AI in SMR: improving decision making, predicting problems and optimising maintenance planning

The use of telematics data and AI can enable fleet managers to make informed decisions, predict component failures, and optimise maintenance schedules.  

The conversation at the September Fleet200 Strategy Network also touched on the challenges of integrating AI with existing systems, the importance of accurate data, and the need for cultural shifts in fleet management to embrace predictive maintenance.  

A case study showed a 95% probability of a vehicle’s component failure, leading to proactive maintenance and cost savings.  

Those fleet decision-makers and suppliers taking part in the debate also saw a potential for AI to inform future buying decisions and the strategic value for fleet management. 

The discussion focused on the successful application of predictive analytics and machine learning in fleet management, highlighting significant cost and downtime reductions. 

Action points 

• Explore ways to better integrate the predictive maintenance system with your operations team to improve buy-in and adoption. 
• Analyse the cost-benefit analysis of the predictive maintenance system, including the impact on operational efficiency and downtime reduction. 
• Investigate opportunities to expand the predictive maintenance system to cover a wider range of vehicle types and technologies, including electric and hydrogen vehicles. 
• Be aware of the potential shortage of qualified technicians at the workshops you use. This can impact their maintenance capabilities, scheduling and therefore vehicle downtime. 

Predictive analytics and machine learning in fleet management 

• Predictive analytics and machine learning in fleet management has been successful, with highlights being significant cost and downtime reductions. 
• The use of fleet management data and telematics data via a cloud system provides independence from specific fleet management systems. 
• The strategic value of analytics in helping fleet managers make future decisions is valuable.  
• The scarcity of qualified technicians was addressed, with a focus on diagnostic approaches to predict component failures and implement defensive maintenance. 

Cost savings and unscheduled maintenance 

• A facilities management company in major cities used AI and unscheduled maintenance costs were significantly reduced. 
• The importance of a proactive maintenance approach to avoid unscheduled maintenance and its associated costs is key 
• The ratio of scheduled to unscheduled maintenance and the financial impact of unscheduled maintenance on collection and delivery drivers should be a concern 
• The potential for predictive analytics to save costs by identifying and addressing component failures before they become major issues is a key benefit. 

AI efficiency and component issues 

• AI can be used to align component issues across different vehicle models. 
• The importance of not relying on averages and the need for a more sophisticated approach to data analysis was highlighted. 
• AI also has the potential to inform future vehicle buying decisions. 
• Used AI to help fleet managers identify and address issues before they become major problems. 

Challenges and opportunities in fleet management 

• Accurate data and strategic planning is essential for AI tools to be worth the investment in money and time. 
• The success of predictive analytics in reducing downtime and the potential for cost savings is significant. 
• Education and culture in fleet management 
• Education and culture change in fleet management to embrace predictive analytics and proactive maintenance is essential. 
• As is the importance of involving all stakeholders, including drivers and planners, in the predictive maintenance process. 

Workshop capacity and efficiency 

• AI can be used to optimise workshop capacity and efficiency, aligning workshop capacity with fleet needs. 
• The importance of having a network of workshops to handle maintenance efficiently is key. 
• There is a need for training and apprenticeships to address the shortage of qualified technicians in the fleet management industry.