Analyzing industrial clusters using measures of structural complexity management

Abstract

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.

Product-requirement-model to approach the identification of uncertainties in battery systems development

“…In contrast to a DSM, a DMM does not only contain one class of information but focuses the relationships between two classes (e.g. domain of a component) [25,26].…”

“…The DSM/DMM methods support understanding the dependencies and interactions within a product system and the exchange of information across domains. Even though, the DSM/DMM framework lacks the information about multiple and time-dependent relationships and dependencies between single node pairs such as requirements and their importance during product development [25,26].…”