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By Mel Duvall
When most chief information officers think about implementing a Green IT strategy, they think about ways to reduce data center energy costs, purchasing more energy-efficient hardware, implementing virtualization technologies, or possibly installing software to automatically shut off desktops when not in use.
But how many think about using the analytical power of their database and business intelligence systems to unlock savings - both energy and cost savings?
That is exactly what BNSF Railway Company of Fort Worth, Texas, has done in its extensive North American freight train operations. Using the knowledge already contained in its enterprise data warehouse system, BNSF has figured out how to run its locomotives more fuel efficiently, and has shared that information with its engineers, encouraging all of its employees to become Fuel MVPs.
Not only has it been a boost to the company's overall environmental strategy, it's also helped put a lot Green back on the company's bottom line.
On tracks where the MVP program has been leveraged, BNSF has been able to cut fuel consumption by 1% to 1.5%. To put that into perspective, in 2008 BNSF spent $4.6 billion on fuel. If a 1% savings could be achieved across the company's entire network, the payback would be $46 million.
BNSF's journey towards achieving greater fuel efficiency through business intelligence began in 2006. John Krebs, a manager of technology services in BNSF's business intelligence division, says that at the time the company was reviewing with its engineers the elements that were tracked as part of a scorecard system. The scorecard is a means of tracking engineer performance to capture best practices.
"Essentially, we were trying to understand what it was the best engineers did so well," says Kreb. One of the ideas brought forward by the engineers was to track fuel efficiency. The engineers wanted to know who was the most fuel efficient operator in the company, and more importantly, why.
BNSF already captured a wide range of data from its freight trains and network operations in its Teradata enterprise data warehouse system (EDW). For example, fuel consumption information is gathered at wireless download base stations from the trains' onboard computers and transferred to the EDW.
The business intelligence team then began pulling out a wide range of other variables from the data warehouse, such as length and tonnage of trains, the grade of a track, type of freight being hauled, and weather conditions, to develop a picture of which engineers were achieving the greatest fuel efficiency under comparable circumstances.
"We were able to do this fairly quick—about three months—and because most of the information was already in the system, the cost was only about $30,000," adds Kreb.
Next: Sharing Green Best Practices
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