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

Abstract

Product and design analytics is emerging as a promising area for the analysis of largescale 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.

Design for Marketing Mix: The Past, Present, and Future of Market-Driven Product Design

“… combine customer preference and technological obsolescence [79],  create new choice modeling scenarios [80],  explore the viability of Twitter as s source for product opinions that could inform models describing purchasing decisions [81],  employ sparse coding and sparse restricted Boltzmann machines to yield high-accuracy predictions of preference [82],  and, create market segments from online reviews focused on individual product attributes (such as zoom on a camera) and to identify attribute important rankings [83].…”

Predictive Model Selection for Forecasting Product Returns

“…Forecasting product returns is defined as the prediction of the quantity and timing of product returns or cores, which minimizes the additional costs in remanufacturing. Simple and naive approaches include either using the proportion of returns to sales with a known life cycle length [1] or limiting to the special market environments, such as take-back programs [4] or lease contracts [5]. Various advanced approaches were proposed such as causal analysis, simulation/ soft-computing, and statistical methods [3].…”

“…In remanufacturing operations, design problems can be formulated [4,5]. For example, different specifications of computers can be remanufactured with a combination of different cores, which will provide different market shares.…”

The use of analytics in the design of sociotechnical products

“…In other work, a continuous preference trend mining algorithm is developed that captures hidden trends of customer purchase patterns from large accumulated transactional data (Ma & Kim, 2013). This algorithm also allows engineering designers in the early stages of design to determine effective product configurations that meet customer preferences and meet technological obsolescence requirements.…”