• DOI:10.1007/s10588-012-9132-z
• Corpus ID: 6579331

Project dynamics and emergent complexity

@article{Schlick2011ProjectDA,
  title={Project dynamics and emergent complexity},
  author={Christopher M. Schlick and S{\"o}nke Duckwitz and Sebastian Schneider},
  journal={Computational and Mathematical Organization Theory},
  year={2011},
  volume={19},
  pages={480-515}
}

• C. Schlick, S. Duckwitz, S. Schneider
• Published 4 January 2011
• Computer Science
• Computational and Mathematical Organization Theory

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.