AI in the Chain

Context

Digital twin technology creates a virtual replica of physical assets, processes and entire supply chains. These dynamic models mirror operations in real time by ingesting data from sensors, IoT devices, enterprise systems and external sources. The concept originated in engineering and aerospace but has rapidly expanded into manufacturing, logistics and retail. As supply chains grew more complex and unpredictable, companies started using digital twins to see what is happening across their networks and to test “what‑if” scenarios before committing resources.

By 2025 the digital twin market is projected to reach $125.7 billion with a compound annual growth rate of about 39 %. Large enterprises have embraced the technology — 70 % of C‑suite executives are actively investing in digital twins. They report productivity gains of 30–60 %, 20 % reductions in material waste and time‑to‑market improvements of 50 %. These performance gains are why digital twin adoption is accelerating across industries.

Why digital twins matter for supply chains

Traditional supply‑chain planning tools rely on averages and historical data. They fall short when unexpected disruptions arise or when managers want to explore multiple scenarios. Digital twins overcome these limitations by providing:

• Risk‑free scenario planning: Digital twin software offers a safe environment to test “what‑if” scenarios before changes are implemented in the physical world. Companies can examine how plant closures, demand spikes, raw‑material shortages or new regulations impact service levels and costs. A steel manufacturer cited in a recent study used a digital twin to evaluate relationships between demand, supply and production volatility; the project uncovered risks 12 weeks ahead, improved EBITDA by two percentage points and cut inventory by 15 %.
• Real‑time visibility and a single source of truth: Digital twins integrate data streams from across the supply chain, giving managers a complete view of operations. This visibility helps spot bottlenecks, optimise throughput and respond faster to disruptions. McKinsey notes that digital twins create a “single source of truth” where data is properly structured and accessible, leading to consistent insights.
• Improved forecasting and operational performance: Retailers using digital twins have achieved 20–30 % better forecast accuracy and cut delays and downtime by 50–80 %. Manufacturers use digital twins to redesign production schedules, reducing processing time by 4 % and saving 5–7 % monthly. These benefits cascade across transportation and inventory, enabling companies to right‑size buffers and improve service levels.
• Enhanced collaboration: Because a digital twin is a shared platform, suppliers, carriers and internal teams can access the same data and simulations. This improves communication, alignment and joint decision‑making.

Limitations of traditional approaches

Before digital twins, scenario planning was done using spreadsheets, static simulations or deterministic optimisation models. These methods have several drawbacks:

• Limited number of scenarios: Analysts could only evaluate a handful of cases because each simulation required manual model setup and data preparation.
• No real‑time feedback: Traditional models used static snapshots of data and could not update decisions based on what was happening in the supply chain at that moment.
• Siloed views: Data was scattered across systems, resulting in inconsistent assumptions and poor coordination between procurement, production and logistics.
• Difficulty capturing rare events: Traditional forecasting models struggle to anticipate extreme disruptions (e.g., pandemics, geopolitical crises or cyberattacks). This results in underpreparedness and overreliance on safety stock.

How AI‑powered digital twins revolutionise scenario planning

AI‑enabled digital twins combine virtual models with machine‑learning and optimisation algorithms. Here’s how they transform decision‑making:

1. Rapid and granular simulation: Using AI, digital twins can generate thousands of scenarios across different time horizons. They support historical analysis, present optimisation and future projections. Planners can model how shifting production to near‑shoring facilities affects service and cost or how adding micro‑fulfilment centres influences last‑mile delivery.
2. Predictive risk sensing: Machine‑learning algorithms detect anomalies in sensor data and external feeds, providing early warnings of equipment failures, port closures or supplier delays. This risk sensing allows companies to re‑route shipments proactively and adjust capacity.
3. Scenario‑based financial analysis: Digital twins integrate operational and financial metrics, enabling planners to evaluate the margin impact of each scenario. For example, the steel manufacturer’s digital twin simulation improved profitability by two points and cut inventory by 15 %.
4. Continuous learning: Digital twins ingest new data in real time and use reinforcement learning to refine decision policies. Over time, the model learns which actions yield the best outcomes, leading to self‑optimising networks.
5. Enhanced human collaboration: AI does not replace planners. Instead, it augments their expertise by presenting insights and letting humans decide on trade‑offs. As John Vickers of NASA notes, the ultimate vision is to design and test equipment virtually, then link physical builds back to their digital counterparts for continuous improvement.

Step‑by‑step roadmap for implementing digital twins

1. Identify high‑value use cases. Start by selecting a process where the potential benefits justify investment. Good candidates include inventory optimisation, production scheduling, logistics routing or contingency planning for critical suppliers.
2. Create a digital baseline. Map the physical process, assets and data flows. Integrate data from ERP, MES, WMS, TMS, sensors and external sources (e.g., weather or market data) into a unified data model. The goal is to build a living representation of your supply chain.
3. Choose the right platform. Select digital‑twin software that supports real‑time data ingestion, AI/ML integration and scalability. Ensure it can model discrete events (like factory operations) and continuous flows (like shipping and inventories).
4. Develop predictive and optimisation models. Use machine‑learning algorithms to forecast demand and lead times and optimisation models to balance cost, service and risk. Integrate these models within the digital twin so that each scenario triggers an appropriate response.
5. Pilot and iterate. Begin with a pilot on a single product line or region. Test different scenarios—such as supplier shutdowns or demand spikes—and compare the model’s recommendations with current plans. Measure improvements in lead time, cost and inventory.
6. Scale across the network. Expand the twin to additional plants, warehouses and carriers. Use lessons from the pilot to improve data quality and model accuracy. As adoption spreads, the organisation will gradually move from reactive firefighting to proactive planning.
7. Invest in people and governance. Create cross‑functional teams of planners, data engineers and data scientists. Provide training to interpret simulation results and translate insights into action. Establish governance to monitor model performance, update assumptions and ensure ethical AI practices.
8. Integrate with other AI tools. Combine the digital twin with generative AI for network design (as discussed in the companion article) and with AI chatbots for automated communication. The synergy enables a fully connected supply chain, where decisions about routing, inventory and customer communication are co‑ordinated through a single platform.

Conclusion

Digital twins offer supply‑chain teams the ability to see, predict and optimise operations like never before. By providing a single source of truth, enabling risk‑free scenario planning and delivering real‑time visibility, AI‑powered twins turn complexity into competitive advantage. Evidence shows they deliver major improvements: productivity gains of up to 60 %, reduced waste, faster time to market, two‑point EBITDA improvements and double‑digit inventory reductions, and forecast accuracy gains of 20–30 % while halving delays. Organisations that start with focused pilots and invest in data integration, people and governance will quickly build momentum. As digital twins mature and merge with generative models and other AI tools, supply chains will become more adaptive and resilient, capable of thriving amid uncertainty.

References

1. Simio article on the transformation of digital twin software – highlights market size projections, executive adoption rates and productivity and waste improvements.
2. Simio study on scenario planning – explains how digital twins provide risk‑free experimentation, with a case where a steel manufacturer improved EBITDA and cut inventory.
3. Simio article on real‑time visibility – notes that digital twins create a single source of truth and deliver better forecasting accuracy and significant reductions in delays and downtime.
4. Maersk logistics insights – emphasises digital twins’ role in enabling end‑to‑end visibility, predictive analytics, scenario planning and collaboration across stakeholders.