0
Research Papers

Continuous Preference Trend Mining for Optimal Product Design With Multiple Profit Cycles

[+] Author and Article Information
Jungmok Ma

Enterprise Systems Optimization Laboratory,
Department of Industrial and Enterprise
Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: jma15@illinois.edu

Harrison M. Kim

Associate Professor
Enterprise Systems Optimization Laboratory,
Department of Industrial and Enterprise
Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: hmkim@illinois.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 23, 2013; final manuscript received February 14, 2014; published online April 11, 2014. Assoc. Editor: Bernard Yannou.

J. Mech. Des 136(6), 061002 (Apr 11, 2014) (14 pages) Paper No: MD-13-1226; doi: 10.1115/1.4026937 History: Received May 23, 2013; Revised February 14, 2014

Product and design analytics is emerging as a promising area for the analysis of large-scale data and usage of the extracted knowledge for the design of optimal system. The continuous preference trend mining (CPTM) algorithm and application proposed in this study address some fundamental challenges in the context of product and design analytics. The first contribution is the development of a new predictive trend mining technique that captures a hidden trend of customer purchase patterns from accumulated transactional data. Unlike traditional, static data mining algorithms, the CPTM does not assume stationarity but dynamically extracts valuable knowledge from customers over time. By generating trend embedded future data, the CPTM algorithm not only shows higher prediction accuracy in comparison with well-known static models but also provides essential properties that could not be achieved with previously proposed models: utilizing historical data selectively, avoiding an over-fitting problem, identifying performance information of a constructed model, and allowing a numeric prediction. The second contribution is the formulation of the initial design problem which can reveal an opportunity for multiple profit cycles. This mathematical formulation enables design engineers to optimize product design over multiple life cycles while reflecting customer preferences and technological obsolescence using the CPTM algorithm. For illustration, the developed framework is applied to an example of tablet PC design in leasing market and the result shows that the determination of optimal design is achieved over multiple life cycles.

FIGURES IN THIS ARTICLE
<>
Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.

References

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. [CrossRef]
Tucker, C. S., and Kim, H. M., 2011, “Trend Mining for Predictive Product Design,” ASME J. Mech. Des., 133(11), p. 111008. [CrossRef]
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.
Tucker, C. S., 2011, “Data Trend Mining Design for Predictive Systems Design,” Ph.D. thesis, University of Illinois, Chicago, IL.
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.
Environmental Protection Agency, 2011, “Electronics Waste Management in the United States Through 2009,” U.S. EPA, May, Report EPA No. 530-R-11-002.
Sodhi, M. S., and Reimer, B., 2001, “Models for Recycling Electronics End-Of-Life Products,” OR Spektrum, 23(1), pp. 97–115. [CrossRef]
Fishbein, B. K., 1998, “EPR: What Does it Mean? Where is it headed?,” P2: Pollution Prevention Rev., 8(4), pp. 43–55. [CrossRef]
Product Stewardship Institute, 2012, “Extended Producer Responsibility State Laws.” Available at: http://productstewardship.us (accessed in May 2013).
Wagner, S., 2003, Understanding Green Consumer Behaviour: A Qualitative Cognitive Approach, Consumer Research and Policy Series, Taylor & Francis Group.
Environmental Protection Agency, 2011, “Benefits of the Remanufacturing Exclusion: Background Document in Support of the Definition of Solid Waste Rule,” June, Washington, DC.
Hucal, M., 2008, “Product Recycling Creates Multiple Lives for Caterpillar Machines,” Peoria Magazines, September.
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.
Parker, D., and Butler, P., 2007, “An Introduction to Remanufacturing.” Available at: http://www.remanufacturing.org.uk (accessed in May 2013).
Kusiak, A., and Smith, M., 2007, “Data Mining in Design of Products and Production Systems,” Annu. Rev. Control, 31(1), pp. 147–156. [CrossRef]
Böttcher, M., Spott, M., and Kruse, R., 2008, “Predicting Future Decision Trees From Evolving Data,” Proceedings of ICDM’08, pp. 33–42. [CrossRef]
Böttcher, M., 2011, “Contrast and Change Mining,” Wiley Interdiscip. Rev.: Data Mining Knowledge Discovery, 1(3), pp. 215–230. [CrossRef]
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/
Ma, J., Kwak, M., and Kim, H. M., 2014. “Demand Trend Mining for Predictive Life Cycle Design,” J. Clean. Prod. [CrossRef]
Vapnik, V. N., 1998, Statistical Learning Theory, Wiley-Interscience, Hoboken, NJ.
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Hyndman, R., Koehler, A., Ord, J. K., and Snyder, R., 2008, Forecasting with Exponential Smoothing: The State Space Approach, Springer-Verlag, Berlin, Heidelberg.
Quinlan, J. R., 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann Series in Machine Learning, Morgan Kaufmann Publishers.
Quinlan, J. R., 1986, “Induction of Decision Trees,” Mach. Learn., 1(1), pp. 81–106. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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.
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.
Decker, R., and Trusov, M., 2010, “Estimating Aggregate Consumer Preferences From Online Product Reviews,” Int. J. Res. Market., 27(4), pp. 293–307. [CrossRef]
De'ath, G., 2002, “Multivariate Regression Trees: A New Technique for Modeling Species-Environment Relationships,” Ecology, 83(4), pp. 1105–1117. [CrossRef]
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.
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. [CrossRef]
Hyndman, R., and Khandakar, Y., 2008, “Automatic Time Series Forecasting: The Forecast Package for R,” J. Stat. Softw., 27(3), pp. 1–22.
Quinlan, J. R., 1992, Learning With Continuous Classes, World Scientific, Singapore, pp. 343–348.
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.
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. [CrossRef]
R Development Core Team, 2008, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
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. [CrossRef]
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. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

Product life cycle in leasing market

Grahic Jump Location
Fig. 2

Overall flow of methodology

Grahic Jump Location
Fig. 3

A schematic of CPTM Algorithm

Grahic Jump Location
Fig. 4

Graphical example of trend embedded data generation

Grahic Jump Location
Fig. 5

Example of model tree

Grahic Jump Location
Fig. 6

Architecture of optimal design with CPTM

Grahic Jump Location
Fig. 7

Data from stationary linear mapping function and generated future data

Grahic Jump Location
Fig. 8

Data from stationary nonlinear mapping function and generated future data

Grahic Jump Location
Fig. 9

Comparison of one time-ahead prediction accuracy between static and dynamic model

Grahic Jump Location
Fig. 10

Comparison of 1, 2, 3, and 4 time-ahead prediction accuracy between static and dynamic model

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In