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Industrial AI Podcast
Porsche combines bayesian networks with modern approaches to optimize production planning
Aug 16, 2023
Simon Dürr from Porsche and Prof. Dr. Marco Huber discuss the optimization of production planning using Bayesian networks. They delve into incorporating customer behavior and market variations, the creation and reinforcement learning of network structures, and the challenges of implementation in a large organization. They also highlight the importance of plant orders and the potential application in other industries.
31:48
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Quick takeaways
- The project aims to redesign the planning and ordering process at Porsche using AI and new technologies, addressing limitations of the current SAP-based system and accommodating global market demands.
- The AI model developed utilizes bi-agent networks and reinforcement learning algorithms to generate virtual planned orders, improving transparency, flexibility, and stability in both the customer ordering and production planning processes.
Deep dives
Introduction and Background
Simon and Marco introduce themselves and their roles at Porsche and the University of Stuttgart, respectively. They explain the background of the project, which aims to redesign the planning and ordering process at Porsche using AI and new technologies.
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