Autonomous systems 101: Optimizing manufacturing processes with AI

Bottles move on a conveyor belt in a factory

This is one part of a four-part introductory series about autonomous systems. To learn more, read the rest of the series and the free e-book linked at the bottom of this post. 

In manufacturing, the name of the game is optimization. With many complex processes working together to build a product or solve a logistical issue, cutting even a little waste can turn into big savings over days, weeks and months.

A major revolution in manufacturing optimization was automation, in which machines carried out the process of building products without as much human intervention. As the manufacturing sector grows, businesses look for more ways to make building products easier and more cost-effective.

That’s where autonomous systems come in. By adding AI to machines, processes or lines, manufacturers have a new weapon in their fight for optimization and efficiency. Engineers are using these new tools that can adjust to their environments and adapt in real time to better meet operational objectives. This stands to disrupt not only how we think about industrial processes, but how humans and machines can work together for maximum impact.

Autonomy, control systems and advancements in AI

Manufacturers know that there are limits to automation—namely, that it’s limited in flexibility and requires high levels of precision and unchanging operating conditions. Think about a modern car assembly line: As a car moves down the line, everything must be just right: location, angle, position. If something is even slightly off, the machines responsible for any aspect of that car can, and probably will, miss their mark (or signal for a human to intervene).

The next step in the evolution of these automated systems is “autonomous” systems. Instead of the automation of a predetermined set of steps, autonomous systems use AI to learn from their environments and engage them dynamically. These machines adopt strategies, rather than rote recipes for action, and then execute them in response to their environment.

The Microsoft approach to autonomous systems

Intelligent, autonomous machines can learn to account for challenges like resource scarcity, managing fail-safe conditions and adaptive object manipulation. To accelerate this learning, Microsoft has pioneered a method that allows control and process experts to teach the AI agent using their own expertise. This allows the AI agent to learn similarly to how a human would: a little at a time, based on a lesson plan from an expert. This includes getting feedback and using that feedback to improve. This AI training methodology allows the most expert engineers to impart their wisdom to the AI agent without needing a data science background, to get the AI agent trained quickly.

The AI agent can then be deployed to assist or advise humans or even work autonomously to optimize a system or process to significantly reduce waste, cost and time.

Read more about autonomous systems: