Introduction to Supply Chain Digital Twins and AI.pdf
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What is a digital twin? For a physical product, it can be its digital replica (e.g., a 3D model). For processes, Artificial Intelligence (AI)-driven, open platforms allow for building digital twins and equip them with simulation capabilities (e.g., AnyLogic models integrated with NVIDIA Omniverse). A supply chain digital twin is more than a digital replica or process simulation.
A supply chain is a cyber-physical system integrating material, financial, and information flows. A supply chain is not a physical product that you can observe and touch. Nor is it a process we have designed centrally, ensuring full observability and controllability. Of course, a supply chain has many physical, tangible elements, such as warehouses, factories, and trucks. That being said, a supply chain is widely intangible and invisible. Most of what we know about the supply chain is its data and reports. A digital twin can help us know much more and translate this knowledge into competitive advantage and business value growth.
» A supply chain digital twin is a dynamic, virtual representation of a physical supply chain that mirrors its structure, behavior, and processes, with data being updated frequently enough to be adequate for system-level decision support. It integrates (i) information systems, (ii) real-time digital technologies such as IoT sensors, cloud computing, and blockchain, and (iii) supply chain analytics tools such as artificial intelligence, simulation, and advanced optimization—with continuous data flows to model, monitor, analyze, and optimize supply chain operations across multiple layers (e.g., product, process, organization, network, and network-of-networks). More than a static simulation, it supports descriptive, predictive, and prescriptive decision-making by enabling real-time visibility, scenario planning, interactive optimization and simulation, text- and data-driven risk assessment, reasoning, ubiquity, synchronization and end-to-end collaboration. Its key components include digital and physical assets, information flows, performance metrics, and human-AI interfaces, embedded within a technological-organizational framework that spans the supply chain lifecycle. (Ivanov & Gusikhin, 2026)
A digital twin is not magic or a buzzword. It is a working management system. It shows the supply chain as it truly is, delivering insights that translate into competitive advantage. Supply chains are not monuments to be preserved over time in their form and structure. It is not like a house we build, and the house will exist in this form for 100 years. Supply chains are organisms that get sick, recover, change behaviors, learn, succeed, fail, enter conflicts—live.
Supply chains change, and so do their digital twins. Essentially, a digital twin is a living model of the supply chain. It connects, orchestrates, and synchronizes suppliers, plants, warehouses, production lines, logistics routes, demand signals, disruption alerts, capacities, and constraints into a dynamic environment with real-time and adequately updated data.
Digital twins are not just technological solutions to automate the supply chain. It is a new way of managing the supply chain. Despite all the progress in supply chain management, the gap between “what sales forecast and planners think will happen” and “what the network actually delivers” is still too large. Digital twins help close this gap and frame a novel human–AI collaboration space, being integrators of models, data, structures, and processes. Traditional, isolated decisions like demand forecasting, scheduling, MRP, inventory control, and production planning are integrated into new, data- and AI-driven frameworks, giving rise to new planning, control, and decision-making areas, responsibilities, and organizational units, such as orchestration, synchronization, fulfillment, transformation, and regeneration. Digital twins are triggering tectonic shifts and bringing us into a new era in supply chain management.
Of course, digital twins are not free of challenges and barriers. Incomplete or inaccurate data and a lack of visibility can break the flow. Over-reliance on automation and AI-driven orchestration without human guidance and synchronized processes can even worsen the existing planning and control chaos. Managing the supply chain using digital twins requires consistency across functions and organizational transformations. Trust regarding data quality, availability, and reasoning, hallucinations in the case of missing data points, high energy and computational costs, reasoning inconsistencies and non-deterministic responses, ethical, societal, legal, and governance challenges, and cybersecurity issues should be considered.
Though when it works, the ways a supply chain is managed and operates will change. Performance improves since decisions are taken based on dynamically updated models, real data, and in a systemic context. Production plans and inventory stabilize because buffers and order releases reflect reality, not intuition. Disruptions are managed proactively, not reactively. A properly developed supply chain digital twin can enable strategic and operational performance improvements, end-to-end visibility, agentic AI integration in decision-making, and supply chain stress testing. It creates a new approach to manage the supply chain.
This resource offers an introduction to supply chain digital twins in a concise but comprehensive form, covering management, modeling, and technology perspectives. It is also designed as a supplementary satellite resource for the text resource Global Supply Chain and Operations Management (Ivanov et al., 2025), advancing the chapters devoted to supply chain digitalization, analytics, and AI.
In this resource, we offer an introduction to major concepts and principles of designing and implementing digital twins of supply chains. The resource aims at delineating major features of supply chain digital twins and explaining technologies and methodologies to develop them. Throughout the resource, numerous practical examples and insights are provided to illustrate theoretical concepts. It also reviews and explains novel frameworks and concepts related to data-driven digital twins and agentic AI. Without relying heavily on mathematical derivations, the resource offers a structured presentation and explanation of major concepts and methods to build/improve supply chain digital twins and use them for decision-making support and enterprise management in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering backgrounds. This text resource can be used for teaching and learning supply chain digital twins and AI in a structured way and is comprised of:
5 Main theoretical concepts
5 Practical examples
5 anyLogistix models.
Graduate/PhD students and supply chain professionals alike would benefit from a structured and didactically oriented, concise presentation of the concepts, principles, and methods to build and deploy supply chain digital twins.
I thank my research associates at the Digital-AI Supply Chain Lab at the Berlin School of Economics and Law, Phu Nguyen, Hiran Prathapage, and Anna Putintseva, for their great help with proofreading the manuscript, creating artwork, and preparing the slides for the resource, which are available on the companion website www.prof-ivanov.de.
The author wishes to thank Ms. Jialin Yan, Senior Editor at Springer Nature, Ms. Ramya Prakash, Production Editor at Springer, and the entire Springer production team for their assistance and guidance in successfully completing this resource. We also thank all colleagues working in the supply chain digital technology and AI areas for publishing their works and sharing their knowledge. Last but not least, I cordially thank my family, who supported me enormously during the work on this resource.

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