Machine Learning

Thanks to its ability to gather gigantic, disparate data sets, and then swiftly analyze the information to provide insights, the role of artificial intelligence in logistics is a valuable one as it helps consumer goods leaders make decisions more quickly, accurately, and efficiently.

While maturity levels vary for artificial intelligence in logistics and the supply chain, it has the potential to facilitate more accurate and efficient decision-making.

Shifting consumer demands, geopolitical uncertainty, labor constraints, and residual pandemic-era behaviors can all have a negative impact on supply chain management. As a result, more companies are leveraging AI in the supply chain to improve their supply chain resilience. 

Thanks to the versatility of its benefits — what business function wouldn’t want to have their mountains of data quickly analyzed? — AI can be applied to supply chain activities in numerous ways, and the future of AI in the supply chain is promising.

Infusing AI in the supply chain isn’t magic, but it can be an extremely powerful tool for supply chain management.

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

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.