The Enterprise Architecture Body of Knowledge as an Evolving Discipline

  • Hadi Kandjani
  • Peter Bernus
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 141)


Enterprise Architecture (EA) as an area of interdisciplinary study relies on models, methods and theories of many disciplines. The article explores the linkage between the needs of enterprise problem domains, the evolution of domain specific disciplines, and the EA body of knowledge. A cybernetic view is presented in an attempt to explain the effects of an important driver of discipline development, namely the change in the complexity of application domains. For the EA discipline (EAD), as any other developing discipline, there should exist a commonly accepted terminology, allowing interdisciplinary theories to be stated, which in turn facilitate the creation of cross disciplinary models and methodologies. While there already exists a fundamental and generalised theory of EA, GERAM, it is a minimalist theory, not prescribing any particular reference models or any concrete methodology, thus there is a constant need to relate domain specific results to the generalised theory, whereupon the evolution of one needs to have impact on the other. In this article we treat the discipline-as-a-system, and use Beer’s Viable System Model (VSM) to discuss three basic components of EAD as a viable system. A ‘co-evolution mechanisms’ for EAD is proposed, and a cybernetic model of co-evolution applied to EAD. We also discuss a cybernetic model of EAD using Checkland’s model for discipline development.


Enterprise architecture discipline Unified theory Viable system model Co-evolution path model Enterprise architecture cybernetics 


  1. 1.
    IFIP-IFAC-Task-Force: GERAM: generalised enterprise reference architecture and methodology, Chapter 2, Version 1(3): 6–3 (1999) In: Bernus, P., Nemes. L., Schmidt, G. (eds.) Handbook of Enterprise Architecture, Springer, Heidelberg (2003)Google Scholar
  2. 2.
    ISO15704: Industrial automation systems - Requirements for enterprise-reference architectures and methodologies. Geneva: ISO TC184.SC5.WG1. Geneva, ISO TC184.SC5.WG1 (2000, Amd.2005)Google Scholar
  3. 3.
    ISO/IEC/IEEE 42010: Systems and software engineering – Architecture description, Recommended Practice for Architectural Description of Software-intensive Systems. ISO/IEC JTC1/SC7/WG42 (2011)Google Scholar
  4. 4.
    DoDAF: DoD Architecture Framework Version 2.0, US DoD, Washington (2009)Google Scholar
  5. 5.
    TOGAF: TOGAF 1 – TOGAF 9.1 (Versions of the TOGAF Architecture Framework). Open Group (1999–2011)Google Scholar
  6. 6.
    Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine, (2nd Rev. Ed 1961). MIT Press, Cambridge (1948)Google Scholar
  7. 7.
    Ashby, W.R.: An Introduction to Cybernetics. Chapman & Hall, London (1956)Google Scholar
  8. 8.
    Beer, S.: Decision and Control: The Meaning of Operational Research and Management Cybernetics. Wiley, New York (1966)Google Scholar
  9. 9.
    Beer, S.: Diagnosing the System for Organizations. Wiley, New York (1985)Google Scholar
  10. 10.
    Boulding, K.E.: General systems theory-the skeleton of science. Manag. Sci. 2(3), 197–208 (1956)CrossRefGoogle Scholar
  11. 11.
    von Bertalanffy, L.: General System Theory-Foundations and Developments. George Braziller, Inc., New York (1968)Google Scholar
  12. 12.
    Pask, G.: Conversation, Cognition and Learning. Elsevier, Amsterdam (1975)Google Scholar
  13. 13.
    Doumeingts, G.: La Methode GRAI [PhD Thesis]. Bordeaux, France: University of Bordeaux I (1984)Google Scholar
  14. 14.
    Doumeingts, G.: GIM, grain integrated methodology. In: Molina, A., Kusiak, A., Sanchez, J. (eds.) Handbook of Life Cycle Engineering, Models and Methodologies, pp. 227−288. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  15. 15.
    Suh, N.P.: The Principles of Design. Oxford University Press, New York (1990)Google Scholar
  16. 16.
    Suh, N.P.: Axiomatic Design: Advances and Applications. Oxford University Press, New York (2001)Google Scholar
  17. 17.
    Suh, N.P.: Complexity: Theory and Applications. Oxford University Press, New York (2005)Google Scholar
  18. 18.
    Holland, J.H.: Complex adaptive systems. Daedalus 121(1), 17–30 (1992)Google Scholar
  19. 19.
    Gell-Mann, M.: Complex adaptive systems. In: Cowan, G.A., Pines, D., Meltzer, D. (eds.) Complexity: Metaphors, Models, and Reality, pp. 17−45. Addison-Wesley, Reading (1994)Google Scholar
  20. 20.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRefGoogle Scholar
  21. 21.
    Wooldridge, M.J.: An Introduction to Multiagent Systems. Wiley, New York (2002)Google Scholar
  22. 22.
    Kensing, F., Simonsen, J., Bodker, K.: MUST: a method for participatory design. Hum. Comput. Interact. 13(2), 167–198 (1998)CrossRefGoogle Scholar
  23. 23.
    Bødker, K., Kensing, F., Simonsen, J.: Participatory IT Design: Designing for Business and Workplace Realities. MIT Press, Cambridge (2004)Google Scholar
  24. 24.
    Hammer, M., Stanton, S.A.: The Reengineering Revolution: A handbook. Harper Business, New York (1995)Google Scholar
  25. 25.
    Ashby, W.R.: Design for a Brain: The Origin of Adaptive Behavior. Wiley, New York (1960)CrossRefGoogle Scholar
  26. 26.
    Senge, P.M.: The Fifth Discipline: The Art and Practice of the Learning Organization, Book Review. Doubleday, New York (1993)Google Scholar
  27. 27.
    Nonaka, I., Takeuchi, H.: The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York (1995)Google Scholar
  28. 28.
    Beer, S.: The Heart of Enterprise: The Managerial Cybernetics of Organization. Wiley, New York (1979)Google Scholar
  29. 29.
    Beer, S.: Brain of the Firm, 2nd edn. Wiley, New York (1981)Google Scholar
  30. 30.
    Kandjani, H., Bernus, P.: Engineering self-designing enterprises as complex systems using extended axiomatic design theory. In: Proceedings of the 18th IFAC World Congr. Milan, Italy, IFAC Papers On Line, vol. 18(1), pp. 11943−11948. Elsevier, Amsterdam (2011)Google Scholar
  31. 31.
    Ashby, W.R.: Adaptiveness and equilibrium. Br. J. Psychiatry 86(362), 478–483 (1940)CrossRefGoogle Scholar
  32. 32.
    Conant, R.C., Ashby, W.R.: Every good regulator of a system must be a model of that system. Int. J. Syst. Sci. 1(2), 89–97 (1970)CrossRefGoogle Scholar
  33. 33.
    Geoghegan, M.C., Pangaro, P.: Design for a self-regenerating organisation. Int. J. Gen Syst 38(2), 155–173 (2009)CrossRefGoogle Scholar
  34. 34.
    Umpleby, S.A.: Ross Ashby’s general theory of adaptive systems. Int. J. Gen Syst 38(2), 231–238 (2009)CrossRefGoogle Scholar
  35. 35.
    Anderton, R.H., Checkland, P.B.: On learning our lessons, Internal Discussion Paper, Lancaster, UK, Department of Systems, University of Lancaster. 2/77 (1977)Google Scholar
  36. 36.
    Checkland, P.: Systems Thinking, Systems Practice. Wiley, Chichester (1996)Google Scholar
  37. 37.
    Industry-University Consortium, An Implementation Procedures Manual for Developing Master Plans for Computer Integrated Manufacturing. In: Williams, T.J. (ed.) Technical Report 155, Purdue Laboratory for Applied Industrial Control, Purdue University, West Lafayette, IN, USA (1992)Google Scholar
  38. 38.
    Bernus, P., Nemes, L., Williams, T.J. (eds): Architectures for Enterprise Integration, p. 368. Chapman and Hall, London (1996)Google Scholar
  39. 39.
    CIM Reference Model Committee: A reference model for computer integrated manufacturing from the point of view of industrial automation. Int. J. Comput. Integr. Manufact. 2(2), 114−127 (1989)CrossRefGoogle Scholar
  40. 40.
    Williams, T.J.: The purdue enterprise reference architecture and methodology (PERA). Comput. Ind. 24(2−3), 141–158 (1994)CrossRefGoogle Scholar
  41. 41.
    Bernus, P., Nemes, L.: A framework to define a generic enterprise reference architecture and methodology. In: Proceedings ICARV’96 4th International Conference on Control, Automation, Robotics and Vision, vol. 3/3, pp. 88−92. Nanyang Technological University, Singapore (1994)Google Scholar
  42. 42.
    Bernus, P., Nemes, L.: A framework to define a generic enterprise reference architecture and methodology. Comput. Integr. Manuf. Syst. 9(3), 179−191 (1996) (Elsevier, Amsterdam)CrossRefGoogle Scholar
  43. 43.
    Zhou, M., Nemes, L., Shinonome, M., Hashimoto, H., Fuse, A., Bernus, P., Uppington, G.: A framework for design: a virtual manufacturing enterprise and its implementation. In: Mo, J. Kimura, F. (eds.) DIISM ‘98. IFIP TC5 WG.5.3/5.7 3rd International Working Conference on the Design of Information Infrastructure Systems for Manufacturing, pp. 339−343. Kluwer, Dordrecht (1998)Google Scholar
  44. 44.
    Vesterager, J., Bernus, P., Larsen, L.B., Pedersen, J.D., Tølle, M.: Use of GERAM as basis for a virtual enterprise framework model. In: Mo, J., Nemes, L. (eds.) Global Engineering, Manufacturing and Enterprise Networks (Proc DIISM2000), pp.75−82. Kluwer, Dordrecht (2000)Google Scholar
  45. 45.
    Tølle, M, Bernus, P.: Reference models supporting enterprise networks and virtual enterprises. Int. J. Networking Virtual Organ. 2(1), 2−15. Inderscience, Olney Bucks (2003)Google Scholar
  46. 46.
    Létray, Z., Bernus, P.: The link between the functional and physical structure in computer integrated manufacturing. In: Proceedings APMS/Compcontrol IV. Budapest: OMIKK-Technoinform, pp. 954−964 (1985)Google Scholar
  47. 47.
    Kalpic, B., Bernus, P.: Business process modelling through the knowledge management perspective. Int. J. Knowl. Manage. 10(3), 40–56 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Centre for Enterprise Architecture Research and Management (CEARM), School of ICTGriffith UniversityBrisbaneAustralia

Personalised recommendations