Developing a Customer Oriented Lean Production System Using Axiomatic Design and Fuzzy Value Stream Mapping

  • Ömer Faruk Yılmaz
  • Gökhan ÖzçelikEmail author
  • Fatma Betül Yeni
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 279)


This chapter proposes a comprehensive methodology to design a customer-oriented production system. To this end, an axiomatic design (AD) based methodology is developed by employing one of the most widely used lean techniques, value stream mapping (VSM). The methodology is designed in three stages: (i) analyzing the current state, (ii) applying the AD method, and (iii) designing the future state. To consider the inherent vagueness of the processes, a fuzzy approach is embedded into the VSM within the context of the proposed methodology, in the first and third steps. To show the applicability of the proposed methodology, a real case study from a water-meter producer is conducted. The lead time is an indicator to show how fast a company responds to customer demands. Therefore, it is employed as a metric to compare current and future states. The process and lead times are defined by using triangular fuzzy numbers (TFNs) to capture the ambiguity in work-in-process (WIP) levels in the company. Computational results show the effectiveness of the proposed methodology in terms of comparison metric, i.e. manufacturing lead-time. This study provides a guideline for academicians and researchers and aims to be a stepping stone for future studies.


  1. 1.
    Babic, B.: Axiomatic design of flexible manufacturing systems. Int. J. Prod. Res. 37(5), 1159–1173 (1999)CrossRefGoogle Scholar
  2. 2.
    Bahadir, M.C., Satoglu, S.I.: A novel robot arm selection methodology based on axiomatic design principles. Int. J. Adv. Manuf. Technol. 71(9–12), 2043–2057 (2014)CrossRefGoogle Scholar
  3. 3.
    Bi, Z.M., Lang, S.Y., Wang, L.: Design of reconfigurable manufacturing systems with strongly coupled nature. Chin. J. Mech. Eng. 20(1), 91–95 (2007)CrossRefGoogle Scholar
  4. 4.
    Braglia, M., Frosolini, M., Zammori, F.: Uncertainty in value stream mapping analysis. Int. J. Logist.: Res. Appl. 12(6), 435–453 (2009)CrossRefGoogle Scholar
  5. 5.
    Büyüközkan, G., Göçer, F.: Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Appl. Soft Comput. 52, 1222–1238 (2017)CrossRefGoogle Scholar
  6. 6.
    Cevikcan, E., Durmusoglu, M.B.: Minimising utility work and utility worker transfers for a mixed-model assembly line. Int. J. Prod. Res. 49(24), 7293–7314 (2011)CrossRefGoogle Scholar
  7. 7.
    Cevikcan, E., Durmusoglu, M.B., Baskak, M.: Integrating parts design characteristics and scheduling on parallel machines. Expert Syst. Appl. 38(10), 13232–13253 (2011)CrossRefGoogle Scholar
  8. 8.
    Cochran, D.S., Eversheim, W., Kubin, G., Sesterhenn, M.L.: The application of axiomatic design and lean management principles in the scope of production system segmentation. Int. J. Prod. Res. 38(6), 1377–1396 (2000)CrossRefGoogle Scholar
  9. 9.
    Duda, J.W., Cochran, D.S., Castaneda-Vega, J., Baur, M., Anger, R., Taj, S.: Application of a lean cellular design decomposition to automotive component manufacturing system design (No. 1999-01-1620). SAE Technical Paper (1999)Google Scholar
  10. 10.
    Durmusoglu, M.B., Satoglu, S.I.: Axiomatic design of hybrid manufacturing systems in erratic demand conditions. Int. J. Prod. Res. 49(17), 5231–5261 (2011)CrossRefGoogle Scholar
  11. 11.
    Ertay, T., Satoğlu, S.I.: System parameter selection with information axiom for the new product introduction to the hybrid manufacturing systems under dual-resource constraint. Int. J. Prod. Res. 50(7), 1825–1839 (2012)CrossRefGoogle Scholar
  12. 12.
    Farid, A.M.: An axiomatic design approach to production path enumeration in reconfigurable manufacturing systems. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3862–3869. IEEE (2013)Google Scholar
  13. 13.
    Farid, A.M., Ribeiro, L.: An axiomatic design of a multiagent reconfigurable mechatronic system architecture. IEEE Trans. Industr. Inf. 11(5), 1142–1155 (2015)CrossRefGoogle Scholar
  14. 14.
    Farid, A.M.: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J. Intell. Manuf. 28(2), 353–369 (2017)CrossRefGoogle Scholar
  15. 15.
    Franco, G.N., Batocchio, A.: Towards an axiomatic framework to support the design of holonic systems. In: 12th International Workshop on Database and Expert Systems Applications, pp. 654–659 (2001)Google Scholar
  16. 16.
    Giambalvo, J., Vance, J., Hoffenson, S.: Toward a decision support tool for selecting engineering design methodologies. In: ASEE Annual Conference and Exposition, Columbus, Ohio, pp. 25–28 (2017)Google Scholar
  17. 17.
    Han, W.M., Zhao, J.L., Chen, Y.: A virtual cellular manufacturing system design model based on axiomatic design theory. In: Applied mechanics and materials, vol. 271, pp. 1478–1484. Trans Tech Publications (2013)Google Scholar
  18. 18.
    Hapke, M., Jaszkiewicz, A., Slowinski, R.: Fuzzy project scheduling system for software development. Fuzzy Sets Syst. 67(1), 101–117 (1994)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Holzner, P., Rauch, E., Spena, P.R., Matt, D.T.: Systematic design of SME manufacturing and assembly systems based on axiomatic design. Procedia CIRP 34, 81–86 (2015)CrossRefGoogle Scholar
  20. 20.
    Houshmand, M., Jamshidnezhad, B.: An extended model of design process of lean production systems by means of process variables. Robot. Comput.-Integr. Manuf. 22(1), 1–16 (2006)CrossRefGoogle Scholar
  21. 21.
    Jadeja, S.B., Khandare, S.S., Batish, A.: B37 İmplementation of JIT methodology through axiomatic design approach (Advanced machining technology). In: Proceedings of International Conference on Leading Edge Manufacturing in 21st century: LEM21, vol. 2009, no. 5, pp. 705–709 (2009)Google Scholar
  22. 22.
    Jadeja, S.B., Khandare, S.S., Batish, A., Patel, D.: Integrating lean manufacturing and six sigma by means of axiomatic design principles. In: ASME 2008 International Mechanical Engineering Congress and Exposition, pp. 449–455 (2009b)Google Scholar
  23. 23.
    Kulak, O., Cebi, S., Kahraman, C.: Applications of axiomatic design principles: a literature review. Expert Syst. Appl. 37(9), 6705–6717 (2010)CrossRefGoogle Scholar
  24. 24.
    Luo, Z., Yu, X., Liu, J., Tang, H., Zhou, B., Chu, L.K.: Reconfigurability and reconfigurable design theory. J.-Tsinghua Univ. 44(5), 577–580 (2004)Google Scholar
  25. 25.
    Matt, D.T.: Application of Axiomatic Design principles to control complexity dynamics in a mixed-model assembly system: a case analysis. Int. J. Prod. Res. 50(7), 1850–1861 (2012)CrossRefGoogle Scholar
  26. 26.
    Mokhtar, A., Houshmand, M.: Introducing a roadmap to implement the universal manufacturing platform using axiomatic design theory. Int. J. Manuf. Res. 5(2), 252–269 (2010)CrossRefGoogle Scholar
  27. 27.
    Molina, J., Sanchez, A.: Design of discrete-event controllers for flexible manufacturing system using informal product requirements. IFAC Proc. 45(6), 218–223 (2012)CrossRefGoogle Scholar
  28. 28.
    Puik, E., Telgen, D., van Moergestel, L., Ceglarek, D.: Assessment of reconfiguration schemes for reconfigurable manufacturing systems based on resources and lead time. Robot. Comput.-Integr. Manuf. 43, 30–38 (2017)CrossRefGoogle Scholar
  29. 29.
    Rauch, E., Dallasega, P., Matt, D.T.: Axiomatic design-based guidelines for the design of a lean product development process. Procedia CIRP 34, 112–118 (2015)CrossRefGoogle Scholar
  30. 30.
    Rother, M., Shook, J.: Learning to see: value stream mapping to add value and eliminate waste. Brazil Lean Enterprise Institute, Sao Paulo-SP (1998)Google Scholar
  31. 31.
    Salonitis, K.: Design for additive manufacturing based on the axiomatic design method. Int. J. Adv. Manuf. Technol. 87(1–4), 989–996 (2016)CrossRefGoogle Scholar
  32. 32.
    Seyedhosseini, S.M., Taleghani, A.E., Makui, A., Ghoreyshi, S.M.: Fuzzy value stream mapping in multiple production streams: a case study in a parts manufacturing company. Int. J. Manag. Sci. Eng. Manag. 8(1), 56–66 (2013)Google Scholar
  33. 33.
    Shah, S.A., Smith, J.J., Cochran, D.S.: Guiding manufacturing enterprises to achieve long-term business sustainability using the collective system design approach. In: MATEC Web of Conferences, vol. 223, p. 01015 (2018)Google Scholar
  34. 34.
    Shirwaiker, R.A., Okudan, G.E.: Contributions of TRIZ and axiomatic design to leanness in design: an investigation. Procedia Eng. 9, 730–735 (2011)CrossRefGoogle Scholar
  35. 35.
    Suh, N.P.: The Principles of Design. Oxford University Press, New York (1990)Google Scholar
  36. 36.
    Suh, N.P.: Applications of axiomatic design. In: Integration of Process Knowledge into Design Support Systems, pp. 1–46. Springer, Dordrecht (1999)Google Scholar
  37. 37.
    Suh, N.P.: Axiomatic Design: Advances and Applications. Mit-Pappalardo Series in Mecha (2001)Google Scholar
  38. 38.
    Taha, Z., Soewardi, H., Dawal, S.Z.M.: Axiomatic design principles in analysing the ergonomics design parameter of a virtual environment. Int. J. Ind. Ergon. 44(3), 368–373 (2014)CrossRefGoogle Scholar
  39. 39.
    Uppala, A.K., Ranka, R., Thakkar, J.J., Kumar, M.V., Agrawal, S.: Selection of green suppliers based on GSCM practices: using fuzzy MCDM approach in an electronics company. In: Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, pp. 355–375 (2017)Google Scholar
  40. 40.
    Vinodh, S.: Axiomatic modelling of agile production system design. Int. J. Prod. Res. 49(11), 3251–3269 (2011)CrossRefGoogle Scholar
  41. 41.
    Vinodh, S., Aravindraj, S.: Axiomatic modeling of lean manufacturing system. J. Eng. Des. Technol. 10(2), 199–216 (2012)Google Scholar
  42. 42.
    Weber, J., Förster, D., Kößler, J., Paetzold, K.: Design of changeable production units within the automotive sector with axiomatic design. Procedia CIRP 34, 93–97 (2015)CrossRefGoogle Scholar
  43. 43.
    Yılmaz, Ö.F., Durmuşoğlu, M.B.: Evolutionary algorithms for multi-objective scheduling in a hybrid manufacturing system. In: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, pp. 162–187. IGI Global (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ömer Faruk Yılmaz
    • 1
  • Gökhan Özçelik
    • 1
    Email author
  • Fatma Betül Yeni
    • 1
  1. 1.Industrial Engineering Department, Engineering FacultyKaradeniz Technical UniversityTrabzonTurkey

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