Artificial intelligence and machine learning are dominating news headlines and events across a wide range of industries. As business segments begin to adopt these technologies in meaningful ways, the $4 trillion logistics industry still runs in a largely analog fashion. This is surprising given the heavily data-driven nature of logistics.
The industry is ready for digital transformation. There’s a clear need for increased efficiency. A typical supply chain transaction involves five to 25 different parties and handoffs. A staggering amount of data and potential outcomes are introduced in this process – far too many for humans to optimize without advanced technology. With the overwhelming complexity, supply chain leaders are increasingly interested in how data intelligence can simplify and supplement their predictions and business decisions.
The current approach & tools can’t keep up
According to the IHS Global Insight World Trade Services, many players in today’s global supply chain are using technology created decades ago – or, in some cases, collecting data completely offline. Because these tools aren’t sophisticated and haven’t been updated for today’s supply chain, data ends up inaccessible and inaccurate which leads to being reactive and likely wrong. We all know what that means – more inventory, lower margins and less than happy customers.
Big isn’t better. It is time to get smarter and deploy data science. Here’s how predictive logistics can help.
Most supply chain decisions are already based on predictions (defined as the best information we have, along with years of experience and tribal knowledge). With the advancement of machine learning and artificial intelligence, it is in the industry’s best interest to embrace this technology. The way predictive logistics works is by first pooling and then cleaning data that logistics decision makers have at their disposal. AI and machine learning are then applied to interpret and understand the vast quantities of data in a way the human mind cannot. Ultimately, this process produces an accurate prediction of what logistics leaders can expect to see happen in the global supply chain weeks in advance. This technology is useful for carriers, forwarders, terminals, shippers and 3/4PLs, because it enables them to plan further in advance and far more accurately.
Supply chain leaders need to embrace this new technology and start by simply reexamining their data through the lens of AI and machine learning. Then, they should think about the corresponding change to your operating model and decision making process. Being proactive and more accurate with the help of intelligent, predictive data will drive less inventory, lower cost, and greater revenue.