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A Process Model and Data Mining to Support Designing Families of Services

[+] Author Affiliations
Seung Ki Moon, Timothy W. Simpson, Soundar R. T. Kumara

Pennsylvania State University, University Park, PA

Paper No. DETC2007-34687, pp. 591-600; 10 pages
doi:10.1115/DETC2007-34687
From:
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 33rd Design Automation Conference, Parts A and B
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4807-8 | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME

abstract

Product family design is a cost-effective way to achieve mass customization by allowing highly differentiated products to be developed from a common platform while targeting individual products to distinct market segments. Recent trends seek to apply and extend principles from product family design to new service development. In this paper, we extend concepts from platform-based product family design to create a novel methodology for module-based service family design. The new methodology helps identify a service platform along with variant and unique modules in a service family by integrating service-based process analysis, ontologies, and data mining. A function-process matrix and a service process model are investigated to define the relationships between the service functions and the service processes offered as part of a service. An ontology is used to represent the relationships between functional hierarchies in a service. Fuzzy clustering is employed to partition service processes into subsets for identifying modules in a given service family. The clustering result identifies the platform and its modules using a platform level membership function. We apply the proposed methodology to determine a new platform using a case study involving a family of banking services.

Copyright © 2007 by ASME
Topics: Design , Data mining

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