The Problem
The client operates a large fleet of high-fuel consumption cargo vehicles. While they have operated these vehicles for several years, a recent upgrade to a make and model making up >30% of their long-haul fleet provides significant payload capacity and fuel efficiency enhancements. With increasing fuel costs and sensitivities to carbon emissions, the client hypothesized that “a better way” exists to employ their fleet in their distribution network.
Task and Approach
The client asked for help looking at new ways to deploy the fleet to take advantage of the fleet upgrades.
Research I conducted through problem discovery sessions, client marketing presentations, and client stakeholder interviews provided me with a robust understanding of the client’s goals and constraints. I then conducted a deep quantitative analysis to assess the baseline or “business as usual” fleet deployment using metrics such as on time delivery (OTD), fuel consumption, and crew utilization as metrics of interest. This step included analysis of historical routes operated, amount of cargo delivered, and crew and equipment hours required over a 20-year period amongst all vehicles utilized in their operation.
With the baseline analysis complete, I then leveraged various techniques including simulation, cluster analysis, neural network modeling, and network optimization to develop a new fleet deployment strategy and a planning algorithm to help logistics planners take advantage of the new strategy. My analysis showed that a hub-and-spoke logistics model would yield significant throughput enhancements while saving >10% in fuel consumption for the same demand. I also provided a playbook that specified payload and vehicle assignment to the most common origin-destination pairs.
Results
The client adopted the recommended hub and spoke distribution model. As the business case was so compelling, and the new model satisfied the overarching constraints such as vehicle capability and crew duty times, the client was also willing to waiver additional non-safety constraints to enable the new model. The client also achieved the projected fuel savings and avoided additional equipment investments that would have been required without this new employment strategy.