Transportation Management And The Promise Of Machine Learning Posted on : Dec 11 - 2018

Transportation management systems have a proven ROI. Primarily, a TMS can save companies money by lowering their freight spend. An ARC Advisory Group strategic report on the ROI of TMS found that respondents indicated freight savings of approximately 8% with the use of a TMS application. But that does not mean that there is not room for improvement. ARC is excited about the promise of machine learning to allow a TMS to better handle competing objectives and discover nonobvious impacts on performance.

The primary reason companies buy a transportation management system is for freight savings. These freight savings can be attributed to simulation and network design, load consolidation and lower cost mode selections, and multi-stop route optimization.

But few companies would buy a TMS if it would lead to declining service levels. A transportation management system maintains the service levels by understanding the origin to destination lead times and using that as a constraint during the optimization run. There are also analytics associated with the system. For example, a shipper can analyze which carriers are too often late, and which lanes and destinations often receive late shipments. Consequently, it is not surprising that most companies using a TMS maintain or improve their service levels.

This sounds complex and sophisticated, and it is, but machine learning promises to allow us to go deeper, and to capture nonobvious tradeoffs.

Let me give an example. TMC, a division of C.H. Robinson that provides managed transportation services, published a white paper called Multi-stop Trucking: How It Affects Load Acceptance and Pricing. What it shows is that for multi-stop truckloads, every additional stop lowers the on-time delivery level.

"On single-stop truckload shipments, 80 percent of loads that picked up late still delivered on time. Multi-stop loads are different... The more stops there are, the worse the on-time delivery percentage if one of the early pickups is delayed or late... Trucks that picked up late on a three-stop load, for example, averaged on time delivery only 71 percent of the time."

Existing TMS solutions will calculate the many situations where multi-stop loads save money. The TMS understands the lead times. It assumes the lead times will be adhered to and the loads will be delivered on time. But the transportation management system does not show in-line analytics to a planner that say, for example, "if you go forward with this shipment, there is only an X% chance the last customer on the route will receive their load on time." Existing TMS solutions are just not built in a way where these kinds of relationships can be discovered and easily acted upon. View More