AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization

• URL: http://arxiv.org/abs/2509.11691v1
• Date: Mon, 15 Sep 2025 08:45:17 GMT
• Title: AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization
• Authors: Lukas Rauh, Mel-Rick Süner, Daniel Schel, Thomas Bauernhansl,
• Abstract summary: This paper proposes a new process model for the lifecycle management of AI assets in manufacturing.<n>The proposed process model aims to support organizations in practice to systematically develop, deploy and manage AI assets across their full lifecycle.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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