Special Issues

Supply Chain Forum: An International Journal

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Implementing the Physical Internet: Supply Chain Intelligence, Business Innovation and Transition Pathways

Purpose

The Physical Internet (PI) is moving from vision to reality. Beyond foundational concepts, organizations are now experimenting with pilots, demonstrators, living labs and large-scale initiatives that test how PI principles can be implemented, governed and scaled.

This Special Issue seeks rigorous, practice-oriented and empirically grounded contributions that provide actionable insights for managers, policymakers and supply chain stakeholders. The Special Issue is associated with the IPIC 2026 International Conference, June 2026, France, but submissions are open to all authors.

Scope and Orientation

We welcome papers focusing on the implementation and operationalization of the Physical Internet, including:

• Empirical studies, qualitative, quantitative or mixed methods
• Case studies, pilots, demonstrators and living labs
• Practice-based research and industry-driven innovations
• Funded projects and cross-industry initiatives

Conceptual contributions are welcome only if strongly connected to real implementation challenges and managerial implications.

Topics of Interest

• PI use cases, pilots and demonstrators
• Business models and value creation in open and shared logistics
• Governance and coordination mechanisms for interoperable networks
• Supply chain intelligence, data sharing and digital platforms
• Transition pathways, roadmaps and maturity models
• Managerial and organizational challenges of PI adoption
• Public policy, regulation and public–private collaboration
• Sustainability, circular logistics and environmental performance

All submissions are expected to adopt a sound scientific methodology while clearly demonstrating managerial relevance and actionable lessons learned.

Keywords

Physical Internet, Supply Chain Intelligence, Business Models, Use Cases, Living Labs, Demonstrators, R&I Projects, Transition Roadmaps, Governance, Collaboration, Innovation Management, etc.

Guest Editors

• Dr. Louis FAUGERE, Amazon Research & Georgia Institute of Technology, USA
• Prof. Markus GERSCHBERGER, University of Applied Sciences Upper Austria & Austrian Supply Chain Intelligence Institute – ASCII, Austria
• Prof. Matthieu LAURAS, KEDGE Business School, France

Deadline

• 15 September 2026 – Submission platform opened
• 30 November 2026 – Paper submission deadline

Submission

Manuscripts must follow the author guidelines of Supply Chain Forum: An International Journal and be submitted via the journal’s online system: https://www.tandfonline.com/journals/tscf20 .

All papers will undergo a blind peer-review process.

IMA Journal of Management Mathematics

🌐 Special Issue website

Mathematical Models and Decision Analytics for AI-Driven and Cyber-Physical Supply Network Systems

Digital innovation, resilience imperatives, and advances in Artificial Intelligence and cyber-physical technologies are reshaping supply networks. Consequently, disruptions, evolving managerial demands, and the convergence of IoT and digital twins are transforming logistics, manufacturing, urban distribution, last-mile delivery, and global freight.

These dynamics call for adaptive frameworks grounded in mathematical modelling and decision analytics. Such approaches are closely aligned with interoperable and integrated visions such as the Physical Internet. At the same time, AI and machine learning, real-time simulation, and cyber-physical orchestration are enabling increasingly prescriptive decisions.

Accordingly, management mathematics plays a central role in this transformation by providing methodological foundations through optimization, stochastic control, simulation, and hybrid mathematical-AI approaches. Ultimately, these enable the translation of emerging technologies into actionable strategic, tactical, and operational solutions for the design and management of next-generation supply networks.

Key Areas of Interest

• Developing AI-augmented mathematical models and optimisation frameworks capable of integrating unstructured contextual information to dynamically refine network design, service configuration, and operational constraints.
• Enhancing simulation, forecasting, and decision analytics in supply chain management, logistics, and transportation through advanced scenario generation, digital twins, and real-time sensitivity analysis driven by cyber-physical systems.
• Designing hybrid intelligent decision support systems in which AI supports model formulation, parameter estimation, constraint generation, and result interpretation.
• Advancing Physical Internet-oriented logistics and interoperable service design, including mixed passenger–freight operations, shared logistics platforms, and synchromodal transportation systems.

Objective

This Special Issue aims to bridge advanced artificial intelligence and cyber-physical technologies with the domain of management mathematics to reshape how supply networks, logistics, and transportation systems are modelled, analysed, and optimised.

It seeks to explore how mathematically grounded modelling, optimisation, stochastic control, and simulation can be synergistically combined with data-driven and AI-based decision analytics to design resilient, sustainable, and interoperable supply network systems, and to demonstrate their potential for transforming real-world logistics systems.

Scope and Topics

• Mathematical models for AI-driven and cyber-physical supply networks, including network design, multi-echelon flows, capacity planning, and service design
• Optimisation frameworks with economic performance metrics, including cost, NPV, and risk
• Hybrid mathematical–AI frameworks integrating optimisation, simulation, machine learning, and reinforcement learning for adaptive decision-making
• Digital-twin-based modelling and real-time decision analytics for logistics, manufacturing, urban distribution, and last-mile delivery
• Simulation, discrete-event and agent-based models, and their coupling with data-driven or learning-based decision policies
• Cyber-physical orchestration and control of interconnected logistics services, including Physical Internet and synchromodality
• Quantitative models for risk, disruption, and resilience management in global and regional supply network systems
• Decision support systems for stakeholders in AI-enabled supply networks, including public authorities, shippers, carriers, and infrastructure managers
• Data-driven forecasting, demand sensing, and predictive analytics embedded in mathematical planning and scheduling models
• Design of scalable algorithms and decomposition or approximation schemes for large-scale supply network optimisation under uncertainty
• Leveraging large language models for supply chain analytics, including semantic analysis of unstructured data, automated scenario generation, and hybrid mathematical-LLM frameworks

Instructions for Authors

Authors should submit a cover letter and a manuscript by 31 December 2026, via the Journal’s online submission site. Please see the author instructions on the website.

When submitting via the submission site, please select the Special Issue title, “Mathematical Models and Decision Analytics for AI-Driven and Cyber-Physical Supply Network Systems”, to ensure that the manuscript will be reviewed for this Special Issue.

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Papers will be subject to a strict review process managed by the Guest Editors, and accepted papers will be published online individually before print publication.

Important Dates

• Submission Open: 01 September 2026
• Submission Deadline: 31 December 2026
• First Reviews Due: 31 March 2027
• Revised Manuscripts: 30 June 2027
• Final Decision: 31 August 2027

Guest Editors

• Frederick Benaben, Professor, Institut Mines Telecom, Albi, France & Adjunct Professor at Georgia Institute of Technology, USA
• Walid Klibi, Professor, Kedge Business School, Bordeaux, France
• Olivier Labarthe, Associate Professor, Kedge Business School, Bordeaux, France
• Chen Wang, Associate Professor, Tsinghua University, China