How Li & Fung uses AI and platforms for supply chain orchestration

Li & Fung, the Hong Kong-based supply chain services provider, has integrated artificial intelligence into its digital platform to automate key sourcing decisions, from initial costing through to factory selection and order management. The company, which coordinates production across a network of suppliers for global fashion and retail brands, is using AI to process vast amounts of supplier data, match orders to factories based on capability and capacity, and generate cost estimates more quickly than traditional manual processes allow.

What is Li & Fung's digital platform strategy?

Li & Fung has shifted from a traditional intermediary model to a technology-enabled orchestration platform that connects brands with suppliers. The company's platform consolidates data from thousands of factories across its network, allowing it to provide visibility and coordination across fragmented supply chains. This digital infrastructure underpins the company's AI deployment, as machine learning models require consistent, structured data to function effectively.

The platform approach reflects broader industry trends towards transparency and speed. Rather than simply brokering relationships, Li & Fung positions itself as a technology provider that manages complexity, particularly for brands that lack the scale or expertise to build direct supplier relationships across multiple countries and product categories.

How does Li & Fung use AI for factory matching?

Li & Fung uses machine learning algorithms to match incoming orders with suitable factories from its supplier network. The AI analyses multiple variables including factory capabilities, production capacity, quality track record, compliance certifications, lead times and geographic location. By processing this data automatically, the system can recommend optimal production partners far more quickly than manual evaluation.

This capability is particularly valuable when brands place complex orders that require specific technical expertise or tight delivery windows. The AI can filter thousands of potential suppliers to identify those with the right equipment, available capacity and proven performance in similar product categories. The system also learns from past orders, refining its recommendations based on which factory matches led to successful outcomes.

What role does AI play in costing and quotation?

AI-powered costing tools enable Li & Fung to generate price estimates more rapidly by drawing on historical order data, current material costs, labour rates and factory efficiency metrics. Traditional costing processes often require manual negotiation and multiple rounds of quotation between buyers and suppliers. Automated systems can compress this timeline by instantly accessing benchmark data and calculating likely costs based on product specifications.

The AI models account for variables such as order volume, fabric type, construction complexity and delivery requirements. They can also flag potential cost drivers early in the design phase, allowing brands to make informed decisions about materials or construction methods before committing to production. This predictive capability helps avoid budget overruns and reduces the back-and-forth typically required to finalise pricing.

How is AI applied to order management and tracking?

Once orders are placed, AI tools monitor production progress and flag potential delays or quality issues. Li & Fung's platform aggregates real-time data from factories, including production milestones, inspection results and shipping status. Machine learning models can detect patterns that indicate risk, such as a factory falling behind schedule or quality metrics trending downward, and alert supply chain managers to intervene.

This predictive monitoring helps mitigate disruptions before they escalate. If a factory is likely to miss a deadline, the AI can suggest alternative production plans or reallocate portions of an order to other facilities with available capacity. The system also provides brands with visibility into their orders across multiple suppliers and geographies, consolidating information that would otherwise be scattered across emails, spreadsheets and separate factory systems.

What are the key use cases across Li & Fung's operations?

Li & Fung's AI deployment spans several core functions within its supply chain orchestration model:

• Supplier discovery and vetting: AI screens potential new factories by analysing compliance records, financial stability, production capabilities and quality history, accelerating the onboarding process.
• Demand forecasting: Machine learning models analyse historical order patterns and market signals to help Li & Fung and its brand clients anticipate production needs and allocate capacity.
• Quality prediction: AI models use data from past inspections and factory performance to predict which orders may face quality challenges, enabling proactive quality control.
• Logistics optimisation: Algorithms evaluate shipping routes, consolidation opportunities and customs requirements to reduce transportation costs and delivery times.
• Sustainability tracking: AI tools monitor supplier environmental and social compliance data, helping brands meet transparency and reporting requirements.

What challenges does AI address in fashion supply chains?

Fashion supply chains are characterised by complexity, fragmentation and volatility. Brands typically work with dozens or hundreds of suppliers, each with different capabilities, systems and standards. Coordinating production across this ecosystem requires constant communication, data reconciliation and decision-making. AI helps automate routine tasks, such as checking factory availability or calculating lead times, freeing human teams to focus on exceptions and strategic relationships.

The technology also addresses information asymmetry. Brands often lack detailed visibility into factory operations, relying on periodic updates and manual reporting. By digitising supplier data and applying machine learning, Li & Fung can provide more granular, real-time insights into production status, capacity constraints and potential risks. This transparency is increasingly important as brands face pressure to demonstrate supply chain accountability and respond quickly to disruptions.

However, AI deployment in this context depends on data quality and supplier participation. Factories must adopt digital tools and share information consistently for the platform to function effectively. Li & Fung's ability to drive this adoption stems from its scale and long-standing supplier relationships, which give it leverage to set standards and invest in technology upgrades across its network.