Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
2011-01-04
Nonlinear Sciences
Adaptation and Self-Organizing Systems
110 pages, 6 figures, book chapter
Scientific paper
The paper presents theoretical and empirical analyses of project dynamics and emergent complexity in new product development (NPD) projects. A model-driven approach is taken and mathematical models of cooperative work are formulated based on the theory of vector-autoregressive (VAR)and hidden Marvov (HM)processes. To validate the models with field data, a case study was carried out in an industrial company. Furthermore, concepts and measures of complex systems science are analyzed. To evaluate emergent complexity in NPD projects, an information-theory measure-termed "effective measure complexity" (EMC)- is chosen, because it can be derived from first principles and can be calculated efficiently. EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, EMC is of particular interest for evaluating time-dependent complexity. The formulated VAR and HM models provide the base for the calculation of several closed-form solutions of EMC, solutions that allow an explicit complexity assessment based on the model's independent parameters. Finally, the theoretical complexity analyses are elucidated in practical terms through an applied example of optimizing project organization design. The objective is to minimize emergent complexity by choosing the "best" team design from within a predefined group of developers with different productivities in a simulated NPD project. The application shows that EMC is not only a highly satisfactory quantity in theory but it has also leads to useful results in organizational optimization.
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