@Article{Lick+Wegel+Kühn,
							Cite-key = "Lick2023Met", 
							Year= "2023", 
							Number= "1", 
							 Volume= "Industry 4.0 Science 39", 
							Pages= "106-113", 
							Journal   = "Industry 4.0 Science",
							 Title= "Methods for Designing Enterprise Architecture in Manufacturing Companies - EAM as Enabler for the Design of Transferable AI Solutions", 
							Author= "Jonas Lick, Arthur Wegel and Arno Kühn, Fraunhofer IEM, Paderborn", 
							Doi= "https://doi.org/10.30844/I4SE.23.1.106", 
							 Abstract= "A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions
in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape.
The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.", 
							 Keywords= "enterprise architecture management, EAM, artificial intelligence, AI, transferability of
AI solutions, transferable AI solutions, transferable AI, IT system landscape, IT landscape, data management", 
							}