Editor’s Choice: 5 Ingredients for Predicting Accurate Shipment ETAs

Note: Today’s post is part of our “Editor’s Choice” series where we highlight recent posts published by our sponsors that provide supply chain insights and advice. This article comes from Vivek Vaid, CTO at FourKites, and examines how to predict accurate shipments ETAs.

shipment ETAPredictive intelligence is a big deal these days. In talking to one of our customers, it occurred to me that despite the advances FourKites has demonstrated in machine learning and data science, the concept is still a black box to many of our stakeholders. We thought we would take this opportunity to start unpacking some of that and communicating how we are able to do the things we do.

Let’s start with the basic question: What exactly is predictive intelligence? And how does that differ from traditional business intelligence?

AI is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals who have a cognitive ability to get better through experience. What this means in the world of FourKites’ real-time supply chain visibility is to learn that mechanical breakdowns or busy rest areas will result in longer transit times. This may seem obvious, but when you are looking across 100 countries, the number of these learnings quickly outgrows what a human can keep track of, e.g. idiosyncrasies of hours of service (HOS) regulations, commercial traffic hours, warehouse congestion, individual driver, route or carrier learnings, and the list goes on.

In the landscape of data science applications, predictive analytics is a special class of algorithms that tell you what will happen vs. other techniques that may focus on explaining why something that happened in the past occurred.

Predictive analytics at FourKites is primarily about solving customer issues in anticipation of a problem. For instance, a basic use case might be that unusual traffic causes a change in the ETA (estimated time of arrival) which could cause a shipment to miss its anticipated delivery window. You might be thinking, “That’s not data science!” – and you would be right if the mechanism for doing this work was simply rule-based.

To read the full article and learnt the top five ways to predict accurate shipment ETAs, click HERE.

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