The benefits of adopting artificial intelligence (AI) in manufacturing are undeniable. However, operationalizing AI beyond the prototype, especially when involved with cyber-physical production systems (CPPS), remains a significant challenge due to the technical system complexity, a lack of implementation standards and fragmented organizational processes. To this end, this paper proposes a new process model for the lifecycle management of AI assets designed to address challenges in manufacturing and facilitate effective operationalization throughout the entire AI lifecycle. The process model, as a theoretical contribution, builds on machine learning operations (MLOps) principles and refines three aspects to address the domain-specific requirements from the CPPS context. As a result, the proposed process model aims to support organizations in practice to systematically develop, deploy and manage AI assets across their full lifecycle while aligning with CPPS-specific constraints and regulatory demands.
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Rauh et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69c37c33b34aaaeb1a67f044 — DOI: https://doi.org/10.1016/j.procs.2026.02.094
Lukas Rauh
Mel-Rick Süner
Daniel Schel
Procedia Computer Science
University of Stuttgart
Fraunhofer Institute for Manufacturing Engineering and Automation
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