AI in Supply Chain: 13 Real Use Cases

Most supply chain leaders have already settled whether AI belongs in their operation. The harder decision is which of the dozens of potential applications can produce a return you can defend to finance, and which your current systems can support. An AI recommendation only creates value when something downstream acts on it.

Only 23% of supply chain organizations have a formal AI strategy. For many organizations, that means they still evaluate AI use cases as individual projects, making investment decisions one at a time. The starting point is the problem you want AI strategy to solve. 

The 13 use cases below show where those applications fit across the supply chain and what each requires.

TL;DR: 13 AI Supply Chain Use Cases

Every figure in that table is published by the operating company or by an independent professional body. Vendor-reported numbers are excluded, which is why eight rows carry no result.

Planning and Inventory AI Use Cases

Planning is a common starting point for supply chain AI because the data is structured and the financial impact is easier to measure. . Both use cases here depend on clean, reliable data.

1. AI-Powered Demand Forecasting

Best for: Operations managing volatile demand across many SKUs, locations, channels, or seasons.

Demand forecasting is a common AI application because demand depends on more than historical sales patterns. Machine learning models can incorporate factors such as promotions, competitor pricing, weather, local events, and demand shifts between related products. They can produce forecasts for specific products and locations and update them as demand changes.

Walmart built its own multi-horizon recurrent neural network to forecast across short, medium, and long horizons at once. The model uses past demand patterns, planned events, and current global and local trends.

The model needs several years of clean sales history, with stockouts identified. A period with zero sales because a product was out of stock does not represent zero demand. Without that distinction, the model can learn to under-forecast products that were previously unavailable. The same applies to promotions and other planned events. A maintained event and promotion calendar gives the model information that historical sales alone cannot provide.

Good data gives planners a better forecast. The next question is whether they use it to change inventory decisions. If planners keep overriding it, improvements in forecast accuracy may have little effect on safety stock or replenishment decisions.

2. Dynamic Inventory Optimization and Replenishment

Best for: Multi-location operations balancing service levels against inventory and working-capital costs.

Where forecasting predicts demand, inventory optimization decides how to respond. It helps decide how much stock to hold, where to hold it, when to reorder, and when to move stock between locations. The models set safety stock based on  service-level targets and demand variability for each  item and location, then recalculate those levels as conditions change. 

Walmart’s Self-Healing Inventory reroutes overstocked product to locations that need them. One system alone has saved Walmart more than $55 million since its deployment in Mexico City. Rebalancing existing stock can reduce  markdowns at the overstocked location while preventing  lost sales at locations with shortages, making it especially valuable in networks with many stocking points.

Those decisions depend on accurate inventory data. If the inventory position is wrong by 4%, a rebalancing engine may send stock that is unavailable while leaving available stock in place.  Cycle-count discipline and near-real-time inventory updates from the Warehouse Management System (WMS) matter as much as the optimizer itself.

Inventory optimization also changes the work required to move product through the network. Lower inventory levels and more frequent rebalancing can increase shipments, receipts, and putaway work. If those additional costs aren’t included in the model, freight and handling can erode some of the working-capital savings. 

Warehouse AI Use Cases

Inside the warehouse, AI can help decide how work gets done. Those decisions have to reach the WMS or another system that directs warehouse activity. This makes system integration a basic requirement for warehouse AI use cases. 

3. AI-Driven Slotting and Warehouse Layout Optimization

Best for: High-SKU warehouses where travel time, poor slot placement, or capacity constraints limit productivity.

Slotting determines where products should be stored to reduce the distance workers travel during picking. It considers factors such as product velocity, cube, weight, order affinity, and pick-path geometry. Machine learning adds the ability to identify patterns in order data, such as products that are frequently picked together, and use those patterns to improve product placement. It can also update recommendations as demand changes, giving warehouses a way to adjust slotting more often than an annual review. 

The recommendations depend on accurate product and location data. The system needs reliable information on each product’s dimensions and weight, along with each storage location’s capacity. Many warehouses find that their item data is incomplete or based on estimates. 

That creates a problem when the recommended location cannot hold enough inventory. A fast-moving product may need several replenishment trips during the day, adding work that can offset the time saved from shorter pick paths.

The warehouse also has to account for the labor required to reslot inventory.  Moving a product takes time to retrieve, transport, and put away. Frequent reslotting can consume some of the labor savings from reduced travel, increasing the final spend.  

4. AI-Driven Task Orchestration and Pick Optimization

Best for: Busy facilities with multiple task types, workers, robots, zones, and changing priorities during a shift.

Task orchestration decides which work should happen next and who or what should do it. During a shift, priorities change as trailers arrive, orders are expedited, or work falls behind in a particular zone. The system can reassign tasks based on worker location, available equipment, task priority, and current workload. This reduces the manual reprioritization supervisors have to manage throughout the shift. 

A multibillion-dollar international health, hygiene, and nutritional products manufacturer implemented Blue Yonder Luminate Warehouse Tasking with Open Sky Group across eleven warehouses already standardized on Blue Yonder WMS and Warehouse Labor Management (WLM). The implementation reduced manual task adjustment and improved on-time trailer departure and dock utilization. The system also accounts for working hours, reducing the need for manual changes around weekends, holidays, and shift changes. 

For this to work, task orchestration needs a direct connection to the WMS. The WMS has to be able to assign the task, notify the worker, and record what happened. Labor standards also need to reflect how work is performed. If the system assigns work based on outdated priorities or unrealistic labor expectations, workers and supervisors may have to correct those instructions manually. 

Task optimization, however, directly affects what happens after picking. Higher picking throughput can increase the workload at packing, staging, or the dock. If those areas cannot handle the additional volume, the queue simply moves downstream.

5. Predictive Maintenance for Warehouse Automation and MHE

Best for: Facilities where conveyors, sortation, Automated Storage and Retrieval Systems (ASRS), forklifts, robotics, or other material handling equipment (MHE) create significant downtime risk.

Warehouse equipment often shows signs of wear before it fails. Changes in vibration, motor current, temperature, or cycle counts can indicate that a component is starting to degrade. Predictive maintenance models use these signals to flag potential failures early.

Maintenance teams can then plan a repair during scheduled downtime instead of dealing with an unexpected stop during a shift. For equipment such as sortation systems or ASRS that sits on the critical path, avoiding one major outage can justify the cost of monitoring.

The challenge is teaching the model what those signals mean. It needs records of past failures to connect changes in equipment data with actual breakdowns. A facility may have years of sensor data but few maintenance records showing what failed and when. Newer robotics and automation can have extensive sensor data with very few failures, leaving little history to train a model. In those cases, facilities may use vendor models trained on data from other equipment.

Predictive maintenance changes the maintenance team’s work as well. More repairs can be planned around scheduled downtime, which affects technician schedules, spare-parts planning, and the skills needed on the team.

6. Computer Vision for Quality and Inventory Checks

Best for: Operations with high inspection workloads, frequent damage, labeling requirements, or manual inventory verification.

Computer vision uses cameras and AI models to inspect products as they move through the warehouse. It can check for damage, incorrect items or quantities, labeling errors, and expired products. The same technology can read storage locations and pallet configurations to support inventory checks, reducing manual inspection and counting.

Amazon uses Project P.I. at fulfillment centers across North America. Items pass through imaging tunnels where the system uses optical character recognition to check for damage, incorrect color or size, and expired products. When the system flags an item, an associate reviews it and decides whether to resell, donate, or otherwise use the product.

For this to work, the cameras need a clear and consistent view of the products. Lighting, product position, and camera placement affect what the system can see, so the conveyor or workstation may need adjustment. The model needs labeled images of each product, too. When new products are added, the system must capture and label images before it can check them reliably.

There is a trade-off in how much the system flags for review. If it catches too few problems, defective products can pass through. If it catches too many, associates spend time checking products that are fine. At that point, the manual review work can outweigh the labor saved by automated inspection.

Labor AI Use Cases

Managing labor starts with understanding how much work needs to be done and how long it takes. AI can help planners turn changing workloads into clearer labor expectations. 

7. AI-Powered Labor Forecasting

Best for: Warehouses with variable order volume, seasonal peaks, or unpredictable staffing demand.

A labor forecast shows how many hours an operation will need to complete its expected workload. It takes expected order volume and translates it into labor requirements by function, shift, and skill. For example, a change in the mix of picking, replenishment, and other tasks can change the required hours even when total order volume stays the same.

To make that calculation, the forecast needs to know how much time each task requires. Engineered labor standards provide those time expectations. A labor management system provides the workload and performance data used to create and maintain the standards. Those standards need to reflect the current process because changes to layouts, equipment, or order profiles can change how long a task takes.

With that information, managers can plan staffing around upcoming work. They can see where demand is likely to exceed available hours, adjust shifts, and identify where cross-trained employees may be needed.

Open Sky Group’s “Powered by People” perspective puts this use case in context: genersative AI can support the people doing the work. Better labor forecasts give managers a clearer view of staffing needs and help them use their workforce more effectively.

8. Workforce Optimization and Dynamic Staffing

Best for: Operations facing overtime pressure, uneven labor utilization, or changing workload during the day.

Labor forecasting sets staffing requirements before a shift. Workforce optimization adjusts those plans as the work changes. If more orders arrive in one area than expected, the system can recommend moving available workers from another area. It can flag when overtime is likely and identify cross-trained workers who can fill a gap.

Those decisions depend on current labor and workload information. The system needs to know what work is open in each area, who is working, where they are, and what tasks they are trained to perform. Cross-training determines how much flexibility managers have when moving people between functions. A recommendation to move four people from packing to picking only works if those workers are qualified to pick and their schedules allow the move. 

The move itself takes time, too. Workers have to get to the new area and adjust to the new task. If people move too often, that transition time can reduce the productivity gains from better labor allocation. 

Transportation AI Use Cases

Transportation is one area where AI can have a clear financial impact. Better load planning, routing, and carrier selection can reduce freight costs, while the savings can be measured directly against transportation spend. 

9. AI-Assisted Load Building, Routing, and Carrier Selection

Best for: Shippers with significant freight spend, many lanes or carriers, or complex consolidation decisions.

A transportation plan must answer several questions at once: which orders should travel together, which carrier should handle each load, and which route the driver should follow. Optimization models can evaluate these decisions against freight cost, service commitments, available capacity, delivery windows, and equipment requirements. They can even recalculate the plan when orders or conditions change.

UPS provides one of the clearest examples with ORION, its On-Road Integrated Optimization and Navigation system. As of December 2015, ORION had saved UPS more than $320 million against a total project cost of $250 million. At full deployment across 55,000 drivers, UPS expected annual savings of $300 million to $400 million, plus 10 million gallons of fuel and 100,000 metric tons of CO2.

The model needs current transportation data to make those decisions. Rates, carrier capacity, service levels, delivery requirements, and shipment constraints must be accurate. The recommendation then needs to reach the system that handles transportation execution. 

A transportation management system provides the rate and carrier information the optimizer uses and manages the tender, execution, and settlement processes that follow. Open Sky Group helps build this foundation through TMS implementation and advisory services.

This plan directly impacts warehouse work. A consolidated load may require picking and staging orders in delivery sequence. If the warehouse cannot build the load that way, the transportation savings can create extra dock work.

10. Predictive ETA and Shipment Exception Management

Best for: Transportation networks where delays, demurrage, premium freight, customer updates, or manual exception handling drive up costs.

A shipment’s planned arrival time is based on what was known when the load was dispatched. Once the shipment is on the road, conditions can change. Traffic, missed appointments, loading delays, weather, and other disruptions can push the actual arrival later than planned. A static ETA does not account for those changes.

Predictive ETA models keep recalculating the expected arrival using shipment tracking data and patterns from previous deliveries. When the predicted arrival moves past a customer’s delivery window or another important milestone, the system can flag the shipment as an exception.

The quality of that prediction depends on how much information is available during transit. Contracted truckload and container shipments often provide more tracking data than less-than-truckload, spot freight, or shipments handled by third parties. Gaps in tracking leave the model with less information, which makes it harder to identify a delay early.

When tracking data is available, the system can identify a delay before the scheduled delivery time. The transportation team then has time to reschedule the delivery, adjust labor at the destination, arrange different transportation, or update the customer. If the delay is identified only when the shipment is already late, most of those options become harder or more expensive.

Network and Cross-System AI Use Cases

The final group operates above any single system. These use cases analyze relationships across warehouse, transportation, labor, and financial data, making them most dependent on integration and hardest to pilot within one function.

11. Supplier and Network Risk Monitoring

Best for: Companies managing many suppliers, multiple supplier tiers, long lead times, or significant disruption exposure.

Supply chain disruptions often start before a company knows they will affect its own operations. A port closure, severe weather event, financial problem at a supplier, or geopolitical event may threaten a material several steps upstream. AI-based risk monitoring brings these external signals together with supplier and supply chain data to identify which materials, facilities, and orders could be affected.

That requires visibility into the supplier network. Knowing your direct suppliers is not enough when a disruption starts at a sub-tier supplier that provides a component or raw material. Building that visibility often requires suppliers to share information about their own suppliers, so the challenge is partly commercial and contractual. The technology can only work with the network information available to it.

The other challenge is deciding which risks deserve attention. A system that flags every possible disruption can leave teams with more alerts than they can review. Companies need clear thresholds for severity and exposure so teams can focus on disruptions that could affect their operations.

12. Cross-System Anomaly Detection and Cost-to-Serve Analysis

Best for: Multi-system operations where WMS, TMS, LMS, ERP, billing, and customer data are difficult to analyze together.

Cost-to-serve analysis brings together the warehouse, transportation, labor, billing, and returns costs associated with serving a customer or fulfilling an order. When that information sits across separate systems, it is difficult to see the full cost of an individual customer, SKU, or service. AI can analyze the combined data to identify unusual cost patterns and changes that may be difficult to spot in individual systems.

Open Sky Group’s Kaleidoscope connects data from the WMS, TMS, LMS, ERP, and other systems already in place. Its Supply Chain Insights lens applies machine learning and AI to that combined data, including modeling cost-to-serve down to the SKU level and identifying anomalies in real time. For example, the system could identify a kitting process that is billed differently across distribution centers or a transportation lane whose costs have changed over time.

The analysis depends on consistent data across those systems. A “shipment” in the TMS may not represent the same event as a “shipment” in the WMS, for example. Those differences need to be reconciled before the data can support a reliable cost or performance analysis.

Once the costs are visible together, companies can use that information to make decisions about pricing, service levels, and operational changes. A customer that appears profitable based on revenue alone may look different when the full cost of fulfillment is included.

13. Natural-Language Querying of Supply Chain Data

Best for: Teams that depend heavily on analysts, dashboards, or manual reports to answer routine operational questions.

Supply chain data is spread across systems, tables, and predefined reports. Finding a specific piece of information often requires knowing where that data lives and how the relevant fields relate to one another. Natural-language querying removes that technical step by letting users describe what they need in plain language.

Supply Chain Chat, another Kaleidoscope lens from Open Sky Group, applies natural language processing across connected supply chain data. Agents can be configured for functions such as sales support, operations, and load planning, so the system can work with the data and questions relevant to each function.

The underlying data still determines what the system can answer reliably. If different systems use inconsistent definitions or contain incomplete information, a natural-language query can produce an answer that sounds precise but is based on flawed data. Teams must have consistent definitions, connected data, and appropriate governance in place before relying on the results.

How to Prioritize AI Supply Chain Use Cases

Four questions help determine whether a use case is ready for investment: 

• Is there a measurable problem worth solving? Define the cost in dollars, hours, or service failures before selecting a technology.
•       Does the required data already exist, and can you trust it? Check whether the model’s required fields  are captured with enough accuracy to support the use case. 
• Can the AI recommendation change the workflow? Trace the recommendation through to execution, and confirm that the relevant system or person can act on it within the required operating window.
• Can you prove the result in a controlled pilot? Define the comparison, the measurement period, and the success threshold before the pilot begins. .

If you have a use case in mind but aren’t sure whether your current environment can support it, Open Sky Group Advisory Services can help you assess the systems, data, and processes involved. The assessment can identify what needs to change and whether the expected return justifies the investment. 

What These AI Use Cases Require From Your Systems

Almost every use case above depends on data from existing operational systems. Before committing to a pilot, check what those systems capture today and whether the data is accurate enough for the use case. 

•       Demand and inventory data from planning and ERP systems. Sales history with identified stockouts, current on-hand positions, and open purchase orders.
•       Warehouse events from the WMS. Task-level records of what was picked, put away, and replenished, and by whom.
•       Labor data from the LMS or WLM. Engineered standards that reflect the current process, plus actual performance history by function and shift.
•       Shipment information from the TMS. Rates by lane and carrier, tendering history, service commitments, and delivery outcomes.
•       Equipment signals from automation. Sensor telemetry from conveyors, sortation, ASRS, and robotics, paired with structured failure records.
•       Integrations between those systems. A way to move data e between platforms, and return recommendations to the system that executes them.

The same systems can support very different levels of AI readiness. An operation with a current WMS, disciplined cycle counting, and accurate labor standards may have the data needed to pilot several use cases. An operation with a heavily modified legacy system may need significant integration and data remediation first. That work needs to be part of the business case.

Build the Right Foundation for Supply Chain AI

Across all 13, the use cases that pay back are the ones your systems can already feed and act on. Planning and transportation use cases may be easier to pilot when the required data and execution processes are already in place. Cross-system use cases require data from several platforms, which adds integration work before the AI application can be tested. 

Open Sky Group works across WMS, TMS, labor management, robotics and automation, and cross-system analytics. That gives our advisors a view of the systems and processes behind these AI applications. If you’re deciding which use case to pilot, our advisors can assess your current environment, identify gaps, and build the business case for the next step.  

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