Data, emerging technologies, and the circular economy: how Accenture and AWS are unlocking environmental and business impact

This post was contributed by Ilan Gleiser, Principal Specialist, Emerging Technologies at AWS, Joshua Curtis, Circular Intelligence Global Lead and Patrick Ford, Circular Intelligence North America Lead, Accenture Sustainability Services

It is well documented that the circular economy is an opportunity for positive impact on business and society. Accenture’s analysis presents an economic opportunity of $4.5tn value is at stake for the global economy to 2030 by departing from our current ‘take-make-waste’ economic system [1]. Ellen MacArthur Foundation outlines the importance of circularity as a solution to climate change, with 45% of the required carbon emission reductions to achieve a 1.5-degree world coming from how we make and consume products [2].

It’s clear that resource use and circularity are critical to the creation of a sustainable, healthy economy. But how do we realize this value? How does a business identify where and how circular strategies can create financial and environmental impact?

In this post, we will explore the challenges to achieving accurate and actionable data on circular economy performance and impact, and the solutions that lie in emerging technologies. Join us as we explore opportunities for kicking off and accelerating the data transformation needed to drive authentic, impact-driven progress on circularity.

Achieving data-driven circularity

The importance of transitioning to circular business models is why the European Commission is, for the first time, making measurement and disclosure of resource use and circular economy impacts mandatory for companies. The newly-launched European Sustainability Reporting Standards (ESRS) include a requirement [3] for companies – where material – to report on circular economy metrics like:

1. the percentage of material used for products and packaging that are renewable, recycled or re-used
2. the volume of waste by stream that is recovered by destination
3. the financial effects of material risks and opportunities arising from resource use

But how do we calculate these metrics? How do we collect, aggregate, and analyze the data in a way that doesn’t require significant time and resources year on year? How do we not only do this to understand where we are, but also to determine where we need to go?

While there is an increasing number of new sustainability measurement products being presented to the market, there is no one ‘tool’ to answer these questions. In fact, these questions themselves are not new — they are the essence of data-driven decision-making which is the foundation of any profitable business. The circular measurement challenge is a data challenge and must be approached as such.

What is new – and evolving – is the potential application of digital technologies to support the transformation of circular economy data management for companies. For example, the advancement of the Internet of Things (IoT) enables tracking of product movement and health through use phases; machine learning algorithms can help companies identify patterns in circular economy data, like trends in demand for certain recycled materials; and blockchain technology can be used to create a transparent and secure ledger of circular-related transactions, enabling stakeholders to track and verify the movement of materials and products through the value chain.

As we continue to tackle the circular measurement challenge, it is essential to approach it with a data-driven mindset. Digital technologies have the potential to revolutionize how we manage and analyze circular economy data, allowing us to create a more sustainable and efficient economy for the future. Accenture and AWS are collaborating to make these applications a reality.

The challenges to circular data transformation

To help ensure digital solutions are effective in managing circular economy performance, it’s crucial to design them to address specific challenges faced by businesses. Let’s begin by exploring these challenges.

First, selecting the right metrics themselves is not straightforward. We mentioned the European Commission’s regulations ESRS E5 on resource use and the Circular Economy. They provide headline metrics for business disclosure. The Circular Target-Setting Guidance from the Circular Economy Indicators Coalition (CEIC), a partnership between The Platform for Accelerating the Circular Economy (PACE) and Circle Economy (supported by Accenture) provides an overview of leading measurement methodologies and approaches for business implementation.

These are important starting points for business across industries, but they don’t account for the specific value chains or functional priorities of businesses in different sectors. For example, the metrics to measure circular economy performance (and therefore the data required) vary significantly for a fashion retailer compared to an oil and gas major. Ultimately, selecting the right metrics must be led by each business, drawing on the wealth of supporting materials, best practices and market standards.

Next comes the hard part: identifying, collecting and transforming the data. Comprehensive circular measurement relies on data from across the value chain, often not tracked in existing enterprise systems. The foundational data itself is simple enough – what materials are being used and where do they come from; what waste is being produced and where is it going – are tangible examples. The challenge is collecting that data across product lines, business units, and geographies and then transforming the data to be usable. For example, when calculating your percentage of materials that are recycled, renewable or re-used (as ESRS E5 requires), materials data must be segmented in ways not currently built into enterprise data capture. Without technology, this requires line-by-line segmentation based on data that is available e.g. through supplier declarations. The bottom line is that collecting data to measure circular performance is an arduous process, requiring time and costs, and is hindered by data gaps. To do this at the business level, in a way that enables action, is not only helped by digital technologies, it depends upon them.

Finally, companies must transform this data into actionable insights to guide decision-making. Circularity is not an end, but a means to optimize planetary and business impact. To accelerate this impact, resource use data like the above example regarding materials that are recycled, renewable or re-used, must be connected with other internal and external data sets like sales data and emissions intensity factors. Companies must understand how different material choices impact carbon emissions, as well as procurement costs and business profitability. The true story of corporate circularity is of trade-offs and investment requirements to capture long-term value. Without a comprehensive approach to circular economy measurement and data transformation, understanding those trade-offs properly and making impact-driven decisions is impossible. This again adds complexity to data collection and analysis, with the only solution for ongoing insight generation being an automated, centralized approach, like a circular and/or sustainability data lake, which combines data sets and applies analytics solutions for calculation and visualization.

Figure 1. The Circular Business Hub: Illustration of data visualization for circular economy performance management. Companies must overcome challenges to achieve measurement and visualization of circularity at each pillar of the business value chain.

The role of emerging technologies

Accenture and AWS are collaborating to bring the best of their combined data, technology and sustainability expertise to transform circular economy data management. AWS offers the broadest set of capabilities in artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), big data analytics, and high-performance computing (HPC) in the market. Accenture is the world’s leading integrator of AWS solutions and technologies – they’ve completed over 1,100 projects with us over 15 years of partnership.

Teaming up on the circular economy, Accenture brings its 12+ years of client experience in circular economy strategy and implementation, and 100+ global circular economy experts, to guide the application of these digital solutions to unlock value for joint customers from data-driven circularity.

Accenture has developed core assets as part of a suite of circular intelligence solutions, powered by AWS. These include industry-specific KPI frameworks, a foundational data model and a proof-of-concept dashboard to act as a platform for client co-development and customization. Building upon these assets, a core priority of this collaboration is to enable the automation of circular data ingestion, transformation and analysis to enable ongoing performance measurement and generation of actionable insights for companies across the value chain. To do this, we’re leveraging Velocity, a co-funded and co-developed platform that adds new cloud innovations up to 50% faster, to develop the data architecture which pulls in resource use and circular economy-related datasets into a central circular data lake.

A circular data lake (Figure 2) works by taking raw data from siloed sources and bringing it into a unified, organized system. This process is made possible using the AWS Transfer family, which collects data and brings it into a raw layer.

Figure 2. Reference Architecture of an automated circular data lake for managing circular economy measurement and insight generation

Once the data is in the raw layer, one or more ETL (extract, transform, and load) workstreams are triggered. These ETL workstreams work together to clean, refine, and organize the data. To facilitate the ETL process, the reference architecture employs several AWS Glue jobs and AWS Lambda functions. These functions and jobs help to ensure that the ETL workflows are fully orchestrated and operate efficiently and effectively, to produce the refined data necessary for circular metrics and KPIs.

Once the data is fully curated and prepared, it’s ready for business intelligence and machine learning (AWS Athena, Amazon Redshift, and Amazon SageMaker). These services enable companies to derive value from the data by analyzing it, identifying patterns and trends. By doing so, it’s possible to transform siloed data from a wide range of sources into actionable insights that can drive circular business progress.

The circular data lake is designed to centralize all data related to a business’ resource use and subsequent impact. With this, it must be designed and implemented in connection with other environmental objectives like climate and nature-related data sources. Indeed, the circular data lake is designed to be a component of an organization’s overall sustainability data lake fabric, creating a single system of record for ESG data management.

Building upon the circular data architecture, created and tested with joint customers, Accenture and AWS are building solutions for companies focused on creating value from data in functional areas of circular transformation. This means going beyond just measuring circular economy performance (which we refer to as ‘foundational’ use cases) to applying digital solutions for value creation from circular insights (these are ‘advanced’ use cases).

Figure 3. Overview of foundational and advanced circular intelligence use cases at each stage of an industry-agnostic value chain

Figure 3 shows examples of circular economy use cases split between foundational and advanced. Circular intelligence helps companies measure the impact of their investments and compare results over time. For more examples of how digital technologies enable a circular economy, please see this blog post.

Figure 3. Overview of foundational and advanced circular intelligence use cases at each stage of an industry-agnostic value chain

The circular intelligence maturity journey

Let’s look at an example of how AWS services and solutions are being used to enable foundational and advanced use cases in the procurement stage of the product development lifecycle.

Example: how to transition away from virgin, non-renewable materials to optimize for decarbonization, cost reduction and risk management?

The materials that companies buy for products and services are a critical component of their environmental impact, while the reliance on virgin, finite materials also presents a growing supply chain risk for many industries.

To achieve foundational circular procurement decision-making, machine learning algorithms can analyze past procurement data and identify patterns and insights to make better purchasing decisions. For example:

• With AWS’s Intelligent Document Processing solutions, companies can streamline the ingestion of bills of materials across product lines. By automating the procurement data ingestion process, companies can measure their circular input footprint (share of circular materials per product and/or packaging type), as well as the cost and carbon impact of circular inputs. This allows for faster and more accurate decision-making when selecting sustainable alternatives.
• Another powerful tool is Amazon Textract, which can automatically ingest supplier declarations, saving time and increasing accuracy. By digitizing these declarations, companies can easily track progress toward their circular procurement goals and identify areas that require improvement.

These applications are a significant value add for companies in reducing the time required to assess circular performance in procurement, while enabling decisions that account for trade-offs, and can accelerate the highest value initiatives.

The value opportunity doesn’t end there, however.

• With generative AI services, like Amazon Bedrock, companies can build on this foundation for predictive analytics, real-time scenario modeling and advanced forecasting – AWS Partner Simudyne, uses high-performance computing enabled simulations, coupled with LLM powered chatbots, to provide recommendations of how companies can decarbonize their supply chains, and therefore reduce their scope 3 emissions.
• By leveraging market data and research to identify opportunities for alternative materials and supply chain risk assessment, companies can gain an edge over the competition. For example, AWS partner Good Chemistry, uses HPC clusters, orchestrated by AWS Batch, to design new materials and accelerate the identification of molecules to substitute or destroy toxic chemicals from the production process and environment.
• By using SageMaker for its geospatial capabilities, companies are proactively addressing potential environmental risks. By monitoring deforestation in real-time with satellite data, IOT sensors and drones, coupled with machine learning algorithms, regulators, insurance companies and businesses can be aware of the risk of their current material footprint.

Overall, the use of AWS emerging technologies helps companies, governments and other stakeholders, transition towards circular materials in the short and long term, making sustainability a priority in their procurement process.

Call to action

Each company’s journey on circular economy measurement will look different, depending on industry, strategic priorities, and technical maturity. At Accenture and AWS, we believe solutions must be fit-for-purpose and co-designed for the specific business context. While there are growing options for ‘plug-and-play’ sustainability solutions, specifically in carbon management, developing the data foundation for automated baselining across the value chain and moving towards value-creating technology applications requires a level of customization and co-development using existing products and repeatable solutions where available.

The first step to get started is defining your circular economy blueprint with key metrics across each area of your business. This is the foundation for a data-driven strategy and many companies are working hard to collect data for reporting on circular performance on an annual basis. To then understand and plan for foundational and advanced circular measurement use cases, it is key to assess your current functional and technical maturity to measure and manage these metrics. Identifying where the raw data lives, how it is collected and stored, and who is responsible for it is critical to map the journey towards automation. From there, it is a case of prioritization of metrics and use cases through engaging with stakeholders across different business groups. The end-users of the data and insights must be involved in the full journey to ensure the applied solution serves the required needs.

The next step is to design, build and test. Cross-functional teams will need to work together to integrate solutions as part of a circular data lake, and develop the user interface for practical, day-to-day business decision making. Proof-of-Concepts (POCs) are valuable in building the foundational technical architecture, illustrating the value with a tangible output, and establishing buy-in from leadership and key stakeholders. This can then prime you for rapid deployment and scale.

From there, deployment must be thought of as a multi-generational journey. Companies should focus on their key requirements for a Minimum Viable Product (MVP) solution that supports their core business priorities. Be it cross-value chain performance management, or specific use case advancement, the MVP must also include solutions for data pipeline automation and a data governance strategy. Accenture and AWS are supporting companies across these phases of implementation, including the development of targeted POCs and MVPs to tackle the key challenges your business is facing on circular measurement and prove the value of emerging technologies applied to these challenges. This landscape is rapidly evolving and our joint assets are developing with every implementation; co-development with leading businesses is at the heart of this.

Conclusion

Circular economy measurement requires more than an off-the-shelf sustainability solution; it is a data and technology problem that requires a comprehensive approach. By leveraging the power of AWS and Accenture’s expertise, businesses can unlock the full potential of the circular economy. By capturing data into a purpose driven data lake and running advanced analytics, our joint approach provides businesses with actionable insights and recommendations for optimizing resource usage and reducing waste. By prioritizing sustainability and embracing greater intelligence, businesses can drive positive environmental and business impacts, paving the way for a more sustainable future.

The content and opinions in this blog are those of the third-party author and AWS is not responsible for the content or accuracy of this blog.

References

[1] Lacy, Long and Spindler, The Circular Economy Handbook: Realizing the Circular Advantage (2019)
[2] Ellen MacArthur Foundation, Completing the Picture: How the Circular Economy Tackles Climate Change (2021)
[3]European Sustainability Reporting Standards in scope for companies with a turnover above €150 million in EU