Chainalytics helped a 3PL optimize its transportation network by evaluating the optimal fleet size with existing weight tonnage. The result? Improved utilization today and a five-year roadmap for anticipated weight tonnage increases in the future.
Our customer was a third-party logistics provider operating their own hubs, distribution centers, and a fleet of 350 trucks across India. They run an interleaved transportation network where a truck is loaded and starts from the origin hub, stops at branches on the way to drop/pick up shipments, and reaches the destination hub for unloading. During the truck’s return journey back to the origin hub, the roles of the origin and destination hubs reverse.
Specifically, executives wanted to understand the following:
- Is the current fleet size optimal?
- Is the distribution of trucks across different hubs optimal?
- Are we using suitable capacity trucks on routes?
- How many trucks should we add to handle growth over the next five years?
Collecting data and identifying opportunities for improvement
The Chainalytics team met with the logistics operations team to understand the current network of hubs, branches, and routes. The customer and our team chose the actual shipment data for one month to build the baseline model. They operated multiple transportation assets – private fleet, dedicated fleet, and for-hire trucks and also used numerous truck types – from vans to larger capacity vehicles. The former is used for local delivery and larger capacity vehicles for long-haul routes.
Truck utilization was enhanced from the existing 65% to 88%, an improvement of 35%, and reduced the unit cost per unit weight shipped, which is an important metric.
One initial observation was that the truck volume utilization on most routes was low and there were instances of routes where higher capacity trucks were used, but a lower capacity truck on those routes would have been more efficient. They also had a higher proportion of trucks at the main hub, while the number of loads originating from the main hub was not exceptionally high. There was an opportunity to reposition some of the trucks from the main hub to other hub locations.
Building a model with real-world constraints
We chose Blue Yonder Transportation Modeler software to build the model. The different components modeled include the hubs and branch network and fleet details such as truck types, capacity, business rules, and input costs.
The customer stipulated some business constraints while building the model. They did not want the ratio of dedicated fleet to private fleet to exceed 15%. A vehicle allocated to a hub should always return to the hub. The hub and branches on an existing route would remain the same and shipments destined to a specific branch would be part of an existing route. The routes serviced by for-hire trucks had to be serviced by for-hire trucks and could not shift to fleet.
A strategy for improving utilization by 35%
Chainalytics worked with key stakeholders and the operations team to develop three key unconstrained scenarios with different weight tonnages – current, growth projection for the next two years, and growth projection for the next five years. All scenarios focused on the optimal selection of trucks of different capacities on routes and optimal allocation of trucks to hubs.
The product shipped tended to “cube out.” Hence the cubic volume was the critical measure to optimize. The modeling recommended improved truck cubic utilization. Truck utilization was enhanced from the existing 65% to 88%, an improvement of 35%. The model recommended the optimal equipment usage on routes while ensuring allocation of larger-size trucks as the weight tonnage increased. This specific optimization reduced the unit cost per unit weight shipped, which is an important metric. The truck profile showed a decrease in the required number of 10-ton trucks and an increase in 16-ton trucks with increased weight tonnage.
A future roadmap with better utilization and lower costs
With Chainalytics’ help, the customer can chart the future roadmap that included investing in higher capacity 16-ton trucks, reallocation of trucks across hubs, and retiring trucks near end of life. The benefits realized included improving truck utilization by selecting the right weight capacity truck on each route, thereby lowering the unit cost per unit weight.
Optimizing your transportation network is always recommended. No matter the constraints on your network, hidden efficiencies are there, waiting to be found. Reach out to us and see how Chainalytics can assist in exposing savings and moving goods faster. Using one-of-a-kind tools and approaches like digital assets and managed analytics services, we consistently deliver actionable insights and measurable outcomes to our clients.
