Data & Analytics

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.

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.

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.

This report leverages location analytics to analyze changes in consumer behaviors and offers insights that can help retailers and CPG companies better cater to today’s brick-and-mortar visitors. Read the white paper to find out.

Amazon reveals details of its first fully autonomous mobile warehouse robot.

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.