Originally published by Fleet Management Weekly
In the ever-evolving landscape of fleet management, technology continues to play a pivotal role in shaping the way businesses operate and serve their customers. From streamlining operations to improving customer service, embracing technological advancements has become a hallmark of success. One area where technology is making significant strides is in the realm of fleet maintenance approvals, where the fusion of machine learning and human expertise is transforming the way decisions are made. In this article, we delve into the realm of machine learning-driven maintenance approvals and how this innovation is revolutionizing customer service for fleet management companies.
At Wheels, we are committed to enhancing the service we provide our customers by using both tried and tested technologies as well as newer cutting-edge solutions. From utilizing machine learning (ML) for maintenance approvals to leveraging optical character recognition (OCR) and robotic process automation (RPA) for streamlined administrative processes, we’re revolutionizing how we serve our clients.
Machine Learning in Maintenance Approvals
Maintenance approvals involve assessing the nature of the repair, its cost, and whether it aligns with customer-specific thresholds. However, the process can be time-consuming and resource intensive. Machine learning is an innovative solution that streamlines the process and enhances customer service efficiency. An algorithm leverages the collective decisions made by maintenance advisors over time. It then learns the patterns and trends associated with these decisions and pairs them with customer specific parameters. For instance, if a BMW repair request falls within a certain price range and has previously been consistently approved, the algorithm approves the request. Using this technology allows our maintenance advisors to play a more strategic role in decision-making and cost negotiation elevating the overall quality of service provided to both customers and vehicle operators. With machine learning, maintenance vendors receive instant decisions on maintenance requests, so our customers’ drivers are back on the road as soon as possible.
OCR and RPA for Enhanced Speed and Quality
The joint forces of optical character recognition (OCR) and robotic process automation (RPA) are a game-changer when it comes to dealing with vast amounts of administrative documents and data processing. Whether it’s toll violations, paper invoices or insurance card requests, OCR engines meticulously scan and interpret information, extracting key attributes like license plate numbers, violation types, dollar amounts and more. RPA bots then take the data gleaned from OCR and utilize predefined scripts to navigate through different systems and vendor portals. These technologies streamline tasks that were once manual and time-intensive, such as keying in data, updating systems, or making payments. The RPA bots work around the clock, enhancing both speed and accuracy, reducing turnaround times so that customers receive swift responses and resolutions. And again, like with machine learning, employees can focus on tasks that require creativity, strategy and problem-solving.
Investigating Machine Learning for Odometer Estimates
We rely upon accurate odometer data to provide maintenance reminders to drivers. Our current system captures odometer data from fuel program transactions and maintenance shop visits. Yet sometimes this data is not available. We can make estimates based on generalized rules such as 25 miles a day for one fleet and 50 for another. However, in the spirit of continuous improvement, we are piloting machine learning algorithms to analyze unique driver behavior and driving history. The algorithm comprehends patterns, learns from them, and refines mileage predictions. This approach personalizes mileage estimates to the specific driver as opposed to using overall fleet estimates increasing the accuracy of the odometer data used for important maintenance reminders.
GPS Tracking for Trip Logs and Mileage Reporting
Our mobile app incorporates GPS tracking. Using this feature, drivers can produce automated trip logs to feed directly into monthly or annual mileage reporting. Many drivers are required to keep meticulous trip logs for regulatory reasons. And it is a productivity tool for drivers who use their vehicles for personal and business miles. They can set up the tool to track their business miles during business hours. It will then automate the submission of their mileage every month. GPS is also a very important feature of our reimbursement program. Many of our customers want to reimburse drivers based upon evidence of miles driven and they rely on our GPS tracking in the app to provide the evidence to support those reimbursement amounts.
Machine Learning for Damage Detection During Vehicle Inspections
Many of our customers require regular annual inspections because vehicles are sometimes turned in with unreported damage. We are testing the use of machine learning to streamline this process. Using an app, a driver will be able to photograph or record a video of the vehicle and a machine learning algorithm will analyze the data and determine whether there is a scratch, dent, defect or flaw on the exterior of the vehicle. Using this technology, drivers will be able to satisfy inspection requirements without having to make a physical trip for an inspection.
Machine learning, OCR and RPA are just a few of the technologies we are using and investing in to improve the way we serve our customers. At Wheels, we foster a culture of innovation, and we are committed to leveraging advanced technologies to deliver exceptional service to our clients and their drivers.
About the Author:
Tim O’Hara is the Chief Information Officer at Wheels.