Digital Product Passports for Intelligent Decision Support in Circular Supply Chains

Data-driven innovation is emerging as a key enabler of the transition towards a circular economy. Digital Product Passports (DPPs) offer structured, interoperable, lifecycle-wide information about products, materials, components, and their associated value chains. Driven by regulatory developments, particularly within the European Union, DPPs are expected to enhance transparency, traceability, interoperability, and information exchange across supply chains. By providing information on product composition, origin, environmental impact, maintenance history, repairability, ownership, usage, and end-of-life pathways, DPPs can create new opportunities for circular business models, resource-efficient operations, and sustainable supply chains.

However, the value of DPPs and related digital infrastructures extends beyond information provision and regulatory compliance. Lifecycle and operational data generated through DPPs, together with data from other digital sources, can provide a foundation for intelligent operational, tactical, and strategic decision-making across circular operations and supply chains. A key research challenge is therefore how heterogeneous lifecycle and operational data can be integrated, interpreted, and transformed into actionable decisions that improve circularity, sustainability, resilience, resource efficiency, and supply chain performance.

Organisations increasingly face the need to balance efficiency, resilience, circularity, and environmental sustainability under conditions of uncertainty and rapid change. Volatile demand, resource constraints, supply disruptions, changing product conditions, uncertain product returns, and the transition towards circular business models require decision-support capabilities that extend beyond traditional planning approaches focused primarily on cost and productivity.

Recent advances in artificial intelligence, machine learning, optimisation, simulation, digital twins, mathematical programming, agent-based modelling, and advanced analytics create new opportunities to transform lifecycle and operational data into predictive and prescriptive insights. The Research Topic therefore seeks interdisciplinary research that moves beyond the provision of lifecycle transparency and demonstrates how lifecycle and operational data can be transformed into effective decisions. Contributions are particularly encouraged where digital technologies are connected explicitly to operational, tactical, or strategic decision-making and where their implications for circularity, sustainability, resilience, resource efficiency, and supply chain performance are evaluated.

Objectives

This Research Topic aims to:

• Investigate how Digital Product Passports and other data-driven infrastructures enhance lifecycle transparency, traceability, interoperability, and information availability across circular operations and supply chains.

• Explore how lifecycle and operational data from DPPs and complementary digital sources can support operational, tactical, and strategic decision-making.

• Develop and validate intelligent decision-support methods, including artificial intelligence, machine learning, predictive and prescriptive analytics, optimisation, simulation, digital twins, agent-based modelling, mathematical programming, and advanced analytics.

• Develop data-driven decision-support frameworks for production planning and scheduling, inventory and resource management, logistics and transportation, maintenance, product recovery, repair, reuse, refurbishment, remanufacturing, recycling, and circular supply chain network design.

• Examine data-driven decision-making under uncertainty and dynamic conditions, including uncertain product returns, changing product conditions, resource constraints, demand volatility, and supply disruptions.

• Evaluate how DPP-enabled and other data-driven decision-support approaches contribute to circularity, sustainability, resilience, resource efficiency, and overall supply chain performance.

Themes

• Digital Product Passports, Data Foundations, and Digital Infrastructures: Digital Product Passports, lifecycle information systems, data platforms, architectures, standards, interoperability, lifecycle data management, data quality, governance, trust, ownership, privacy, and security. Contributions may also examine organisational readiness, regulatory requirements, policy considerations, implementation challenges, and the role of DPPs as data infrastructure for circular decision-making.

• Intelligent Decision Support for Circular Operations and Supply Chains: Decision-support systems, artificial intelligence and machine learning, predictive and prescriptive analytics, explainable and trustworthy AI, hybrid approaches combining AI, analytics and optimisation, and agent-based or autonomous decision-support systems.

• Data-Driven Optimisation, Simulation, and Digital Twins: Mathematical programming, optimisation, simulation, simulation-optimisation, digital twins, virtual decision-support environments, agent-based and hybrid modelling frameworks, multi-objective and multi-criteria decision-support approaches, and decision-making under uncertainty, disruption, and dynamic operating conditions.

• Data-Driven Circular Operations and Recovery Systems: Maintenance planning, lifecycle management, production planning and scheduling, inventory and resource management, product recovery, repair, reuse, refurbishment, remanufacturing, recycling, disassembly, end-of-life management, recovery pathway selection, and material recovery optimisation supported by lifecycle and operational data.

• Data-Driven Logistics and Circular Supply Chain Design: Reverse logistics, transportation planning, product collection, inspection, sorting, redistribution, supplier and recovery-partner selection, facility location, capacity planning, closed-loop supply chain network design, and resilient and sustainable supply chain configuration using DPPs and other digital data sources.

• Emerging Technologies and Digital Integration: Integration of DPPs with IoT, cyber-physical systems, blockchain, real-time monitoring systems, digital twins, enterprise systems, data platforms, and digital ecosystems.

• Industrial Applications, Sustainability, and Policy: Applications in manufacturing and industrial equipment, electronics, batteries and electric vehicles, renewable energy systems, construction, critical raw materials, and other product and industrial sectors. Contributions may address circularity measurement, sustainability assessment, environmental impact evaluation, resilience analysis, performance trade-offs, governance implications, regulatory and policy frameworks, and industry adoption of data-driven decision-support systems.

Types of Contributions Welcomed

Original empirical research; methodological and computational developments; artificial intelligence and machine learning applications; predictive and prescriptive analytics; optimisation and mathematical programming studies; simulation, simulation-optimisation, and digital twin applications; multi-objective and multi-criteria decision-support frameworks; industrial case studies and validated applications; hybrid methodological approaches integrating multiple analytical and computational techniques; DPP-enabled decision-support frameworks and applications; studies integrating DPPs with other digital infrastructures and technologies; policy and governance studies linked to DPP-enabled data-driven circular decision-making; and interdisciplinary research spanning operations management, supply chain management, sustainability, engineering, information systems, and public policy.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review

Keywords: Digital Product Passport, DPP, Circular Supply Chains, Intelligent Decision Support, Lifecycle Data, Artificial Intelligence, Machine Learning, Optimisation, Mathematical Programming, Simulation, Digital Twin, Agent-Based Modelling, Reverse Logistics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.