The just-completed expansion of the Panama Canal to support larger container vessels is an important and highly visible milestone in supply chain management. Delivering goods in roughly half the time that it would take by alternate ocean routes will save shipping costs – assuming the goods arrive in good condition. But what if the goods are damaged during loading, unloading or while at sea? What if the container never made it on the vessel? That’s where “in-transit visibility” plays an important role in supply chain management.
Businesses that ship or receive electronic products and components know all too well the hazards of the shipping process – they may incur shock through mishandling or accidents or be damaged by heat or humidity while in transit. Until recently, these were just the risks of doing business. But today, sophisticated sensor technology can mitigate those risks.
Now a sensor attached to the inside or outside of a shipping container, truck trailer or railcar can detect abnormalities and transmit them in real-time to the shipper and/or receiver. Using a single system of engagement, logistics managers can receive real-time alerts, exception warnings and event management notices. The manager can not only anticipate late deliveries but can be proactive in taking alternative measures to prevent a supply chain disruption.
For example, a sensor detects a shock to a load of electronic components in a railcar moving through a switchyard or a shipping container pitching on high seas days ahead of the scheduled delivery. The receiving manager knows from the sensor’s report there is a high likelihood that some or all of the components have been damaged and will likely not be deliverable to the customer. But instead of waiting for the shipment to arrive, the manager can order replacement components for expedited delivery, saving time and grief for himself and the customer.
Incidents like this, discovered in real-time, are captured and analyzed with data from many sources including: electronic data interchanges (EDI), sensors, global positioning systems (GPS), telematics, mobile, automatic identification systems (AIS), weather, traffic, social media and other sources to provide end-to-end in-transit inventory visibility. The data is correlated and analyzed from enterprise resource planning (ERP) systems, transportation management systems (TMS), carriers, third-party logistics (3PL) firms, suppliers and customers to ensure that shipments arrive on time. The solution displays an interactive global map of every route in the supply chain network—down to individual carriers, segments, and ports. Big data analytics and machine learning – generating models that predict arrival times and the likelihood of a supply chain disruption – are critical components of in-transit visibility.
Specialized machine learning algorithms, combined real-time shipment location data and other situational information enable customers to optimize their supply chain networks. As a result, logistics managers can develop a deep understanding of which shipments to expedite, which carriers have capacity, which ports appear to be congested, what time is best for clearing customs and what shipping lanes are best for road, ocean, and rail.
The benefits of employing an in-transit visibility system such as I’ve described can include a reduction in logistics and transportations costs; increased on-time deliveries; decreases in days of inventory on-hand; improved carrier performance; even possible reductions in insurance requirements and/or premiums. Companies seeking a competitive advantage need a solution that can integrate all forms of data as well as apply predictive and prescriptive analytics to transform the data into actionable information. That’s why an application for in-transit visibility is key to obtaining operational benefits quickly and affordably. With such a solution, supply chain leaders will always know the location, condition and estimated arrival times of their shipments and products in transit.
Jim Hayden is Senior Vice President of Data Science and Solutions, at Savi Technology, which is pioneering sensor analytics solutions that create operational intelligence from the Internet of Things (IoT). Applying big data technologies to machine-generated data, Savi solutions are trusted to run the world’s largest and most complex asset tracking and monitoring network, serving the U.S. Department of Defense, Allied military and more than 1,000 commercial companies around the world. Hayden has 20 years’ experience conducting award-winning work on the practical application of analytics methods and technologies including knowledge discovery, data mining and predictive analytics. Before joining Savi, Hayden was Vice President, Analytics at TEOCO, a leading provider of analytics solutions to communications service providers worldwide. He also served as Vice President and Chief Strategy Officer at Mantas, a global analytics leader in the Compliance and Anti-Money Laundering software market.