How to Use AI for Circular Manufacturing

Summary

Artificial intelligence has gained traction across the manufacturing industry. Factories are using it to refine product designs, optimize workflows and bring needed flexibility to robots, but its potential can go further. It may prove an essential tool in creating a circular manufacturing economy.

What is circular manufacturing?

Circular manufacturing refers to production and consumption that revolves around reuse instead of making disposable products from new materials. In the current model, manufacturing follows a linear pattern, starting with extracting raw materials and ending with users disposing of old items. A circular economy aims to reduce waste on both ends by connecting them.
 
Instead of relying on virgin materials, circular manufacturers get most of their resources from recycling and reuse. The process should also minimize energy waste to make the cycle as efficient as possible. It’s critical, considering the manufacturing sector is responsible for 12% of all greenhouse gas emissions in the U.S.
 
Implementing a circular economy would not solve all the industry’s environmental issues, but it is a goal worth striving for. The world generated 62 million metric tons of electronic waste alone in 2022, and producing new electronic devices requires energy-hungry and damaging mining processes. A circular economy would reduce the environmental impact of both sides.

How AI enables a circular manufacturing economy

As beneficial as circular manufacturing can be, it’s challenging to implement. AI can help organizations overcome these obstacles in several ways.

1. Designing for circularity. Part of enabling a circular economy is designing products for consumers to recycle and reuse instead of disposing of them. Many current designs make circularity difficult because extracting materials from old products requires energy-intensive processes. Manufacturers could address this by making things with eventual extraction in mind, and AI could find opportunities to incorporate this philosophy.
 
Machine learning models can identify ways to use recycled materials, create less wasteful designs or tweak existing plans to enable easier post-usage resource recovery. Several companies have already seen positive results from doing so, including producing clothes with close to no wasted fabric or using up to 20% recycled metal in electronics.

2. Optimizing supply chains. Circular manufacturing also requires a restructured supply chain. Logistics networks must make it simpler for consumers to recycle products after using them and connect these recycling facilities with factories. Shortening the distance between points will also help, as it reduces transportation-related emissions.
 
AI is an ideal supply chain analysis tool because these networks are so complex. Machine learning excels at discovering patterns in large datasets and predicting the impact of subtle changes. As a result, it can pinpoint nearby recycling facilities to partner with, find opportunities to enable smoother reverse logistics and shorten delivery routes.

3. Minimizing production-related waste. The manufacturing process will also need to adapt to enable circularity. Recycling and reuse typically produce a lower volume of usable materials, so production must become more resource-efficient. Lowering energy consumption will also help in the sustainability push.
 
AI has already driven many waste reduction improvements in this industry. Machine vision and similar automated systems have drastically reduced human error, resulting in less material loss. AI-guided robots are highly precise, enabling less material-intensive designs. Consequently, a lower volume of incoming resources will not hinder production.

4. Improving recycling streams. AI can also make circular manufacturing more viable by boosting recycling facility efficiency. Current sorting and reclamation methods often produce waste and miss opportunities to recover valuable materials. AI’s precision and speed optimize the process, making it a reliable source for manufacturers.
 
Machine vision systems can sort recoverable materials with close to 100% accuracy, keeping reusable resources out of landfills. AI could also help such automated solutions work faster, allowing recycling facilities to remain productive despite labor shortages. As these centers produce more recycled materials, manufacturers will be able to rely less on virgin feedstocks.

5. Finding ongoing optimization opportunities. Creating a circular manufacturing sector will be a long, slow process full of ongoing improvements. AI is the ideal way to find and analyze the efficacy of such areas to improve, making circularity more financially viable.
 
AI can remove hundreds of thousands of tons of CO2 from industrial waste streams through carbon capture, reduce production errors and find green transportation opportunities. It can also simulate various organizational changes to identify which strategies would yield the most impactful results. Taking the guesswork out of the equation can significantly streamline the path that leads to a circular economy.

AI can pave the way for a greener future

A circular manufacturing industry is crucial for a truly sustainable future, but building one will be challenging. AI offers the efficiency, insight and accuracy needed to turn this goal from a dream into a reality. As more research pours into this area, AI’s potential for long-term sustainability will only grow.