Circularity is data-driven

Digital technologies often cycle through phases of being the ‘next big thing.’ Currently, AI dominates discussions, just as Blockchain did five years ago, the Internet of Things a decade ago, and RFID twenty years ago

Although none of these technologies became as widespread as hyped, each has found niche applications where their unique properties have been transformative.

The other megatrend that fills my timeline is the circular economy. I often see posts about where one or another these technologies is offered up as the enabler for the circular economy, but none of these technologies in isolation is going to be the decisive factor in replacing the linear economy with a circular one. Breaking linear lock-in is impossible without these technologies, but they have to be seen simply as tools within a data-driven culture.

A data-driven culture is a mindset where decisions are made based on data analysis and interpretation rather than intuition, personal experience, or opinion. This is essential for the circular economy because we are dealing with an entirely new paradigm in which intuition, experience and opinion count for little. Curiosity and experimentation enabled by data are far more valuable.

Four interconnected capabilities

A data-driven culture, regardless of the technology that underpins it, rests upon 4 equally important and intrinsically connected capabilities. Weakness in any single one undermines the culture as a whole.

The role of digital technologies is to enable these capabilities. Each of them has a particular function and benefits. All of them need to be used to create a cohesive whole.

1) Data Integration

Information siloes are the greatest barrier to creating a data-driven culture. Breaking them down through effective data integration practices is essential.

That involves structuring all the different, isolated data sources within the organisation, combining technical and business processes to create a single-source-of-truth that can be trusted and adopted throughout the company. That’s a massive challenge which organisations of all types and sizes are working toward because without it progress of all kinds (including circularity) is blocked. Employees must be able to source data that they need and to trust it.

The circular economy transition will require vast amounts of complex data inputs. Not just that which is already existing in locked siloes, but new data sources from upstream in the value chain and – most critically - from downstream where data sets from the use phase and post-use phase of products will need to be captured and processed.

Answering this need is the place of ‘big data’ solutions, the catch-all term for a variety of advanced techniques for the processing, storage, distribution and management of high-volume, fast-moving data.

2) Data Analytics

However well managed it is, raw data is useless. Data analytics is the transformation of data into insights that can acted upon.It’s in the field of data analytics that artificial intelligence will play a vital role.

There simply isn't enough room in this article to mention all if the ways in which AI enabled data analytics will enable the circular economy. This Ellen MacArthur Foundation paper provides a great overview.

I'll give one example that I believe will prove very significant.

Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) will be essential perspectives and the role of technology will be to enable those insights on the scale that organisations will need in order to make such a massive and critical realignment of businesses.

Manufacturers will need to carry out Life Cycle Assessments of hundreds of products and thousands of component parts. That will require AI to manage the big data problem that this represents but even then it's only half the story. Those LCA calculations must be analysed and interpreted in a way that will give clear, actionable insight to managers.

In this article from McKinsey & Company the authors offer an excellent 'cleansheeting' model for decision making in manufacturers that want to "advance their carbon-reduction goals—and their broader sustainability goals—while operating profitably".

In one case study, they analyse the potential for both CO2e abatement and cost reduction of a single component, an injection-moulded plastic part for the filter housing of a coffee machine.

Step one calls for creating a cost and carbon-emissions model of how the part is currently designed and produced, a classic LCA-LCC exercise. Step two analyses the cost / carbon abatement implications of 7 potential initiatives across changes to design, material sourcing and manufacturing processes.

The results show a compelling environmental and financial case for change.?

However, remember that this kind of analysis would need to be carried out on thousands of individual components. That can only be achieved through the use of AI to automate LCA-LCC, Gen AI to interpret the results, and the creation of a digital twin of each part.

The third and fourth capabilities, data capture and data sharing are, very closely linked

3) Data Capture

An essential technology for capturing data in a product’s life cycle will be the Internet of Things (IoT); devices with?sensors, processing ability,?software?and other technologies that can connect and exchange data with other devices and systems.

This paper from the Ellen MacArthur Foundation explores the ways IoT enabled ‘smart’ products, or as they term it, 'intelligent assets' can enable circularity.

Through IoT technologies products will be able to communicate three pieces of information essential for them to be managed through multiple use cycles - Location, Condition and Availability

Location

Knowing the location of an asset is important for a number of reasons.

The ability to map resources is essential for the efficient sharing, repair, reuse, refurbishment and recycling of assets. The logistics required for the repair of assets can be effectively planned, so can the operations and infrastructure required for the reverse loop of circular supply chains.

The closer the assets are to the points of reuse or recycling facilities, the lower the transportation costs and environmental impact. Not only does this enhance the overall sustainability of the circular economy, but it also has important implications for its economic viability.

This is especially important for businesses that have mobile, high-value assets deployed across multiple locations, since operational performance depends on balancing resource utilisation, rapidly redeploying resources, and keeping assets in service. Think of companies like Select Plant Hire or car sharing models like Zipcar UK .

Localising circular economy activities in that way can also strengthen regional economies and reduce dependence on global supply chains, making the system more resilient to disruptions.

Circular cities like Amsterdam provide a great example here. The city government is working towards the reuse of building materials within the city becoming standard practice and has used cloud computing technologies to create a digital inventory of locally available materials for architects and construction companies to use. The success of this can be seen in the neighbourhood of Buiksloterham, where 80% of materials in public spaces are now made from materials that are reused, recycled, or bio-based.

Condition

The condition of an asset determines its potential for reuse, repair, or repurposing. High-quality assets can be directly reused, while others may need refurbishing.

Monitoring the condition of assets also allows for better maintenance and timely interventions, which will extend their useful life. This data will also provide valuable insights to product designers by identifying those factors that limit a products' use life and reuse potential.

Most importantly, visibility of an asset's condition helps in determining the residual value of assets, aiding in economic viability assessments for reuse or recycling.

This level of visibility can be achieved through IoT technologies.

Giving products unique digital identities that can be accessed in the cloud via QR codes (as Digimarc do) could give access to information like the number of wash-cycles that a washing machine has completed and whether it has a full maintenance history. Another example would be the sensor technologies deployed by MOVUS through which the performance and condition of industrial plant is monitored.

Availability

Linear supply chains pull resources and materials through the value chain on the implicit (but wrong!) assumption that an unlimited supply exists to meet it.

By contrast, the reverse loop of the circular supply chain has to push a limited pool of resources toward reuse. So, it's essential that when products and materials become available for reuse that this is visible.

In that way companies can ensure a steady supply of materials and products for reuse, remanufacturing, and recycling. They can balance supply and demand, avoiding overproduction and manage market dynamics by influencing the pricing and competitiveness of secondary materials and products.

Visibility of availability is also a pre-requisite for the sharing economy. Zipcar UK for example manage their fleet of cars through IoT technology so that their customers can see not only which cars are available for use but where they are and whether they are adequately fuelled / charged for use - location, condition and availability.

The Zipcar UK example above is also a good illustration of how essential data sharing becomes in the circular economy.

4) Data Sharing

Data sharing is the process of making information available to internal and external stakeholders (both upstream and downstream).

Data sharing is a prerequisite to the circular economy. No single company can adopt the circular economy in isolation as no company holds all the data they need to enable the necessary strategies. Data has to be shared across entire value chains and the entire life cycle of products.

Enabling the circular economy will depend upon then ability to track and trace product and material flows throughout their life cycle. Opaque value chains, the lack of visibility of material flows, are in my view the single largest barrier to adopting the circular economy.

Perhaps the most important technological enabler for the circular economy will be the emergence of the ‘internet of materials’, which Accenture have defined as:

“A decentralised data system building on a standardised digital dataset registering material types and volumes sold, collected and treated across markets”

This is certainly essential and is a major part of the data integration effort that needs to be seen across whole value chains. However, it has to begin with enabling technologies that make it possible to read the specifications, material content and real-time condition of a product.

To service that need, the EU has introduced the Digital Product Passport as a key component of the Ecodesign for Sustainable Products Regulation (ESPR), set to be implemented during the next 12 months.

The DPP is a digital record that provides comprehensive information about a product and its entire value chain. This includes everything from the origin of the product, materials used, environmental impact, and disposal recommendations.?For this every product will need a unique identifier through which that information can be accessed in the cloud, this will be enabled by Auto-ID technologies like RFID or, in most cases, barcodes.

The major issue with any kind of data sharing is data security and privacy.

For example, as the circular economy needs data on the location, condition and availability of products in their use phase, then we as users of those products must consent to others in the value chain (particularly the manufacturer) being able to monitor those.

Similarly, those manufacturers must consent to share information about the bill of materials of their products with those organisations that need to recover those materials at the end of their life.

Naturally, these issues raise many concerns about personal privacy and corporate confidentiality. All parties must be assured that data shared is secure and used only for the purposes of enabling circularity.

This is where blockchain becomes essential. Blockchain is THE enabling technology that links data collection and sharing.

Circularise apply blockchain in a way that enables full traceability but allows value chain actors to both select which data points to share with others. Their 'smart questioning' approach also allows actors to find relevant information.

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So, there are no 'killer apps', no single solutions and no 'magic bullets'. The circular economy needs all of these technologies to be applied well and creatively, but above all their role is to support a holistic data-driven culture.

Creating that culture requires much more that simply choosing a software package.