Ongoing disruption requires companies to make a fundamental shift in their supply chain strategy. What makes this transformation so difficult is that the scope of change required is extensive, involving many internal (and external) stakeholders, data and technology. 

Managing a supply chain is a complex task which has only grown more intricate in recent years due to globalization, a volatile geo-political landscape, technological advancements, and the increasing demands of consumers. 

In today's fast-paced logistics landscape, actionable data is critical to meet the demands of consumers' expectations. Navigating the complexities of data management amid labor shortages, volatility, and evolving consumer behavior poses significant challenges.

By harnessing AI for activities such as load optimization, route planning, predictive analytics and dynamic scheduling, businesses can start to find ways to trim their carbon footprint.

Leaders also said they've struggled to hire and retain skilled supply chain-focused workers in the last two years. 

As we’ve seen over the last few years, planning and forecasting alone haven’t been enough to deal with the delays and shortages rippling through global supply chains.

The Elon Musk-owned EV company handed over less than 387,000 cars in the first three months of 2024, down 8% year-over-year.

A growing economy, drought conditions at the Panama Canal, and systems not designed to plan for trucking schedules could all spell trouble for the Port of LA in the months to come.

With proper human oversight, NLP can reduce supply chain challenges even before they occur, thus potentially saving companies substantial time and money.

The company's ADI provides recommendations to either accept or reject a tendered load by analyzing a variety of factors.