Ryder is an American transportation and logistics company that operates both in North America and the United Kingdom. It is especially known for its fleet of commercial rental trucks. The company has roughly 6.7 billion in freight under management based on supporting 42 clients and optimizing 14 million loads per year.
Doing managed trans well requires the use of a robust transportation management system. Ryder uses JDA. But their TMS (transportation management system) is several versions behind the current version. They are looking to upgrade the solution, but in the meantime, the RPA provides significant time savings for the planners and analysts that use this system.
Ryder selected a solution from Kofax. The system was easy enough to use by business users, rather than developers, who learned to create the rules that automate the processes. There is a design studio where the bots are created. Many of the bots have a logic that says, “if you see this, do this“. The bots reside in a management console. Users can create a schedule to trigger bot tasks. Or, bots can be manually initiated.
Ryder started the implementation in November 2017 and launched their first bot in January 2018 which was quicker than upgrading their TMS. In addition, only two people were dedicated to the implementation. If necessary, new bots can be built in about two weeks.
There are several places that RPA is used in the planning cycle, but one example is for automating client-specific business rules not contained in the optimization engine. For example, a client-specific rule might be “when shipping to this client, do not use the standard carriers in the route guide but use these carriers that have higher insurance coverage“. Another example, a bot can examine the loads and determine which loads must ship sooner and must be optimized immediately, and which loads can be pulled out of the optimization queue for optimization at a later time. When you have more time to optimize a load, it increases the chances of saving money through load consolidation.
The RPA implementation has had good payback. They now have 133 bots running that execute 1300 daily tasks. The automation has allowed them to redeploy 6 transportation analysts. Optimization runs that use to take 1.5 hours can now be done in half an hour. And the accuracy is better because the bots do not make data entry mistakes.