Through my work over the past twenty years, I have found myself in a fascinating place.
Globalisation, the awesome power of technology and the ever present threat of climate change have come to dominate my world view. How is it possible, as an individual, to react to such enormous issues in a positive and practical way?
During my career, I have been lucky to meet people that are passionately engaged in answering just that question. They have introduced me to ideas and tools that have transformed my professional and personal thinking. I have come to know many wonderfully innovative new companies, applying technology specifically to enable the growth of a sustainable economy. However, I have still to see one that has that elusive balance of feasibility, viability and value that’s needed to make a real commercial or systematic impact. This is particularly true with regard to what needs to be done when making the transition to the circular economy.
This is the first in a series of posts in which I’ll attempt to identify opportunities. Leverage points and gaps where feasible solutions can be commercialised quickly, have clear positive impact and offer the opportunity to create something new.
A great starting point
I have long been interested in the technical barriers to the transition to the circular economy, and specifically the data sharing issues. Circular economy business models, with their emphasis on product as a service, mean that the stakeholders in an asset change according to where it is in its lifecycle.
The component materials, location, condition and availability of a product are no longer the sole concern of its current owner-user. Those with an interest in its future re-use or the potential value of its components also have a legitimate stake in that asset, and if they are unable to access that information then the resource efficiencies that the circular economy is predicated upon are lost. So, data must follow the same loop as the physical products and materials that it relates to.
A white paper published in 2018 by the World Economic Forum – ‘Harnessing the Fourth Industrial Revolution for the Circular Economy’ – was the first document that I had seen that explored this issue in any depth. It looks at two contrasting markets (consumer electronics and plastic packaging) and argues that both face the same 5 ‘circular challenges’
1. Opaque value chains – Lack of transparency on material origin, content, condition and destination
2. Linear product design – Circular design alternatives are often not understood, considered or contextualised
3. Linear lock‐in – Difficulties developing viable circular business models in yet linear systems
4. Inefficient collection and reverse logistics – Material leakage and fragmentation impeding economies of scale
5. Insufficient sorting and pre‐processing infrastructure – Lack of efficient facilities delivering the mono‐streams needed for high quality recycling‘Harnessing the Fourth Industrial Revolution for the Circular Economy’ – WEF 2018
The paper goes on to suggest the solutions that might be applied to address each of these five challenges, including design tools, value-based return incentives and product passports. Each of these are enabled by emerging technologies such as AI, the IoT, cryptographic anchors and machine learning. Crucially, this captured data is then shared via the ‘Internet of Materials’ – “a decentralized data system connecting data on different products and materials through standardized communication protocols. Data should be supplied by producers as products are sold, tying in data on material provenance and product design. Ensuring data confidentiality and anonymity are key here to avoid competitive and anti‐trust challenges.” During the product’s life, users can add to this data so that location, condition and availability can be shared as required too. In this way, data does follow the same loop as the physical products and materials that it relates to.
I cannot fault the vision described in the white paper. To my mind this is the technological ecosystem that has to emerge for the circular economy to be realised, and I have enthusiastically shared this paper since I first read it.
Furthermore, I know of a number of companies that already have solutions that could address many of these challenges. Technologically, it’s all achievable, but there’s more to it than technology.
Wheels within wheels
However. I’ve also struggled with the following ‘circular challenges’ on how to address the previously listed ‘circular challenges’.
Where’s the entry point?
The joy of all things linear is that we know where to begin. It’s at the beginning.
Within a loop, the interdependency of elements is not so easy to delineate, and it always appears that removing one piece breaks the circle completely. As a result, anyone looking to find the point in the Internet of Materials loop at which they can intervene for the best effect is faced with a daunting ‘chicken and egg’ paradox.
Clearly, they are interdependent but are they equally interdependent? If not, it’s essential to identify which of the five ‘circular challenges’ would, when solved, enable most progress to be made in the others.
The cold start problem
The ‘cold start problem’ is an issue faced by most AI entrepreneurs.
Any AI value proposition is that it gives its customers’ game changing powers of prediction, analysis and decision making, through their product’s ability to wrangle ‘big data’. The more data that is collected, the more useful, and the more valuable a service becomes – this is known as the “data network effect”. Once started, it should gather momentum and become a self-perpetuating source of value for the AI company and its users alike. But, how do you start when you have little or no data? Clearly, the Internet of Materials will be a desolate place until there’s worthwhile data to share.
Digital linear lock-in
The linear lock-in challenge described in the WEF paper applies to the digital realm as much as it does to the material.
The WEF paper identifies the three key issues of linear lock-in as, firstly, “difficulties identifying viable business models in yet linear systems”. Secondly, “lack of infrastructure and capabilities for product lifetime extension (e.g. IoT for remote diagnostics)” and finally, “lack of data to assess conditions and value of used products”.
So, solving the challenges of the second and third elements is clearly an opportunity for tech entrepreneurs. The problem is, any tech entrepreneur will also face the same first issue – “difficulties identifying viable business models in yet linear systems”.
The ubiquitous cloud
In the late 90s, I remember sitting through a lot of PowerPoint slides in which people described the glories of the emerging technology landscapes. A universal feature was a diagram (they weren’t yet ‘infographics’) in which various bits and pieces were linked together by drawing a dotted line into a simple drawing of a cloud. In essence, the presenters were saying, ‘you don’t need to worry about how this bit actually works’, it’ll all get sorted ‘in the cloud’. As we all learned, ‘the cloud’ is not some kind of magic box, it’s fraught with issues. So, seeing the return of the ubiquitous cloud in the centre of Accenture’s diagram (see page 15 of the paper) filled me with a mixture of nostalgia and dread.
The WEF paper rightly says that standards, regulation, open-source interoperability and a globally distributed architecture are needed, this is the main constriction for all growth related to the infrastructure required by 4IR technologies. So, finding a way in which solutions can operate and create value independently of the ubiquitous cloud will be essential. New solutions that derive their value solely from the cloud will not be viable and so cannot kick-start the process.
During the next few weeks, I’ll address each of these issues one by one. In doing so, I’m sure that I’ll raise just as many questions as I hope to answer but I hope that by having more eyes upon them we can start to make some kind of progress.
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