Longterm Forecasting with Short-term Demand Data of a New Product, Journal of the Korean Operations Research and Management Science Society, 33(1), 45-59.
  8. Kaufman, L. and Rousseeuw, P. J. (2009), Finding groups in data : an introduction to cluster analysis, Wiley.com.
  9. KIET (2012), Apparel stock market status and Implications, Policy report of Ministry of Knowledge Economy, http://www.korea.kr/archive/ expDocView.do?docId=32370.
  10. Kim, G. S., Kim, J. h., Kim, H. T., Suh, C. J. and Erh, Y. Y. (2011), Production and operations management system of the green era, 200- 212, Bobmunsa, Seoul, Korea.
  11. Kim, J. H. (2004), ROC and Cost Graphs for General Cost Matrix Where Correct Classifications Incur Non-zero Costs1), Communications for statistical applications and methods, 11(1), 21-30. https://doi.org/10.5351/CKSS.2004.11.1.021
  12. Kim, K. H., Kim, J. S., Kang, H. I. and Jun, C. H. (2000), Market Forecasting Modeling Using the Diffusion Model for the Strategic Items in Information/Telecommunication Aria, Electronics and Telecommunications Trends, 15(6), 178-189.
  13. Kim, O. N. (2008), Demand forecasting system, how to build one, LG Economic Research Institute, Seoul, Korea.
  14. Kim, U. C. (2006), Modern Statistics, 4th, 427-428, Youngji publishers, Seoul, Korea.
  15. Koo, H. Y. and Min, D. K. (2013), Forecasting renewable energy using delphi survey and the economic evaluation of long-term generation mix, Journal of the Korean Institute of Industrial Engineers, 39(3), 183-191. https://doi.org/10.7232/JKIIE.2013.39.3.183
  16. Lim, J. I. and Oh, H. S. (1992), A Study on the New Product Forecasting Methodology, Journal of the Korean Institute of Industrial Engineers, 18(2), 51-63.
  17. Mahajan, V. and Muller, E. (1979), Innovation diffusion and new product growth models in marketing, The Journal of Marketing, 55-68.
  18. Mahajan, V. (Ed.) (1986), Innovation diffusion models of new product acceptance, Ballinger.
  19. Min, J. H. and Jeong, C. W. (2009), A GA-based Classification Model for Predicting Consumer Choice, Journal of the Korean Operations Research and Management Science Society, 34(3), 29-41.
  20. Park, S. B., Kim, J. W., Jeon, S. I., Kim, C. W., Choi, E. J., Lee, C. H. and Huh, Y. S. (2012), Effective demand forecasting methods and practices, Samsung Economic Research Institute, Seoul, Korea.
  21. Rousseeuw, P. J. (1987), Silhouettes : a graphical aid to the interpretation and validation of cluster analysis, Journal of computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
  22. Shin, S. C. and Song, M. S. (2003), Bias Reduction in Split Variable Selection in C4. 5, Communications of the Korean statistical society, 10(3), 627-635. https://doi.org/10.5351/CKSS.2003.10.3.627
  23. Shmueli, G., Patel, N. R. and Bruce, P. C. (2008), Data mining for business intelligence : Concepts, techniques, and applications in Microsoft Office Excel with XLMiner, Wiley.com.
  24. Thomassey, S. and Fiordaliso, A. (2006), A hybrid sales forecasting system based on clustering and decision trees, Decision Support Systems, 42(1), 408-421. https://doi.org/10.1016/j.dss.2005.01.008
  25. Thomassey, S. and Happiette, M. (2007), A neural clustering and classification system for sales forecasting of new apparel items, Applied Soft Computing, 7(4), 1177-1187. https://doi.org/10.1016/j.asoc.2006.01.005
  26. Ye, J. S. and Kim, M. S. (2005), New Marketing, 295-307, Pakyoungsa, Seoul, Korea.
  27. Turney, P. (1995), Cost-sensitive classification : Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research (JAIR), 2.