Continuous preference trend mining for optimal product design with multiple profit cycles

Journal of Mechanical Design, Transactions of the ASME

Volumn 136, Issue 6, 2014, Pages

  Ma, Jungmok ^a   Kim, Harrison M ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

continuous preference trend mining; design for multiple profit cycles; model tree; product and design analytics

Indexed keywords

DATA MINING; LIFE CYCLE; OBSOLESCENCE; OPTIMAL SYSTEMS; PERSONAL COMPUTERS; PRODUCT DESIGN; SALES;

CONTINUOUS PREFERENCE TREND MINING; DESIGN FOR MULTIPLE PROFIT CYCLE; LARGE SCALE DATA; MINING ALGORITHMS; MINING TECHNIQUES; MODEL TREES; PRODUCT AND DESIGN ANALYTIC; STATIC DATUM; TRANSACTIONAL DATA; TREND MININGS;

PROFITABILITY;

EID: 84898629908     PISSN: 10500472     EISSN: None     Source Type: Journal    
DOI: 10.1115/1.4026937     Document Type: Article

References (43)

1. Tucker, C. S., and Kim, H. M., 2008, "Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization," ASME J. Mech. Des., 130(4), pp. 991-1000.
2. Tucker, C. S., and Kim, H. M., 2011, "Trend Mining for Predictive Product Design," ASME J. Mech. Des., 133(11), p. 111008.
3. Van Horn, D., Olewnik, A., and Lewis, K., 2012, "Design Analytics: Capturing, Understanding and Meeting Customer Needs Using Big Data," ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2011), Paper No. DETC2012-71038.
4. Tucker, C. S., 2011, "Data Trend Mining Design for Predictive Systems Design," Ph.D. thesis, University of Illinois, Chicago, IL.
5. Rai, R., 2012, "Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews," ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2011), Paper No. DETC2012-70493.
6. Environmental Protection Agency, 2011, "Electronics Waste Management in the United States Through 2009," U.S. EPA, May, Report EPA No. 530-R-11-002.
7. Sodhi, M. S., and Reimer, B., 2001, "Models for Recycling Electronics End-Of-Life Products," OR Spektrum, 23(1), pp. 97-115. (Pubitemid 33692895)
8. Fishbein, B. K., 1998, "EPR: What Does it Mean? Where is it headed?," P2: Pollution Prevention Rev., 8(4), pp. 43-55.
9. Product Stewardship Institute, 2012, "Extended Producer Responsibility State Laws." Available at: http://productstewardship.us (accessed in May 2013).
10. Wagner, S., 2003, Understanding Green Consumer Behaviour: A Qualitative Cognitive Approach, Consumer Research and Policy Series, Taylor & Francis Group.
11. Environmental Protection Agency, 2011, "Benefits of the Remanufacturing Exclusion: Background Document in Support of the Definition of Solid Waste Rule," June, Washington, DC.
12. Hucal, M., 2008, "Product Recycling Creates Multiple Lives for Caterpillar Machines," Peoria Magazines, September.
13. King, A., Miemczyk, J., and Bufton, D., 2006, "Photocopier Remanufacturing at Xerox uk a Description of the Process and Consideration of Future Policy Issues," Innovation in Life Cycle Engineering and Sustainable Development, D. Brissaud, S. Tichkiewitch, and P. Zwolinski, eds., Springer Netherlands, pp. 173-186.
14. Parker, D., and Butler, P., 2007, "An Introduction to Remanufacturing." Available at: http://www.remanufacturing.org.uk (accessed in May 2013).
15. Kusiak, A., and Smith, M., 2007, "Data Mining in Design of Products and Production Systems," Annu. Rev. Control, 31(1), pp. 147-156. (Pubitemid 46809572)
16. Bottcher, M., Spott, M., and Kruse, R., 2008, "Predicting Future Decision Trees From Evolving Data," Proceedings of ICDM'08, pp. 33-42.
17. Bottcher, M., 2011, "Contrast and Change Mining," Wiley Interdiscip. Rev.: Data Mining Knowledge Discovery, 1(3), pp. 215-230.
18. Klinkenberg, R., 2004. "Learning Drifting Concepts: Example Selection vs. Example Weighting," Intell. Data Anal., 8(3), pp. 281-300. Available at: http://www.iospress.nl/
19. Ma, J., Kwak, M., and Kim, H. M., 2014. "Demand Trend Mining for Predictive Life Cycle Design," J. Clean. Prod.
20. Vapnik, V. N., 1998, Statistical Learning Theory, Wiley-Interscience, Hoboken, NJ.
21. Fixson, S. K., 2004, "Assessing Product Architecture Costing: Product life cycles, Allocation Rules, and Cost Models," ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2004), Paper No. DETC2004-57458.
22. Duverlie, P., and Castelain, J. M., 1999, "Cost Estimation During Design Step: Parametric Method Versus Case Based Reasoning Method," Int. J. Adv. Manuf. Technol., 15(12), pp. 895-906. (Pubitemid 30531741)
23. Seo, K., Park, J., Jang, D., and Wallace, D., 2002, "Approximate Estimation of the Product Life Cycle Cost Using Artificial Neural Networks in Conceptual Design," Int. J. Adv. Manuf. Technol., 19(6), pp. 461-471. (Pubitemid 34573277)
24. Zhao, Y., Pandey, V., Kim, H. M., and Thurston, D., 2010, "Varying Lifecycle Lengths Within a Product Take-Back Portfolio," ASME J. Mech. Des., 132(9), p. 091012.
25. Hyndman, R., Koehler, A., Ord, J. K., and Snyder, R., 2008, Forecasting with Exponential Smoothing: The State Space Approach, Springer-Verlag, Berlin, Heidelberg.
26. Quinlan, J. R., 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann Series in Machine Learning, Morgan Kaufmann Publishers.
27. Quinlan, J. R., 1986, "Induction of Decision Trees," Mach. Learn., 1(1), pp. 81-106.
28. Witten, I., and Frank, E., 2005, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., The Morgan Kaufmann Series in Data Management Systems, Elsevier Science.
29. Cheung, K.-W., Kwok, J. T., Law, M. H., and Tsui, K.-C., 2003, "Mining Customer Product Ratings for Personalized Marketing," Decision Support Syst., 35(2), pp. 231-243.
30. Archak, N., Ghose, A., and Ipeirotis, P. G., 2011, "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Manage. Sci., 57(8), pp. 1485-1509.
31. Ferreira, L., Jakob, N., and Gurevych, I., 2008, "A Comparative Study of Feature Extraction Algorithms in Customer Reviews," 2008 IEEE International Conference on Semantic Computing, pp. 144-151.
32. Abulaish, M., Jahiruddin, Doja, M. N., and Ahmad, T., 2009, "Feature and Opinion Mining for Customer Review Summarization," Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI'09, Springer-Verlag, pp. 219-224.
33. Decker, R., and Trusov, M., 2010, "Estimating Aggregate Consumer Preferences From Online Product Reviews," Int. J. Res. Market., 27(4), pp. 293-307.
34. Death, G., 2002, "Multivariate Regression Trees: A New Technique for Modeling Species-Environment Relationships," Ecology, 83(4), pp. 1105-1117.
35. Kwak, M., and Kim, H. M., 2011, "Market-Driven Positioning of Remanufactured Product for Design for Remanufacturing With Part Upgrade," ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2011), Paper No. DETC2011-48432.
36. Yue, S., Pilon, P., and Cavadias, G., 2002, "Power of the Mannkendall and Spearman's Rho Tests for Detecting Monotonic Trends in Hydrological Series," J. Hydrol., 259(14), pp. 254-271. (Pubitemid 34175777)
37. Hyndman, R., and Khandakar, Y., 2008, "Automatic Time Series Forecasting: The Forecast Package for R," J. Stat. Softw., 27(3), pp. 1-22.
38. Quinlan, J. R., 1992, Learning With Continuous Classes, World Scientific, Singapore, pp. 343-348.
39. Wang, Y., and Witten, I. H., 1997, "Inducing Model Trees for Continuous Classes," Proceedings of the 9th European Conference on Machine Learning Poster Papers, pp. 128-137.
40. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H., 2009, "The Weka Data Mining Software: An Update," SIGKDD Explor. Newsl., 11(1), pp. 10-18.
41. R Development Core Team, 2008, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
42. Kwak, M., Kim, H. M., and Thurston, D., 2012, "Formulating Second-Hand Market Value as a Function of Product Specifications, Age, and Conditions," ASME J. Mech. Des., 134(3), p. 032001.
43. Shrestha, D. L., and Solomatine, D. P., 2006, "Machine Learning Approaches for Estimation of Prediction Interval for the Model Output," Neural Netw., 19(2), pp. 225-235.