@inproceedings{8760e4e897ca495a899c23013d3541fd,
title = "Analyzing industrial clusters using measures of structural complexity management",
abstract = "Companies organize in industrial clusters to exchange knowledge, to identify new options for cooperation and to improve the regional competences for a special industry sector. For optimizing industrial clusters, it is necessary to assess factors influencing performance or effectivity of industrial clusters. This evaluation or analysis of cluster's performance can reveal strengths and weaknesses of the cluster. Interpreting the weaknesses might detect activities for improving the performance of the industrial cluster. For this performance analysis, we use measures and metrics of structural complexity management to investigate the cluster's inner structure, e.g. the cooperation and linkage between employees of companies, which are in the same cluster. We applied the measures at the MAI Carbon cluster and interpreted the results of the performance analysis. The user data of the cluster's online platform serve as the basis for this analysis.",
keywords = "Cluster structure, DMM, DSM, Industrial cluster, Structural complexity",
author = "Schmidt, {Danilo Marcello} and Marc Haas and Daniel Kammer and Julian Wilberg and Kissel, {Maximilian Philipp} and Udo Lindemann",
year = "2015",
doi = "10.3139/9783446447264.005",
language = "English",
series = "Modeling and Managing Complex Systems - Proceedings of the 17th International DSM Conference",
publisher = "Carl Hanser Verlag",
pages = "41--51",
editor = "Browning, {Tyson R.} and Eppinger, {Steven D.} and Schmidt, {Danilo Marcello} and Udo Lindemann",
booktitle = "Modeling and Managing Complex Systems - Proceedings of the 17th International DSM Conference",
note = "17th International Dependency and Structure Modeling Conference, DSM 2015 ; Conference date: 04-11-2015 Through 06-11-2015",
}
