• Christopher Schlick
  • Bruno Demissie
Part of the Understanding Complex Systems book series (UCS)


Industrial companies operating today persistently face strong competition and must adapt to rapid technological progress and fast-changing customer needs. Under these conditions, if companies want to gain a competitive advantage in global markets, they must be able to successfully develop innovative products and effectively manage the associated product development (PD) projects. To shorten time-to-market and lower development/production costs, PD projects often undergo concurrent engineering (CE). In their landmark report, Winner et al. (1988) define CE as “a systematic approach to the integrated, concurrent design of products and their related processes, including manufacture and support. This approach is intended to cause the developers, from the outset, to consider all elements of the product life cycle from conception through disposal, including quality, cost, schedule, and user requirements.” A large-scale vehicle development project in the automotive industry offers a good example. In the late development stage, such a project involves hundreds of engineers collaborating in dozens of CE teams. The CE teams are usually structured according to the subsystems of the product to be developed (e.g. body-in-white, powertrain, interior systems, electronics etc.) and are coordinated by systems integration and management teams of responsible engineers who know the entire product (see e.g. Midler and Navarre 2007). Under an integrated approach to concurrent design of products and processes, multi-disciplinary teams are formed to develop recommendable configurations of the intended subsystems. These configurations should satisfy all constraints and contribute the different types of technical expertise and methodological approaches to problem-solving needed in order for a parallel execution of work processes to be successful (Molina et al. 1995). The constraints and requirements imposed on the design by the various engineering disciplines (engineering design, production engineering, control engineering etc.) are discussed by the subject-matter experts in team meetings and are mapped onto specific design parameters in a process of intensive collaboration. To avoid unnecessary system integration problems, in CE the teamwork usually follows a continuous integration rhythm with regular team meetings that are typically held at intervals of just a few weeks. Additional team meetings, e.g. to solve time-critical or quality-critical problems and to find sound compromises for conflicting constraints that have arisen during the design process, are held as needed.


Cooperative Work Monte Carlo Experiment Concurrent Engineering Design Structure Matrix Concurrent Design 
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.


  1. Bialek, W., Nemenman, I., Tishby, N.: Predictability, complexity and learning. Neural Comput. 13(1), 2409–2463 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  2. Braha, D., Maimon, O.: The design process: Properties, paradigms, and structure. IEEE Trans. Syst. Man Cybern. 27(2), 146–166 (1997)CrossRefGoogle Scholar
  3. Chalmers, D.J.: Strong and weak emergence. In: Clayton, P., Davies, P. (eds.) The Re-emergence of Emergence, pp. 244–256. Oxford University Press, Oxford (2002)Google Scholar
  4. Durst, R., Kabel, D.: Cross-functional teams in a concurrent engineering environment—principles, model, and methods. In: Beyerlein, M., Johnson, D., Beyerlein, S. (eds.) Virtual teams, pp. 163–210. Elsevier Science Ltd., London (2001)Google Scholar
  5. Eppinger, S.D., Browning, T.: Design structure matrix methods and applications. MIT Press, Cambridge, MA (2012)Google Scholar
  6. Eversheim, W., Schuh, G.: Integrierte Produkt- und Prozessgestaltung. Springer, Berlin (2005) (in German)CrossRefGoogle Scholar
  7. Feldhusen, J., Grote, K.-H., Nagarajah, A., Pahl, G., Beitz, W., Wartzack, S.: Vorgehen bei einzelnen Schritten des Produktentstehungsprozesses (in German). In: Feldhusen, J., Grote, K.-H. (eds.) Pahl/Beitz Konstruktionslehre—Methoden und Anwendung erfolgreicher Produktentwicklung, 8th edn, pp. 291–410. Springer, Berlin (2013)Google Scholar
  8. Grassberger, P.: Toward a quantitative theory of self-generated complexity. Int. J. Theor. Phys. 25(9), 907–938 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  9. Huberman, B.A., Wilkinson, D.M.: Performance variability and project dynamics. Comput. Math. Organ. Theor. 11(4), 307–332 (2005)CrossRefzbMATHGoogle Scholar
  10. Li, L., Xie, Z.: Model selection and order determination for time series by information between the past and the future. J. Time Ser. Anal. 17(1), 65–84 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  11. Lindemann, U., Maurer, M., Braun, T.: Structural complexity management. An approach for the field of product design. Springer, Berlin (2009)CrossRefGoogle Scholar
  12. Loch, C.H., Terwiesch, C.: Coordination and information exchange. In: Loch, C., Kavadias, S. (eds.) Handbook of New Product Development Management, pp. 315–343. Butterworth Heinemann, Amsterdam (2007)Google Scholar
  13. Luczak, H., Wimmer, R., Kabel, D., Durst, R. (2000). What engineers do learn from team effectiveness models: An investigation of applicability and utility of team effectiveness models in production systems. In: Proceedings of the 7th International Conference Human Aspects of Advanced Manufacturing: Agility and Automation, HAAMAHA, pp. 1–8, (2000)Google Scholar
  14. Luczak, H., Mühlfelder, M., Schmidt, L.: Group task analysis and design of computer supoorted cooperative work. In: Hollnagel, E. (ed.) Handbook of Cognitive Task Design, pp. 99–127. Lawrence Erlbaum Associates, Mahwah, NJ (2003)Google Scholar
  15. Midler, C., Navarre, C.: Project management in the automotive industry. In: Morris, P.W.G., Pinto, J.K. (eds.) The Wiley Guide to Managing Projects, pp. 1368–1388. Wiley, New York, NY (2007)CrossRefGoogle Scholar
  16. Mihm, J., Loch, C.: Spiraling out of control: Problem-solving dynamics in complex distributed engineering projects. In: Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) Complex Engineered Systems: Science Meets Technology, pp. 141–158. Springer, Berlin (2006)CrossRefGoogle Scholar
  17. Mihm, J., Loch, C., Huchzermeier, A.: Problem-solving oscillations in complex engineering. Manag. Sci. 46(6), 733–750 (2003)CrossRefGoogle Scholar
  18. Molina, A., Al-Ashaab, A., Ellis, T.I.A., Young, R.I.M., Bell, R.: A review of computer aided simultaneous engineering systems. Res. Eng. Design 7(1), 38–63 (1995)CrossRefGoogle Scholar
  19. Mühlfelder, M.: Das kollektive Handlungsfeld—Ein psychologisches Konzept zur Modellierung interpersonal koordinierten Handelns (in German). Ph.D. Thesis, Europa-Universität Flensburg, (2003)Google Scholar
  20. Mütze-Niewöhner, S., Luczak, H.: Prospective job design and evaluation in early stages of production system design. In: Proceedings of the Seventh International Symposium on Human Factors in Organizational Design and Management, ODAM 2003, pp. 323–328, (2003)Google Scholar
  21. Nicolis, G., Nicolis, C.: Foundations of Complex Systems—Nonlinear Dynamics, Statistical Physics, Information and Prediction. World Scientific, Singapore (2007)CrossRefzbMATHGoogle Scholar
  22. Petz, A., Schneider, S., Duckwitz, S., Schlick, C.M.: Modeling and simulation of service systems with design structure and domain mapping matrices. J. Mod. Proj. Manag. 3(3), 65–71 (2015)Google Scholar
  23. Reinertsen, D.G.: The Principles of Product Development Flow—Second Generation Lean Product Development. Celeritas Publishing, Redondo Beach, CA (2009)Google Scholar
  24. Rohmert, W.: Formen menschlicher Arbeit (in German). In: Rohmert, W., Rutenfranz, J. (eds.) Praktische Arbeitsphysiologie. Georg Thieme Verlag, Stuttgart, New York (1983)Google Scholar
  25. Schlick, C.M., Beutner, E., Duckwitz, S., Licht, T.: A complexity measure for new product development projects. Proceedings of the 19th International Engineering Management Conference, pp. 143–150, (2007)Google Scholar
  26. Schlick, C.M., Duckwitz, S., Gärtner, T., Schmidt, T.: A complexity measure for concurrent engineering projects based on the DSM. In: Proceedings of the 10th International DSM Conference, pp. 219–230, (2008)Google Scholar
  27. Schlick, C.M., Duckwitz, S., Gärtner, T., Tackenberg, S.: Optimization of concurrent engineering projects using an information-theoretic complexity metric. Proceedings of the 11th International DSM Conference, pp. 53–64, (2009).Google Scholar
  28. Schlick, C.M., Schneider, S., Duckwitz, S.: Modeling of cooperative work in concurrent engineering projects based on extended work transformation matrices with hidden state variables. In: Proceedings of the 14th International Dependency and Structure Modeling Conference, DSM 2012, pp. 411–422, (2012).Google Scholar
  29. Schlick, C.M., Schneider, S., Duckwitz, S.: A universal complexity criterion for model selection in dynamic models of cooperative work based on the DSM. In: Proceedings of the 15th International Dependency and Structure Modeling Conference, DSM 2013, pp. 99–105, (2013a).Google Scholar
  30. Schlick, C.M., Duckwitz, S., Schneider, S.: Project dynamics and emergent complexity. Comput. Math. Organ. Theor. 19(4), 480–515 (2013b)CrossRefGoogle Scholar
  31. Schlick, C.M., Schneider, S., Duckwitz, S.: Estimation of work transformation matrices for large-scale concurrent engineering projects. In: Proceedings of the 16th International Dependency and Structure Modeling Conference, DSM 2014, pp. 211–221, (2014).Google Scholar
  32. Schlick, C.M., Schneider, S., Duckwitz, S.: Estimation of work transformation matrices for large-scale concurrent engineering projects. J. Mod. Proj. Manag. 3(3), 73–79 (2015)Google Scholar
  33. Shalizi, C.R.: Methods and techniques of complex systems science: An overview. In: Deisboeck, T.S., Kresh, J.Y. (eds.) Complex Systems Science in Biomedicine, pp. 33–114. Springer, New York (2006)CrossRefGoogle Scholar
  34. Smith, R.P., Eppinger, S.D.: Identifying controlling features of engineering design iteration. Manag. Sci. 43(3), 276–293 (1997)CrossRefzbMATHGoogle Scholar
  35. Stahl, J., Mütze, S., Luczak, H.: A method for job design in concurrent engineering. Hum. Fact. Ergon. Manuf. 10(3), 291–307 (2000)CrossRefGoogle Scholar
  36. Steward, D.V.: The design structure system: A method for managing the design of complex systems. IEEE Trans. Eng. Manag. 28(3), 71–74 (1981)CrossRefGoogle Scholar
  37. Summers, J.D., Shah, J.J.: Mechanical engineering design complexity metrics: Size, coupling, and solvability. J Mech. Des. 132(2), 1–11 (2010)CrossRefGoogle Scholar
  38. Tackenberg, S., Duckwitz, S., Kausch, B., Schlick, C.M., Karahancer, S.: Organizational simulation of complex process engineering projects in the chemical industry. J. Universal Comp. Sci. 15(9), 1746–1765 (2009)Google Scholar
  39. Tackenberg, S., Duckwitz, S., Schlick, C.M.: Activity- and actor-oriented simulation approach for the management of development projects. Int. J. Comp Aided Eng. Technol. 2(4), 414–435 (2010)CrossRefGoogle Scholar
  40. Tatikonda, M.V., Rosenthal, S.R.: Technology novelty, Project complexity and product development project execution success. IEEE Trans. Eng. Manag. 47, 74–87 (2000)CrossRefGoogle Scholar
  41. Terwiesch, C., Loch, C.H., De Meyer, A.: Exchanging preliminary information in concurrent engineering: Alternative coordination strategies. Organ. Sci. 13(4), 402–419 (2002)CrossRefGoogle Scholar
  42. Winner, R.I., Pennell, J.P., Bertrand, H.E., Slusarczuk, M.M.: The role of concurrent engineering in weapons system acquisition. IDA-Report R-338, Institute for Defense Analyses; Alexandria, VA, (1988)Google Scholar
  43. Yassine, A., Joglekar, N., Eppinger, S.D., Whitney, D.: Information hiding in product development: The design churn effect. Res. Eng. Des. 14(3), 145–161 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christopher Schlick
    • 1
    • 2
  • Bruno Demissie
    • 2
  1. 1.Institute of Industrial Engineering and ErgonomicsRWTH Aachen UniversityAachenGermany
  2. 2.Fraunhofer Institute for CommunicationInformation Processing & Ergonomics FKIEWachtbergGermany

Personalised recommendations