The notion of Circular Economy (CE) dates back nearly 50 years. While there seems to be no single creator, the principles behind it were first explored as early as the 1970’s. Interest in this model is growing recently, as digital transformation in industry is seen as a driving force enabling the foundational elements of the concept.

The Ellen MacArthur Foundation defines the circular economy as a system where materials never become waste and nature is regenerated. In a circular economy, products and materials are kept in circulation through processes like maintenance, reuse, refurbishment, remanufacture, recycling, and composting. In a broad sense, the circular economy addresses global challenges, like climate change, biodiversity loss, waste, and pollution, by decoupling economic activity from the consumption of finite resources. The three main principles of a circular economy are: minimizing or eliminating waste and pollution, increasing the operational longevity of products and materials, and revitalizing natural systems.

By contrast, in a linear economy, natural resources are turned into products that are ultimately destined to become waste because of the way they have been designed and manufactured. It is based on a culture of mass production of disposable products with short shelf life, such as mobile phones, fashion items, clothes, etc. This process is often summarized as "take, make, waste”.

A circular economy aims to transition from this approach to a more sustainable system geared towards sharing, refurbishing, and recycling to generate a model that reduces the creation of waste and pollution.

Where Does Digital Transformation Fit in a Circular Economy?

Digitalization can been seen as one of the enablers of CE thanks to its building visibility and intelligence into products and assets such as knowledge of the location, condition and availability of assets. The application of new digital technologies in a circular economy then changes, from a tool for maximizing profits to a tool for reducing environmental impact.

According to an article published in Science Direct, digitalization can help boost the transformation towards a more sustainable circular economy. Digitalization enables more efficient processes in companies, helps reduce waste, promotes longer life for products, and minimizes the transaction costs.

The authors note that the combination of cyber physical systems, big data, data mining, data analytics, Internet of Things (IoT), and new business models could provide major opportunities towards more sustainable industrial value creation, value capture, and CE. Increasing use of digital technologies such as utilization of artificial intelligence or blockchain technology brings novel ways to improve traceability and transparency throughout product lifetime. Smart, connected products allow producers to monitor, control, analyze, and optimize products’ performance while collecting usage data.

Let’s look closer at some of the areas where digitalization can enable a circular economy:

• Product creation phase support: In this group we can include new digital technologies such as design systems, simulation and R&D aids, such as CAD/CAM/CAE solutions, Digital Twins, AI/ML etc. These technologies allow the creation of a product, virtually before making it into its physical prototype, starting from simulations and mathematical analyses and employing characteristics of reuse of existing materials, constraints in terms of environmental impact, durability, etc.
  It is interesting to note that in this phase there are also positive impacts from the numerous efforts by international and government bodies that impose standards aimed at creating compatible products, and therefore reducing waste and providing quality guidelines that also include the reduction of environmental impact such as energy classifications.
• Product creation support: In this group we can include new digital technologies belonging to the so-called Industry 4.0/5.0 such as IoT, predictive analysis systems, and smart factories. All technologies that allow extremely efficient production in terms of energy use/reuse and reduced environmental impact.
  In this category we can also include all the new digital technologies related to additive manufacturing if they are adopted to produce products on-demand and therefore eliminating warehouse stocks that we can consider a useless waste of natural resources.
• Support for procurement and distribution: The adoption of new digital technologies such as GPS systems, fleet monitoring, automated warehouses, and management of the product conservation chain (especially food) has allowed the creation of a supply chain and distribution mechanisms that are extremely efficient in terms of saving energy resources such as fuel and electricity.
• Support for market data analysis: The research underlying the creation of new products or the improvement of existing ones, now relies on an extremely vast amount of data, ranging from consumer data, to procurement data, to production data, and finally to product feedback data. The use of new digital technologies such as AI and machine learning provide the information necessary for the creation of new products that meet the expectations of the circular economy.
• The inherent risks of Digital Transformation: Many, if not most of these new technologies are AI-based, presenting advanced methods to collect, transmit, and analyze massive amounts of raw data that is transformed into decision making information. This extreme technological sophistication allows industry to reach eco-sustainability goals never achieved before, and thus, making it mandatory to protect them.  

An attack on an AI application or even a simple malfunction or an inadvertent manipulation could have life-threatening implications. In the industrial sector, for example, an attacker could tamper with the ML training data, where even seemingly harmless changes, such as altering the color of individual pixels, can have a major effect. Certain manipulated properties might feed through into the trained model that no human observer would ever spot. In a similar vein, an attacker could alter the pre-processing of the training data, the training parameters, or even the finished trained model to cause mistakes further down the line. The attack surfaces of the machine learning lifecycle are many and protection from manipulation is critical. Manipulation of any data or any algorithm used within the machine learning lifecycle can have disastrous consequences. In addition, the confidentiality of sensitive data and intellectual property contained in it must also be protected, as the training data could e.g. reveal the inner workings of a component. Even the AI application itself or its underlying data about the relevance of specific training parameters might represent intellectual property in this respect.

How to license and protect these new assets? Wibu-Systems is adeptly addressing the surging demand with robust and adaptable solutions. You can read about them in this Primer, Artificial Intelligence: Protection and Licensing.