AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914038024241152 |
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| author | Rauh, Lukas Süner, Mel-Rick Schel, Daniel Bauernhansl, Thomas |
| author_facet | Rauh, Lukas Süner, Mel-Rick Schel, Daniel Bauernhansl, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization Rauh, Lukas Süner, Mel-Rick Schel, Daniel Bauernhansl, Thomas Software Engineering 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. |
| title | AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.11691 |