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Production yield optimization
Optimize control processes in changing environments
Autonomous systems for production yield optimization built on Microsoft Project Bonsai bring AI from research labs to real-life manufacturing process control. By leveraging machine teaching, deep reinforcement learning, and advanced simulation technologies, Neal Analytics designs, trains, and deploys AI agents by customer’s manufacturing lines.
The AI agent combines classical process inputs, real-life heuristics, and relevant environmental variables to help optimize production yield while considering a broad set of potentially competing priorities. Once deployed, the AI agent (aka “Project Bonsai brain”) will either advise operators or directly oversee the manufacturing control system while remaining under operator supervision.
Without the availability of realistic process simulators, autonomous systems are not possible as they are the critical part of enabling reinforcement learning to be possible.
Self-trained AI through reinforcement learning
With machine teaching, process specialists can build AI-powered control systems that will leverage their expertise effectively without the need to become AI experts.
Human augmentation with AI or AI supervision by humans
This can significantly increase operator effectiveness, reduce quality issues, and avoid downtime.
How PepsiCo makes the perfect Cheetos with the help of autonomous systems
PepsiCo is a leading food and beverage manufacturer, and Cheetos are one of its most famous snacks. But how can PepsiCo ensure they manufacture the perfect Cheetos every time, even as the production environment changes?
Neal Analytics worked with the Microsoft AI engineering team and Cheetos manufacturing experts to build, train, and deploy an autonomous system leveraging the Microsoft Project Bonsai platform. This solution helps PepsiCo ensure the perfect Cheetos snack comes out each time.
Leverage the power of reinforcement learning to build your next AI solution