AI: An Enabler to Circular Economy or Hindrance?

Author: Sneha Kumari

We have grown rapidly, in almost every industry from industrial to technology. During this growth phase we have taken and utilized enough resources from the environment around us and have not found a way to replenish them, thus leaving a negative impact. The resources around us are finite and we need to start thinking of smarter models to reduce/eliminate waste back into our processes with actionable steps.

Circular Economy is the answer to remove/repurpose waste by feeding it back into the system/loop/process. All the while doing this, it is possible to remain economically viable. According to a McKinsey report from 2019, a circular economy in Europe can generate 1.8 Trillion Euros by 2030 while creating jobs and generating environmental benefits.

The rate at which a linear economy is hurting us can create monumental irreversible impacts and to counter this we need a model that can work fast and be agile. In this digital age, we are training machines to think and be creative. How about putting the newly trained machine to some good use? AI is most often compared to creation similar to how humans would and train/develop like humans think. Using AI to make our supply chains work smarter than before, we can witness powerful effects of having a circular business model.

Let us walk through some of the aspects supply chain through a product life cycle that can benefit from AI implementation.

Starting with a product design phase, AI has the right capabilities to analyze complex models and come up with designs that support CE concepts with a powerful capability to shrink and analyze volumes of data.  AI can help pick the products, materials that serve the circular economy purpose.  According to Ellen MacArthur Foundation, AI has the ability to create products, components, materials with better designs suited for CE, operate circular business models and optimize infrastructure to ensure circular product and material flows.

Then work with suppliers to create these designs. Design will play a pivotal role in defining the process used to create it. Again, sharing data and collaborating with suppliers will be key to create value supply chain networks.

After identifying the source of the right raw materials, how about using energy-efficient source of delivery like zero-emissions vehicles or solar-powered logistics centers. Not to forget to use responsible and reusable packaging methods.

Once the right materials have been procured, AI can be used in the intelligent/smart manufacturing of products using better pattern recognition, prediction, optimized planning and implementing solutions with robots.

AI can play a huge role in optimizing planning and purchasing of materials. With end to end data access and integration across upstream supply chains. AI can help with modeling techniques that can help predict the demand thus enabling the purchase of the right inventory in the right amounts. This can help reduce the problem of stockout/ excess inventory.

Once distributed to the channels, the key is to close the loop in the CE model. Moving to reverse logistics, this is where it can get complex to locate where the products are, the condition of the products, the life of the components left that can be reused, re-manufactured. This is where AI can help locate, sort, and match materials that can be purchased back and re-fed in the value chain.

While all this mentioned above, makes it look like AI is the solution and enabler to circular economy, this will take a lot of careful and intelligent human intervention to make this model successful. And of course, all this needs to be conducted ethically. There are many research showing the human influence that can manipulate AI models. Correct input and design are required to be input in an AI model before it can provide outputs.

We can already see few companies treading this path in adopting AI to their CE models.

  • TOMRA: Implementing AI to intelligently identify the life of food produce and avoid waste rather employ the intelligent use of waste
  • Stuffstr: AI is helping this company to bring back clothes to retailers and then pricing them smartly to sell in secondary markets. According to the CEO of Stuffstr: the carbon footprint in the household items bought each year exceeds the emissions of the entire US. auto fleet. And almost 85% of US textiles end up in landfill — about 70 pounds per person every year — yet most are eligible for recycling.”
  • Optoro: This company is using AI to enable circularity concepts in reverse supply chain. AI helps them sort returns and replenish the inventory back with whatever is re-usable.

While AI is one answer to supporting our circular economy initiatives, it still has the danger of leaving a carbon footprint behind. This can prove counter-intuitive to what CE really stands for. An article from MIT released a report that the power required to train the neural networks can lead to emissions of about 626K pounds of carbon dioxide which is comparable to about 5 times the lifetime emissions of the average US car. The more complex the problem gets, the more complicated the neural networks get and so the more emissions.

Data security and privacy is another concern that is raised through the implementation of AI. Quantum encryption technology is a field that is being explored to close the gap with data security but still needs further innovation. An integration of Internet of Things (IoT), wireless networks and cloud computing can also prove helpful in enabling circular economy concepts. One of the research shows Virtualization as one of the not very common forms of digitization that can enable CE as well that helps in designing products that are repairable acting as an enabler to CE.

While we discuss the benefits of digitization for driving circularity, it needs to start with a behavior change from the consumers and governments. One of the ideas is to “Co-Create” between companies by sharing expertise, data, and knowledge base. This will help create transparency between organizations both upstream and downstream supply chains. Transparency all along the supply Value chain will be key to watch AI become a success in building a circularity.

A mindset shift is required to move the world towards circularity and adopt the practices not just at the level of organization but by consumers in small day to day activities. There could not be a better time to start working in this direction than NOW.

References:

http://news.mit.edu/2020/artificial-intelligence-ai-carbon-footprint-0423

https://www.mckinsey.com/business-functions/sustainability/our-insights/artificial-intelligence-and-the-circular-economy-ai-as-a-tool-to-accelerate-the-transition

https://www.ellenmacarthurfoundation.org/assets/downloads/Artificial-intelligence-and-the-circular-economy.pdf

https://www.greenbiz.com/article/3-companies-using-power-ai-advance-circular-economy

http://www.theijes.com/papers/vol7-issue9/Version-3/I0709035563.pdf

https://www.sciencedirect.com/science/article/pii/S2212827118305432?via%3Dihub

https://www.greenbiz.com/article/how-big-data-and-ai-can-make-supply-chains-better-consumers-and-planet

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