Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T08:32:22.269Z Has data issue: false hasContentIssue false
  • English
  • Français

Special Issue: Platform product development for mass customization

Published online by Cambridge University Press:  28 January 2005

GEORGE HUANG ,
ROGER JIAO  and
MITCHELL M. TSENG
GEORGE HUANG
Affiliation:
Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong
ROGER JIAO
Affiliation:
School of Mechanical and Production Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore 639798
MITCHELL M. TSENG
Affiliation:
Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong
Get access Rights & Permissions [Opens in a new window]

Extract

Platform product development is a contemporary approach to agile product development for mass customization. Distinctive product variants are derived or customized from a platform that is defined as components and subsystems commonly shared across a product family. A well-organized platform is essential to connect different parties of an enterprise including soliciting customer needs through order fulfillment to field service. Hence, it is also critical to achieve the economy of scale by identifying repetitive applications of share tooling, knowledge, and other resources. In this Special Issue, we attempt to cover the different aspects of platform product development. The topics span a wider range of artificial intelligence (AI) disciplines, from knowledge representation to knowledge support systems. It identifies a fertile area not only for applying AI to contemporary issues of design and manufacturing systems but also for enriching the methodology for developing AI systems in manufacturing enterprises. Indeed, we consider that numerous successful applications in the industrial sectors highlight a future direction for productive AI applications in industry.

Type
GUEST EDITORIAL
Information
AI EDAM , Volume 18 , Issue 1 , February 2004 , pp. 1 - 2
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)