Comprehensive management function models applied to heterogeneous industrial networks The comprehensive management function models (CMFM) represent a solution for the growing heterogeneity in the management plane of the industrial domain. We present the internal structure of the CMFM with a metamodel. The use of semantic techniques and knowledge formalization is a first step towards management automation from a humancentric point of view. This humancentric character also helps to keep the overview and comprehensive understanding in the vast field of the network management. The evaluation of the CMFM against wellknown and widely used network management systems explains the extent of the novelty of this approach. The CMFM ease the management of heterogeneous industrial networks and are a first step towards the application of further automation measures like artificial intelligence.

Smart manufacturing and DVSM based on an Ontological approach Abstract   Smart manufacturing is characterized as transparent shop floor production, rapid and intelligent responses to dynamic changes, and a utilization of highperformance intercooperation networks. Smart manufacturing and a global appetite for personalized products have transitioned industry from mass production into the age of mass customization. Increased autonomy is slowly changing customer expectations as well, enabling customers to modify a product design not only during an order, but sometimes even long after placing an order. In this context, this paper fills a gap by presenting a datacentric infrastructure to enable interaction with a xe2x80x9cglobal, virtual data space,xe2x80x9d which overcomes the problems with traditional direct access methods such as interoperability and compatibility. Using a CyberPhysical System (CPS), resource monitoring on the shopfloor as well as multiple parities beyond the enterprise boundary will be interconnected through this datacentric infrastructure. A semantic knowledge management system, which encompasses product lifecycle knowledge and manufacturing process ontology, is developed as the data schema in the datacentric infrastructure. In comparison to relational databases which are effective at handling paper forms and tabular structure, the flexible schema of graph databases enable these to handle dynamic and uncertain variables. These capabilities are deemed critical for a platform supporting realtime information exchange between customer, manufacturer and collaborators. One advantage of such a system allowing for realtime information exchange is that it enables last minute order changes by the customer, allowing for product design changes even after production has started on the order. The other advantage is that it allows manufacturing managers to monitor the productivity of customerdirected, dynamic manufacturing processes by utilizing Dynamic Value Stream Mapping (DVSM) methods.