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Transportation management systems have a proven ROI.
Primarily, a TMS can save companies money by lowering their freight spend. Mean freight savings of 8 percent occur after companies implement a TMS. But that does not mean that there is not room for improvement. I am excited about the promise of machine learning to allow a TMS to better handle competing objectives and discover non-obvious impacts on performance.
Few companies would buy a TMS if it would lead to declining service levels. A TMS 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 it is, but machine learning promises to allow us to go deeper, and to capture non-obvious trade-offs.
Let me give an example. TMC, division of C.H. Robinson, 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 percent 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.
But transportation management systems — particularly network-based solutions — are rich in data. Machine learning depends on big data sets. The problem above would be solved using machine learning. The system is provided with a variety of raw data but then also provided with a target. In this case, the system is asked to predict on-time deliveries (OTDs). How does OTD change based on multi-stops? Based on region? Based on real-time milestone data? And potentially based on lots of other data sets, even data sets that exist outside the TMS? Then, the algorithm attempts to understand how to match the input to the performance metric. And with machine learning, there is a feedback loop; the system continues to get smarter as time goes by.
It is not necessarily just freight costs and service levels that need to be traded off. There are any number of metrics that can be targeted for improvement. But the big data accumulated in TMS and complementary solutions can lead to a fuller understanding of how all sorts of policies and practices affect important transportation performance metrics. It is inevitable that TMS solutions will be built with this technology.
Steve Banker is vice president of supply chain services at ARC Advisory Group.
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