FedEx strives to monetize its Big Data
A network effects business model allows a company to gain more value as more companies use its products or services. The value of the offer increases rapidly because each additional user increases the value of the network. Today, we mainly think of digital companies like Google
But it turns out that big logistics companies are also generating big data, and they’re also working to monetize that data. A concrete example is FedEx
Sriram Krishnasamy, CEO of FedEx Dataworks, said data is much richer than just scanned data. FedEx augments this data with weather and traffic data. FedEx does the obvious with this data; they use predictive analytics to improve the flow of goods through their network and become a more reliable carrier. “The information is just as important as the package,” Krishnasamy said. “We’ve always been good at capturing data. Now it’s in a big data infrastructure” and they can find connections between data that may seem unconnected.
This predictive intelligence is especially important for high-value packages that need to be delivered quickly. A good example is the FedEx Surround solution. FedEx Surround is based on proactive monitoring and intervention controls on a delivery network. FedEx Surround predicts shipping success by combining information about a package with external data such as weather to manage risks surrounding the shipping process. Shipping data includes scans, shipping route, means of transportation, and SenseAware ID, a small Bluetooth tracking sensor that can be placed on a package. This allows a high-end service team to monitor a package’s location and take action if it looks like the package won’t arrive on time. For example, an intervention may consist of a team picking up at-risk packages from a sorting center and depositing them in a van which will leave immediately. But, from origin to destination, says FedEx, there are multiple points of intervention.
The first use of FedEx Surround was tracking critical and sensitive shipments of COVID-19 vaccines as they moved from manufacturers and distributors to medical centers across the United States. In the first year of distributing the COVID-19 vaccine, FedEx delivered approximately 300 million doses across the United States with the help of predictive analytics tools. Vaccine shipments transit through the FedEx network with an average transit time of less than 20 hours. Tracking devices, combined with White Globe teams monitoring shipments, enabled proactive interventions that led to a 99.91 success rate for delivery of vaccines on the day of engagement.
But FedEx Dataworks seeks to do more than improve its ability to be a reliable partner. And they’re looking to leverage their customers’ data, not just their own. Currently they have a proof of concept project using data from one of their biggest customers. Customer data is ingested through an API framework and then combined with data from the FedEx network.
In one use case, customer data is used to help forecast demand and returns in a joint planning process. The better the forecast for demand and returns, the more the customer can save on shipping by injecting the package at the right time and at the right place in the FedEx delivery network. For example, seven-day deliveries are cheaper than express deliveries. The correct injection point is not necessarily the closest FedEx sorting center to the customer. Traffic and congestion may suggest that injecting parcels into a more distant sorting center will actually improve the chances of on-time delivery.
Over time, machine learning will be leveraged to improve predictions. FedEx will also learn from its failures. To the extent that joint forecasting allows inventory to be positioned in the right place and shipped using lower-cost services, FedEx and its customers stand to benefit. The customer benefits by paying for the service that best meets their needs. FedEx benefits from stronger customer relationships.
There are other opportunities to monetize data. FedEx ships to 220 countries. Commercial documentation is required for international shipments. Properly classifying goods for duties and ensuring goods are not shipped to bad actors (filtering denied parties) may seem trivial. It’s devilishly tricky. Machine learning based on big data can help FedEx serve its customers better and better.
Another opportunity is fraud prevention. FedEx has validated addresses in their databases. This could create an opportunity to expose fraudulent transactions.
In conclusion, the business benefits of the network effect are not exclusive to large digital companies. Large carriers have the opportunity not only to improve their service, but also to monetize this data in various ways.