Browse > Article
http://dx.doi.org/10.7232/JKIIE.2014.40.3.291

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

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)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.40, no.3, 2014 , pp. 291-298 More about this Journal
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
Product Life Cycle; Demand Forecasting; Clothing Industry; Data Mining;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 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.
2 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.   DOI   ScienceOn
3 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.
4 Thomassey, S. and Fiordaliso, A. (2006), A hybrid sales forecasting system based on clustering and decision trees, Decision Support Systems, 42(1), 408-421.   DOI   ScienceOn
5 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.   DOI   ScienceOn
6 Ye, J. S. and Kim, M. S. (2005), New Marketing, 295-307, Pakyoungsa, Seoul, Korea.
7 Turney, P. (1995), Cost-sensitive classification : Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research (JAIR), 2.
8 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.
9 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.
10 Kaufman, L. and Rousseeuw, P. J. (2009), Finding groups in data : an introduction to cluster analysis, Wiley.com.
11 KIET (2012), Apparel stock market status and Implications, Policy report of Ministry of Knowledge Economy, http://www.korea.kr/archive/ expDocView.do?docId=32370.
12 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.   DOI
13 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.
14 Kim, O. N. (2008), Demand forecasting system, how to build one, LG Economic Research Institute, Seoul, Korea.
15 Mahajan, V. and Muller, E. (1979), Innovation diffusion and new product growth models in marketing, The Journal of Marketing, 55-68.
16 Kim, U. C. (2006), Modern Statistics, 4th, 427-428, Youngji publishers, Seoul, Korea.
17 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.   과학기술학회마을   DOI   ScienceOn
18 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.   과학기술학회마을
19 Mahajan, V. (Ed.) (1986), Innovation diffusion models of new product acceptance, Ballinger.
20 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.   과학기술학회마을
21 Bowerman, B. L., O'Connell, R. T. and Koehler, A. B. (2005), Forecasting, time series, and regression : an applied approach, South-Western Pub.
22 Buzzell, R. D. (1966), Competitive behavior and product life cycles, New ideas for successful marketing, 46-68.
23 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.   과학기술학회마을
24 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.
25 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.   DOI   ScienceOn
26 Gorden, A. D. (1999), Classification, Chapman and Hall/CRC, New York, USA.
27 Hastie et al. (2001), The Element of Statistical Learning, Springer.