Artificial intelligence can help boost the circular economy, says McKinsey research

Artificial intelligence can help boost the circular economy, says McKinsey research

Combining artificial intelligence and circular economy can accelerate a shift towards a regenerative system fit for the future, new research by the McKinsey organisation has shown.

The advantages of such an approach are substantial. Research shows that a circular economy in Europe can create a net benefit of €1.8 trillion by 2030, while addressing mounting resource-related challenges, creating jobs, spurring innovation, and generating environmental benefits.

It is clear that new approaches and solutions are needed to put us on an accelerated transition to a better model. New technologies, including faster and more agile learning processes with iterative cycles of designing, prototyping, and gathering feedback, are needed for the complex task of redesigning key aspects of our economy.

Artificial intelligence (AI) can play an important role in enabling this systemic shift. A growing number of initiatives are exploring how AI can create new opportunities to address some of the world’s most important challenges.3

The McKinsey research offers a first look into the cross-section of two emerging megatrends: how AI can accelerate the transition to a circular economy. It provides an initial examination of how AI can enhance and enable circular economy innovation across industries in three main ways:

  1. Design circular products, components, and materials. AI can enhance and accelerate the development of new products, components, and materials fit for a circular economy through iterative machine-learning-assisted design processes that allow for rapid prototyping and testing.
  2. Operate circular business models. AI can magnify the competitive strength of circular economy business models, such as product-as-a-service and leasing. By combining real-time and historical data from products and users, AI can help increase product circulation and asset utilisation through pricing and demand prediction, predictive maintenance, and smart inventory management.
  3. Optimise circular infrastructure. AI can help build and improve the reverse logistics infrastructure required to “close the loop” on products and materials, by improving the processes to sort and disassemble products, remanufacture components, and recycle materials.

The AI opportunity in accelerating the transition towards a circular economy for consumer electronics is up to $90 billion a year in 2030. Applications here include: selecting and designing specialist materials; extending the lifetime of electronics through predictive maintenance; and automating and improving e-waste recycling infrastructure through the combination of image recognition and robotics.

Combining the power of AI with a vision for a circular economy represents a significant, and as yet largely untapped, opportunity to harness one of the great technological developments of our time to support efforts to fundamentally reshape the economy into one that is regenerative, resilient, and fit for the long term.

Creating a broader awareness and understanding of how AI can be used to support a circular economy will be essential to encourage applications which span, and go beyond, the areas of circular design, operating circular business models, and optimising circular infrastructure. Ultimately, AI could be applied to the complex task of redesigning whole networks and systems, such as rewiring supply chains and optimising global reverse logistics infrastructure, in any sector.

Both collaboration between relevant stakeholders and a degree of oversight will be needed to support these systemic applications of AI, ensuring that data can be shared in an open and secure manner, and that AI is developed and deployed in ways that are inclusive and fair to all.

Further information

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January 25, 2019