Increasing fleet reliability: Real-world recommendations for preventative maintenance
Fast facts
- Customer
Middle-mile logistics partner for a multinational e-commerce company
- Business challenge
Faced with rising maintenance costs and inefficient scheduling as its vehicle warranties expired, our client needed a tailored preventative maintenance plan to reduce costs, extend vehicle lifespans, and improve reliability.
- Geographical coverage
United States
- Solution implemented
MSX developed a data-driven risk modeling solution, using warranty data and survival analysis to create a preventative maintenance schedule. This approach aimed to reduce repair costs, extend vehicle lifespans, and improve reliability, potentially avoiding up to an additional $13 million in spend annually.
Addressing rising costs and inefficiency
The logistics partner for a multinational e-commerce company faced two key challenges as their fleet of vehicles approached the five-year mark – when warranties on many of their vehicles would expire:
In short, our logistics client needed a solution that could quantify the financial risk associated with maintaining its fleet post-warranty and propose a preventative maintenance plan that would extend vehicle lifespans, reduce downtime, and improve reliability, all while being cost-effective.
Insights for data-driven maintenance
We approached the client’s logistics team with a data-driven, advanced risk modeling solution that leveraged our expertise in the automotive sector and our ability to work with OEMs to obtain critical warranty data. The solution combined statistical techniques – specifically a reliability analysis – with practical recommendations for vehicle maintenance.
Step 1: Data collection
Our first step was to gather warranty data from the OEMs, which would allow us to understand the failure modes and maintenance requirements for each vehicle model in their fleet. We successfully negotiated with one OEM to provide warranty data not only for our client’s fleet vehicles but also for all similar model vehicles in its fleet, enriching our analysis. In the case of the second OEM, while direct data sharing wasn’t feasible, we leveraged a workaround by scraping the warranty data from its online vehicle information site, which allowed us to obtain the necessary details for its vehicles.
Step 2: Risk modeling
Using the warranty and maintenance data, we created a reliability model for each major vehicle component - such as the transmission, drivetrain, and electrical systems. We employed survival analysis to model the risk of failure for each component over time, allowing us to quantify the probability of failures at various mileage points. This modeling enabled us to predict how the risk of failure would evolve as vehicles aged, particularly after the expiration of their warranties.
Step 3: Preventative maintenance strategy
Once we had a clear understanding of the risks associated with each vehicle and component, we moved on to crafting a detailed preventative maintenance schedule. This schedule was designed to reduce the likelihood of costly, unplanned repairs by performing timely, cost-effective maintenance activities before failures occurred. Our approach considered the maintenance needs of 12-15 critical vehicle components for each OEM, proposing specific interventions (such as replacing batteries or servicing drivetrains) at optimized intervals.
Step 4: Financial analysis and impact simulation
Using the preventative maintenance schedule, we ran simulations to estimate the financial impact of the proposed activities. The goal was to quantify how much our client could save in terms of reduced downtime and repair costs. For example, we found that by implementing our preventative maintenance strategy for the drivetrain and transmission systems, they could avoid up to an additional $13 million in spend annually.
The key to our solution was that it not only helped our client optimize maintenance costs but also allowed the organization to plan for parts and services more effectively. By reducing unplanned downtime and minimizing the need for emergency repairs, it could maintain fleet reliability while cutting costs.
Benefits of a preventative maintenance model
While the full preventative maintenance strategy has not yet been implemented across the client’s entire fleet, the results from our model have already provided significant insights:
The foundation for long-term savings
As the program continues to evolve, MSX is exploring automation and AI to further streamline rule-based entries and validations. Early estimates suggest that these innovations could significantly reduce workloads for high-volume, low-complexity claims, freeing up dealer teams to focus on higher-value activities and further improving cycle times.
This project exemplifies the power of data-driven decision-making in fleet management. By combining advanced risk modeling and preventative maintenance strategies, we provided this organization with the tools to optimize its fleet maintenance, reduce operational costs, and enhance vehicle reliability. Although the full operational results are yet to be realized, the financial and strategic framework we’ve put in place gives the business a clear path to achieving long-term savings and efficiency gains.
