The 2x2 Matrix: DPPs and the Paradigm Shift Toward Dynamic, AI-Driven Supply Chains
- Carsten Stöcker
- Oct 1, 2024
- 23 min read
How Digital Product Passports Evolve from Static Data Repositories to μERP Systems, Enabling Real-Time Optimization in Cyber-Physical Ecosystems
Abstract
This article explores the current trends and future potential of Digital Product Passports (DPPs), using a 2x2 Matrix to categorize them into four DPP types: Data Vaults, Smart Compliance, Insight Engine, and Cyber-physical μERP. These categories represent the progression from static data systems focused on compliance to dynamic, API-driven platforms enabling real-time interaction and automation.
We are introducing the verifiable Digital Product Passport (vDPP), which combines trust chaining and digital signatures for data provenance, enabling third parties to verify DPP assertions and mitigate misleading claims.
Key challenges surrounding identity management and authorization for Persons of Legitimate Interest (PLIs) are addressed through enterprise identity wallets.
A deep dive into the μERP concept demonstrates how DPPs can evolve into micro ERP systems that manage real-time processes like predictive maintenance, circular economy integration, and supply chain automation.
The article includes a case study of Spherity’s EV Battery Passport, highlighting how dynamic data, secure APIs, and identity management optimize battery lifecycle management, from real-time value tracking to recycling.
This discussion is crucial for industry practitioners working on supply chain digitization, circular economy initiatives, and the adoption of DPPs, as it offers insights into how DPPs can drive efficiency, sustainability, and compliance across sectors.
1. Introduction to Digital Product Passports (DPPs) and Their Evolution Beyond Static Data
Digital Product Passports (DPPs) track key information about products. They capture data on origin, sustainability, materials, and certifications. This helps businesses, regulators, and consumers understand a product’s journey, especially it environmental impact. Traditionally, DPPs focus on transparency and compliance.
The European Union’s Eco-design for Sustainable Products Regulation (ESPR) defines how DPPs should be used. The goal is to promote sustainable products. DPPs help ensure that products meet eco-design standards. They also enable traceability from manufacturing to disposal. This improves the reduction of waste and carbon emissions, keeping critical raw materials in the economic cycle while shifting economic practices towards sustainability and accountability in supply chains regarding ESG and biodiversity goals.
Beyond ESPR: The Future of DPPs
However, DPPs are evolving. Their role goes beyond compliance and transparency. In this article, we take a broader view of DPPs. They are no longer just digital records. DPPs are becoming dynamic systems. These systems provide real-time data and enable interaction through APIs.
DPPs can now help with:
Product lifecycle management: Tracking a product’s status from manufacturing to recycling.
Customer engagement: Offering real-time updates, product recommendations, and interactive features.
Value-added Services and Supply Chain Automation: Connecting with external systems to trigger actions like predictive maintenance, better utilization, supply chain updates, and automation of responsible take-back systems including 2nd life marketplaces, sorting, logistics, re-manufacturing, and recycling.
Our View of DPPs Beyond ESPR
We use the term DPP in a broader context, beyond its original intent and definition under the ESPR. The DPP infrastructure is interoperable, making it a foundation for many more use cases that go beyond ESPR compliance. Without such an interoperable infrastructure, many of these use cases wouldn’t be able to deliver their full potential for value creation.
We see DPPs as the core infrastructure enabling innovative services and business models across industries, supporting everything from real-time supply chain automation to Industry 4.0, sustainability, and circularity initiatives.
2. Emerging Trends Driving the Evolution of Digital Product Passports (DPPs)
DPPs are now starting to play a critical role in broader business contexts. This chapter explores the major trends influencing the development of DPPs today.
2.1 Rise of Data-Driven Transparency and Compliance
One of the most important trends is the growing demand for data-driven transparency. Consumers, governments, and businesses increasingly demand to know the origins, materials, and environmental impact of the products they purchase and use.
Regulations like the Eco-design for Sustainable Products Regulation are setting the standard for this type of transparency by requiring companies to disclose detailed information about the lifecycle of their products. This trend is creating a shift from static data in DPPs (e.g., certifications and compliance documents) to more dynamic and interactive DPPs that track real-time updates on product conditions.
2.2 Integration with Cyber-Physical Systems and IoT
As businesses adopt cyber-physical systems (CPS) and the Internet of Things (IoT), DPPs are becoming more embedded in operational environments. IoT devices can provide continuous, real-time data about product usage, performance, and condition. This allows companies to monitor products throughout their lifecycle — from manufacturing to usage and eventual recycling.
Cyber-Physical Systems: These systems integrate the physical and digital worlds, allowing machines, sensors, and products to communicate and act autonomously. DPPs become hubs of real-time product data, enabling companies to track performance, predict failures, and manage product lifecycle events.
IoT Integration: IoT devices embedded in products feed data into DPPs, making them dynamic and interactive. Real-time monitoring can trigger automated actions, such as scheduling maintenance or adjusting product parameters remotely.
This trend underscores how DPPs, connected to IoT devices, can become active participants in product management, not just passive data holders.
However, the ESPR regulation does not yet foresee the integration of dynamic IoT data with DPPs. The focus of ESPR is on static information, such as product origin, material composition, and compliance certifications. The only exception currently is the EU Battery Regulation, which mandates the integration of dynamic battery health data into Battery DPPs, allowing updates via a Battery Management System (BMS) or Vehicle Telematic System on a regular basis or before a major battery lifecycle event, such as selling the vehicle or the battery itself.
We expect that this is just the beginning. More use cases leveraging DPP infrastructure will likely start to integrate dynamic lifecycle data into DPPs, enabling real-time management and optimization throughout the product’s life. This shift will further enhance the value of DPPs in automation and circular economy initiatives.
2.3 Shift to Circular Economy Models
The circular economy is gaining momentum as industries strive to reduce waste, optimize resource use, and keep critical resources in a circular loop.
In this model, products are designed for longevity, reuse, repair, and recycling. DPPs play a crucial role in enabling circular systems by capturing data on material composition, product usage, and end-of-life management.
Circular Design: DPPs can feed real-time data about how products are used, maintained, and disposed of back into the design process. This helps manufacturers design better products that are easier to recycle or refurbish.
Take-Back and Recycling Programs: DPPs can manage the logistics of take-back programs, tracking where products are in the circular economy and ensuring that they are properly recycled or reused.
Responsible End-of-Life Management: When a product reaches the end of its useful life, its DPP can provide the information necessary to determine the best disposal or recycling pathway, ensuring minimal environmental impact.
In the long term, intelligent DPPs can route products through circular end-of-life processes to make informed decisions about their next steps. The DPP can assess whether a product should be sold on a second-life market, including providing a certified pre-owned assertion or opt for remanufacturing or recycling. The DPP can also manage the logistics for sorting and configuring remanufacturing or recycling machines by providing dismantling and recycling instructions. This smart routing ensures that the product is handled in the most sustainable and efficient way, maximizing resource recovery and minimizing environmental impact.
2.4 AI and Predictive Analytics
As DPPs become more dynamic, they increasingly integrate with predictive analytics and artificial intelligence (AI). AI allows companies to gain insights into product performance, customer behavior, and supply chain efficiency. Predictive analytics can forecast when products will need maintenance, how they will perform in certain conditions, or when components should be replaced.
Predictive Maintenance: By analyzing data from IoT sensors, AI can predict when products are likely to fail and trigger automatic maintenance actions through DPPs.
Supply Chain Optimization: AI analyzes product lifecycle data to optimize inventory levels, reduce waste, and ensure efficient resource allocation.
AI and predictive analytics play a key role in shifting DPPs into dynamic, interactive systems. These tools enable the transformation from static, analytical DPPs into micro-ERP systems capable of autonomously managing the product lifecycle.
2.5 Growth of Value-Added Services
The value-added services market is expanding rapidly, and DPPs are at the center of this growth. By providing real-time product data, DPPs can offer new services that enhance the customer experience or improve business efficiency.
Subscription Models: DPPs can support subscription-based services, where companies offer products as a service (e.g., a monthly fee for equipment maintenance or access to premium features).
Automation of Services: DPPs connected to APIs can automatically schedule repairs, update product software, or trigger supply chain actions when conditions change.
Value-added services, such as predictive maintenance, product upgrades, and customer support automation, are driving DPPs toward interactive, dynamic models that allow businesses to automate processes and optimize product lifecycle management.
2.6 Consumer-Centric Experiences and Personalization
In consumer markets, there is a growing expectation for personalized products and services. DPPs provide the data necessary to deliver these experiences. By tracking product usage, preferences, and conditions, DPPs allow companies to offer tailored recommendations, upgrades, and services.
Customer Engagement: DPPs enable real-time interaction with consumers, offering personalized product recommendations, loyalty rewards, or targeted marketing based on how the product is used.
Loyalty Programs and Gamification: Brands can leverage DPPs to engage customers by rewarding sustainable behaviors (e.g., recycling products) or offering incentives for using certain features.
This trend reinforces the next level customer engagement, especially in the upper quadrants where dynamic, personalized services are delivered through real-time interactions with the product’s DPP.
2.7 Standardization and Interoperability
As DPPs evolve, the need for standardization and interoperability is becoming more important. With global supply chains and complex ecosystems, it’s crucial for DPPs to follow common standards so they can be used across borders and industries. These standards ensure that data can be shared and trusted across different stakeholders, including manufacturers, regulators, consumers, and machine users of a supply chain system.
Open Standards for DPPs: Organizations are pushing for open standards that define how DPP data is stored, shared, and accessed. This is critical for ensuring that DPPs can function effectively across global markets and supply chains.
Interoperable Systems & Seamless API Integration: The integration of DPPs with other enterprise systems, such as enterprise resource planning (ERP) platforms, manufacturing execution systems (MES), product information management (PIM), and customer relationship management (CRM) tools, requires interoperability. APIs play a central role in ensuring that DPPs can communicate with these systems seamlessly.
Standardization of DPPs is — from our point of view — a significant enabler of real-time interaction, as value-added APIs become more widely adopted, enabling real-time interaction between DPPs and external systems.
How These Trends Shape the 2x2 Matrix
The trends outlined in this chapter — data-driven transparency, CPS integration, circular economy models, AI, value-added services, consumer personalization, and standardization — are pushing DPPs to evolve rapidly.
These trends are driving Spherity’s 2x2 matrix described in Chapter 3, where DPPs move from static to dynamic, and from passive data analysis to real-time, interactive systems that drive new business models and unlock value across both customer engagement and value-added services.
3. Explanation of the 2x2 Matrix for Digital Product Passports (DPPs)
Our 2x2 matrix offers a clear framework to understand how Digital Product Passports (DPPs) evolve across two dimensions: from static data to dynamic data, and from data analysis to real-time interaction via APIs.
This structure shows how DPPs transition from passive data repositories to fully interactive systems that can automate processes, manage product lifecycles, and engage with customers in real time.
Axis 1 — “Data”: Static Data → Dynamic Data
This axis represents the type of data stored and accessed through the DPP. It highlights the progression from static, unchanging information to dynamic, real-time data that updates based on the product’s lifecycle, usage, or external factors.
Static Data: This includes unchanging information, such as product specifications, materials, origin, or certificates. This data is essential for transparency, compliance, and basic consumer trust but lacks flexibility or adaptability.
Dynamic Data: As products are used, their data changes. Dynamic data includes real-time performance, condition monitoring, or updates based on external triggers. This type of data adds value by enabling operational decisions, such as maintenance schedules or resource optimization, enhancing the DPP’s role in the product’s lifecycle.
Axis 2 — “Engagement”: Data Analysis → Real-time Interaction (via APIs tailored to the product lifecycle)
This axis addresses how data is used within the DPP, showing the shift from static analysis to real-time interaction. The DPP evolves from being a passive repository of product information to an active, connected system.
Data Analysis: In this phase, DPPs serve as repositories for static or semi-static data that can be analyzed retrospectively. Companies or consumers use this data to assess quality, provenance, compliance, or sustainability. However, the interaction is one-sided and does not involve real-time input or feedback loops.
Real-time Interaction (via APIs): The DPP becomes part of a real-time ecosystem. APIs tailored to the product’s lifecycle allow for interaction between the DPP and external systems or users. This could include automated maintenance, compliance checks, or dynamic customer engagement. The DPP isn’t just a source of data; it enables interaction, automation, and value-added services.
Lower Left Quadrant: Data Vault
This quadrant represents the most basic use case where DPPs focus on providing static information for retrospective analysis. In industries like cosmetics, electronics, furniture, textiles, or tires, where compliance and transparency are critical, this static data is essential but lacks the flexibility for more dynamic interaction.
Examples:
Product Certificates: Documents that detail material composition or certifications, which do not change over time.
Sustainability Reports: Static data used for conducting audits or lifecycle assessments.
A company uses a DPP to verify a product’s compliance with sustainability standards based on the static information stored. However, this data is not used to make real-time decisions or interact with external systems.
Lower Right Quadrant: Insight Engine
In this quadrant, the DPP evolves to include dynamic data, but the interaction remains focused on analysis. The DPP collects real-time or updated information, such as sensor data, but this data is used primarily to understand trends or inform future decisions.
Examples:
Condition Monitoring: The DPP tracks a product’s temperature, usage frequency, or wear over time.
Predictive Maintenance: Data is used to analyze when maintenance might be required based on the product’s condition, but this analysis does not trigger automated actions.
Adding Supply Chain Events of the Product Life-cycle: The DPP captures and tracks key supply chain events throughout the product’s lifecycle, such as shipping, handling, and distribution. This data can be used for decision-making, monitoring, and reporting compliance or performance metrics.
A machine’s DPP collects real-time performance data, helping the company predict when maintenance will be needed. While the data is dynamic, the system does not automatically trigger repairs or interact with external systems in real time.
Upper Left Quadrant: Smart Compliance
This quadrant introduces a level of interaction through APIs, but the data remains static. External systems or users can query and interact with the DPP to verify static information or trigger certain actions, such as warranty registration or compliance checks. However, the data itself does not change in real time.
Examples:
Warranty Verification Systems: An external system uses the DPP’s API to verify warranty terms or product authenticity based on static data.
Regulatory Compliance Automation: APIs connect to regulatory systems to check product origin or certifications automatically.
Customs Checks: Customs authorities can leverage DPPs to verify a product’s provenance and assess risks of it being illegitimate (e.g., firearms, drugs) or non-compliant (with import controls or regulations like the ESPR). By using DPP APIs, customs can validate key product data. DPP APIs allow customs to gather shipment, manifest, and seller/importer data at scale, helping identify risks and streamline compliant products through import fast lanes, reducing delays and preventing illegitimate items from entering the market.
Upper Right Quadrant: Cyber-physical Micro-ERP (μERP)
This quadrant represents the most advanced use case for DPPs, where dynamic data is combined with real-time, real-time interaction. The DPP evolves into a fully functional micro ERP system, allowing it to manage the product’s lifecycle, automate processes, and interact with both customers and systems autonomously.
Examples:
Product Lifecycle Management (PLM): The DPP tracks the product’s entire lifecycle, from manufacturing through usage to recycling, dynamically updating data in real time.
Cyber-Physical Systems (CPS): In IoT environments, DPPs act as hubs that communicate with machines, sensors, and enterprise systems, allowing real-time product monitoring and automated actions.
End-of-Life and Recycling Systems: The DPP tracks products through their entire lifecycle to end-of-life, including real-time updates during recycling. Integrated with data carrier scanning and system automation features, the DPP communicates with recycling machines and enterprise systems to automate material recovery, issue recycling assertions for tax credit management, and ensure proper tire disposal.
A smart device’s DPP monitors real-time usage data, automatically triggering maintenance services when needed. APIs enable the DPP to interact with external service providers and logistics partners, ensuring parts are replaced and customers are notified without human intervention. The DPP serves as a micro ERP system, controlling key product operations while feedback back usage information to improve the circular design of a product.
From this perspective, a micro ERP can also be seen as a micro data & transaction hub dedicated to managing all transactions related to a single product. In this sense, it functions as middleware, seamlessly connecting interactive DPPs with external systems for transportation, value-added services, and end-of-life or recycling operations. This integration ensures real-time data flow and interaction between the product and its broader ecosystem, enabling smarter decisions and optimized lifecycle management.
Key Insights from the 2x2 Matrix
Shift from Static to Dynamic Data: The move from static to dynamic data represents the growing demand for real-time product information. Whether for customer engagement or operational optimization, dynamic data enables DPPs to provide more value throughout the product’s lifecycle.
Interactivity through APIs: The evolution from passive data analysis to real-time, interactive systems demonstrates how DPPs are becoming integral to product lifecycle management, enhanced value-added services, and the circular economy. APIs enable DPPs to communicate with other systems, offering automation, customer engagement, and value-added services.
Upper Right Quadrant as the Future of DPPs: The upper right quadrant, where dynamic data and real-time interaction come together, represents the future of DPPs. In this model, DPPs function as μERP systems that can autonomously manage product lifecycles, drive value-added services, and engage with customers in real time.
4. Deep Dive: Static DPPs with Digital Identity and Trust Chaining
While static Digital Product Passports (DPPs) are often seen as basic repositories of product information, incorporating digital identity and trust chaining can significantly enhance their value. These enhancements help distinguish static DPPs based on their ability to ensure data authenticity, protect sensitive information, and enable secure access to relevant parties. Below are three tiers of static DPPs, each defined by its level of data verifiability and access control.
Introducing the “DPP Identity Matrix”
To better understand the role of digital identity and trust chaining in static DPPs, we introduce a new 2x2 matrix. This matrix explores the intersection of PLI Access Control and Data Provenance:
Axis 1: PLI Access Control
This axis tracks the degree to which secure access controls are implemented to restrict data access based on the digital identity of PLIs. Moving from no access control (open access) to full access control for verified PLIs.
Axis 2: Data Provenance
This axis represents the degree to which the authenticity and provenance of the DPP’s data can be verified. Moving from unverified public data to fully authenticated, digitally signed data with traceable origins.
By combining identity wallets, authorization credentials, and provenance chaining with DPPs, Spherity has developed a robust suite of foundational tools to address the PLI and data provenance challenges within the “DPP Identity Matrix.”
4.1 Naïve DPP
The most basic form of a static DPP offers publicly accessible static data. These DPPs serve as open repositories for general product information, such as origin, material composition, or basic compliance certificates. They are designed to be easily accessible to any user but do not offer any mechanisms for verifying the authenticity or provenance of the data.
Use Case: A consumer checks the material origin of a product or basic warranty information without requiring authentication.
Limitation: There is no guarantee that the data is accurate, current, or authenticated, which limits its use in more critical applications.
Note: Many DPP solutions on the market today are even “double naïve” or “blind DPPs” because they lack a semantic model, which prevents these solutions from offering semantic interoperability. This is a critical limitation because semantic interoperability ensures that data can be meaningfully understood and exchanged across different systems and stakeholders. Without this capability, DPPs cannot fully support integration with global supply chains, value-added services, or recycling and circular economy systems.
For companies looking to implement DPPs, it’s essential to evaluate the semantic capabilities and the approaches to identity and trust of their DPP providers. At Spherity, we are addressing the blind spots of DPPs by focusing on ensuring semantic interoperability and providing strong identity and trust frameworks. Our solution enables access-controlled, verifiable DPPs that offer trustworthy, interoperable data exchange for our customers across various industries.
4.2 Access Controlled DPP
This type of DPP introduces secure access controls for Persons of Legitimate Interest (PLI), who can be either natural persons or legal entities. Legal entities can be commercial businesses, regulators, or government entities. Sensitive information, such as dismantling instructions, is often restricted to specific authorized individuals, organizations, or regulators. By integrating digital identity solutions, DPP providers can ensure that only verified PLIs can access confidential or restricted data.
Use Case: An automotive manufacturer provides secure access to dismantling instructions for electric vehicle batteries only to certified recycling companies. Access is controlled and granted only to authorized PLIs through verifiable credentials.
Other Examples: Intellectual property rights (IPR) holders accessing proprietary design data, or regulators reviewing sensitive compliance documents.
Value: This access control mechanism ensures sensitive data remains secure, preventing unauthorized access to confidential information that could be misused or lead to safety risks.
4.3 Verifiable DPP (vDPP)
DPPs at this level offer verifiable data that can be authenticated using digital identity and trust-chaining mechanisms. This ensures that third parties can verify the provenance and accuracy of the data provided in the DPP. For instance, in industries where product safety is critical, these DPPs allow users to confirm the authenticity of instruction manuals or safety data sheets, and verify that the document is the latest version available. This contributes to enhanced safety, compliance, and trust in the product.
Use Case: A safety inspector verifies that the technical manual being used for servicing a machine is authentic and up-to-date. This ensures that outdated or tampered documents are not used in maintenance, reducing safety risks.
Value: Enhances trust in the product by guaranteeing that critical information such as safety instructions is both authentic and the most current version, contributing to compliance and safety.
4.4 Access Controlled Verifiable DPP
The most advanced form of a static DPP combines verifiable data with secure access controls for authorized PLIs. This ensures both the authenticity of the data and the security of sensitive information. PLIs can access sensitive data such as quality assurance (QA), asset testing, dismantling instructions, safety manuals, or proprietary documents only if they have been authenticated as legitimate users. This DPP is ideal for industries that require both trust and confidentiality in data sharing.
Use Case: A recycling company accesses dismantling instructions for electric vehicle batteries through a verifiable DPP. Both the recycling company’s identity and the data’s authenticity are verified, ensuring that sensitive information is securely and accurately transferred.
Other Examples: Restricted access to sensitive technical documents for regulators, and secure sharing of confidential business or compliance information.
Value: This combination ensures the highest level of data integrity, enabling businesses to securely manage and share sensitive data while maintaining confidence in the authenticity and relevance of the information.
From State of Art to Technology Leadership
The majority of DPP solutions currently available are Naïve DPP implementations, providing only basic static data without mechanisms for verifying authenticity or controlling access to sensitive information. These solutions primarily focus on compliance and transparency but fall short of meeting the demands of more advanced use cases that require higher levels of trust and security.
The ESPR, while outlining the need for access control for Persons of Legitimate Interest (PLIs), leaves open questions about the specific requirements, trust models, and the technical implementation of PLI mechanisms.
At Spherity, we believe that DPPs must be verifiable, enabling third parties to verify assertions about DPP attributes and check the authenticity of the data. This ensures that users can trust the integrity and accuracy of the information provided, which we combine with PLI access control mechanisms.
We expect that all advanced DPP concepts such as Insight Engine, Smart Compliance, and the Cyber-physical μERP will all come with digital identity for provenance and access control.
At Spherity we are already today working on these concepts by blending for instance dynamic battery data for commercial electric vehicles with identity wallets and our API-first approach for our cross-sectoral DPP solutions for interoperable and secure access-controlled verifiable DPPs.
5. Deep Dive: DPPs with a “μERP-insde”
A micro ERP (μERP) can be defined as a lightweight, product-specific Enterprise Resource Planning system integrated directly with a Digital Product Passport (DPP). This μERP is tailored to manage the circular life cycle of a specific product, ensuring that all interactions — whether by human users or systems — are managed securely throughout the product’s life. The μERP serves as an interactive platform for tracking, updating, and managing product data, from manufacturing and use to end-of-life and recycling, within a decentralized framework.
The μERP Paradigm Shift
In traditional ERP systems, multiple products and their associated Digital Product Passports (DPPs) are stored in large, centralized databases. These systems often make it difficult to locate, access, and interact with specific DPPs, especially in complex cyber-physical value chains. This inefficiency hinders supply chain transparency and the transition to a circular economy, where products need constant traceability across multiple life stages and actors.
By contrast, in a decentralized circular system, each product is securely linked to its own DPP — a highly specific, micro ERP that provides business logic to autonomously interact with its environment. We refer to this as the μERP.
This model ensures that both human and system users can easily access relevant data throughout the product’s lifecycle, unlocking numerous benefits without the need for a centralized system, which may be inaccessible to many supply chain actors. The benefits include:
Customer engagement: Interactive DPPs enable customers to access personalized information and engage with value-added services, such as maintenance tips, product authenticity checks, and recycling options.
Value-added services: Manufacturers, retailers, and service providers can offer tailored services linked to the DPP, enhancing customer experience and fostering loyalty.
Circular benefits: By having real-time visibility into product data, actors in the supply chain can efficiently manage the product’s end-of-life, including recycling, refurbishment, or remanufacturing.
In the tire DPP and GDSO use case, for example, Digital Product Passports are essential for providing full traceability of the tire’s lifecycle, from manufacturing to end-of-life recycling. They play a critical role in ensuring regulatory compliance, supporting sustainability goals, and offering precise information about material composition to enable proper recycling.
At the end-of-life stage, tires equipped with RAIN RFID chips — which serve as the data carrier for the tire, similar to a QR code but designed specifically for B2B use cases — interact seamlessly with recycling systems and supply chain infrastructure. The tire is identified through its RFID chip, which contains a unique Tire ID. This ID is linked to the Digital Product Passport, storing key data such as the tire’s composition (e.g., rubber, steel, carbon black) and specific recycling instructions. The recycling system retrieves the DPP data and configures the process to ensure the tire is recycled in accordance with its composition and relevant regulations.
Once the tire is scrapped, the DPP is retired, marking the tire’s lifecycle completion. Recycling assertions, such as the recovery of recycled carbon black, are issued and linked to the DPP retirement event, which serves as proof of scrapping. This provides traceability, ensures proper material recovery, and supports sustainability reporting, tax credit filing, and compliance.
The μERP linked to each product’s DPP enables real-time interaction and updates without the need for a centralized database. This allows previously unknown supply chain actors to seamlessly interact with the product at any stage of its life, making the system highly adaptable and responsive to changes. This decentralized model builds upon interoperable DPPs, where each product maintains its own trust model and history, enabling a secure, transparent, and efficient supply chain.
In contrast, a centralized approach lacks the flexibility, scalability, and dynamic interaction needed for efficient circular processes. Decentralized, product-linked μERPs offer greater transparency, faster decision-making, and better alignment with the principles of the circular economy, where trust, provenance, and real-time data sharing are critical.
Advantages of a Decentralized μERP-Driven DPP System:
Interoperability: Seamless interaction between different stakeholders, systems, and products, fostering a network of trusted supply chain partners.
Scalability: Ability to handle vast networks of products and supply chain actors without the complexity and bottlenecks of centralized databases.
Trust and Security: Decentralized systems ensure that each DPP and μERP operate with their own trust model, making data tampering or unauthorized access significantly harder.
This shift is critical for the future of cyber-physical value chains and the efficient transition to a circular economy. By decoupling DPPs from monolithic ERP systems and embedding μERPs directly into products, businesses can unlock unprecedented opportunities for engagement, service innovation, and sustainability.
6. Spherity Case Study: Dynamic Data, APIs, and Identity for Battery Lifecycle Management
A few days ago, Spherity’s Digital Battery Passport went into production with a major commercial vehicle OEM, marking a significant advancement in the use of dynamic data and API-driven interactions.
This milestone highlights the power of combining identity management, data provenance, and authorization credentials with real-time data to optimize the lifecycle of EV batteries — from production to recycling.
Dynamic Data and API Integration
By leveraging dynamic data APIs, the EV Battery Passport continuously tracks battery health, usage, and performance in real time. For example, during the vehicle’s operational phase, telematics systems and battery management systems (BMS) automatically record critical performance indicators like temperature, charging cycles, and discharge rates. These data points are sent to the DPP through secure APIs, enabling real-time analysis for predictive maintenance and value calculations.
One key advantage of working with commercial vehicles rather than passenger cars is that battery usage data does not carry the same personal identifiable information (PII) and GDPR challenges. In a commercial vehicle fleet context, the focus is primarily on efficiency and battery performance, making it much easier to move a dynamic battery passport into production without complex privacy concerns. This makes the implementation of real-time, dynamic data systems more straightforward, accelerating the adoption of dynamic battery passports.
For instance, the Battery Passport calculates the real-time value of a battery by monitoring its wear and tear. This data informs not only maintenance schedules but also provides key insights into the battery’s end-of-life value for second-life applications or recycling.
Optimizing Remanufacturing and Recycling Processes
At the end of the battery’s life, Spherity’s Digital Battery Passport facilitates removal, remanufacturing, and recycling processes. By integrating the Battery Passport with Circular Process APIs, recycling companies can access dismantling instructions, material composition data, and real-time battery condition information. This allows for optimized sorting, remanufacturing, and recycling operations, ensuring maximum recovery of critical materials.
For example, the Passport’s data can guide a recycling facility to determine whether a battery should be refurbished for a second life or dismantled for raw material recovery. This decision is informed by data collected throughout the battery’s lifecycle, which is authenticated and verified via secure identity wallets and provenance chains.
Authentication/Authorization
At Spherity, we distinguish between natural and legal Persons of Legitimate Interest (PLI). Given the nature of commercial vehicles, we address the PLI challenge by focusing on legal PLIs through the use of enterprise identity wallets to manage PLI access rights.
Through the enterprise wallet, only authorized entities — such as OEMs, recyclers, or regulators — are granted access to specific parts of the battery’s lifecycle data. This ensures that sensitive data is protected while enabling the right stakeholders to make informed decisions.
For example, during the recycling phase, a registered recycler can access the Battery Passport to view dismantling instructions and verify the battery’s origin and usage history. This access is controlled via verified PLIs, ensuring only authorized actors interact with critical battery data. Meanwhile, regulatory authorities can audit the data to ensure compliance with EU Battery Regulations.
Additionally, in collaboration with Bundesanzeiger Verlag (BANZ), Spherity issues identity and recycler role credentials. This partnership ensures that data underpinning these credentials is accurate and primary-source validated, further enhancing trust and security across the battery lifecycle ecosystem.
Data Provenance
Data provenance is crucial for ensuring that information about battery lifecycle management is accurate and trustworthy, especially when it comes to compliance with EU Green Claims regulations. In cases of litigation or to avoid misleading claims, having verifiable data about the battery’s origin, battery testing, battery homologation, condition, and lifecycle is essential. This is particularly important when batteries are entering second-life markets, or going into recycling and remanufacturing. Without robust verification mechanisms, there is a significant risk that circular processes can be manipulated, leading to the potential failure of the system.
A useful comparison can be drawn from the past when car owners manipulated the odometer to inflate the value of their vehicle in the used car market. In a similar way, dishonest actors could falsify data on battery performance, lifespan, or condition to gain financially in second-life or recycling markets.
Spherity’s solution prevents these risks by signing all Digital Battery Passport and DPP data with digital identities and establishing and verifying trust chains for data provenance. This ensures that battery assertions are trustworthy, making the entire after-sales and ultimately the circular process more resilient and preventing manipulation by dishonest players.
A μERP for Battery Lifecycle Management and Energy-Mobility Sector Coupling
By integrating real-time data, identity management, and Circular Process APIs, Spherity’s Digital Battery Passport operates like a micro ERP system. It manages battery information across its entire lifecycle — from production and usage to recycling — ensuring optimized processes, better sustainability, and compliance with regulatory frameworks like the EU Battery Regulation.
The next step for us is to leverage the DBP μERP APIs for advanced use cases, such as battery pre-qualification, fleet charging forecasting, and the creation of virtual batteries for aggregation in load shifting, peak shaving, and bidirectional charging. This integration enables energy-mobility sector coupling, providing standardized and secure methods for electric grid operators to engage with decentralized flexibilities. This is crucial in helping balance energy demand and supply in modern electric grids, contributing to grid stability and the optimization of renewable energy sources.
Through this approach, Spherity not only ensures transparency and sustainability in the EV industry but also opens up new opportunities for value-added services, predictive maintenance, and efficient recycling, creating a complete solution for the modern battery ecosystem.
Conclusion
The paradigm shift in Digital Product Passports (DPPs) is transforming them from static data repositories into dynamic, AI-driven systems that optimize supply chains and product lifecycles. This evolution is crucial for both consumer and industrial product businesses, as it enhances transparency, efficiency, and sustainability. There is a significant opportunity to unlock through real-time data integration, predictive maintenance, and circular economy models.
To learn more about how our VERA DPP product can revolutionize your business, reach out to explore our innovative DPP solutions. We provide ready-to-use systems that integrate seamlessly with legacy enterprise IT from day one. Discover how we manage Person of Legitimate Interest (PLI), add provenance to DPP solutions, and offer API-first products. We also provide cross-sectoral DPPs, pre-defined templates, and white-labeling options to support your business needs. Contact us to take the next step in DPP innovation.
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About Spherity
Spherity is a German decentralized digital identity software provider, bringing secure identities to enterprises, machines, products, data, and even algorithms. Spherity provides the enabling technology to digitalize and automate compliance processes in highly-regulated technical sectors. Spherity’s products for enterprise wallets and object identity empower cyber security, efficiency, and data interoperability among digital value chain actors.
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