Predictive Maintenance in the Fleet Management Industry

Keeping vehicles in optimum operating condition is a business imperative for every company in the fleet management industry. Historically, a significant percentage of overall operational costs have been tied to vehicle maintenance. In an attempt to keep their fleets in good condition, most companies relied on time-based maintenance schedules. While these routines do utilize some degree of statistical analysis, they are, by design, static and rigid.

Continued advancements in IoT, telematics, AI (artificial intelligence), and ML (machine learning) technologies are paving the way to a much better approach.

The Problem

Time-based vehicle maintenance cannot account for the actual—real-time—operating conditions of the vehicle. This lack of performance insight results in vehicle maintenance that is performed too early (unnecessary) or too late (post failure). This lack of predictive capability can create a host of operational and competitive handicaps for the fleet operator.

• Vehicle breakdown: Vehicles break down due to component malfunctions not detected during the periods between fixed maintenance points. Vehicles must be retrieved for repair.
• Reduced lifespan:  Vehicles are permanently removed from service due to catastrophic failure or repeated failure of specific components. This creates higher vehicle inventory turnover.
• Unnecessary maintenance: Fixed/static maintenance schedules often result in vehicles being called in for service before service is needed. This can create a self-induced shortage of fleet vehicles.
• Higher Costs: Each of the above problems creates higher operational cost for the fleet operator. Reduction of these costs would improve competitive position and financial results.
• Poor customer satisfaction: Vehicle breakdowns, poorly planned maintenance schedules, lack of connected vehicle insights and higher consumer pricing can all contribute to dissatisfied customers.

The Solution – Predictive Maintenance

Over the next decade predictive maintenance will be replacing preventive maintenance across every machine-intensive industry. This will certainly apply to the fleet management sector. Predictive maintenance, enabled by Edge-native AI technologies and solutions, will provide fleet operators with the means to revolutionize the maintenance of their vehicles.

Edge-native AI predictive maintenance brings endpoint intelligence to the process by….

• • Leveraging AI-enabled telematic control unit (TCU) data
    • AI models trained and embedded at the vehicle TCU/sensor/device endpoint
    • Raw TCU data collected and analyzed in real time
    • Creation of predictive insights into vehicle health and performance
    • Automated processing and routing of maintenance alerts to designated recipients
    • Continuous learning and evolution of predictive algorithms based on historical data
  • Providing reliable, cost-effective, scalable, vehicle connectivity
    • Vehicle sensor data processed locally providing rapid insights into real-time conditions
    • Big data architecture to support high-volume processing of TCU data
    • Minimized reliance on cloud infrastructure reduces cost and improves reliability
    • Predictive maintenance technology that can be scaled across an entire fleet of vehicles (regardless of size)

The Impact

Fleet management companies that invest in an Edge-native AI predictive maintenance solution will finally be able to maximize the performance and reliability of their vehicles. The impacts will be both operational and financial and will include the following:

• Driver safety: Promotes vehicle and driver safety via real-time monitoring of the health and performance of critical vehicle components
• Use of existing infrastructure: Edge-native AI solutions can be deployed on new or existing TCU infrastructure
• Enhanced visualization: Ability to connect data for an entire fleet of vehicles onto single-pane-of-glass visualization tools
• Optimized fleet utilization: Predictive maintenance keeps vehicles in prime operating condition and helps prevent unexpected failure
• Increased vehicle lifespan: AI-enabled predictive maintenance can significantly increase the service-life of fleet vehicles
• Reduced operational costs: Overall reduction in the cost of fleet maintenance combined with increased availability and longer lifespans produces better bottom line results
• Improved client satisfaction: Improved vehicle performance and reliability creates higher levels of customer satisfaction and market share.