Big data, part 3: seeing your fleet in multiple dimensions

Editor’s note: This is the third of a 4-part series on the evolution of big data in trucking. This installment shows how transportation firms can use advanced business intelligence tools to gain new insights. For part 1 click here and for part 2, go here.

Barton Logistics uses business intelligence to find patterns it its data that can be acted on to improve performance.Barton Logistics uses business intelligence to find patterns it its data that can be acted on to improve performance.

As the volume of data in your enterprise increases, so do the opportunities to harness it and find new ways to improve and leapfrog the competition.

Barton Logistics, a non-asset transportation company based in Medina, Texas, contracts with owner operators and carriers to offer shipper customers a virtual fleet of 100 trucks and trailers.

The company uses the LoadMaster transportation management system from McLeod Software, but “we had questions that canned reports couldn’t answer,” says Criss Wilson, vice president of operations.

In 2012, he began working with McLeod Software to develop a multi-dimensional data cube using a SQL Server database service, which McLeod calls its business intelligence foundation.

Wilson says the cube has made his analysis faster and more expansive. He is now able to analyze a stream of data like revenue, net margins or rate per mile by different dimensions such as by time, shipment type, customer and carrier, among other possibilities.

“We are looking for a pattern that we can act upon,” he says. When a pattern is identified, Wilson adds a new metric to a user dashboard. Dispatchers have two screens on their desktops, one of which they use to monitor the dashboard. As values change into an actionable realm they turn from green to red.

As one example of a pattern, Wilson says he uses the cube to predict changes in rates caused by capacity leaving the market due to the electronic logging device mandate. This insight shows lanes where contract rates need to be held steady so that carriers will be able to function after they convert to electronic logs, he says.

“If I set my rate too low to the customer, I am going to win the business, but I may run small carriers out of business,” he says. “The analysis showed where pricing in lanes moves carriers into a dangerous red zone.”

One of the nation’s largest flatbed carriers, Boyd Bros., has developed a data warehouse and business intelligence tools to analyze its four companies that operate a combined 1,100 trucks.

Each company uses a separate transportation management system (TMS), but Boyd Bros. can now use Microsoft Access and Excel applications as the interface to the data warehouse to analyze its entire business, says Dwight Bassett, chief financial and operating officer.

The Clayton, Ala.-based carrier is able to analyze utilization and fuel efficiency by tractor type and by driver. Its analysts can also compare key metrics for revenue, deadhead mileage, operating ratios and other areas of performance by company division and fleet manager, he says.

At any moment during the day, Bassett says that analysts can quickly identify a solution to problems that arise.

“We’ve got an issue now with a customer that we think requires a great deal of tarping for drivers,” Bassett says. Within 10 minutes, he says an analyst was able to find the loads from the customer that needed tarping and the number of stops for each.

“We were only paid one tarping fee but were doing a lot more than that,” he says.

Every morning, reports are emailed to people in operations and customer service that show key metrics from the previous day. The reports identify the tasks they are expected to work on that day such as come up with solutions for loads in the system with low revenue, as measured in terms of revenue-per-day and per hour.

Any load that generates less than $600 in a 24-hour period becomes an action item, he says, to change the pickup or delivery times, to increase the rate, or to find new customers.

Stay tuned for part 4 to see what video can add to big data and how fleets can compare their key metrics to others in the industry.