Harnessing AI for Digital Product Passports: Paving the Future of Sustainable Supply Chains

In today’s interconnected global economy, the demand for transparency and sustainability in supply chains is higher than ever. One of the key mechanisms aimed at achieving this goal is the Digital Product Passport (DPP), as introduced by the European Commission. The DPP provides consumers and businesses with detailed information about a product’s lifecycle, from material sourcing to disposal. With AI advancements, companies now have the potential to streamline and enhance DPP implementation, ensuring the future of sustainable supply chains.

Understanding the Digital Product Passport

In 2022, the European Commission unveiled the Ecodesign for Sustainable Products Regulation (ESPR), a framework promoting the creation of more durable, reusable, and sustainable products. One of its cornerstones is the Digital Product Passport. Embedded into products via QR codes or RFID tags, DPPs contain extensive information on the product’s composition, its sustainability features, and supply chain data. This provides businesses, regulators, and consumers greater insight into a product’s environmental impact.

The Growing Need for DPPs

The DPP is a vital tool to ensure manufacturers and consumers align with the broader goals of sustainability and circular economy. Industries, particularly in Europe, are under increasing pressure to track and report environmental impacts, reduce carbon emissions, and limit waste. The DPP’s role extends beyond local markets, influencing global supply chains by requiring comprehensive data on where components are sourced and how products are designed, manufactured, and disposed of.

However, the volume of data necessary for each product, and the complexity of tracking that data across global supply chains, present a considerable challenge. This is where AI can step in to facilitate the process.

AI’s Role in Automating DPP Creation

One of the primary challenges in implementing DPPs is the enormous amount of data that must be collected, processed, and maintained across every stage of a product’s lifecycle. Manual data management is insufficient for the scale required. Here, Artificial Intelligence (AI) can significantly streamline these processes.

AI-powered systems can:

• Automate Data Collection: By leveraging machine learning, AI can capture real-time data from suppliers, manufacturing processes, and logistics networks. Through IoT sensors and smart devices, AI can monitor material sources, emissions, and energy usage throughout the product’s creation.
• Ensure Data Accuracy: AI can cross-verify data from multiple sources to ensure accuracy, avoiding issues such as double counting of emissions or incorrect material sourcing reports. Generative AI, for instance, could simulate and predict data inconsistencies, flagging them for verification.
• Predict Environmental Impact: Using predictive analytics, AI can forecast the lifecycle impacts of products, such as energy use or waste production, based on historical data. This allows companies to adjust processes early in the production cycle to optimize for sustainability.

Enhanced Supply Chain Transparency with AI

A key promise of DPPs is increased transparency throughout the supply chain. Historically, lack of visibility into the sourcing of materials, ethical labour conditions, and emissions along a product’s lifecycle has been a challenge for both manufacturers and consumers.

AI can significantly improve supply chain visibility by:

• Tracking Materials in Real Time: AI can integrate data from across the globe, giving manufacturers an up-to-date picture of where materials are sourced and how they are being processed. This real-time tracking is crucial for industries like electronics, which have complex, multi-tiered supply chains.
• Identifying Risks and Optimizing Routes: AI’s ability to process vast amounts of supply chain data enables predictive risk analysis. Companies can foresee potential disruptions, such as shortages in raw materials or delays in shipping, and mitigate these risks by rerouting materials or finding alternative suppliers.
• Ensuring Compliance: DPPs demand strict adherence to sustainability standards. AI can automatically cross-check supplier data with regulatory standards, ensuring compliance across multiple jurisdictions. For example, AI can help identify if certain materials are sourced from conflict zones or if ethical labor practices were followed in production.

AI’s Potential for Supply Chain Optimization

The long-term benefits of AI in supply chains extend far beyond DPPs. The implementation of AI across industries can:

• Enhance Efficiency: AI can optimize production processes to use less energy, minimize waste, and maximize the use of recyclable materials. This is especially relevant to industries like electronics, which produce millions of tons of e-waste annually.
• Drive Circular Economies: One of the major objectives of the ESPR is to foster a circular economy. AI can help by predicting when products will reach the end of their life cycle and initiating recycling or refurbishment processes. In this way, materials can be recirculated into the supply chain, reducing the need for new raw materials.

A viable and relevant use case for Blockchain

Blockchain may finally have a viable and relevant use case with Digital Product Passports (DPPs) by ensuring the immutability and security of product data across supply chains. Since DPPs rely on transparent and trustworthy records, blockchain offers a decentralized, tamper-proof ledger to store product information—ranging from material sourcing to recycling processes—without risk of alteration. By integrating AI with blockchain, supply chains can ensure not only real-time data processing but also permanent, verifiable records that enhance accountability, support regulatory compliance, and foster consumer trust in sustainable products.

Overcoming Challenges in AI and DPP Integration

Despite its potential, there are challenges in integrating AI into the DPP ecosystem. AI systems rely on vast quantities of high-quality data. If suppliers or manufacturers fail to provide accurate or complete data, the AI system’s output could be compromised. Additionally, there is the challenge of interoperability—ensuring that different AI systems used by various companies along the supply chain can communicate and share data seamlessly.

Furthermore, companies may face challenges in adjusting their existing infrastructure to accommodate the AI technologies required for DPP compliance. Many legacy systems lack the integration capabilities needed for modern AI solutions, requiring substantial investments in technology upgrades.

Future Outlook: AI, DPPs, and a Greener World

The promise of AI-driven Digital Product Passports is profound. As global supply chains grow in complexity, AI offers the ability to manage and analyze the vast data networks required to make DPPs feasible. Companies that invest in AI technologies today are not only preparing for future regulations but are also positioning themselves as leaders in sustainable innovation.

By fostering greater transparency, accountability, and optimization, AI can ensure that DPPs fulfil their promise of driving sustainability throughout global supply chains. More importantly, it signals a new era where technology can power the shift towards more ethical, environmentally-conscious production processes, benefiting businesses, consumers, and the planet alike.

Portera, an expert partner in Digital Product Passports

Portera has established itself as a leader in AI and data-driven transformations, guiding businesses through end-to-end digital product journeys across multiple industries. Over the years, Portera has demonstrated expertise in delivering innovative solutions, streamlining supply chains, and leveraging AI for sustainable practices. In 2024, Portera’s client Danone received a Gartner Award for Innovation for its Digital Product Passport (DPP) experience, showcasing Portera’s capability in driving cutting-edge digital initiatives that enhance product transparency and sustainability.