Predictive Analytics Could Pave the Path to Increased Visibility
This post is part 2 of a 3-part series exploring the topic of visibility.
At the heart of achieving real-time visibility is data. It’s the constant flow of data from multiple, connected systems that provides clear visibility into the status of active shipments. Of course, that’s when the right systems are tracking the right information. At the same time, historical data allows us to see patterns in past shipments – transport times, arrival times, delays, etc… Looking forward, systems will be able to analyze vast amounts of data and help logistics teams plan for tomorrow, not just manage today.
According to a recent study by The Hackett Group, 66 percent of supply chain leaders say advanced supply chain analytics will be critically important to their supply chain operations in the next 2 to 3 years. And, predictive is a top-5 priority for supply chain, according to the 2017 Geodis Supply Chain Worldwide Survey.
Today, many transportation operations are relying on historical data to inform decisions, but often the data isn’t being analyzed for future improvements. The logistics industry is on the verge of leaping forward beyond descriptive and diagnostics intelligence, which can show what happened and why it happened, and into the promise of predictive to automatically adjust behavior to achieve desired outcomes.