11 benefits and use cases for AI in inventory management

5 benefits of AI in inventory management

AI can help improve inventory management processes in a variety of ways. Learn more.

1. More accurate inventory

Human error causes numerous inventory management problems, including overstocking, understocking and discrepancies between financial records and physical inventory. These errors are inefficient and can expose companies to potential fraud.

AI models can use real-time data from RFID tags or other sensors to detect and reconcile anomalies as they happen. This preemptive action helps maintain better stock counts.

2. Cost reduction

The accuracy of AI-driven stocktaking and predictive analytics can help managers reduce storage costs, minimize deadstock and release capital that would otherwise be tied up in excess inventory. In some cases, AI agents can help negotiate better supplier terms by identifying bulk purchasing opportunities.

Of course, accurate stocktaking also provides valuable benefits, as the data gives managers insight into their current inventory levels.

3. Improved demand forecasting

AI-based demand forecasting uses machine learning to analyze historical sales, market trends and external factors, such as seasonality or weather, to predict future product demand.

These systems continuously learn and adjust forecasts in near real time, which can lead to more accurate forecasts and smarter purchasing decisions by managers because of the higher quality of forecasts.

Precision in forecasting can help companies avoid stockouts or overstocking and help with the negotiation of supplier contracts.

4. Faster decision-making

While traditional decision support tools enable a high-level analysis of inventory, AI forecasting provides more granular insights, such as which regions, or even which cities, will likely experience spikes in demand.

These AI capabilities can lead to better pricing and promotions as well as improved decision-making. For example, a manager may decide to ramp up supply in advance of a local demand or in response to an emerging trend because of AI-enabled data insights.

AI forecasting can also respond quickly to disruptions like supply chain delays or sudden demand spikes.

5. Waste reduction

Overstocking is not just an issue of cost control. AI can help managers reduce waste of perishable goods by aligning procurement with consumer demand, which can help reduce overproduction, spoilage in transit and issues with storage.

For companies with sustainability initiatives, AI can also optimize delivery routes to reduce gas consumption and suggest changes to packaging design to make the packaging more sustainable.

6 use cases for AI in inventory management

AI's capabilities are applicable to several different aspects of inventory management.

1. Supplier risk mitigation

Supply chain risk mitigation is complex and involves analysis of geopolitical disruptions, weather patterns and historical delivery timelines. AI can suggest alternate routes and other suppliers if needed.

In addition, what appears to be a problem on the supplier's part may be beyond their control. AI and machine learning can help inventory managers analyze the factors that influence supplier performance, helping them improve processes.

2. Automated replenishment

AI systems can trigger automatic purchase orders when stock dips below predefined thresholds, avoiding the issues that accompany a lack of product.

For example, retailers can use AI to monitor stock in stores, regional distribution centers and central warehouses, then have AI replenish high-turnover items, which reduces the need for manual intervention and can prevent stockouts.

3. Batch tracking and recall management

Consumer safety is critical in industries like pharmaceuticals or food, and inventory managers must be able to quickly find tainted items in the event of an urgent recall.

AI-based quality management systems can track batches using serial numbers or RFID tags, enabling managers to trace a specific batch throughout the supply chain.

4. Warehouse optimization

Robots, computer vision systems and drones are becoming standard in modern warehouses and fulfillment centers, and AI drives all of those technologies. Even when human labor is heavily involved in handling goods, AI helps streamline picking, packing and sorting by arranging storage locations based on turnover rates, item relationships and order analysis.

Warehouse designers may also use AI to plan layouts with more efficient storage.

5. Dynamic pricing adjustments

Retail and wholesale pricing is influenced by stock levels, demand fluctuations and information about competitors.

AI models can analyze this complex matrix of factors and automatically adjust pricing while also taking into account other factors, such as storage costs and transportation.

6. Returns management

Goods going back to the retailer, warehouse or originator is also known as reverse logistics.

AI can play a part in reverse logistics, from predicting returns based on historic patterns to reintegrating the inventory and pricing returned goods for resale. AI analysis of return patterns can also help identify any potential issues with product quality or over-selling.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.