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Increasing fleet reliability

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:

During the first few years of vehicle ownership, our client’s costs were manageable, as major repairs were covered by manufacturer warranties. However, after four or five years, those warranties would no longer cover significant repairs, exposing them to potentially millions of dollars in additional maintenance expenses. With vehicles from several OEMs, they faced further complications. Maintenance and failure patterns varied across these OEMs, requiring tailored strategies for each.
The existing maintenance schedule was insufficient for their needs. The schedule did not account for the rigorous demands placed on the fleet, leading to inefficiencies in downtime and suboptimal financial performance. In addition, our client lacked clear insights into the financial liabilities of extending vehicle lifespans and whether a more robust preventative maintenance program could mitigate risks and reduce costs.

In short, our logistics client needed a solution that could quantify the financial risk associated with maintaining their fleet post-warranty and propose a preventative maintenance plan that would extend vehicle lifespans, reduce downtime, and improve reliability, all while being cost-effective.

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:

During the first few years of vehicle ownership, our client’s costs were manageable, as major repairs were covered by manufacturer warranties. However, after four or five years, those warranties would no longer cover significant repairs, exposing them to potentially millions of dollars in additional maintenance expenses. With vehicles from several OEMs, they faced further complications. Maintenance and failure patterns varied across these OEMs, requiring tailored strategies for each.
The existing maintenance schedule was insufficient for their needs. The schedule did not account for the rigorous demands placed on the fleet, leading to inefficiencies in downtime and suboptimal financial performance. In addition, our client lacked clear insights into the financial liabilities of extending vehicle lifespans and whether a more robust preventative maintenance program could mitigate risks and reduce costs.

In short, our logistics client needed a solution that could quantify the financial risk associated with maintaining their 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 their 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 their online vehicle information site, which allowed us to obtain the necessary details for their 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 them to plan for parts and services more effectively. By reducing unplanned downtime and minimizing the need for emergency repairs, they could maintain fleet reliability while cutting costs.

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 their 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 their online vehicle information site, which allowed us to obtain the necessary details for their 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 them to plan for parts and services more effectively. By reducing unplanned downtime and minimizing the need for emergency repairs, they 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 their entire fleet, the results from our model have already provided significant insights:

Cost savings

By following our recommendations, they could avoid up to an estimated $13 million in spend annually just by reducing the frequency and cost of major repairs in critical vehicle components.

Financial risk mitigation

Our calculations indicated that without preventative measures, our client would face a 227% increase in repair costs once warranties expired. While this increase cannot be substantially reduced, our focus is on making this out-of-warranty jump as small as possible. Our preventative maintenance schedule provided a clear path to reducing these future liabilities.

Improved reliability

The reliability modeling showed that implementing our maintenance schedule would significantly improve vehicle reliability, leading to less downtime and more efficient operations.

Benefits of a preventative maintenance model

While the full preventative maintenance strategy has not yet been implemented across their entire fleet, the results from our model have already provided significant insights:

Financial risk mitigation

Our calculations indicated that without preventative measures, our client would face a 227% increase in repair costs once warranties expired. While this increase cannot be substantially reduced, our focus is on making this out-of-warranty jump as small as possible. Our preventative maintenance schedule provided a clear path to reducing these future liabilities.

Cost savings

By following our recommendations, they could avoid up to an estimated $13 million in spend annually just by reducing the frequency and cost of major repairs in critical vehicle components.

Cost savings

By following our recommendations, they could avoid up to an estimated $13 million in spend annually just by reducing the frequency and cost of major repairs in critical vehicle components.

The foundation for long-term savings

The project has provided our client with a clear understanding of future maintenance costs and the advantages of a structured preventative maintenance plan. To see the full benefits, they need to implement and operationalize the recommended maintenance activities. The methodology and tools we provided give them a solid foundation to continue optimizing their fleet management strategy.

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 their 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 them a clear path to achieving longterm savings and efficiency gains.

The foundation for long-term savings

The project has provided our client with a clear understanding of future maintenance costs and the advantages of a structured preventative maintenance plan. To see the full benefits, they need to implement and operationalize the recommended maintenance activities. The methodology and tools we provided give them a solid foundation to continue optimizing their fleet management strategy.

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 their 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 them a clear path to achieving longterm savings and efficiency gains.

Fast facts

Customer: Middle-mile logistics partner for a multinational e-commerce company

Business Opportunity: Challenge: Faced with rising maintenance costs and inefficient scheduling as their vehicle warranties expired, our client needed a tailored preventative maintenance plan to reduce costs, extend vehicle lifespans, and improve reliability.

Geographical coverage: United States

Solutions 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. knowledge.

Fast facts

Customer: Middle-mile logistics partner for a multinational e-commerce company

Business Opportunity: Challenge: Faced with rising maintenance costs and inefficient scheduling as their vehicle warranties expired, our client needed a tailored preventative maintenance plan to reduce costs, extend vehicle lifespans, and improve reliability.

Geographical coverage: United States

Solutions 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. knowledge.