Inventory Intelligence: How AI is Transforming Supply Chains

Inventory management is the secret behind how companies order, store, move and sell raw materials, components and final products. 

Deloitte reports that around 60% of executives saw improved demand forecasting and stock management when using AI in 2024, while 70% plan to adopt such tools by the end of 2025. 

The use of AI in inventory management helps ensure the right stock is in the right place, at the right time. It minimises waste, controls costs and ultimately keeps operations running smoothly. 

Understanding inventory management in supply chains

Using real-time data and technologies such as barcodes, RFID, AI and more, businesses can manage stock efficiently while reducing human error. These tools allow them to monitor every item as it moves from supplier through the warehouse to the consumer – helping staff react quickly to changes or disruptions. 

By ensuring an accurate view of inventory across the supply chain, professionals can ensure fewer delays and faster fulfilment, leading to better brand perception. This also helps to avoid both stockouts – when popular items become unavailable – and overstocking, which leaves more unsold goods than planned. 

These modern inventory systems also play a key role in international trade. By digitising paperwork and streamlining product flow, companies can now meet trade regulations and move goods across borders with much fewer delays. 

Case study: Amazon

Amazon maintains its supply chain edge with a Vendor Managed Inventory (VMI) model that gives suppliers real-time insight into warehouse stock levels and sales performance. Instead of waiting for Amazon to raise purchase orders, suppliers take the lead, restocking when products hit predetermined thresholds. This hands-off approach on Amazon’s part clears the way for smoother, leaner operations.

With millions of SKUs (stock keeping units) moving through its system, Amazon removes layers of admin by scrapping the need for manual purchase orders. Suppliers track demand and replenish in sync with actual sales data, cutting down instances of stockouts and surplus inventory – meaning faster deliveries and fewer disappointed customers.

“Today, in virtually every corner of the company, we’re using Generative AI to make customers' lives better and easier,” explained CEO Andy Jassy earlier this year. “In our fulfillment network, we’re using AI to improve inventory placement, demand forecasting and the efficiency of our robots – all of which have improved cost to serve and delivery speed.”

How global supply chains utilise inventory management

For successful businesses, the process of inventory management usually starts with demand forecasting. By analysing the likes of previous sales, market activity and seasonal trends, companies can predict the quantities of goods they will need. 

Once forecasts are set, procurement teams place orders with suppliers based on current stock and lead times. Next, approved items are entered into the inventory system and placed in storage, with barcodes to enable efficient tracking.

Warehouses themselves are organised so stock is easy to access, with high-demand items often stored closer to packing areas. Inventory systems will then monitor stock levels constantly, so that when a consumer places an order, the system updates immediately. 

Products can then be picked, packed and dispatched until stock drops below a certain point, when the cycle starts again. 

This ensures a constant supply so businesses can keep up with demand. 

Some large companies manage inventory across multiple locations using systems that combine ERP and WMS software connected through APIs or middleware.

Meanwhile, automation tools like barcode scanners, RFID and AI streamline tasks such as purchase order issuance and delivery tracking, while multi-warehouse systems allocate stock efficiently based on demand. It’s also important to ensure ongoing staff training and regular audits to check and maintain data accuracy. 

Case study: H&M

H&M has updated how it manages inventory, turning to data and analytics to ensure stores carry what local shoppers actually want. By tapping into regional sales figures, live demand signals and trend forecasts, the retailer now fine-tunes stock levels to match real-world buying behaviour.

AI shapes decisions on where products go and when, with the aim of preventing both overstock and stockouts – the twin challenges that plague fast-fashion chains. With better balance, each H&M store becomes more responsive, carrying stock that reflects its unique customer base. This keeps things moving and supports the chain’s agile approach to fashion.

Daniel Erver, the retailer's CEO, told Reuters in January: “H&M’s improved operating profit margin in the fourth quarter [of 2024] was partially attributable to a better product assortment.” 

He adds that tweaks to design and supply timelines may “reduce the timeline from product conception to in-store availability by up to 50%”. 

Though H&M reports a slight dip in turnover ratio in 2025, the broader picture shows inventory flowing faster and more efficiently. Distribution centres are working smarter and logistics is running more smoothly – meaning inventory meets demand with less delay, less waste and stronger margins.

The future of inventory management

Investing in smart, automated inventory systems can cut costs, boost efficiency and help companies stay competitive. 

In the coming years, AI will forecast needs with greater precision using behavioural, market and environmental data, while autonomous systems will handle picking, packing and restocking with minimal human oversight. 

Cloud-based and IoT-enabled platforms will also provide real-time global visibility, enabling instant collaboration across supply chains while smart sensors and RFID will ensure perfect tracking of goods. Predictive analytics could also guide procurement, sustainability and supplier partnerships, helping companies minimise waste and carbon footprints.

As e-commerce and omni-channel fulfilment continue to expand, systems will dynamically balance stock across digital and physical networks. Future inventory platforms will serve as intelligent ecosystems – self-learning, adaptable and central to building faster, greener and more resilient global operations.

Case study: Cisco

Cisco gave its inventory strategy a digital overhaul, relying on demand-driven supply models to match stock more closely with actual market requirements. This approach trims surplus by ensuring components and finished goods arrive only when needed, not before.

At the core is real-time data visibility across Cisco’s vast manufacturing network. From component sourcing to final delivery, predictive analytics monitor movement and adjust stock levels in line with current demand. Forecasting tools plug directly into production schedules, keeping availability high while avoiding stockpiling or tying up capital unnecessarily.

Joe McMorrow, Vice President of Supply Chain Transformation at Cisco, explains: "My team drives supply chain strategy for a secure, adaptable and sustainable supply chain. We focus heavily on digital transformation, including AI use cases like forecasting with causal AI, machine learning in quality management and generative AI for knowledge management. AI is embedded in digital workflows to improve responsiveness, efficiency and sustainability."

These changes ripple across Cisco’s supply chain. Suppliers are able to stay in sync through stable production plans and lean inventory. This allows stock to flow better as shipment timing improves and the entire network gains flexibility without losing control.

Top 10 Vendors

• SAP Extended Warehouse Management (EWM)
• Oracle Fusion Cloud Inventory Management
• Microsoft Dynamics 365 Supply Chain Management
• Manhattan Associates Warehouse Management
• Blue Yonder Supply Chain Management
• Oracle Warehouse Management Cloud
• Infor Supply Chain Management
• NetSuite ERP Inventory Management
• Softeon Warehouse Management System (WMS)
• Cin7 Omni