Skip to main content Skip to main navigation


AI Asset Management: a Case Study with the Asset Administration Shell (AAS)

Lukas Rauh; Mike Reichardt; Hans Dieter Schotten
In: 2022 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE International Conference on Emerging Technologies and Factory Automation (ETFA-2022), 27th Internetional Conference on Emerging Technologies and Factory Automation, September 6-9, Stuttgart, Germany, IEEE, 2022.


Driven by the goal of maintaining competitiveness within the shift toward personalized production, manufacturing companies are increasingly adopting Artificial Intelligence (AI) in their manufacturing facilities, empowered by the digital transformation, Big Data, and Cyber-Physical Systems (CPS). However, the complexity caused by the continuously growing variety of AI implementation frameworks, the initial high investment in resources, and the lack of industry-wide AI standards are hindering the rapid adoption of industrial AI. To improve asset interoperability for AI assets and thereby ensure sustainable reliability by reducing investment risks, previous work has proposed an information model to wrap AI assets into the Industry 4.0 framework. Therefore the approach utilizes the Asset Administration Shell (AAS) as a realization standard of the Digital Twin (DT) in the manufacturing Industry 4.0 context. This paper provides additional practical experience from implementing the AI AAS concept in a real use case as a basis for initiating a standardization process of the resulting AI AAS information model.