Skip to main content
Log in

Project dynamics and emergent complexity

  • Manuscript
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

This paper presents a theoretical analysis of project dynamics and emergent complexity in new product development (NPD) projects subjected to the management concept of concurrent engineering. To provide a comprehensive study, the complexity frameworks, theories and measures that have been developed in organizational theory, systematic engineering design and basic scientific research are reviewed. For the evaluation of emergent complexity in NPD projects, an information-theory quantity—termed “effective measure complexity” (EMC)—is selected from a variety of measures, because it can be derived from first principles and therefore has high construct validity. Furthermore, it can be calculated efficiently from dynamic generative models or purely from historical data, without intervening models. The EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, it is particularly interesting to evaluate the time-dependent complexity in NPD and to uncover the relevant interactions. To obtain analytical results, a model-driven approach is taken and a vector autoregression (VAR) model of cooperative work is formulated. The formulated VAR model provided the foundation for the calculation of a closed-form solution of the EMC in the original state space. This solution can be used to analyze and optimize complexity based on the model’s independent parameters. Moreover, a transformation into the spectral basis is carried out to obtain more expressive solutions in matrix form. The matrix form allows identification of the surprisingly few essential parameters and calculation of two lower complexity bounds. The essential parameters include the eigenvalues of the work transformation matrix of the VAR model and the correlations between components of performance fluctuations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Amaral LAN, Uzzi B (2007) Complex systems—a new paradigm for the integrative study of management, physical, and technological systems. Manag Sci 53(7):1033–1035

    Article  Google Scholar 

  • Arnold D (1996) Information-Theoretic analysis of phase transitions. Complex Syst 10(2):143–155

    Google Scholar 

  • Ay N, Der R, Prokopenko M (2010) Information driven self-organization: the dynamic system approach to autonomous robot behavior. Santa Fe institute working paper 10-09-18

  • Baldwin CY, Clark KB (2000) Design rules: the power of modularity. MIT Press, Cambridge

    Google Scholar 

  • Bialek W (2003) Some background on information theory. Unpublished working paper, Princeton University

  • Bialek W, Nemenman I, Tishby N (2001) Predictability, complexity and learning. Neural Comput 13(11):2409–2463

    Article  Google Scholar 

  • Billingsley P (1995) Probability and measure, 3rd edn. Wiley, New York

    Google Scholar 

  • Bosch-Rekveldt M, Jongkind Y, Mooi H, Bakker H, Verbraeck A (2011) Grasping project complexity in large engineering projects: the TOE (technical, organizational and environmental) framework. Int J Proj Manag 29(6):728–739

    Article  Google Scholar 

  • Braha D, Bar-Yam Y (2007) The statistical mechanics of complex product development: empirical and analytical results. Manag Sci 53(7):1127–1145

    Article  Google Scholar 

  • Braha D, Maimon O (1998) The measurement of a design structural and functional complexity. IEEE Trans Syst Man Cybern, Part A, Syst Hum 28(4):527–535

    Article  Google Scholar 

  • Brockwell PJ, Davis RA (1987) Time series: theory and methods. Springer, New York

    Book  Google Scholar 

  • Browning T (2001) Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Trans Eng Manag 48(3):292–306

    Article  Google Scholar 

  • Carlile PR (2002) A pragmatic view of knowledge and boundaries: boundary objects in new product development. Organ Sci 13(4):442–455

    Article  Google Scholar 

  • Cataldo M, Wagstrom PA, Herbsleb JD, Carley KM (2006) Identification of coordination requirements: implications for the design of collaboration and awareness tools. In: Proceedings of the 2006 ACM conference on computer supported cooperative work (CSCW 2006), Banff, Alberta, Canada, pp 353–362

    Google Scholar 

  • Cataldo M, Herbsleb JD, Carley KM (2008) Socio-technical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity. In: Proceedings of the 2nd international symposium on empirical software engineering and measurement (ESEM’08), Kaiserslautern, Germany, pp 2–11

    Chapter  Google Scholar 

  • Chaitin GJ (1987) Algorithmic information theory. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Chalmers DJ (2002) Strong and weak emergence. In: Clayton P, Davies P (eds) The re-emergence of emergence. Oxford University Press, Oxford, pp 244–256

    Google Scholar 

  • Colver LJ, Baldwin CY (2010) The mirroring hypothesis: theory, evidence and exceptions. Harvard Business School working paper 10-058

  • Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York

    Book  Google Scholar 

  • Crutchfield JP, Feldman DP (2003) Regularities unseen, randomness observed: levels of entropy convergence. Chaos 13(1):25–54

    Article  Google Scholar 

  • Crutchfield JP, Ellison CJ, James RG, Mahoney JR (2010) Synchronization and control in intrinsic and designed computation: an information-theoretic analysis of competing models of stochastic computation. Santa Fe Institute working paper 10-08-015

  • Cummings JN, Espinosa JA, Pickering CK (2009) Crossing spatial and temporal boundaries in globally distributed projects: a relational model of coordination delay. Inf Syst Res 20(3):420–439

    Article  Google Scholar 

  • de Cock K (2002) Principal angles in system theory, information theory and signal processing. PhD thesis, Katholieke Universiteit Leuven

  • Danilovic M, Browning TR (2007) Managing complex product development projects with design structure matrices and domain mapping matrices. Int J Proj Manag 25(3):300–314

    Article  Google Scholar 

  • Denman J, Kaushik S, de Weck O (2011) Technology insertion in turbofan engine and assessment of architectural complexity. In: Proceedings of the 13th international dependency and structure modeling conference (DSM 2011), pp 407–420

    Google Scholar 

  • El-Haik B, Yang K (1999) The components of complexity in engineering design. IIE Trans 31(10):925–934

    Google Scholar 

  • Ellison CJ, Mahoney JR, Crutchfield JP (2009) Prediction, retrodiction, and the amount of information stored in the present. Santa Fe Institute working paper 09-05-017

  • Eppinger SD, Browning T (2012) Design structure matrix methods and applications. MIT Press, Cambridge

    Google Scholar 

  • Gebala DA, Eppinger SD (1991) Methods for analyzing design procedures. In: Proceedings of the ASME conference on design theory and methodology, Miami, FL, pp 227–233

    Google Scholar 

  • Gokpinar B, Hopp WJ, Iravani SMR (2010) The impact of misalignment of organizational structure and product architecture on quality in complex product development. Manag Sci 56(3):468–484

    Article  Google Scholar 

  • Grassberger P (1986) Toward a quantitative theory of self-generated complexity. Int J Theor Phys 25(9):907–938

    Article  Google Scholar 

  • Griffin A (1997) The effect of project and process characteristics on product development cycle time. J Mark Res 34(1):24–35

    Google Scholar 

  • GrĂĽnwald P (2007) The minimum description length principle. MIT Press, Cambridge

    Google Scholar 

  • Hölttä-Otto K, Magee CL (2006) Estimating factors affecting project task size in product development—an empirical study. IEEE Trans Eng Manag 53(1):86–94

    Article  Google Scholar 

  • Horn RA, Johnson CR (1985) Matrix analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Huberman BA, Wilkinson DM (2005) Performance variability and project dynamics. Comput Math Organ Theory 11(4):307–332

    Article  Google Scholar 

  • Kellogg KC, Orlikowski WJ, Yates J (2006) Life in the trading zone: structuring coordination across boundaries in postbureaucratic organizations. Organ Sci 17(1):22–44

    Article  Google Scholar 

  • Kerzner H (2009) Project management: a systems approach to planning, scheduling, and controlling. Wiley, Hoboken

    Google Scholar 

  • Kim J, Wilemon D (2003) Sources and assessment of complexity in NPD projects. R&D Manage 33(1):15–30

    Article  Google Scholar 

  • Kim J, Wilemon D (2009) An empirical investigation of complexity and its management in new product development. Technol Anal Strateg Manag 21(4):547–564

    Article  Google Scholar 

  • Kreimeyer M, König C, Braun T (2008) Structural metrics to assess processes. In: Proceedings of the 10th international dependency and structure modeling conference (DSM 2008), pp 245–258

    Google Scholar 

  • Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Physical Review E 69(6)

  • Krattenthaler C (2005) Advanced determinant calculus: a complement. Linear Algebra Appl 411(2):68–166

    Article  Google Scholar 

  • Lancaster P, Tismenetsky M (1985) The theory of matrices, 2nd edn. Academic Press, Orlando

    Google Scholar 

  • Lebcir MR (2011) Impact of project complexity factors on new product development cycle time. University of Hertfordshire Business School working paper. https://uhra.herts.ac.uk/dspace/handle/2299/5549, University of Hertfordshire Business School

  • Li W (1991) On the relationship between complexity and entropy for Markov chains and regular languages. Complex Syst 5(4):381–399

    Google Scholar 

  • Li M, Vitanyi P (1997) An introduction to Kolmogorov complexity and its applications, 2nd edn. Springer, New York

    Book  Google Scholar 

  • Lind M, Marcus B (1995) An introduction to symbolic dynamics and coding. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Lindemann U, Maurer M, Braun T (2009) Structural complexity management: an approach for the field of product design. Springer, Berlin

    Book  Google Scholar 

  • Luenberger DG (1979) Introduction to dynamic systems. Wiley, New York

    Google Scholar 

  • LĂĽtkepohl H (2005) New introduction to multiple time series analysis. Springer, Berlin

    Book  Google Scholar 

  • Maurer M (2007) Structural awareness in complex product design. Doctoral dissertation, Dr Hut Verlag, Munich.

    Google Scholar 

  • Maylor H, Vidgen R, Carver S (2008) Managerial complexity in project-based operations: a grounded model and its implications for practice. Int J Proj Manag 39(1):15–26

    Google Scholar 

  • Mihm J, Loch C (2006) Spiraling out of control: problem-solving dynamics in complex distributed engineering projects. In: Braha D, Minai AA, Bar-Yam Y (eds) Complex engineered systems: science meets technology. Springer, Berlin, pp 141–158

    Chapter  Google Scholar 

  • Mihm J, Loch C, Huchzermeier A (2003) Problem-solving oscillations in complex engineering. Manag Sci 46(6):733–750

    Article  Google Scholar 

  • Mihm J, Loch C, Wilkinson D, Huberman B (2010) Hierarchical structure and search in complex organisations. Manag Sci 56(5):831–848

    Article  Google Scholar 

  • Murmann PA (1994) Expected development time reductions in the German mechanical engineering industry. J Prod Innov Manag 11(3):236–252

    Article  Google Scholar 

  • Neumair A, Schneider T (2001) Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27(1):27–57

    Article  Google Scholar 

  • Nicolis G, Nicolis C (2007) Foundations of complex systems—nonlinear dynamics, statistical physics, information and prediction. World Scientific, Singapore

    Book  Google Scholar 

  • O’Leary MB, Mortensen M (2010) Go (con)figure: subgroups, imbalance, and isolates in geographically dispersed teams. Organ Sci 21(1):115–131

    Article  Google Scholar 

  • Papoulis A, Pillai SU (2002) Probability, random variables and stochastic processes. McGraw-Hill, Boston

    Google Scholar 

  • Prokopenko M, Boschetti F, Ryan AJ (2007) An information-theoretic primer on complexity, self-organization and emergence. In: Proceedings of the 8th understanding complex systems conference

    Google Scholar 

  • Puri NN (2010) Fundamentals of linear systems for physical scientists and engineers. CRC Press, Boca Raton

    Google Scholar 

  • Rissanen J (1989) Stochastic complexity in statistical inquiry. World Scientific, Singapore

    Google Scholar 

  • Rissanen J (1996) Fisher information and stochastic complexity. IEEE Trans Inf Theory 42(1):40–47

    Article  Google Scholar 

  • Rissanen J (2007) Information and complexity in statistical modeling. Springer, Berlin

    Google Scholar 

  • Rivkin JW, Siggelkow N (2003) Balancing search and stability: interdependencies among elements of organizational design. Manag Sci 49(3):290–311

    Article  Google Scholar 

  • Rivkin JW, Siggelkow N (2007) Patterned interactions in complex systems: implications for exploration. Manag Sci 53(7):1068–1085

    Article  Google Scholar 

  • Rogers JL, Korte JJ, Bilardo VJ (2006) Development of a genetic algorithm to automate clustering of a dependency structure matrix. National Aeronautics and Space Administration, Langley. Research Center, Technical memorandum NASA/TM-2006-214279

  • Schlick CM, Beutner E, Duckwitz S, Licht T (2007) A complexity measure for new product development projects. In: Proceedings of the 19th international engineering management conference, pp 143–150

    Google Scholar 

  • Schlick CM, Duckwitz S, Gärtner T, Schmidt T (2008) A complexity measure for concurrent engineering projects based on the DSM. In: Proceedings of the 10th international dependency and structure modeling conference (DSM 2008), pp 219–230

    Google Scholar 

  • Schlick CM, Duckwitz S, Gärtner T, Tackenberg S (2009) Optimization of concurrent engineering projects using an information-theoretic complexity metric. In: Proceedings of the 11th international dependency and structure modeling conference (DSM 2009), pp 53–64

    Google Scholar 

  • Schlick CM, Schneider S, Duckwitz S (2011) Modeling of periodically correlated work processes in large-scale concurrent engineering projects based on the DSM. In: Proceedings of the 13th international dependency and structure modeling conference (DSM 2011), pp 273–290

    Google Scholar 

  • Schlick CM, Schneider S, Duckwitz S (2012) 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) (in press)

    Google Scholar 

  • Shalizi CR (2006) Methods and techniques of complex systems science: an overview. In: Deisboeck TS, Kresh JY (eds) Complex systems science in biomedicine. Springer, New York, pp 33–114

    Chapter  Google Scholar 

  • Shalizi CR, Crutchfield JP (2001) Computational mechanics: pattern and prediction, structure and simplicity. J Stat Phys 104:817–879

    Article  Google Scholar 

  • Shaw R (1984) The dripping faucet as a model chaotic system. Aerial Press, Santa Cruz

    Google Scholar 

  • Shtub A, Bard JF, Globerson S (2006) Project management—processes, methodologies, and economics, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Sinha K, de Weck O (2009) Spectral and topological features of “real-world” product structures. In: Proceedings of the 11th international dependency and structure modeling conference (DSM 2011), pp 65–77

    Google Scholar 

  • Smith RP, Eppinger SD (1997) Identifying controlling features of engineering design iteration. Manag Sci 43(3):276–293

    Article  Google Scholar 

  • Sosa ME (2008) A structured approach to predicting and managing technical interactions in software development. Res Eng Des 19:47–70

    Article  Google Scholar 

  • Sosa ME, Eppinger SD, Rowles CM (2004) The misalignment of product architecture and organizational structure in complex product development. Manag Sci 50(12):1674–1689

    Article  Google Scholar 

  • Suh NP (2005) Complexity—theory and applications. Oxford University Press, Oxford

    Google Scholar 

  • Summers JD, Shah JJ (2003) Developing measures of complexity for engineering design. In: Proc ASME DETC, Chicago, IL, Paper DTM-48633, pp 381–392

  • Summers JD, Shah JJ (2010) Mechanical engineering design complexity metrics: size, coupling, and solvability. J Mech Des 132(2):1–11

    Article  Google Scholar 

  • Steward DV (1981) The design structure system: a method for managing the design of complex systems. IEEE Trans Eng Manag 28(3):71–74

    Article  Google Scholar 

  • Tackenberg S, Duckwitz S, Kausch B, Schlick CM, Karahancer S (2009) Organizational simulation of complex process engineering projects in the chemical industry. J Univers Comput Sci 15(9):1746–1765

    Google Scholar 

  • Tackenberg S, Duckwitz S, Schlick CM (2010) Activity- and actor-oriented simulation approach for the management of development projects. Int J Comput Aided Eng Technol 2(4):414–435

    Article  Google Scholar 

  • Tatikonda MV, Rosenthal SR (2000) Technology novelty, project complexity and product development project execution success. IEEE Trans Eng Manag 47(1):74–87

    Article  Google Scholar 

  • Terwiesch C, Loch CH, De Meyer A (2002) Exchanging preliminary information in concurrent engineering: alternative coordination strategies. Organ Sci 13(4):402–419

    Article  Google Scholar 

  • Ursu E, Duchesne P (2008) On modelling and diagnostic checking of vector periodic autoregressive time series models. J Time Ser Anal 30(1):70–96

    Article  Google Scholar 

  • Weyuker E (1988) Evaluating software complexity measures. IEEE Trans Softw Eng 14(9):1357–1365

    Article  Google Scholar 

  • Winner RI, Pennell JP, Bertrand HE, Slusarezuk MM (1988) The role of concurrent engineering in weapons system acquisition. Ida-report r-338, Institute for Defense Analyses, Alexandria, VA

  • Yassine A, Joglekar N, Eppinger SD, Whitney D (2003) Information hiding in product development: the design churn effect. Res Eng Des 14(3):145–161

    Article  Google Scholar 

Download references

Acknowledgements

The work of the first and second author was supported by grants from the Deutsche Forschungsgemeinschaft (DFG Normalverfahren SCHL 1805/3-3).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher M. Schlick.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schlick, C.M., Duckwitz, S. & Schneider, S. Project dynamics and emergent complexity. Comput Math Organ Theory 19, 480–515 (2013). https://doi.org/10.1007/s10588-012-9132-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-012-9132-z

Keywords

Navigation