Predictive, data-driven product family design

Jungmok Ma, Harrison M. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Predictive design analytics is a new paradigm to enable design engineers to extract knowledge from large-scale, multidimensional, unstructured, volatile data, and transform the knowledge and its trend into design decision making. Predictive, data driven family design (PDFD) is proposed as one of the predictive design analytics methods to tackle some issues in family design. First, a number and specifications of product architectures are determined by data (not by pre-defined market segments) in order to maximize expected profit. A trade-off between price and cost in terms of the quantity and specifications of architectures helps to set the target in the enterprise level. k-means clustering is used to find architectures that minimize within architecture sum of squared errors. Second, a price prediction method as a function of product performance and deviations between performance and customer requirements is suggested with exponential smoothing based on innovations state space models. Regression coefficients are treated as customer preferences over product performance, and analyzed as a time series. Prediction intervals are proposed to show market uncertainties. Third, multiple values for common parameters in family design can be identified using the expectation maximization clustering so that multipleplatform design can be explored. Last, large-scale data can be handled by the PDFD algorithm. A data set which contains a total of 14 million instances is used in the case study. The design of a family of universal electronic motors demonstrates the proposed approach and highlights its benefits and limitations.

Original languageEnglish (US)
Title of host publication40th Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791846315
DOIs
StatePublished - Jan 1 2014
EventASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014 - Buffalo, United States
Duration: Aug 17 2014Aug 20 2014

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2A

Other

OtherASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
CountryUnited States
CityBuffalo
Period8/17/148/20/14

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Keywords

  • Predictive design analytics
  • Preference trend mining
  • Product family design

ASJC Scopus subject areas

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Cite this

Ma, J., & Kim, H. M. (2014). Predictive, data-driven product family design. In 40th Design Automation Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2A). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2014-34753