Real-Time Shipping Visibility, Thanks to Predictive Analytics

package delivery cat on porch feature

Real-time shipping visibility can keep even the finickiest of customers happy (credit: Sticker Mule on Unsplash)

The numbers are in, and results have confirmed a well-known hypothesis: shippers and consumers appreciate real-time shipping visibility of packages in transit, with a truck on a map. They’re almost hypnotized while observing it travel from stop to stop, eventually landing at their loading dock or front door.

A recent study from Verte proved that 91% of consumers are actively tracking their packages. Another study showed that nearly 60% of shippers consider real-time shipping visibility a prerequisite for carrier selection. But the biggest challenge is determining the ultimate value it’s going to provide. Is it just a real-time visibility tool, or does it positively impact key service metrics, inventory controls and revenue?

The buyer finds power in the ability to view their product’s journey, and supply chains must take into consideration the growing demand for frequent updates. If businesses rely on making the customer feel in control, real-time shipping visibility and tracking is certainly an answer, even if that value is hard to quantify.

Eighty-two percent of Americans have recently expressed their high expectations for accurate, real-time tracking and delivery information for their packages, Verte found. Since the current state of our industry depends on end-to-end supply chain visibility, how can we make real-time tracking precise while delivering estimated shipping times that don’t disappoint? The answer may be in varying forms of analytics trends.

Forms of Shipping Analytics to Utilize

There’s consistent growth in shipper and consumer demand. The reasoning behind this is simple: accuracy.

It’s difficult to fully approximate the time a package will take to arrive when shipping is subject to a multitude of factors outside our control. These variables can include extreme weather, driver shortages, holidays and even customs discrepancies. Promises of next-day arrival have raised consumer expectations, leaving many frustrated when they see that one or more of these factors have left their shipment delayed.

They reach out to customer service and demand an explanation, tying up the phone lines and staff of a shipper, carrier or 3PL.

These complications have left logistics companies to evolve existing technology like predictive analytics for a better view into their supply chain. A solid transportation management system (TMS) can already give great insight into the how and why of supply chain delays, but this is only after the fact.

What if you could know beforehand based on previous patterns, and implement this into the shipping estimate given to the customer? Let’s explore the different types of analytics and see how they could modify the future of supply chain management.

Predictive Analytics

This is a fairly simple concept: by compiling data about the past, we can understand patterns that will help us predict future trends. This method of analytics can be seen in retail, where businesses forecast customer behavior and purchasing habits based on historical data. In a similar way, predictive analytics can directly impact logistics management to help make smarter decisions based on extensive data observation.

Diagnostic Analytics

This offers visibility into the positives and negatives that can be found within the data. Once this information is combed through and the supply chain issues are identified, they’ll typically become a part of predictive analytics to prevent mishaps or unfortunate outcomes.

Descriptive Analytics

In the case of a supply chain, this involves gathering and examining data to identify trends, patterns and the often-complex relationship between uncontrollable variables. This becomes the foundation for predictive analytics once it’s compiled. Logistics managers can identify when they might need to adjust their estimated delivery times based on prior data.

Prescriptive Analytics

This uses advanced algorithms to suggest the best course of action in optimizing supply chain operations. It considers both historical and real-time data to recommend precise actions that can be taken to reduce cost and improve efficiency. Having decades of data readily available from multiple sources (transportation schedules, customer demand, inventory levels, etc.) empowers supply chain managers to determine future patterns.

Better Technology Improving Visibility

Shippers and consumers value transparency and desire understanding when their freight and orders are delayed. What if they were notified that their package could be delayed before it actually was? With advanced analytics tactics working together, this possibility is closer than ever. Identifying potential disruptions and optimizing logistics to get the most accurate shipping estimates could save the industry substantial amounts of time and money.

The freight industry will always be at the forefront of fine-tuning technology to gain flexibility and customer loyalty. As consumer demand increases along with the need for real-time shipping visibility, large amounts of data will be necessary to keep up with rapid changes.

These forms of analytics have the potential to revolutionize supply chains by offering great insights into the entire process. Once we can utilize them to their full potential and predict currently unpredictable factors, customer satisfaction is enhanced and inefficiencies we may not know are there can be eliminated.

Christina Ryan is EVP of Managed Services for Redwood Logistics