The Data Foundation AI Still Needs for Supply Chain Management

The promise of artificial intelligence (AI) in the supply chain is immense – aworld of predictive insights, automated workflows, and a new meaning of resilience. Organizations are investing heavily to make this vision a reality; research shows that 61% of supply chain executives are actively investing in predictive analytics and 58% are investing in AI and machine learning.

Yet, for many, the value of AI is hard to grasp. AI-driven recommendations can feel disconnected from reality, and projects either get stuck in pilot mode or fall short of expectations. The issue isn't a failure of algorithms, but a failure of communication. Most AI tools are working with incomplete, siloed data, giving leaders a fragmented view of their own interconnected business. In fact, only 28% of executives feel they have a complete picture of their supply chain network.

For AI to deliver on its promise, it doesn't need a better algorithm – it needs smarter, more unified data. It needs a "common language" that allows it to listen to, understand, and act on signals from the entire business at once. Building this capability starts with a practical framework, integrating data from three essential streams: the factory floor, the logistics network, and the outside world.

The Factory Floor: Grounding AI in Operational Reality

A smart supply chain starts with a smart factory. AI cannot accurately forecast demand or optimize inventory if it’s not aware of what’s happening on the production line. For years, the operational technology (OT) on the factory floor – the systems managing machines and production – has been disconnected from the information technology (IT) in the warehouse and back office. This creates a critical gap that can hinder attempts at enterprise-wide intelligence.

To bridge this gap, effective physical AI needs to be fluent in the factory’s language. This means integrating foundational data streams, including:

Live production data: What is the actual output versus the plan? Are production schedules being met?

Predictive machine alerts: Is a critical piece of equipment showing signs of a slowdown or breakdown? Are integrations of AI, machines, and inventory data enabling AI-powered forecasting and demand planning and enhancing accuracy through predictive analytics?

Material consumption rates: How quickly are we using raw materials on the assembly line?

Real‑time production, machine health signals, and material usage data give AI something it has often lacked: a true picture of what’s happening on the factory floor. It can see that a specific machine is underperforming, predict a production shortfall, and flag the downstream impact on inventory and orders. This is the first step in building a system that doesn't just report on the past but actively prepares for the future, saving significant labor and resource costs along the way.

The Logistics Network: Connecting Production to Fulfillment

Once AI understands production capacity, it must know the realities of the logistics network. This second data stream provides the crucial context of how finished goods are stored, handled, and moved. It involves integrating data from core business systems that have often operated in their own silos, such as:

• Warehouse Management Systems (WMS): What are the real-time inventory levels for every product? What is the current labor capacity for picking and packing?
• Transportation Management Systems (TMS): What are the inbound and outbound shipment schedules? Is carrier capacity available? Where are trucks right now?

When you connect this data, the supply chain starts working like a single, unified system. This is enabled by modern platforms that create a unified data core, a concept some have coined “Cloud 3.0,” where hybrid and multi-cloud environments act as the operational backbone for AI.

Instead of being trapped in separate applications, data from WMS, TMS, and enterprise resource planning (ERP) systems can converge with live production data by integrating AI with logistic automation systems. Together, logistics stops being a rigid, plan-based function and becomes a flexible, responsive operation with physical AI – allowing teams to make immediate, data-driven decisions about shipments, inventory, or labor allocation.

The Outside World: Sensing and Responding to Disruption

Even a perfectly connected internal operation is still vulnerable to the outside world. A truly resilient supply chain AI must be able to sense and respond to constant change. The scale of this external threat is significant; a recent survey found that nearly 75% of enterprises experienced at least one critical risk event in the past year, with IT failures and cyberattacks leading the cause. It’s no surprise, then, that 70% of executives now say that managing supply chain disruptions is a major challenge for their organization.

The solution is to build an ecosystem of sensors and data feeds that provides visibility beyond internal operations. By integrating this third data stream, AI systems can monitor for external risks and opportunities, including:

Supplier and partner data: What is the current component lead time from key suppliers? Are any quality issues emerging?

Logistics and transit data: Is a key shipping lane or port congested? Are there disruptive weather events forecasted?

Sustainability and ESG data: What is the carbon footprint of a shipment? With 58% of organizations identifying reducing their carbon footprint as a key supply chain objective, this is a primary concern.

With access to this external data, AI can fully enable predictive orchestration. For example, if a severe weather event is forecast near a major port, the system can identify containers with critical components in the area and automatically model the impact of the delay on production schedules. This allows the team to move from reactive crisis management to proactive problem-solving, giving them the crucial lead time to reroute shipments, adjust production plans, or find alternative suppliers.

From Internal Efficiency to Radical Customer Transparency

The true power of unifying data from the factory floor, the logistics network, and the outside world is in giving AI the end-to-end visibility needed to deliver on its potential. The impact goes beyond internal efficiency to fundamentally transform the reliability of the entire supply chain network.

Integrating live production data means AI is grounded in operational reality, not assumptions. Extending that intelligence into warehouse and transportation systems enables rapid, data-driven decisions. Layering in external data allows teams to spot emerging risks and anticipate their downstream impacts before they become a crisis.

The impact of this transparency is felt across the entire business network. When an organization's teams are working from the same real-time picture, its partners and customers gain a critical advantage: predictability. Proactive communication replaces costly surprises, and clear visibility into the movement of goods builds trust at every step. Building this unified data ecosystem with physical AI transforms logistics from a hidden, reactive function into a source of confidence and clarity for every stakeholder it touches.