The power of AI in inventory management

Inventory management is critical to the success of any supply chain, as it enables teams to efficiently allocate resources, minimize stockouts and optimize cash flow. It is an important aspect of enhancing operational efficiency, since high inventory levels lead to increased overhead costs, product obsolescence and wasted opportunities. As businesses strive to improve their inventory management, artificial intelligence (AI) provides innovative solutions to conquer these challenges.

Potential of AI in inventory management

Many organizations already automate inventory management tasks with ERP software. However, these tasks are often performed in a static way, requiring regular human monitoring and correction if requirements change or an error occurs.

AI-powered systems can manage inventory dynamically. Utilizing data from a digital twin, AI can

• Monitor inventory levels
• Track consumption patterns
• Automatically generate purchase orders or production plans

By automating these processes dynamically instead of statically, changes are made automatically – which reduces manual error, streamlines operations, and ensures businesses carry the right amount of stock.

AI-supported inventory management in the real world

In one recent customer project, 4flow supported a retail business to optimize re-stocking of its brick-and-mortar stores. Prior to the project, the business simply re-ordered the same products that had been sold.

Together with 4flow, the business implemented an AI-powered software to predict products most likely to sell best based on historical and current sales data from similar stores. Orders were then placed using this prediction, instead of re-ordering the same product.

As a result, the company increased sales and improved inventory management in their stores, increasing potential revenue by 11%.

The steps you can take now

Choosing the correct task and data input for an AI solution is essential for its success. In the case of inventory management, an AI tool may not be able to manage high-level tasks like choosing the inventory management strategy. For example, choosing between using a just in time (JIT) model or a vendor managed inventory (VMI) model affects much more than just a company’s inventory levels and require human expertise.

However, AI can be well suited to handle lower-level tasks, such as increasing required safety stock levels when a market metric is triggered. Defining a scope of work well suited for AI ensures its long-term functionality.

Additionally, choosing the correct data is important to ensure the success of an AI solution. In the case of inventory management, companies should provide the solution with relevant low- and high-level data such as:

• Real-time stock levels
• Product descriptions
• Inventory location
• Current inventory management strategy
• Market data

This blog post is the second in a series of five exploring AI use cases in supply chains. Explore AI for demand forecasting in the previous article, and stay tuned for more practical insights on the 4flow blog.