After being acquired by a major private equity firm, the ninth largest private vehicle fleet in the country was asked to justify their equipment utilization and maintenance costs—and found they had no useable data. This became part of a major initiative to achieve over $30 million in operational improvements.
POWERING MAXIMUM FLEET UTILIZATION WITH FACT-BASED STRATEGIES
The fleet, which served both commercial and residential customers, was comprised of three independent business units and a total of 600 US branches. A central fleet management team supported their 17,000 light and medium-duty vehicles, including fleet engineering, vehicle sourcing, and lease management. Despite an active effort on the part of the Vice President of Fleet to identify and implement best practices throughout the company, operational performance remained inconsistent because of the decision making freedom wielded by branch level managers. Even the use of one maintenance information system did not drive the desired process standardization or provide centralized data collection across all locations.
The Vice President of Fleet looked to Tenzing to collect data and build the analytical models required to ensure future equipment and maintenance decisions would be based on fact.
The fleet management team approached the challenge in three distinct phases, starting each by identifying and gathering the data required for advanced analysis.
Phase 1: Optimizing Vehicle Lifecycles
The team developed a lifecycle model for each vehicle type and concluded that extending vehicle life by up to two years was optimal and achievable. Deferring new vehicle purchases would result in over $20 million in cash flow improvement over the next several years. The decision to extend vehicle life would impact repair costs and equipment requirements, so the team turned next to current maintenance practices.
Phase 2: Managing Maintenance Structure
Although the central team had established service agreements to allow branches to easily contract for outsourced maintenance, only two-thirds did so. Each branch was convinced that the strategy they had chosen was best, but they could not justify their choice with facts. The team modeled technician productivity and vehicle counts to determine which branches needed in-house maintenance staff.
Phase 3: Tying Vehicle Utilization to Customer Density
The business units added equipment as a matter of routine, not in response to increased demand. The team analyzed branch fuel consumption by vehicle type as a proxy for mileage data, which had not been consistently recorded. After normalizing the data for “customer density” (urban/rural), the team analyzed variations in vehicle count and utilization. They identified 1,000 surplus vehicles and put a disposal/redeployment plan in place.
“We knew these opportunities existed but we were unable to build an analytical case for improvement.” – Director of Fleet Maintenance
The equipment lifecycle extension and deployment improvements drove over $26 million in net cash flow improvements over several years. Improvements in maintenance service and parts yielded another $6 million in annual “hard cost” savings. The Vice President of Fleet and his business unit colleagues gained a firm grasp of their fleet operations and can use their increased visibility and analytical models to drive ongoing improvements in cost and efficiency. Business unit leaders achieved a new level of collaboration and now have a comprehensive, enterprise-wide approach to managing their most critical assets. As they put it, the project with Tenzing was the long-overdue “catalyst for change” that enabled them to achieve their goals.
Gaining Critical Branch-level Support
Gaining the support of stakeholders across the business units required sound analysis and fact-based strategies. In most cases, data did not exist at the branch level to overcome the massive gaps in operational insight associated with each opportunity. Fortunately, branch-level vehicle types and counts were available and, with Tenzing’s assistance, the team was able to conduct a comparative analysis across branch locations and business units. This revealed previously unrecognized variations in branch level costs, suggesting that the total savings potential was even higher than expected. Once stakeholder buy-in was secured, the company was ready to move to the next phase: determining how to execute and measure a range of operational improvements.