Predictive Maintenance: How ‘big data’ is powering the future of fleet efficiency
Smarter, more accurate data solutions are helping fleets predict their vehicles’ maintenance needs ahead of time - reducing the risk of downtime, and giving them more control over repair costs.
But how exactly do these ‘predictive maintenance’ strategies work, and what goes into making them a reality across large, complex fleets of vehicles?
For over 20 years, sopp+sopp have worked with some of the UK’s best-known fleets to deliver efficiency-focused, technology-driven accident & repair management solutions, helping them keep their vehicles moving, and eliminate unnecessary costs & downtime proactively.
Here, we explore how predictive maintenance strategies work, and how they’re helping fleets maximise vehicle uptime, control repair costs, and inform more accurate total cost of ownership calculations.
What is ‘predictive maintenance’ in fleet?
Predictive fleet maintenance is the practice of harnessing real-time vehicle data, historic repair analytics, and AI/machine learning models to monitor vehicles’ conditions, and predict when mechanical issues might occur down the line.
This enables fleets to intervene proactively before issues lead to vehicle downtime, or more costly repairs, enhancing operational efficiency and reducing maintenance costs.
What data is needed to drive predictive fleet maintenance?
The data used to fuel predictive maintenance models can vary, depending on the level and granularity of vehicle data the fleet has available. Many fleets commonly utilise:
Vehicle usage data from telematics systems
Historical repair, maintenance, and servicing data
Damage inspection & walkaround reports
Visual data from vehicle scans & underbody inspections
This data is then fed into an intelligent machine learning algorithm, or an AI tool, which looks for correlations across all these data points to identify trends, potential risks, and future vehicle requirements.
How can fleets achieve predictive maintenance models?
Achieving predictive fleet maintenance requires a strong approach to sourcing data from across the vehicle lifecycle, and unifying it appropriately for centralised analysis.
Here are some of the key things fleets need to achieve in order to fuel predictive vehicle strategies:
Getting the data & bringing it together
High-quality vehicle usage & repair data is crucial for effectively predicting maintenance needs. Fleets need to collect and unify in-depth vehicle data from telematics, vehicle sensors, daily walkaround checks, visual assessments/scans, and historic repair & servicing activity in one place to form the basis of their approach.
Before embarking on a predictive maintenance journey, it’s therefore essential that fleets have the processes, technology, and integrations in place across their supply chain to receive this data regularly, accurately, and centrally.
This may include using visual inspection apps for drivers, remote OBD scanners, in-vehicle telematics, or API integrations with repair and maintenance partners to collect real-time and historical data.
To put it simply - fleets need as much vehicle data as possible to build a complete picture of their condition, usage, and history.
Training drivers on proactive reporting
Driver engagement is key for keeping track of vehicles’ conditions, and spotting oncoming issues - especially if the asset is kept by them at their home. By training their drivers on early warning signs, and providing them with accessible pathways to report them, fleets can ascertain even more insightful data to fuel their predictive models.
Automated Vehicle Inspections & Walkarounds
New vehicle inspection solutions allow drivers to visually scan their vehicles during daily walkarounds, or using drive-through gantries for depot-allocated vehicles - using advanced AI models to spot defects that may otherwise go unnoticed.
Using intelligent tech to monitor the trends
Once fleets have connected and centralised their vehicle data, they can begin to harness AI & machine learning algorithms to spot trends in failure causation, timespans, warning signs, and operational impact.
Not only does this help fleets understand how often certain issues occur, but it also allows them to quantify their impact on performance, repair costs, and driver safety.
The key to the success of these algorithms is the strength and quality of the data fed into them. It’s important that fleets ensure the data they use in predictive models is cleansed for accuracy and consistency in order to get the best results from their analysis.
Forecasting issues & optimising service intervals
These historic trends can then form the basis of fleets’ predictive models - helping them forecast when similar issues may occur in the future on a per-vehicle basis, and align their SMR strategies to prevent them.
This forecasting can then be shared with fleets’ management partners, and repair & parts suppliers, to empower proactive intervention, sourcing, and vehicle monitoring. Fleets can even configure automated alerts to ensure these strategies are adopted and upheld with minimal reliance on human input.
What are the benefits of predictive vehicle maintenance?
Aside from reducing reliance on human-led maintenance planning, predictive models can also help fleets bolster efficiency, reduce costs, and develop more intelligent vehicle acquisition strategies.
Here are some of the key benefits predictive maintenance can deliver for fleets:
1 - It helps to proactively reduce vehicle downtime
For fleets, any time for which a vehicle is off the road equates directly to lost revenue. Predictive maintenance models help to spot issues that may otherwise go unnoticed, before they amount to breakdowns or reduced vehicle performance. This helps fleets to keep their vehicles moving with fewer exceptions, boosting day-to-day productivity and financial performance, and avoiding vehicle downtime.
2 - It gives fleets more control over repair & maintenance costs
Predicting vehicle’s maintenance needs in advance gives fleets more opportunity to mitigate repair costs, and prevent smaller issues from leading to more extensive repair work. With advanced planning, fleets are often able to secure more preferable repair rates for common maintenance activities, and even source certain components in bulk to reduce individual part basket costs.
3 - It can extend vehicle lifespans, and improve resale value
Spotting issues early, and preventing them worsening, can help fleets extend vehicles’ operational lifespans, and secure more preferable resale or end-of-lease agreements. Knowing when issues might occur, and where more extensive repairs may be required, can help to optimise retention strategies, and keep vehicles in the optimal condition where it really counts to secure higher valuations.
4 - It informs more accurate Total Cost of Ownership (TCO) calculations
Knowing your fleet’s vehicles inside and out means you’ll know almost every cost involved throughout their lifespan - from purchasing, to maintenance, repair, and return or resale. This means you’ll be able to calculate TCO more accurately before acquiring new assets, helping you inform which vehicles you choose, how you purchase them, and critically, how you can keep their running costs down.
sopp+sopp: Keeping Fleets Moving for 20+ Years
Delivering smarter, data-driven claims solutions
We’re embracing technologies like AI and automation to enhance the speed and accuracy of claims handling, enable predictive repair models, and further streamline our in-house tech solutions. We’re focusing on providing our customers with real-time, data-driven technologies that enable proactive risk prevention, more efficient repair & maintenance management, and robust cost and turnaround controls.
Maximising cost & operational efficiency
Claims inflation continues to be a pressing challenge for fleets, and we’re focused on providing solutions to mitigate and drive down costs. We’re focusing on further strengthening our cost control measures, data sharing, and repair strategies to prioritise both efficiency and transparency. Predictive analytics will also play a critical role in helping our customers make data-informed decisions, ensuring they can manage costs while maintaining operational integrity.
Helping industry professionals adapt to advancing technology
As vehicle technologies advance, equipping experts across our industry with the right skills and support is critical. We’re focused on workforce evolution, with training programmes designed to prepare our people for the demands of advanced vehicle technologies. Wellbeing support also remains a priority, ensuring our teams are ready to deliver the consistently high standard of service our customers expect.
Supporting fleets in sustainability & ESG objectives
Sustainability is a growing priority for fleets, and we’re committed to supporting our customers in delivering on their environmental targets. From using alternative and recycled parts to promoting green repair practices, we’re working towards solutions that not only reduce environmental impact, but also align with increasingly demanding ESG standards. As the adoption of EVs accelerates, our enhanced repair capabilities, and access to detailed automotive market data, are helping fleets adapt to the more complex requirements of zero-emission vehicles.
Fostering collaboration throughout the fleet ecosystem
We recognise that partnership is key to creating a more resilient and integrated fleet ecosystem. By working closely with insurers, repair networks, and technology providers, we’re focused on improving operational efficiencies and fostering a collaborative approach. Strengthening these connections will allow us to deliver faster, more transparent repair solutions that help fleets stay ahead in an ever-changing industry.
Learn more about sopp+sopp’s range of technology-driven, efficiency-focused fleet management solutions: