Imagine you're in charge of maintenance for a fleet of trucks and you have a crystal ball. Instead of waiting for something to go wrong, you could foresee breakdowns and accidents. You could pull vehicles that are most at risk of component or system failure off the road, avoiding costly and dangerous on-the-road repairs.

This might sound like something from a fantasy story, but it's actually the promise of Al and predictive analytics. For the past few years, leaders in the industry have heard about how Al will completely reshape fleet maintenance. And there's evidence to back this up: in 2020, Transport Topics conducted a survey of large American fleets. They estimated that predictive analytics could reduce unplanned maintenance by 7-10%, and fuel costs by 2-3%.

But what will it take to get there? And what should fleet maintenance leaders do when they're ready to embrace this future, but keep hitting bumps in the road along the way?

The good news: even without top-notch data scientists and a cutting-edge tech stack, it's still possible to move the needle. Fleets can take steps that reduce downtime, save thousands per truck, and provide faster, more reliable service.

Maintenance strategy maturity

Despite all the noise about how predictive maintenance can be transformational, there is little said about what it takes to actually achieve this vision. In reality, very few fleets are ready for an advanced predictive maintenance strategy, and even fewer have actually done it.

Many fleets are still waiting until vehicles break down, or implementing rudimentary time or mileage-based rules that aren't responsive to changing conditions and actual usage. The ultimate goal is to have a predictive, or even a prescriptive maintenance strategy – to be proactive instead of reactive.

But because implementing a "smart" maintenance strategy like this requires operational changes (and sometimes radical ones), achieving it is often an uphill battle.

A step-by-step journey

Instead of jumping straight into things like advanced machine learning or data mining, most fleets are on a step-by-step, evolutionary journey to analytical maturity. Each stage offers its own opportunities, and comes with its own unique challenges:

Stage What it means Value it unlocks Roadblocks

Unified data foundation

Bring together disparate sources of data (e.g., maintenance plans, service and repair history, telematics) to create a complete picture of fleet vehicle health

A foundation for understanding fleet maintenance performance; setting the groundwork for advanced analytics

Engineering talent to stand up a data environment (warehouse, lake, or database), build a unified schema, maintain ETL pipelines, and create and maintain an up-to-date data dictionary for business and technical users


Use Business Intelligence (Bl) tools to run basic descriptive reporting (e.g., "how many brake repairs did we conduct over the past month? How is that trending month over month?")

Understand key trends, get an objective view on maintenance performance over time, form hypotheses about correlations and relationships in the data

Data trust and reliability, helping business users self-service despite data complexity

Statistical inference

Use statistical methods to ask and answer sophisticated questions (e.g. using repair data to optimize maintenance intervals)

Move past simple historical summaries to make maintenance recommendations based on data-driven rules and guidance

Expertise in statistical methods, married with domain-specific knowledge of fleets repairs

Machine learning at scale

Use machine learning models to generate maintenance alerts based on individual vehicle failure probability and optimize repair paths

Detect data relationships, patterns, and signals that go far beyond what any human could manually infer from the data

Machine learning talent to build, train, and maintain predictive models


This journey may look daunting. But in reality, it's a critical framework for fleets to evaluate where they are in the process-and identify the next best actions. Even fleets with more conventional maintenance strategies can start making incremental changes and seeing results.

For example, consider a fleet that has just assembled a data warehouse with repair and financial data, and has begun to run historical reports on component repair frequency and costs with Microsoft Power Bl. It might be tempting to think the next step is to jump straight to fancy Al-powered use cases based on telematics data.

But there's much more low-hanging fruit: this fleet would be able to significantly reduce downtime through the smart application of basic statistical analysis. For example, doing basic survival analysis for key components by vehicle type would enable the team to put in place data-driven, rules-based maintenance intervals.

The biggest challenge of all: operationalization

Every stage has unique challenges, but there's one obstacle that maintenance leaders should be mindful of solving at every stage of the journey: the challenge of operationalizing analytics. Gleaning insights from data is one thing, but actually using those insights to effect change at the organizational level and improve business performance is another.

Some questions to consider at each stage include:

  • How do we ensure that insights flow to the point of maintenance decision-making in a timely manner?
  • How do we make sure insights are presented in a way that makes sense to front-line employees such as drivers and technicians?
  • Will employees used to doing things a certain way adapt?
  • Are there warranty constraints or compliance issues that need to be considered before rolling out new insights?

Asking these questions at every stage is crucial to ensure that valuable insights aren't left on the shelf collecting dust.

The bottom line

Fleets have been hearing for years that the ultimate goal is to have an advanced predictive maintenance strategy, but this isn't something that can be achieved overnight. Even fleets that have the talent and data infrastructure to support such a strategy run into barriers when it comes to operationalizing their insights.

Instead of taking an all or nothing approach, fleets should look for the ways they can benefit from implementing analytics right now, even if it means taking small steps to get there.

About Viaduct

Viaduct's machine learning solutions allow fleets to manage, analyze, and utilize the data generated by their connected vehicles. With a platform powered by patented machine learning technology and data analyzed from over 1 million vehicles on the road, Viaduct is helping fleets overcome the biggest barriers to operationalizing machine learning at scale.

For more information, contact [email protected].