Clarifying Fuel Economy May 2007 | Commercial Carrier Journal In the past two years, Barry McGrady has analyzed volumes of fuel economy data for Floyd & Beasley Transfer Co. The cause of poor performance is usually a “smoking gun,” says McGrady—vice president of IT and human resources for the 200-truck carrier based in Sycamore, Ala.—but sometimes his analysis uncovers less obvious causes. Recently, McGrady noticed a sudden drop in fuel economy for one vehicle, and he assumed in correlated with a new driver assignment. But the data showed that a change in drivers happened one month before the incident. As McGrady dug deeper into the data, he spotted the most likely cause—a change of tires. “It happened at the exact time the fuel economy started dropping,” McGrady says. “ I turned the data over to the shop for their action.” For many fleets, 2006 marked a frightening trend, as volatile fuel costs surpassed labor as the top expense. With no price relief in sight, managers are looking more closely at their data to gain more insight and control over the variables in their operations that drive fuel economy. With the right technology, fleet managers like McGrady can quickly identify the cause, intervene and monitor the results for improvement—all without leaving their desks. Finding the root cause “That’s the first test,” McGrady says. “I would prefer to try to deal with people below average. Within that pool, I look at the ones that are the worst first.” McGrady’s spreadsheet draws from two sources. The mpg for each vehicle comes from the electronic control module via Qualcomm’s SensorTracs performance reporting solution. McGrady pulls the current driver-vehicle assignment firle from the company’s McLeod LoadMaster enterprise management system to link each vehicle’s mpg to a driver. Before contacting the bottom performers, McGrady reviews their detailed data in the SensorTracs database to isolate the root cause. “Most of the time, it is what you expect,” says McGrady, who pays special attention to idle time, over-revs—indicative of poor shifting habits—and speed. He has identified idle time and over-revs as the two most common causes of poor fuel economy, but occasionally he finds drivers below the fleet average for mpg because their average speed is higher than the fleet’s average speed. Technology from onboard computing and mobile communications providers offers immediate access to vehicle and driver fuel performance. To pin down the root causes of poor fuel performance, experienced fuel analysts pay close attention to the variation in fuel economy from one period to the next. “Looking at fuel on a day-to-day basis is great,” says Brett Vitrano, operations research analyst with Accelerated Freight Group. “It gives you a good assessment, but you are looking good at a very minute aspect of the picture.” AFG—a 70-truck carrier based in Tehodore, Ala.—uses ADV Monitor, a performance reporting feature of GeoLogic’s multimode communications and tracking system. Every Monday morning, Vitrano downloads data into a spreadsheet to analyze the three variables he believes are the most critical measures: fuel consumption, idle time (as a percentage) and mpg. “By looking at it weekly and monthly it allows me to see whether we are trending in a bad way or a good way,” Vitrano says. “If we see three days where consumption has increased, that is not a good assessment of what out consumption is. If it is trending up for three weeks, that gives me a better understanding for what is truly going on.” Idle percentage is a good example of why trends- as opposed to snapshots-are revealing, Vitrano says. Idle times rise significantly during the summer and winter months due to in-cab cooling and heating. By analyzing the range of movement for idle times throughout the year, Vitrano can set goals for idle times—along with other metrics—for specific periods of the year, and by driver AFG’s analysis shows that idle percentage should remain below 30 percent on average, and mpg should be above 6.o. “We are using logic and math to get an understanding of what is acceptable and what is not in the circumstances that surround it and communicating that to drivers,” Vitrano says. Digging deeper In February 2005, Andersen Logistics implemented an onboard computing and mobile communications system from PeopleNet for one of its regional private fleets. The 16-truck fleet based in Brookly Park, Minn., is one of several operated by Andersen Logistics, a division of window and door manufacturer Andersen Windows. Using PerformX—a real-time driver and vehicle performance evaluation tool from PeopleNet—Andersen Logistics management decided to narrow its focus on three critical variable for fuel economy: over-revs, overspeed and long idle time. When fleet manager John Mathias looked at the initial data, he saw the fleet average for mpg was 6.4. While acceptable by most standards, the data varied widely by vehicle and driver. “Guys were all over the map Mathias says. But the average did not take into account the fleet’s equipment mix, among other factors. Andersen Logistics operates both straight trucks and tractors. To find a more meaningful basis to evaluate fleet performance, Mathias spoke with truck manufacturers to find the optimal thresholds for fuel economy in terms of RPM and speed for each type of power unit. For optimal fuel economy, manufacturers said the optimal threshold for RPM was 2,100 for straight trucks and 1,600 for tractors. The optimal speed for fuel economy was 55 mph, but since 55 mph was not a realistic threshold for drivers on 70 mph highways, the company decided to set PerformX to gather statistics on speed over 65 mph. For idle time, it chose five minutes as the threshold to begin counting stats on long idle time. “Once you have the optimal, figure out the threshold for your fleet,” says Mathias, who adds that performance should be evaluated by location. “We can’t compare ours with the Chicago branch. We operate differently.” Another way to establish performance thresholds is to evaluate the performance of each driver and vehicle using a statistical formula called regression analysis, says AFC’s Vitrano. Regression analysis finds the curve or line that best fits a given set of data points; the equation for this curve or line can be used to project future results. Large corporations often use regression analysis to predict future earnings from historical data. At AFG, Vitrano uses an application from SPSS Software to run a regression analysis to determine what idle time, fuel consumption and mpg should be throughout the year for each driver, and for the fleet, based on past performance. “You can do (regression analysis) for the fleet as a whole, but it is more accurate to see how the driver will perform in the future,” Vitrano says. “We can also say to a driver, ‘We didn’t incorporate results from other drivers into our finding.’” Through regression analysis, the fleet has determined that idle times generally should stay under 30 percent, given most weather condiditions. “Given certain situations, if we see that a drier repeatedly abuses idle time or his fuel card, then we can call him in and lay out our cards, and say ‘Mathematically, this is what should be going on. We need to have a talk and find common groun,’” Vitrano says. Regression analysis is most accurate when used to evaluate drivers that travel over the same route and terrain, but it’s useful even if that’s not the case, Vitrano says. “There is always going to be a margin of error, but the more information I can put my hands on, the more I can perfect things to as close as they can come.” Analyzing fuel economy data taken directly from the vehicle’s ECM is the most precise method for determining the amount of fuel consumed over a given distance. When computing mpg, however, the ECM does not have the intelligence to account for fuel that is consumed because of inefficient routing. “We have two separate ways of measuring fuel economy,” says Jim Gervais, director of maintenance at O&S Trucking. Using its dispatch software, the Springfield, Mo.-based truckload carrier keeps a record of fuel purchases through an interface with its fuel purchase card vendor. It divides the gallons by the dispatch miles—using shortest or practical miles, for example—to get the “overall” mpg for each driver and for the fleet. O&S Trucking, which operates 350 tractors, then cross-references the mpg as reported by its dispatch system versus the ECM; the difference takes into account out-of –route miles. “If a driver takes 50 miles home and back, the 100 miles is not paid miles,” Gervais says. “That is actual fuel we purchased for the truck. That is a good reflection of what it costs to operate the truck. “When we look just at ECM data, we are getting 6.3, 6.4 and above,” Gervais says. “This is a broad range, and there are a lot of variables involved in that. We shoot for 6 mpg for overall fuel economy. You’ve got to get all the other things factored in.” (For more on how to address out-of-route mileage, see “Fuel analytics in real time,” on this page.) Advanced tools “We have actual fuel transactions tied into these tables,” says David Custred, McLeod Software’s director of sales services. For example, a manager could find the average fuel cost and miles for a lane over a certain data range; he then could dig deeper by matching up actual fuel costs to a shipper or consignee in that lane, or to a particular driver or tractor. The manager also could bring in what was billed for each lane, the fuel charges collected and the actual miles that drivers ran for each lane. Advanced costing systems that integrate with transportation management systems also can be used to pinpoint the true cost of each shipment, including fuel. Fleets can determine what shipments are fully compensated with the fuel charge and those that are not, says Ken Manning, president of Transportation Costing Group. For both less-than-truckload and truckload carriers, the fuel surcharge does not cover costs incurred by inefficient routing of shipments, i.e. out –of-route miles; shippers are going to pay a fuel surcharge on the shortest or practical miles, Manning says. For LTL carriers, the fuel surcharge can be especially complex because it is typically based on a percentage of revenue, such as 2.8 percent. Does this surcharge adequately reflect the shipment’s true cost? Not likely, Manning says. |