Inventory Optimisation

AI for inventory optimisation

Inventory optimisation is the process of maintaining optimal inventory levels to meet customer demand while minimising costs. It involves balancing a number of factors, such as stockout risks, carrying costs and ordering costs.

Inventory optimisation is a complex task, as it requires businesses to track a large amount of data, including inventory levels, sales data and supplier lead times. Additionally, businesses need to be able to forecast demand accurately in order to maintain optimal inventory levels. AI inventory optimisation solutions help businesses overcome the limitations of traditional inventory optimisation methods.

Benefits

AI inventory optimisation solutions provide benefits, including:

• Reduced costs: AI solutions can help businesses to reduce inventory costs by minimising stockouts and overstocking.
• Improved customer service: AI solutions can help businesses to improve customer service by ensuring that products are in stock when customers need them.
• Increased sales: AI solutions can help businesses to increase sales by ensuring that they have the right products in stock at the right time.
• Improved profitability: By reducing costs, improving customer service, and increasing sales, AI inventory optimisation solutions can help businesses to improve their profitability.

Who it's for

AI-powered inventory optimisation solutions can benefit businesses in a wide range of industries, including retail, manufacturing, healthcare and logistics. They benefit businesses of all sizes, from small businesses to large enterprises.

Some of the specific roles that can benefit from AI-powered inventory optimisation solutions include:

• Inventory managers
• Supply chain managers
• Demand planners
• Sales managers
• Customer service managers

How it works

AI inventory optimisation solutions typically work by analysing the following types of data:

• Inventory data (such as stock levels, reorder points, and safety stock levels)
• Sales data (such as historical sales data and seasonal sales data)
• Supplier lead times
• Economic data (such as GDP growth and inflation rates)

This data is then used to train a machine learning model to predict demand and generate optimal inventory recommendations. The model can also be used to monitor inventory levels in real time and generate alerts when stockouts or overstocking are detected.

Getting started

As outlined above, by balancing a number of factors, such as stockout risks, carrying costs and ordering costs, AI inventory optimisation solutions help businesses reduce costs, increase sales and improve profitability.

If you're curious to learn more, contact us today.