Using AI and a proper maintenance strategy to keep driver's happy
The world of trucking and logistics is changing, and it is changing quickly. At the core of all these changes are the truck drivers. In recent years improving driver retention, onboarding and driver experience has been at the forefront of all fleet businesses in North America.
Trucking companies have challenges that are uncontrollable and are outside the scope of their manageable perimeter. In 2020, unaffordable insurance costs was the primary reason why most small and medium trucking companies went out of business. A recent study from the ATA concluded that 92% of drivers churn in most trucking companies. How can you run a business if you are hiring a fresh new workforce every year? How do you run an effective business if you are unable to keep up with inevitable and uncontrollable external factors such as driver retention, insurance, government regulations, etc.
Maintenance as a Strategy and Tactic
Tactical Insights – Data that helps you focus on the right problems
Fleet Managers, Maintenance Managers and Operations Managers have a lot of information coming at them. These include service metrics, procurement activities, driver hiring and retention, telematics and data points from all across the business. Fleet managers need technologies that isolate and help them manage their businesses by exceptions. Investment in the right technologies that allow them to do so is critical for their success. You can pull thousands of raw parameters from your telematics but if you’re not told exactly what went wrong and what you have to do, the entire exercise is futile. A combination of classical hours and time-based maintenance coupled with AI is what will drive the future of maintenance technologies.
Change Management and Transformation
Classic fleet management practices such as hours-based, or distance-based preventative maintenance are great frameworks that improve continuity & de-risk fleet operations. However, compared to AI delivered insights, these are only “guestimates” and assumptions. Predictive insights from applications like Pitstop helps you get ahead of the problem by isolating an issue and helping execute a real-world workflow around it. Although it is a completely novel approach, the shift into this school of thought can be minimal, provided the correct stakeholders are brought into the implementation of AI early.
Effective Shop Maintenance: Parts and Labour
Predictive Maintenance allows the fleet leaders to source parts effectively and build accurate forecasts which they can share with their own suppliers and manage supply schedules with improved accuracy. Maintenance applications also help manage mechanic productivity, effectiveness, and quality of service operations. Improved shop maintenance reduces driver wait time and shortage of parts, and does not keep drivers and vehicles on the road waiting on service. Reducing downtime is key to improving driver experience for all types of commercial fleets.
Maintenance improvements through AI can be leveraged to deliver a driver centric management approach in fleets. Vehicle uptime, availability of parts and labour, transparent communication channels are all key drivers (no pun intended) of a positive driver experience. Pitstop can be leveraged to build and deliver driver centric maintenance programs by focusing on the right information at the right time. An improved quarterly driver survey coupled with reduced downtime and effective shop maintenance are the most improved metrics in fleet operations after implementation of AI enabled prognostics software.
By: Rakean Zakir, Customer Success Lead | Pitstop |