Machine Learning

To be sure, the future of AI in the supply chain holds great promise for the companies that can successfully see their investments through.

Artificial intelligence can be used to quickly gather and analyze massive amounts of data flowing from a large number of sources. As a result, AI in the supply chain is a prime candidate for the tech investments that can move the needle in improving business performance.

There’s no shortage of AI logistics use cases, whether it’s through demand forecasting, enhancing warehouse efficiencies, or improving route planning. In all of these cases, AI in the supply chain can translate to streamlining, predicting, and automating key aspects of logistics operations. 

Lack of real-time inventory visibility was chosen by retailers as the top priority of “high importance” that needs to be resolved through technology upgrades or implementations, making it a prime use case for AI in the supply chain.   

While AI in the supply chain is far from a magic salve, both it and IoT can deliver an elevated level of visibility into the supply chain.

AI in the supply chain has many applications, not least of which includes logistics. While historically logistics teams may have operated in siloes and relied on manual, labor-intensive processes, they are now leaning into AI-powered tech to gain greater visibility into their operations.

Integrated data and analytics have been a top investment priority for many years—a trend that has only been accelerated by flexible, agile, scalable cloud platforms and new analytic capabilities that did not exist in the on-premise environment. Here are five keys to building and maintaining a successful Data Products strategy.

Thomas Ortis, CMO of Eboost, an energy drink and supplement company, shares how they’re using digital twins and generative AI to connect with consumers.