
United Parcel Service is working on an ambitious analytics and machine learning project to gather and consolidate data from various applications within the company’s logistics network to better predict package flow, volume and delivery status, writes The Wall Street Journal (July 17, 2018). The predictive analytics tool will gather and analyze more than 1 billion data points per day at full-scale, including data about package weight, shape and size, as well as forecast, capacity and customer data. This allows UPS to know exactly what’s going where, and when it’s going to arrive, much more accurately than before.
The project is an example of how UPS is upgrading technology systems as it faces heavy competition from rivals including FedEx and Amazon as well as ever-growing e-commerce shopping demands. The company still relies on some outdated equipment and manual processes, but it’s opening new automated facilities and working on technology upgrades, such as this one, as part of a $20 billion capital spending plan.
It will give staff more accurate forecasts about the package volume that needs to be processed at UPS facilities on any given day. That will give employees enough lead time to determine whether they need more resources at package and sorting facilities in the event of a higher-volume day. Predictive analytics also could help eliminate bottlenecks in the supply chain because of unforeseen weather or emergency situations. Knowing how upcoming inclement weather will impact the supply chain days in advance will result in better planning.
Developing the tool was an ambitious feat because of how many hundreds of millions of data points needed to be consolidated into one single platform. Until now, forecast, capacity, customer and package data was housed in different applications. The tool is expected to be available to UPS employees by the end of the year via a smartphone, desktop and tablet application.
Classroom discussion questions:
- Why is this project so important to UPS?
- Why is the forecasting system so complex?











