Product platform design and optimization: Status and promise

Research output: Contribution to conferencePaperpeer-review

81 Scopus citations


In an effort to improve customization for today's highly competitive global marketplace, many companies are utilizing product families to increase variety, shorten lead-times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design research has matured rapidly in the past decade, and this paper provides an extensive review of the research activity that has occurred during that time to facilitate product platform design and optimization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with optimization-based approaches to help automate the design of a product platform and its corresponding family of products. Examples from both industry and academia are presented throughout the paper to highlight the benefits of platform-based product development, and the paper concludes with a discussion of promising research directions to help bridge the gap between planning and managing families of products and designing and manufacturing them.

Original languageEnglish (US)
Number of pages12
StatePublished - 2003
Event2003 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Chicago, IL, United States
Duration: Sep 2 2003Sep 6 2003


Other2003 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Country/TerritoryUnited States
CityChicago, IL

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Product platform design and optimization: Status and promise'. Together they form a unique fingerprint.

Cite this