The value of a product lies in its constituent materials and its utility.
Preserving this value is what the circular economy is all about, and artificial intelligence (AI) can help make it happen. The process of preserving value can be broken into six steps.
The challenge for the circular economy starts at the design stage. Creating products from other source materials and designing them so they can be dismantled at a later date takes more than just an understanding of construction: it often requires new materials, too.
Food chemists at the Chilean start-up NotCo drew on Artificial Intelligence (AI) to develop new recipes for a range of products, including mayonnaise and ice cream, that did not require the use of climate-harmful raw materials such as dairy products or beef. Since their database contains huge quantities of recipe data and food information, the AI can include some unlikely-sounding ingredients in its calculations. Who would have thought that extracts of pineapple and cabbage would help make the perfect vegan milk?
This approach works in other spheres too, whether in generating new formulations or even developing new metal alloys with specific properties. This time advantage matters: according to the Massachusetts Institute of Technology, innovation needs to speed up by at least 10 if we are to find solutions to the pressing challenges of our time.
Once a product has been designed, raw materials are required. The use of AI monitoring to prevent plunder and pollution has a key role to play here. Bluefield Technology, for example, analyses satellite data using AI to identify methane leaks from gas pipelines and compressor stations. The environmental nonprofit Imazon uses the same concept in its PrevisIA system to predict where rainforest de-forestation will happen next, thereby making it possible to try to prevent it. So far, the system has proved accurate to within 4 kilometers 85 percent of the time.
There are many ways AI can be used in manufacture, mostly for waste avoidance – fewer offcuts, less energy consumption or quality enhancement.
A research project was conducted to analyse tree trunks after they passed through a CT scanner before being cut up at the sawmill. The AI identified the quality of the different parts of the wood and determined the suitability of each part, be that parquet flooring or construction, violins or shuttering for concrete walls.
This kind of selection and optimised production can also be applied to other fields. In food production, for example, AI systems analyse the quality and ripeness of fruit, thereby allowing resources such as fertilizers to be used only in the quantities required. Imazon uses algorithms to determine which products are packed in paper bags rather than boxes. This is an example of how AI can also reduce consequential harm: less air being transported means fewer goods and vehicles and, therefore, lower transport emissions.
AI also has numerous roles to play when it comes to product utility. It can be incorporated into products and optimise the way they work. This is becoming an increasingly important feature of electrical appliances: smart washing machines, for example, once loaded, can calculate the optimal start time based on energy supply and demand. This is an essential feature as electricity grids are increasingly supplied by renewable sources.
Another application is the AI-supported Green Consumption Assistant co-developed by the Technical University of Berlin.
Integrated into the Ecosia search engine, it works in the same way as the shopping recommendations we are all familiar with from the major online portals but prioritizes products that represent a sustainable alternative.
A search for smartphones, for example, will list re-pairable devices such as the Shiftphone and Fairphone first.
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05. Take Back
One of the biggest challenges facing the circular economy is the creation of reverse supply chains.
Most businesses are optimised for distributing their products to trading centers or direct to end customers. After that, the goods generally become untraceable, making retrieving old equipment or materials difficult.
Successful reverse supply chains depend on one of the AI strengths already mentioned: optical diagnosis. When built into robots, this application can analyse the quality of products and their components. At Ikea in the US, for example, an AI system uses this method to determine the fate of used furniture. Depending on its condition, the furniture is either offered for resale on the Ikea website, donated to the needy, brokered to re-sellers, or declared waste. The benefit is that 85 percent of the furniture remains in use.
If loops in the circular economy are to be closed efficiently, it is important to be able to balance supply and demand, whether by automatically calculating prices for used goods or by using the kind of matching algorithms found in dating apps to connect users on second-hand platforms with the products they are looking for.
The same goes for the creation of sharing business models. These are only as good as the ability of the algorithms to predict the demand for cars, e-scooters, and other shared goods. If the algorithms are not sufficiently accurate, the result is frustration for consumers and costs for providers – and the whole concept of saving resources through the efficient use of consumer goods declines.
AI is, therefore an important element in a number of new product-as-a-service business models.
As seen from the above examples, AI systems can be deployed throughout the material flow, making an important contribution to a fully functioning circular economy.
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Innovation & AI Strategy