Driver Shortage? Use Route Optimization to Get the Most from Your Existing Drivers
The driver shortage is not new news, it is big news and a global problem.
Macroeconomic factors such as the accelerated growth of ecommerce and increased consumer demand for goods overall say that the situation is not likely to change for years. Other country-specific factors, such as Brexit which caused approximately 60,000* European drivers to leave the UK and an aging workforce and competition from other professions in the US, have put pressure on fleet operators to find drivers to meet market demand.
The issues facing the transportation market are structural—even increasing salaries dramatically has not created a flood of driver applicants. Fleet operators need to mitigate the impact of the driver shortage by improving the productivity and retention of existing drivers.
Route optimization plays a critical role in increasing the effective capacity of existing drivers and in helping to keep them.
Driver productivity should be the first place fleet operators look to increase their capacity. Making delivery routes more efficient increases driver productivity.
At one level, route optimization can take excess miles out of the delivery process by evaluating more delivery options to find the right combination of routes and stops. Fleets still building routes manually or using legacy planning tools can see in excess of a 15% increase in driver productivity by reducing miles per stop after implementing modern route optimization technology and automating the route planning process. For larger organizations, this could be the equivalent of hiring tens of additional drivers.
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There is even more that can be done to increase route productivity. By using dynamic delivery appointment scheduling during the customer delivery appointment scheduling process, delivery density—and hence driver productivity—can be improved.
Customers can be offered delivery appointment options that are closer to existing planned deliveries, which reduces the distance between deliveries. This may appear to be counter-intuitive, but customers are happy to accept these kind of delivery options.
With the increasing focus on the environment and the impact of transportation, the concept of an “ecofriendly” delivery option where the customer picks a delivery time that uses less carbon—and mileage—is a win-win for customers and fleets. Customers are happy to help the environment and the fleet is more productive.
Companies implementing options for delivery appointments, such as eco-friendly deliveries, reduce distance per stop by up to 20% over traditional approaches.
Driver retention is also critical as turnover can reach 100% depending upon the transportation market and company. Driving commercially can be very stressful and poor route plans have drivers scrambling to meet their delivery assignments, exacerbating stress levels and causing drivers to look for work elsewhere.
Route optimization solutions enable companies to create routes that better represent the road network, driving conditions, and stop times and to tightly group deliveries to help reduce driver stress. Drivers are more confident they can execute the day’s work and don’t find themselves in mid-route making corrections because the original plan was not completely feasible.
Reducing driver churn can help fleet operators grow their driver pool as the drivers they are working hard to hire become additive and not replacements.
6 Ways Machine Learning Can Improve Fleet Performance
Machine learning can help fleet operators maximize the fleet performance improving the accuracy of data used to plan and execute deliveries. The information generated by GPS-tracked fleets is an excellent data source for machine learning to improve delivery performance.
New technologies such as machine learning can help improve driver retention by identifying down-to-the-driver factors that create individualized driver routes based upon the productivity of the driver. For instance, a veteran driver may be 10% more productive than a new one.
Planning based on driver experience can be the difference between retaining people or seeing a quick exit. Machine learning can also identify which drivers complete their work in less time than planned and can therefore take on more deliveries, which increases driver productivity.
There is no quick fix to the driver shortage problem. While fleet operators should continue to find ways to attract new drivers, they should also place more importance on making existing ones more productive and keeping them with the organization.
Route optimization is an important strategy to increase the effective capacity of existing and increasingly scarce driver resources. How is your fleet operation using route planning to improve driver productivity?