Neural Network Systems Technology and Applications in Product Life-Cycle Cost Estimates

  • Kwang-Kyu Seo


This chapter describes an approximate estimation method for the product life cycle cost (LCC), called an approximate LCC estimation method, which allows the designer to make comparative LCC estimation between the different product concepts. The proposed approach provides the approximate and rapid estimation of product LCC based on high-level information typically known in the conceptual phase. The product attributes at the conceptual design phase and LCC factors are identified and the significant product attributes are determined by statistical analysis. An artificial neural network (ANN) is trained on product attributes and the LCC data from pre-existing LCC studies. This approach does not require a new LCC model.


Artificial Neural Network Model Product Attribute Life Cycle Cost Service Cost Product Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Alting, L., 1993, Life-cycle design of products: a new opportunity for manufacturing enterprises. In Concurrent Engineering: Automation, Tools, and Techniques, A. Kusiak (ed.) (New York: Wiley), 1–17.Google Scholar
  2. Alting, L., and Legarth, J., 1995, Life cycle Engineering and Design, Annals of the CIRP, 44/2: 569–580.CrossRefGoogle Scholar
  3. Beardon, C., 1989, Artificial Intelligence Terminology: A Reference Guide, Halstead Press, New York.MATHGoogle Scholar
  4. Blanchard, B. S., 1979, Life cycle costing—a review, Terotechnica, 1, 9–15.Google Scholar
  5. Breset, H. and van Hemel, C., 1997, Ecodesign, a Promising Approach to Sustainable Production and Consumption. Paris, France: United Nations Environmental Program (UNEP) Industry and Environment.Google Scholar
  6. Clark, T. and Charter, M., 1999, Eco-design Checklists for Electronic Manufacturers, Systems Integrators, and Suppliers, of Components and Sub-assemblies. Scholar
  7. Creese, R. C. and Moore, L. T., 1990, Cost modeling for concurrent engineering, Cost Engineering, 32(6) June, 23–27.Google Scholar
  8. Dowlatshahi, S., 1992, Product design in a concurrent engineering environment: an optimization approach, Journal of Production Research, 30(8), 1803–1818.CrossRefGoogle Scholar
  9. Eisenhard, J. L., 2000, Product Descriptors for Early Product Development: An Interface between Environmental Experts and Designers, M.S Thesis, MIT.Google Scholar
  10. Fabrycky, W. J. and Blanchard, W. J., 1991, Life-Cycle Cost and Economic Analysis (Englewood Cliffs, NJ: Prentice Hall).Google Scholar
  11. Fiksel, J., 1996, Design for Environment: Creating Eco-efficient Product and Processes (New York: McGraw-Hill).Google Scholar
  12. Gershenson, J. and Ishii, K., 1993, Life-cycle design for serviceability. In Concurrent Engineering: Automation, Tools, and Techniques, A. Kusiak (ed.) (New York: Wiley), 363–384.Google Scholar
  13. Hanssen, O. J., 1999, Sustainable Product Systems—Experiences Based on Case Projects in Sustainable Product Development, Journal of Cleaner Production, Vol. 7, pp. 27–41.CrossRefGoogle Scholar
  14. Hubka, V. and Eder, W. E., 1992, Engineering Design: General Procedural Model of Engineering Design (Zurich, Switzerland: Heurista).Google Scholar
  15. Ishii, K., 1995, Life-cycle engineering design. Design for Manufacturability, ASME, DE-81, 39–45.Google Scholar
  16. Paasch, R. K. and Ruff, D. N., 1997, Evaluation of failure diagnosis in concepts design of mechanical systems, Trans. of ASME: J. of Mech. Design, 119(1), 57–67.CrossRefGoogle Scholar
  17. Porter, M. E., 1985, Competitive Advantage: Creating and Sustaining Superior Performance, New York, The Free Press.Google Scholar
  18. Rich, E. and Knight, K., Artificial Intelligence, McGraw-Hill, New York, 1991, pp. 487–509.Google Scholar
  19. Rummelhart, D., Durbin, R., Golden, R., and Chauvin, Y., Backpropagation: the basic theory, Backpropagation Theory, Architectures, and Applications, 1995: 1–34.Google Scholar
  20. Rumrnelhart, D., Widrow, B. and Lehr, M., The basic ideas in neural networks, Communications of the ACM, Vol. 37(3), 1994: 87–92.CrossRefGoogle Scholar
  21. Sousa, I., Eisenhard, J. L. and Wallace, D., 2001, Approximate Life-Cycle Assessment of product Concepts Using Learning Systems, Journal of industrial Ecology, 4/4: 61–81.Google Scholar
  22. Takata, S., Hiraoka, H., Asama, H., Yamoka, N. and Saito, D., 1995, Facility model for lifecycle maintenance system, Annals of the CIRP 44/1: 117–121.CrossRefGoogle Scholar
  23. Tarelko, W, 1995, Control model of maintainability level, Reliability Engineering and System Safety, 47/2: 85–91.CrossRefGoogle Scholar
  24. Utez, H., 1983, Maintainability of production system, Maintenance Management International, 4, 55–68.Google Scholar
  25. Ulrich and Eppinger, S., 1995, Product Design and Development (New York, NY: McGraw-Hill, Inc.)Google Scholar
  26. Vujosevic, R., Raskar, R., Yeturkuri, NV., Jothishankar, MC. and Juang, S.-H., 1995, Simulation, animation and analysis of design disassembly for maintainability analysis, International Journal of Production Research, 33(11), 2999–3022.MATHCrossRefGoogle Scholar
  27. Wani, M. F. and Gandhi, O. P., 1999, Development of maintainability index for mechanical systems, Reliability Engineering and System Scifety, 65(3), 259–270.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • Kwang-Kyu Seo
    • 1
  1. 1.Division of Computer, Information and Telecommunication EngineeringSangmyung UniversityChungnamKorea

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