How PepsiCo makes the perfect Cheetos with the help of Autonomous Systems

How PepsiCo makes the perfect Cheetos with the help of Autonomous Systems

Cheetos manufacturing line

PepsiCo is a leading food and beverage manufacturer, and Cheetos are one of their 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 the 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.


Optimizing production yield while ensuring quality is always a complex challenge. It is no different when it comes to manufacturing an irresistible snack like the Cheetos.

It is difficult for operators and automated control systems alike to ensure consistent quality because of constantly changing variables. Each batch of raw ingredients is slightly different from the previous one. Also, although they can be of the same make and model, equipment across manufacturing lines can have minor differences within their manufacturer’s tolerance range. Finally, like any mechanical equipment, equipment wears over time which also impacts their operating specifications.

To help with these aspects, PepsiCo was looking for a solution that would both ensure consistent quality and optimize product yield


To optimize the production yield, Neal Analytics worked closely with PepsiCo’s manufacturing team, from process experts to operators, and the Microsoft AI engineering team to design, train and deploy a Project Bonsai AI agent, aka a “brain.” This agent helps operators optimize Cheetos output while maintaining quality.

The first step to training the AI agent using the trial-and-error approach of Deep Reinforcement Learning (DRL) was to develop an accurate process simulator. Because of the process complexity, Neal Analytics AI experts developed an AI simulator using a Deep Neural Network architecture.

Using this simulator and PepsiCo’s process experts and operators, the DRL’s so-called “reward function” was defined using the concepts of Machine Teaching and after a series of tests to optimize the solution’s appropriate components from process inputs and outputs to reward function parameters.

Once deployed, the AI agent offers real-time advice to the operators. This advice helps them tune the system to maximize production throughput (lbs. per hour).

It also ensures that key product attributes are kept within specifications.

Altogether, this solution improved the overall system performance by optimizing it for both throughput and quality.

To learn more about this solution, check out this video testimonial by the PepsiCo team and the detailed customer story published by Microsoft on its AI blog.

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