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.

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Fig. 1

Product life cycle in leasing market

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Fig. 2

Overall flow of methodology

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Fig. 3

A schematic of CPTM Algorithm

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Fig. 4

Graphical example of trend embedded data generation

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Fig. 5

Example of model tree

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Fig. 6

Architecture of optimal design with CPTM

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Fig. 7

Data from stationary linear mapping function and generated future data

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Fig. 8

Data from stationary nonlinear mapping function and generated future data

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Fig. 9

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

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Fig. 10

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




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