Project dynamics and emergent complexity

PROJECT DYNAMICS AND EMERGENT COMPLEXITY Christopher M. Schlick Institute of Industrial Engineering and Ergonomics RWTH Aachen University ABSTRACT The present paper presents theoretical and empirical analyses of project dynamics and emergent complexity in new product development (NPD) projects subjected to the management concept of concurrent engineering. A model-driven approach was taken and a vector autoregression (VAR) model of cooperative task processing was formulated. The model is explained and validated based on an empirical study carried out in a German industrial company. Furthermore, concepts and measures of complex systems science were reviewed and applied to project management. To evaluate emergent complexity in NPD projects, an information-theory quantity—termed “effective measure complexity” (EMC)—was selected from a variety of complexity measures, because it can be derived from first principles and therefore has high construct validity. Furthermore, EMC can be calculated efficiently from generative models of task processing or purely from historical data, without intervening models. EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, it is an especially interesting metric to evaluate the time-dependent combinatorial complexity of work processes and to uncover the relevant interactions. The formulated VAR model provided the foundation for the calculation of several closed-form solutions of EMC in the original state space. These solutions can be used to evaluate complexity explicitly based on the model ́s independent parameters. Moreover, a transformation into the spectral basis was carried out to obtain more-expressive solutions in matrix form. These solutions allow identification of the surprisingly few essential parameters influencing complexity, and interpretation of the effects of cooperative relationships directly. The essential parameters include the eigenvalues of the dynamical operator of the VAR model and the correlations between components of unpredictable performance fluctuations. Finally, the theoretical complexity analyses were elucidated in practical terms through an applied example of optimizing project organization design. The objective was to minimize emergent complexity by choosing the “best” team design from within a predefined group of individuals with different productivities in a simulated concurrent engineering project. The application shows that EMC is a theoretically highly satisfactory quantity, which also leads to useful results in organizational optimization regarding key performance indicators, such as the lead time of the project and its empirical variance.