Cohort: 1 

Location: Beijing, China 

Application Title: AI-driven supply chain agent for resilient global operations 

Team Members: Lu Bo (executive sponsor), Che Min (Jammi) Tu (executive sponsor), Guan Wei (executive sponsor), Li Robert (executive sponsor)

The challenge 

Managing a global supply chain has become increasingly complex. Manufacturing, logistics and sourcing activities are distributed across many locations, often spanning different regulatory environments and transport networks. Demand shifts quickly, while supplier reliability and transport conditions can change with little notice. 

In many organisations, supply chain planning relies on periodic forecasts and manual coordination across teams. Data exists across enterprise systems, but it is rarely analysed in a continuous or integrated way. When disruptions occur, such as supplier delays or transport bottlenecks, assessing impact and agreeing on corrective action can take days. This leads to reactive planning and delayed responses. 

Early uses of AI have improved individual functions, including demand forecasting or inventory analysis. However, these tools are often deployed in isolation. Without a shared view across planning and execution, insights remain fragmented and trade-offs are difficult to evaluate in time. 

The solution 

A coordinating AI agent for end-to-end supply chain decisions 

Lenovo developed an AI-driven supply chain agent to support coordinated decision-making across global operations. 

The agent serves as a central layer of intelligence that connects demand forecasting, supplier risk assessment, inventory positioning and logistics planning. Rather than replacing existing systems, it draws on data already generated across operations and applies AI models to evaluate conditions continuously. 

Routine decisions are handled automatically when confidence is high. Situations that require judgment are escalated to human teams with clear context, allowing faster and more informed intervention. 

How it works 

Linking data and decisions in real time 

The system integrates signals from factories, suppliers and logistics partners into a shared operational view. AI models track changes in demand patterns and supplier behaviour, updating forecasts as new information becomes available. 

When potential disruptions are detected, the agent evaluates likely impact and proposes mitigation options. These may include inventory reallocation or adjustments to delivery plans. Decision-makers review recommendations without having to assemble information manually. 

Because the agent operates continuously, assessments that once required days can now be generated much earlier. 

How it differs from conventional approaches 

Reducing reliance on manual coordination 

Traditional supply chain management depends on separate tools for planning and execution. Coordination relies on reports, meetings and manual reconciliation of data. 

Lenovo’s approach integrates intelligence across functions through a single coordinating agent. Instead of optimising each area independently, the system evaluates interactions across the supply chain. This supports quicker responses and more consistent decisions under changing conditions. 

Impact 

• Potential disruptions flagged up to two weeks earlier 
• 5% increase in on-time-in-full delivery performance 
• 50–60% reduction in time required for supply chain decision-making 



Why it matters 

Supporting resilience at global scale 

As supply chains face greater uncertainty, the ability to sense risk early and respond quickly becomes critical. Lenovo’s supply chain agent shows how AI can support this by linking data and decisions across operations. 

By reducing fragmentation and shortening response times, the approach helps organisations manage complexity while maintaining service levels and operational efficiency.