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Machine Learning in Logistics & Supply Chain 6 Use Cases

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16 Examples of AI in Supply Chain and Logistics

Top 3 AI Use Cases for Supply Chain Optimization

Also, by constantly learning over time, it continuously improves on these recommendations as relative conditions change. AI-based automation can assist in the timely retrieval of an item from a warehouse and ensure a smooth journey to the customer. AI systems can also solve several warehouse issues, more quickly and accurately than a human can, and also simplify complex procedures and speed up work. Also, along with saving valuable time, AI-driven automation efforts can significantly reduce the need for, and cost of, warehouse staff. AI in the supply chain helps reduce customer service-and-support time by predicting customer behavior with great precision.

Top 3 AI Use Cases for Supply Chain Optimization

So disruptive that it can be the deciding factor in the success or failure of an enterprise. So, let us explore how this buzzworthy Generative AI can benefit the realm of the supply chain. Now is the time to educate, invest and pilot AI innovations across the supply chain. Early movers will build capabilities, talent pipelines and data foundations that fuel a multi-year advantage. I encourage supply chain leaders to envisage how AI can upgrade planning outcomes, empower teams with dynamic insights and enable higher service levels at lower costs.

Last-mile Delivery

Warehouses store a wide range of products that require different storage and handling strategies. Inefficient storage and retrieval decisions can have severe negative financial impacts. You can also check our data-driven list of supply chain software to find the option that best fits your business.

A branded, interactive digital experience with all the functions of a digital forwarder in a single solution. To make sure that packages are delivered on time and with ease, UPS offers the most optimized navigation system called On-Road Integrated Optimization and Navigation (ORIAN). It ensures that the UPS drivers use the most optimized delivery routes in regard to distance, fuel and time. Supply chain has a lot of components right with managing inputs from raw materials, to manufacturing, warehousing, and distribution to customers.


Misinterpreted input leads to incorrect output, creating a domino effect in which a company’s supply chain strategy may miss the mark. So here lies the age-old challenge that supply chains are by their very nature composed of separate companies with at least three reasons not to share data. First, they may of business that competes with one or more of the other partners. And third, they keep information to themselves to strengthen their hand at the negotiating table. Once the supply chain is optimized for flow, he adds, you can then start installing and executing on predictive quality and maintenance. Another example of how AI is being used can be found at Intel, where several chips are printed on a single wafer using lithography.

Top 3 AI Use Cases for Supply Chain Optimization

By improving asset maintenance, supply chain professionals can significantly decrease maintenance costs. Having a robust demand forecast enables merchants to make smarter decisions around procurement, all the way down to the SKU level. When they know not only which product lines, but which individual SKUs are going to be their best sellers, they can optimize their procurement strategy. Demand forecasting based on machine learning will also optimize inventory carry cost. Merchants will strike a balance between reducing the risk of stockouts and carrying too much inventory. Automated tools based on AI offer better planning and management of warehouses to guarantee the safety of workers and materials.

Read more about Top 3 AI Use Cases for Supply Chain Optimization here.

Top 3 AI Use Cases for Supply Chain Optimization

Written by: Lucia

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