Project dynamics and emergent complexity
Project dynamics and emergent complexity
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DOI：
10.1007/s10588-012-9132-z
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发表时间：
2012-07
影响因子：
1.8
通讯作者：
C. Schlick;Soenke Duckwitz;S. Schneider
中科院分区：
文献类型：
--
作者：
C. Schlick;Soenke Duckwitz;S. Schneider
基于并行工程的管理理念，对新产品开发项目中的项目动态性和涌现性复杂性进行了理论分析。本文综述了组织理论、系统工程设计和基础科学研究中发展起来的复杂性框架、理论和测度。为了评估新产品开发项目中的紧急复杂性，从各种度量中选择了一种称为“有效度量复杂性”（EMC）的信息理论量，因为它可以从第一原理中推导出来，因此具有很高的构造效度。此外，它可以有效地从动态生成模型或纯粹从历史数据中计算，而不需要干预模型。电磁兼容测量一个随机过程的无限过去和未来历史之间的互信息。根据这一原则，评估新产品开发中随时间变化的复杂性并揭示相关的相互作用是特别有趣的。为了得到分析结果，采用模型驱动的方法，建立了协同工作的向量自回归模型。所建立的VAR模型为原始状态空间中电磁兼容的封闭解的计算提供了基础。该解决方案可用于基于模型独立参数的复杂性分析和优化。此外，对谱基进行变换，得到更有表现力的矩阵解。矩阵形式允许识别出令人惊讶的几个基本参数和计算两个较低的复杂度界限。关键参数包括VAR模型功变换矩阵的特征值和业绩波动分量之间的相关性。
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.