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Improving Driver Productivity with Telematics

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Improving Driver Productivity with Telematics

By Bob Hitt, Vice President, Account Management

Introduction

Over 87% of all less than truckload (LTL) drivers use either driver handhelds or in-cab telematics devices, but the effectiveness of these devices isn’t fully realized. Driver detention charges are low even though deliveries frequently exceed the free time allowance. In the case of one carrier, over 4% of all shipments experienced a driver delay in excess of the free time allowance by more than 15 minutes.

Telematics data from drivers or cabs needs to be combined with shipment data to determine driver detention or out-of-route stops. The analytics can be complex – matching algorithms, GPS calculations, interfacing with third-party GPS data sources, and large databases of transactions. Several challenges to driver stop monitoring include:

  • Late driver status updates
  • Driver out of position
  • Large P&D locations
  • Adjacent P&D locations
  • Driver breaks near P&D

These issues can be resolved by developing a model in-house or with a partner that combines telematics, specifically the GPS location, and bill of lading data. By automating most of the process and handling the exceptions with a manual review, carriers can start charging driver detention fees to shippers, get shippers to handle the shipments more quickly, and manage drivers more carefully. This may result in LTL carriers getting one more stop per driver per day, equating to roughly a 6% productivity improvement.

Improving Driver Productivity with Telematics: One More Stop

Much of the focus on the telematics devices used by LTL drivers is their use in updating driver status, recording pickup details, updating delivery status and dispatching pickups to drivers. While these steps improved driver management, linehaul planning and customer visibility, the full benefits of telematics data haven’t been realized. In order to get the most benefit from driver devices, the telematics data must be combined with shipment data to either capture driver detention revenue or reduce driver stop time.

Driver detention charges are low even though deliveries frequently exceed the free time allowance. In the case of one carrier, over 4% of all shipments experienced driver delay greater than 15 minutes in excess of the free time allowance, yet detention charges were applied to less than 0.2% of shipments. For most carriers, this amounts to a missed revenue opportunity of 2-4% of total company revenue.

Typically, drivers provide three status updates that are used for determining wait time: arrived, picked up or delivered, and departed or en route. Unfortunately, the drivers don’t necessarily provide these status updates at the right time. For instance, a driver may depart one customer, arrive at the next customer, and then create a status update or en route and arrived one right after the other. This situation appears like the driver instantly drove to the next customer location, and the previous stop shows up as much longer than it should because the time includes both the stop time and the drive time. Most carriers recognize this potential problem, and omit driver detention charges to avoid inaccurate invoicing.

The key to solving this problem is the combination of ping data and shipment pickup and delivery information. Ping data is provided by the driver telematics device. The devices are scheduled to send GPS coordinates and time at set intervals such as once a minute, every five minutes, or every ten minutes. Based on this information, driver stops can be determined. Shipment data then needs to be combined to compare the stops with the GPS coordinates of the the customer location. Based on the total weight of all shipments picked up or delivered, the free time allowance can be calculated. This allowance time can be compared to the time at the stop based on ping data.

Once the data analytics are in place, exception management can then be applied. Three common situations may cause exceptions due to false readings when using ping data and customer locations. First, very large customer locations may appear like the driver never makes a stop. This anomaly happens when the center point of the customer location is a half mile or more away from the dock door where the driver parks. Setting the geofence too wide creates the second problem of customer locations that are adjacent to each other. In this situation, one long stop might be two different customers. Finally, drivers may take breaks or meals in the truck on a street just outside of a customer location. For these three reasons, exception management with a manual review of the stops significantly improves the integrity of the driver detention charges.

In addition to data analytics and exception management, a detailed communication plan that includes sales, customers and drivers must be implemented. The carrier sales team needs to proactively communicate with customers about the focus on timely loading and unloading times. Additionally, drivers need to be aware of their expectations. While informal conversations between drivers and customers are expected, those conversations cannot interfere with timely loading and unloading of shipments.

In the short term, driver detention revenue will increase. Longer term, customers will work to avoid the detention charges, the the driver delivery times will improve. As driver loading and unloading time decreases, the potential for one additional stop per day per driver will become a reality.

Increasing driver detention revenue or improving driver stops per day can be a major driver of carrier profitability. The key is to use data from multiple sources, plan for exception process, and keep drivers and customers informed about the expectations for loading and unloading times.