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Customer Oriented Product Planning Procedure

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the most important issues in product planning is to identify customer needs and combine them with product technical and trade characteristics. Identification of customer needs was discussed, and product decomposition method was presented in the paper. The Quality Function Deployment method was suggested to be applied as a product and production process data integration tool, where engineering characteristics of a product are combined with its trade characteristics.
Słowa kluczowe
Rocznik
Strony
22--32
Opis fizyczny
Bibliogr. 26 poz., fig.
Twórcy
  • University of Bielsko-Biała, Faculty of Mechanical Engineering and Computer Science, 43-300 Bielsko-Biała
Bibliografia
  • [1] Baier D., Decker R., Schmidt-Thieme L.: Data Analysis and Decision Support. Springer Berlin Heidelberg, 2005.
  • [2] Cheng Q., Zhang G., Liu Z., Gu P., Cai L.: A structure-based approach to evaluation product adaptability in adaptable design. Journal of Mechanical Science and Technology, 25(5), 2011, p. 1081–1094.
  • [3] Chou Y.: Applying Neural Networks in Quality Function Deployment process for conceptual design. Journal of the Chinese Institute of Industrial Engineers, 21(6), 2004, p. 587–596.
  • [4] Cook H., Wu A.: On the valuation of goods and selection of the best design alternative. Research in Engineering Design, 13, 2001, p. 42–54.
  • [5] Fan B., Qi G., Hu X., Yu T.: A network methodology for structure-oriented modular product platform planning. Journal of Intelligent Manufacturing, 26(3), 2015, p. 553–570.
  • [6] Hsu Y., Tai P., Wang M., Chen W.: A knowledge-based engineering system for assembly sequence planning. The International Journal of Advanced Manufacturing Technology, 55(5), 2011, p. 763–782.
  • [7] Iranmanesh H., Thomson V.: Competitive advantage by adjusting design characteristics to satisfy cost targets. International Journal of Production Economics, 115(1), 2008, p. 64–71.
  • [8] Jariri F., Zegordi S. H.: Quality function deployment planning for platform design. The International Journal of Advanced Manufacturing Technology, 36(5-6), 2008, p. 419–430.
  • [9] Jiao J., Tseng M., Dufty V., Lin F.: Product family modeling for mass customization. Computers & Industrial Engineering, 35(3-4), 1998, p. 495–498.
  • [10] Kamrani A., Salhieh S.: Product Design for Modularity. Springer US. 2002.
  • [11] Kim J., Cho H.: Neural Net-based assembly algorithm for flexible parts assembly. Journal of Intelligent and Robotic Systems, 29(2), 2000, p. 133–160.
  • [12] Kutschenreiter-Praszkiewicz I.: Systemy bazujące na wiedzy w technicznym przygotowaniu produkcji części maszyn. Wydawnictwo Naukowe Akademii Techniczno-Humanistycznej, Bielsko-Biała, 2012.
  • [13] Lai L., Liu J.: WIPA: neural network and case based reasoning models for allocating work in progress. Journal of Intelligent Manufacturing, 23(3), 2012, p. 409–421.
  • [14] Lu R., Petersen T., Storch R.: Modeling customized product configuration in large assembly manufacturing with supply-chain considerations. International Journal of Flexible Manufacturing Systems, 19(4), 2007, p. 685–712.
  • [15] Ming X.G., Yan J.Q., Lu W.F., Ma D.Z., Song B.: Mass production of tooling product families via modular future-based design to manufacturing collaboration in PLM. Journal of Intelligent Manufacturing, 18(1), 2007, p. 186–195.
  • [16] Poel I.: Methodological problems in QFD and directions for future development. Research in Engineering Design, 18(1), 2007, p. 21–36.
  • [17] Quiza R., López-Armas O., Davim, J.: Hybrid Modeling and Optimization of Manufacturing. Springer-Verlag Berlin Heidelberg, 2012.
  • [18] Rafiei H., Rabbani M., Kokabi R.: Multi-site production planning in hybrid make-to-stock/make-to-order production environment. Journal of Industrial Engineering International, 2014.
  • [19] Raharjo H., Brombacher A.C., Xie M.: Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials. Computers & Industrial Engineering, 55(1), 2008, p. 253–278.
  • [20] Sakamoto S.: Beyond World-Class Productivity. Industrial Engineering Practice and Theory. Springer-Verlang London, 2010.
  • [21] Sukthomya W., Tannock J.: The training of neural networks to model manufacturing processes. Journal of Intelligent Manufacturing, 16(1), 2005, p. 39–51.
  • [22] Tseng Mm, Jiao J, Su Cj: Virtual prototyping for customized product development. Integrated Manufacturing System, 9(6), 1998, p. 334–343.
  • [23] Xu D., Yan H.-S.: An intelligent estimation method for product design time. The International Journal of Advanced Manufacturing Technology, 30(7-8), 2006, p. 601–613.
  • [24] Yao S., Han X., Yang Y., Rong Y., Huang S. H., Yen D. W., Zhang G.: Computer-aided manufacturing planning for mass customization part 1, framework. The International Journal of Advanced Manufacturing Technology, 32(1-2), 2007, p. 194–204.
  • [25] Yao S., Han X., Yang Y., Rong Y., Huang S. H., Yen D. W., Zhang G.: Computer aided manufacturing planning for mass customization part 2, automated setup planning. The International Journal of Advanced Manufacturing Technology, 32(1), 2007, p. 205–217.
  • [26] Yao S., Han X., Yang Y., Rong Y., Huang S. H., Yen D. W., Zhang G.: Computer aided manufacturing planning for mass customization part 3, information modeling. The International Journal of Advanced Manufacturing Technology, 32(1), 2007, p. 218–228.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e57fa832-fc63-4b4e-ae1e-cd58667818f3
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