AI in the Chain

In 2025, logistics network optimization has evolved from static modeling to dynamic, AI-powered capability. As geopolitical disruptions, nearshoring imperatives, sustainability pressures, and inflationary costs reshape global supply chains, traditional planning methods are proving inadequate. Companies can no longer rely on historical data and linear thinking—they need adaptable, predictive systems that can simulate trade-offs, identify bottlenecks, and drive real-time decisions. This playbook outlines how to harness AI to create an intelligent, resilient logistics network that outpaces today’s volatility.

Step 1: Define Objectives and Scope
Why it matters:
Any redesign effort must begin with crystal-clear goals. Cost reduction, carbon footprint, service levels, and risk exposure all compete for attention—without clarity, AI can’t deliver focused insights.

How to do it:
Bring together stakeholders from logistics, procurement, finance, and sustainability. Align on top KPIs: total landed cost, service level targets, emissions reduction, and network resilience. Consider near-term cost pressures and longer-term ESG commitments.

Prompt example:
“Identify the top three KPIs for our logistics redesign, prioritizing carbon reduction and dual-sourcing risk mitigation.”

Step 2: Build a Digital Twin of Your Logistics Network
Why it matters:
Static spreadsheets can’t keep pace with dynamic global conditions. A digital twin—a virtual replica of your supply chain—enables AI to simulate disruptions and recommend optimal responses in real time.

How to do it:
Use platforms like Dematic’s AI twins, ArcGIS for geospatial overlays, or build in-house models with Python and supply chain APIs. Feed in your internal ERP data, transportation logs, and external signals like port congestion indexes, fuel price changes, and labor data.

Example:
The Port of Corpus Christi built an advanced digital twin to visualize cargo flows, test routing alternatives, and cut port delays—delivering faster throughput and higher service levels.

Step 3: Data Collection and Integration
Why it matters:
Before running scenarios or predictive models, companies must ensure their data is reliable, complete, and integrated. High-quality data is the foundation for meaningful AI insights.

How to do it:
Start by consolidating internal data from your ERP, TMS, and WMS systems, and external data sources (trade data, emissions data, supplier risk indexes). Cleanse and standardize this data to avoid blind spots and ensure the AI models produce actionable outputs.

Example of Data Table for Model Development:

Prompt example:
“Using the table above, identify the top three routes with the highest total landed cost and recommend potential alternatives to optimize performance.”

Step 4: Nearshoring and Dual-Sourcing Modeling
Why it matters:
As highlighted in the 2025 Tariff Impact Report, nearshoring and regional sourcing are growing priorities. However, shifting 20–30% of sourcing closer to home creates complex trade-offs in cost, lead time, and emissions.

How to do it:
Use AI to compare nearshoring options by balancing lead time, emissions, supplier reliability, and tariff exposure. For instance, AI can highlight that moving 20% of EU-bound components from China to Eastern Europe cuts lead times by 40% but increases short-term freight costs by 15%.

Example scenario:
“Simulate dual-sourcing for EV battery components between Vietnam and Eastern Europe, weighing tariffs, lead time, and CO2 emissions.”

Step 5: Demand Sensing and Predictive Freight Allocation
Why it matters:
AI is not just for static network design—it also predicts future flows. McKinsey reports that AI-driven demand sensing boosts forecast accuracy by 30%, minimizing stockouts and reducing reactive shipping.

How to do it:
Deploy ML models to analyze POS data, social sentiment, and macroeconomic trends. Use predictive freight allocation to secure capacity early, avoiding costly last-minute bookings.

Example:
A global electronics brand used AI to pre-book 30% of peak-season freight, reducing expedited shipping costs by 15% and improving on-time delivery.

Step 6: Dynamic Route Optimization
Why it matters:
Accenture’s findings show AI-enabled dynamic routing can cut fuel costs by 20% and improve on-time delivery by 15%. Real-time data on traffic, weather, and labor conditions drive smarter choices.

How to do it:
Feed real-time data into AI models (e.g., XGBoost for predictive delays). Adjust routes dynamically based on live conditions and warehouse readiness.

Prompt example:
“Create a dynamic routing plan for cross-border shipments, factoring in live port congestion and carbon emissions targets.”

Step 7: Automate Monitoring and Alerts
Why it matters:
AI-powered redesign is not a one-off. Automated alerts ensure that your logistics network continuously adapts to shifting risks.

How to do it:
Configure alerts for sudden tariff changes, port delays, or unexpected emissions spikes. Use Slack, email, or dashboard updates to inform decision-makers instantly.

Output:
“Alert: 10% congestion spike at Rotterdam port—consider alternate routing through Antwerp.”

Step 8: Build an AI-Ready Culture
Why it matters:
AI’s success depends on human adoption. As noted in the Deloitte and McKinsey reports, 60% of firms struggle to scale AI beyond pilots due to siloed teams and change resistance.

How to do it:
Foster cross-functional teams that include logistics, IT, and procurement. Train staff on interpreting AI insights and build trust by piloting small wins before scaling up.

Prompt example:
“Create a training plan for logistics staff to interpret AI scenario outputs and apply them to weekly decision reviews.”

Real-World Examples
DHL and Boston Dynamics collaborated to automate sortation hubs, using AI to dynamically allocate inventory and balance peak volumes.
Maersk uses predictive vessel models to avoid delays in congested ports—crucial for avoiding customer disruptions.
Retailers like Decathlon tested nearshoring to Eastern Europe using AI simulations—achieving faster delivery and improved carbon footprint.
China’s rare earth export controls (90% global processing) underscore the urgency of dual-sourcing, as European automotive firms scramble to secure alternative suppliers in the region.

Insights from the Field
The May 2025 Bloomberg article highlighted how U.S. tariffs and China’s export restrictions have accelerated network re-design. Companies are:

• Prebuilding inventory to buffer tariff hikes.
• Lifting and shifting 20–30% of production to nearshore hubs.
• Using AI to simulate multi-objective trade-offs between cost, carbon, and lead time.

Prompts to Support Testing and Action
“Simulate carbon footprint reductions of nearshoring 20% of China-based production to Poland.”
“Estimate how China’s rare earth export restrictions will impact our 2025 EV component costs and availability.”
“Generate a logistics dashboard to compare tariffs, lead times, and emissions under four sourcing scenarios.”

Challenges and Solutions

• Data gaps: Start with historical data to seed AI models, then scale to real-time feeds.
• Integration: Connect AI to ERP, WMS, and sustainability data for a 360-degree view.
• Resistance: Build quick wins to gain team trust—then scale.
• Compliance: Ensure models factor in regulatory limits and supplier codes of conduct.

Future Outlook
AI’s role will continue to evolve:

• Autonomous agents will run daily re-optimizations.
• Quantum computing could unlock deeper scenario insights.
• Real-time carbon tracking will be integrated into route decisions, supporting green supply chains.

Key Prompts to Start
“Map our top 10 most carbon-intensive lanes and propose nearshoring alternatives.”
“Estimate the emissions and lead time benefits of shifting final assembly to regional hubs.”
“Simulate the impact of EU carbon border taxes on sourcing decisions for 2026.”

Conclusion
AI-powered logistics redesign is no longer optional—it’s the foundation for cost savings, sustainability, and competitive edge. By shifting from static plans to dynamic, data-driven scenarios, companies can future-proof their supply chains and thrive in uncertainty.

References
Bloomberg (2025). https://www.bloomberg.com/news/articles/2025-05-28/trump-s-global-tariffs-blocked-by-us-trade-court
World Customs Organization (2025). https://www.wcoomd.org
McKinsey (2024). https://www.mckinsey.com/capabilities/operations/our-insights/next-level-logistics
Accenture (2025). https://www.accenture.com/us-en/insights/operations/ai-supply-chain
DHL Predictive Maintenance Case Study: https://www.dhl.com/global-en/home/insights-and-innovation/white-papers/predictive-maintenance.html
Deloitte (2025). https://www2.deloitte.com/us/en/insights/industry/transportation/ai-in-logistics.html