Machine learning can be used in the supply chain to help improve many different areas. Some of the most common uses of machine learning include:

1. Driving Inventory Optimization: Machine learning can be used to predict demand and optimise inventory levels based on past customer buying patterns. This can help companies identify the optimal ordering frequencies, helping them save costs and meet customer demand.

2. Improving Logistics: Machine learning can help optimise transportation routes, predict the ETA of a shipment, and enhance cargo tracking. Logistics automation helps to reduce operational costs and offer a better customer experience.

3. Automating Purchasing: Machine learning can be used to forecast what items need to be purchased and when. This helps procurement teams automate order placement and form better relationships with suppliers.

4. Detecting Fraud and Risk Management: Machine learning can detect anomalies in payments, which reduces the risk of fraudulent transactions. This helps to protect businesses and customers from fraudulent activity.

5. Enhancing Customer Experience: Machine learning can be used to predict customer behaviour and provide tailored recommendations. This helps to increase customer engagement and loyalty.

Source: https://www.emci.com/blog/how-machine-learning-is-impacting-the-supply-chain