The role of Predictive Maintenance in Fleet Management

In the fleet management industry, predictive maintenance is replacing conventional time-based maintenance plans as an efficient method for vehicle maintenance. Predictive maintenance helps utilize vehicles more efficiently by planning maintenance, only when necessary, reduces vehicle downtime and increases the availability of assets by establishing thresholds of use and monitoring how close a vehicle gets to those thresholds.

Fleet managers may take proactive steps to resolve issues before they arise using predictive maintenance which informs them about the vehicle parts that are most likely to experience a breakdown.

Improved maintenance schedules – Predictive Maintenance in Fleet Management

Predictive maintenance can assist in identifying prospective truck or vehicle issues, such as brake and battery failures and other maintenance issues before they happen. It can also help identify underperforming vehicles and minimize risk by leveraging the vehicle health score and insights, allowing fleet managers to prioritize critical issues and avoid expensive unscheduled maintenance.

In a case study conducted by Pitstop, a Geotab-enabled logistics fleet noticed significant differences in its maintenance schedule. While some trucks followed the standard 3-month preventative maintenance cycle, other vehicles were experiencing lower performance and required servicing every 2-3 weeks.

This inconsistency created a challenge for the fleet, as it was difficult to ensure that all vehicles were in top working condition. To address this issue, the fleet implemented a more rigorous maintenance and monitoring program that kept track of each vehicle’s performance and allowed them to adjust their maintenance schedules accordingly. This allowed them to ensure that all vehicles were properly maintained and that any issues were addressed promptly.

Advanced analytics leading to better maintenance quality

Fleets can use historical data gathered from telematics systems to make a long-term forecast of expected breakdowns and malfunctions.  This allows them to schedule maintenance proactively and intelligently, avoiding the problems of extreme approaches to upkeep. The data may be used to predict when operations like fluid checks, tire rotations, brake inspections, oil changes, and wheel realignments are needed.

Uptake, a pioneer in industrial intelligence software-as-a-service (SaaS), created Uptake Fleet Support, a comprehensive predictive maintenance solution for the transportation sector. It provides users with advanced work order analytics and sensor analytics capabilities to help them predict vehicle failures down to the component level, optimize their maintenance scheduling, reduce vehicle breakdowns, and increase vehicle uptime.

It also supports outputs from the Geotab ESR module and a wide range of connector APIs from fleet and sensor manufacturers, allowing users to leverage additional data to generate electrical systems insights that can prevent roadside failures.   

Cost savings and reduction in vehicle downtimes

Predictive maintenance can save costs by increasing vehicle uptime and optimizing depot operations. It also helps to eliminate costly downtime. It also allows maintenance staff to view and address vehicle failures. Which helps to provide a more dependable, efficient, and safe service for passengers. This can result in $2,000 average cost savings per truck per year for the poorest performing vehicles and a 25% increased vehicle uptime.

Stratio and Keolis have signed a global framework agreement to introduce remote diagnostics and predictive maintenance for Keolis’ networks in fourteen nations. Stratio’s predictive fleet maintenance platform uses advanced AI and machine learning models to monitor, analyze and predict failures in real-time.

Utilizing predictive analytics for predictive maintenance implementation is expected to enhance vehicle uptime and optimize depot operations. It resulted in enhanced passenger service and significant cost savings. Additionally, the predictive maintenance platform will provide Keolis with actionable insights. This will help them better plan and manage their fleet operations, resulting in an overall improved service for passengers.

Questar has developed a Vehicle Health Management (VHM) Platform called Fleet Predictive Maintenance Solution. It utilizes AI and sensor data to identify possible vehicle malfunctions before they trigger built-in error codes. The platform gathers this information through the CAN bus and a variety of sensors including GPS and accelerometers.

Its purpose is to assist fleet operators in reducing the costs associated with repairs, fuel usage, spare parts, accidents, and unnecessary downtime. Fleet operators may reduce their expenses for spare parts by 30%, their fuel consumption by 10%, their accident rate by 20%. And their unnecessary downtime by up to 75% by adopting the VHM platform. Companies in North America, Latin America, and Europe are currently utilizing the VHM platform.

Technology is rapidly improving – Predictive Maintenance in Fleet Management

AI and Machine Learning are playing a crucial role in the predictive maintenance of fleets. Edge-native AI predictive maintenance solutions provide a way to maximize vehicle performance and reliability. It results in improved driver safety, fleet utilization, and customer satisfaction. Developers have specifically designed EdgeAI to operate effectively in environments where power is limited and bandwidth is restricted.

Making it an excellent option for implementing predictive maintenance in fleet operations. With the capability of AI-enabled predictive maintenance solutions to scale across fleets of any size. The entire fleet can use this technology for predictive maintenance.

Tangerine, a data analytics firm that utilizes artificial intelligence, offers solutions for overseeing fleets, shared transportation, and insurance telematics. Tangerine AI utilizes predictive maintenance in fleet management by providing real-time data to inform fleet managers about the parts on a vehicle that are likely to fail, possibly even weeks before the failure is likely to happen. This platform also provides contextual data like weather conditions, traffic, road quality, and driver behavior.

Tangerine’s AI-integrated platform for fleet maintenance can build predictions based on direct measurements. Further can use telematics data to shift from reactive to preventive measures. Fleet managers can also receive easy-to-use reports. They will be ready with all the tools at hand to plan relevant equipment maintenance.  

Predictive Maintenance will continue to revolutionize fleet management

The future of predictive maintenance looks very promising. It is expected that the market will experience significant growth between 2022 and 2030, with a Compound Annual Growth Rate (CAGR) of 29.86%. This growth is due to the increasing demand for predictive maintenance and the cost-cutting capabilities of the technology.

Other contributing factors include further integration of the technology, more data precision, and continued improvements in the area. Additionally, predictive maintenance solutions will become easier to use and wouldn’t require specialized training for employees. 

It is expected that as predictive maintenance continues to advance, it will evolve from solely alerting fleet managers to potential issues to providing them with solutions such as identifying the most suitable repair/maintenance shop for the task, scheduling the work during periods of lower driver utilization, organizing a calendar of appointments, providing driving directions, and securing deals and discounts. The future of fleet management practices is expected to be significantly impacted by the development of predictive maintenance.