A recent project of mine in the New York City metro area highlighted a cost driver with an unexpected material impact on the accuracy of private fleet modeling: tolls. It’s a factor that’s frequently overlooked for strategic network planning, but it can and does make a difference in certain situations.
My colleague, Jeff Zoroya, recently blogged about the importance of accurately modeling private fleet costs in a supply chain network model, considering factors such as distance, time, utilization, and the number of stops per trip. Like the modeling challenges he discussed, the topic of tolls can get complicated if you have a private or dedicated fleet. When using common carriers for point-to-point moves, the cost of tolls is already built into the transportation rates so you don’t need to think about it. But when modeling a private fleet, the impact of tolls needs to be considered in the increasing number of metropolitan areas with toll roads.
We don’t often think of tolls as being a significant component of cost when it comes to operating a private fleet, but in the New York City metro area, the tolls to cross bridges or tunnels or even drive on the New Jersey Turnpike can add up to 10% of total fleet operating costs, almost on a par with fuel costs which often get so much attention! If you want to cross from New Jersey to New York or vice-versa, you’re going to pay a hefty bridge or tunnel toll. Likewise, if you want to get on or off Long Island, you’re going to pay a similarly hefty toll. And if you want to travel north or south in New Jersey, you’re going to pay a NJ Turnpike toll based on your entry and exit point.
Logistics information systems that track delivery activity for private fleets will often capture trip information such as the number of stops, the quantity delivered at each stop, and the time spent at each stop of the delivery. They do not track costs associated with each trip such as total fuel consumed or tolls paid. Fleet costs need to be gleaned from the general ledger data and then allocated to the delivery activities in an appropriate manner.
![[Figure 1] Tolls in the NYC metro area are expensive](http://www.chainalytics.com/wp-content/uploads/2019/08/Blog-Tolls-Private-Fleets-Charlie-Marge-2.png)
In the case of tolls, it would be grossly inaccurate to allocate these costs across all deliveries because only certain origin-destination pairs involve crossing bridges or tunnels or traveling down a toll road. And in the case of multi-stop routes, those toll costs would need to be appropriately shared among the destinations on that route.
Even understanding the particular route a driver would take from a warehouse to a customer can affect the tolls that are incurred. Take the example of traveling between Central New Jersey and Long Island. One might assume a seemingly more direct route through Staten Island over the Verrazano Narrows Bridge. However, for the operator of a private fleet, safety concerns are paramount and the preferred route could be to take the much safer NJ Turnpike toll road and cross over the George Washington Bridge rather than wrestle with the narrow and not well-maintained Brooklyn-Queens Expressway.
Once private fleet tolls are properly reflected in a supply chain network model, they can have a material impact on the choice of distribution center.
Once private fleet tolls are properly reflected in a supply chain network model, they can have a material impact on the choice of distribution center for particular customers in situations like this. At Chainalytics, our culture of modeling ensures that we consider all relevant factors in the analysis to help our clients make investment-grade network decisions. If you’d like to learn more about our proven network modeling approach, reach out for more information or connect with me on LinkedIn to get the conversation started.
Charlie Marge is a supply chain modeling and optimization expert with 25+ years of experience in both consulting and software. At Chainalytics, Charlie leads projects spanning strategic supply chain network design to operational advanced planning & scheduling. He has worked in all major industries with a particular focus in Food, Beverage, and CPG.
