AI Management Model for Production

Henrik Heymann, Jan Hendrik Hellmich, Maik Frye, Dennis Grunert, Robert H. Schmitt

Abstract

<p>Artificial Intelligence (AI) projects in production often end in proof-of-concepts with AI solutions not being continuously maintained along their life cycle. Only by managing multiple AI use cases simultaneously and systematically, companies can achieve an industrial level of usage in their production environment and fully benefit from the technology’s potential. For that purpose, an AI management model is proposed that serves as a framework to capture, design, and optimize AI activities to continuously improve the quality of the AI solutions and the satisfaction of involved stakeholders. Relevant related concepts from quality management (QM) are employed during the creation of the management model distinguishing three categories of processes: management, core, and support. For each category, corresponding processes and sub-processes are provided and explained for orientation in the implementation in specific scenarios. The proposed management model is validated with AI, QM, and production domain experts on a conceptual level. Furthermore, it is applied operationally in the implementation of real-life use cases from production.</p>

Keywords

Artificial Intelligence, Management Model, Quality Management, Production

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IMEKO-TC10-2023-002.pdf

DOI

10.21014/tc10-2023.002

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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