A Hybrid QFD Framework for New Product Development

  • Tsai, Y-C (Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong) ;
  • Chin, K-S (Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong) ;
  • Yang, J-B (School of Management, University of Manchester Institute of Science and Technology, Manchester, UK)
  • 발행 : 2002.12.01

초록

Nowadays, new product development (NPD) is one of the most crucial factors for business success. The manufacturing firms cannot afford the resources in the long development cycle and the costly redesigns. Good product planning is crucial to ensure the success of NPD, while the Quality Function deployment (QFD) is an effective tool to help the decision makers to determine appropriate product specifications in the product planning stage. Traditionally, in the QFD, the product specifications are determined by a rather subjective evaluation, which is based on the knowledge and experience of the decision makers. In this paper, the traditional QFD methodology is firstly reviewed. An improved Hybrid Quality Function Deployment (HQFD) [MSOfficel] then presented to tackle the shortcomings of traditional QFD methodologies in determining the engineering characteristics. A structured questionnaire to collect and analyze the customer requirements, a methodology to establish a QFD record base and effective case retrieval, and a model to more objectively determine the target values of engineering characteristics are also described.

키워드

참고문헌

  1. Akao Y. (1990), Quality function deploy merit: integrating customer requirements into product design, Cambridge, Mass.: Productivity Press
  2. Askin R.G. and Dowson D.W., (2000),'Maximizing customer satisfaction by optimal specification of engineering characteristics', IEE transactions, Vol. 32, pp. 9-20
  3. Bouchereau V. & Rowlands H. (2000), 'Methods and techniques to help quality function deployment (QFD)', Benchmarking: An International Journal, vol. 7, No. 1, pp. 8-19 https://doi.org/10.1108/14635770010314891
  4. Chuang P.T. (2001), 'Combining the Analytic Hierarchy Process and Quality Function Deployment for a Location Decision for a Requirement perspective', Int. J. Adv. Manuf. Technol., vol. 18, pp. 842-849 https://doi.org/10.1007/s001700170010
  5. Curtis C.C. and Ellis L.W., (Sept/Oct, 1998), 'Satisfy customers while speeding R&D and staying profitable', Res. Technology Management, Vol. 41, pp. 23-24 https://doi.org/10.1080/08956308.1998.11671233
  6. Dimancescu S. and Dwenger K. (1996), 'World-Class New Product Development: Benchmarking best practices of agile manufacturers', American Management Association (AMACOM) 1996
  7. Fung R.Y.K., Popplewell K., and Xie J. (1998), 'An intelligent hybrid system for customer requirements analysis and product attribute targets determination', International Journal of Production Research, Vol. 36 No. 1, pp. 13-34 https://doi.org/10.1080/002075498193912
  8. Ganesan K., Khoshgoftaar T.M., and Allen E.B., (2000), ' 'Case based software quality prediction', International Journal of Software Engineering and Knowledse Engineering, Vol. 10, No. 2, pp. 139-152 https://doi.org/10.1142/S0218194000000092
  9. Huang Y. and Miles R. (1996), 'Using case-based techniques to enhance constraint satisfaction problem solving', Applied Artificial Intelligence, 10: 307-328 https://doi.org/10.1080/088395196118524
  10. Haque B.U., Belecheanu R.A., & Barson R.J., et al. (2000), 'Towards the Application of Case Based Reasoning to Decision-Marking in Concurrent Product Development (Concurrent Engineering)', Knowledge-Based System, Vol. 13, pp. 101-112 https://doi.org/10.1016/S0950-7051(00)00051-4
  11. Irgens C.S. (1995), 'Design support based projection of information across the product development life cycle by means of case based reasoning'. IEE Pro.Sci Meas. Techno., Vol. 142 No.5
  12. Kathawala Y. & Motwani J. (1994), 'Implementing quality function deployment: a systems approach', The TQM Magazine, vol. 6, No. 6, pp. 31-37 https://doi.org/10.1108/09544789410073621
  13. Kim J.K., Han C.H., and Choi S.H., et al., (1998) 'A knowledge based approach to the quality function deployment', Computers Ind. Engineering, Vol. 35 No. 1-2, pp. 233-2368 https://doi.org/10.1016/S0360-8352(98)00072-2
  14. Kim K.J., Moskowitz H., & Dhingra A.,et al. (2000), 'Fuzzy Multicriteria Models for Quality Function Deploy ment', European Journal of Operational Reasoning, vol. 121, pp. 504-518 https://doi.org/10.1016/S0377-2217(99)00048-X
  15. Kolodner J. (1993), Case Based Reasoning, Morgan Kaufmann publishers, Inc
  16. Kumar A. (2001), Applying Quality Function Deployment Techniques to Obtain Target Values for the Design of a Novel Hip Projector, Management Project of Manchester School of Management, UMIST, Manchester, UK
  17. Lu M.H., Madu C.N., and Kuei C.H., et al., 'Integrating QFD, AHP and Benchmarking in Strategic Marketing', Journal of Business & Industrial Marketing, Vol. 9 No. 1, 1994, pp.41-50 https://doi.org/10.1108/08858629410053470
  18. Meziane F., Vadera S., and Kobbacy K., et al, (2000) 'Intelligent systems in manufacturing: current developments and future prospects', Integrated Manufacturing System, 11/4, pp. 218-238 https://doi.org/10.1108/09576060010326221
  19. Moskowitz H. and Kim K.J. (1997), 'QFD optimizer: a novice friendly quality function deployment decision support system for optimizing product designs', Computers Ind. Engineering, Vol. 32, No. 3, pp. 641-655 https://doi.org/10.1016/S0360-8352(96)00309-9
  20. Park M.K., Lee I. & Shon K.M. 1998, 'Using Case Based Reasoning for Problem Solving in a Complex Production Process', Expert System With Applcations, Vol. 15, pp. 69-75 https://doi.org/10.1016/S0957-4174(98)00012-8
  21. Schmidt R. (1997), The Implementation of Simultaneous Engineering in the Stage of Product Concept development: A process oriented improvement of Quality Function Deployment, European Journal of Operation Research, 100, pp. 293-314 https://doi.org/10.1016/S0377-2217(96)00291-3
  22. Smyth B. & Keane M.T. 1998, 'Adaptation-Guided Retrieval: Questioning the similarity Assumption in Reasoning', Artificial Intelligence, Vo1.102, pp. 249-293
  23. Stalk G. and Webber A.M., (July-Aug, 1993), 'Japan's dark side of time', Harvard Business Review
  24. Su C.T. and Chang H.H. (2000), 'Optimization of parameter design: an intelligent approach using neural network and simulated annealing', International Journal of System Science, Vol. 31, No. 12, pp. 1543-1549 https://doi.org/10.1080/00207720050217313
  25. Surma J. & Braunschweig B. 1996, 'Case-Base Retrieval in Process Engineering: Supporting Design by Reusing Flowsheets', Engng Applic.Artif. Intell, Vol. 9, No. 4, pp. 385-391 https://doi.org/10.1016/0952-1976(96)00036-X
  26. Tanaka, H., Watada, J., (1988), 'Possibilistic linear systems and their applications to the linear regression model', Fuzzy Sets and Systems, Vol 27, pp. 275-289 https://doi.org/10.1016/0165-0114(88)90054-1
  27. Tanaka H., Haysashi I. & Watada J., (1989), 'Possibilistic linear regression analysis for fuzzy data', European Journal of Operational Research, vol. 40, pp. 389-396 https://doi.org/10.1016/0377-2217(89)90431-1
  28. Temponi C., Yen J. and Tiao W. A. (1999), 'House of quality: a fuzzy logic based requirement analysis', European Journal of Operation Research, 117, pp. 340-354 https://doi.org/10.1016/S0377-2217(98)00275-6
  29. Tsai Y.C. J., Chin K.S. & Yang J.B., (2002), 'Development of Hybrid Quality Function Deployment Framework', Change Management, Proceedings of the $7^t^h$ International Conference on ISO 9000 and TQM, pp. 89-90, Melbourne, Australia
  30. Rao S.S., Nahm A., and Shi Z., et al., (1999) 'Artificial intelligence ad expert systems applications in new product development a survey', Journal of Intelligent Manufacturing, 10, pp. 231-244 https://doi.org/10.1023/A:1008943723141
  31. Vanegas L.V. and Labib A.W. (2001), 'A Fuzzy Quality Function Deployment (FQFD) Model for Deriving Optimum Targets', Int. J. Production Research,Vol. 39, No. 1, pp. 99-120 https://doi.org/10.1080/00207540010005079
  32. Wang J. (1999), 'Fuzzy outranking approach to prioritize design requirement s in quality function deployment', International Journal of Production Research. Vol. 37 No. 4, pp. 899-916 https://doi.org/10.1080/002075499191599
  33. Yagar R.R. (1977), 'Multiple Objective Decision-making using Fuzzy Sets', International Journal of Man-machine Studies, Vol. 9, pp. 375-382 https://doi.org/10.1016/S0020-7373(77)80008-4
  34. Yoder, B. and Mason, D. (1995),Evaluating QFD relationships through the use of regression analysis, in Proceedings of the Seventh Symposium on Quatity Function Deployment, ASI & GOAL/ QPC, American Supplier Institute, Livonia, MI. pp. 35-39
  35. Young A.R., and Allen N. (1996), Concurrent Engineering and Product Specification, Journal of Material processing Technology 61, 181-186 https://doi.org/10.1016/0924-0136(96)02484-3
  36. Zaheh L.A. (1975), 'The Concept of a Linguistic Variable and its Applications to Approximate Reasoning-I', Inf. Science, 8, pp. 199-249 https://doi.org/10.1016/0020-0255(75)90036-5
  37. Zimmermann H., (1976), 'Description and Optimization of Fuzzy Systems', International Journal of General Systems, 2 (4), pp. 209-215 https://doi.org/10.1080/03081077608547470
  38. Zimmermann H., (1978), 'Fuzzy Programming and Linear Programming with Several Objective Functions', Fuzzy Sets and Systems, 1, pp. 45-55 https://doi.org/10.1016/0165-0114(78)90031-3