{"ID":2870803,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11691","arxiv_id":"2509.11691","title":"AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.11691","url_pdf":"https://arxiv.org/pdf/2509.11691v1","authors":"[\"Lukas Rauh\",\"Mel-Rick Süner\",\"Daniel Schel\",\"Thomas Bauernhansl\"]","published":"2025-09-15T08:45:17Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}