Main image of article The 787 Dreamliner Scenario: How Data Can Solve Epic Messes
Following reports of battery failures onboard Boeing’s 787 Dreamliner, the Federal Aviation Administration (FAA) has issued an “emergency airworthiness directive” temporarily grounding the airliners. That’s supremely bad news for Boeing, which poured millions of dollars into the 787’s development. It could also spell trouble for the airlines that purchased the aircraft, including United, Qatar Airways, and LOT Polish Airlines. On January 7, a Boeing 787 on the ground in Boston experienced a battery failure that, in the words of the FAA, “resulted in the release of flammable electrolytes, heat damage, and smoke.” A week later, a 787 flown by a Japanese carrier experienced a similar incident in-flight. The root cause is still under investigation. According to Thomson Reuters, Boeing had managed to deliver 49 new 787s to eight airlines before the battery issue became a full-blown crisis. "We are confident the 787 is safe and we stand behind its overall integrity,” Boeing chairman and CEO Jim McNerney wrote in a statement. “We will be taking every necessary step in the coming days to assure our customers and the traveling public of the 787's safety and to return the airplanes to service.” Boeing intended the 787 to become a cash cow, a next-generation aircraft that would send carriers around the world scrambling for their checkbooks. It features a retooled aerodynamic design, lots of lightweight composite materials in the fuselage and interior (all the better for fuel efficiency), and new electrical and control systems. Instead of acting as a competition-killer, however, the aircraft risks becoming a major PR problem. Boeing relies on massive amounts of collected data to improve its manufacturing and maintenance efforts, of course. As part of that process, it moves 60 petabytes of data around its network; it has also launched several Big Data pilot projects while consolidating its data warehouses and cost-management systems. It has also experimented with launching cloud platforms, most notably with low-disk data projects such as an e-commerce Website, but those efforts are still relatively nascent. And at the moment, Boeing has a big data job on its hands: figure out the root of the battery issue—and how that issue might affect the 787’s other critical systems. Fortunately, modern aircraft come seeded with lots of sensors; combined with a trove of other manufacturing and maintenance data, it’s likely the company can soon find a solution. (Unfortunately, actually fixing the problem may take some time; one aviation expert to Time magazine that, should the FAA require Boeing to replace the batteries and rewire certain infrastructure, it could take up to a year to close the case.)

Intelligent Machines

A couple months ago, General Electric sent its executives to several high-profile tech conferences to pitch the idea of an “Industrial Internet,” which combines the latest in analytics tools and data-gathering sensors with old-school manufacturing. By seeding their fleets of machines and vehicles with sensors, GE argued, companies could receive massive amounts of data about every stage of their operations. Run that data through the appropriate analytics packages, and those companies could squeeze out considerable efficiencies. GE positioned its idea as a particularly good one for the aviation industry, which consists of 20,000 commercial aircraft loaded with 43,000 jet engines composed of lots and lots of moving parts. “If we can change the way that our engines operate by getting real time data, that’s huge,” GE CEO Jeffrey Immelt told an audience at the Dreamforce conference in September, referring to devices’ potential ability to send updates to employees and engineers. If that vision comes to pass—and GE, along with other firms, manages to convince enough industries to sprinkle their products with data-gathering sensors—it could make troubleshooting a somewhat more streamlined process. But that won’t necessarily help Boeing at this delicate moment.   Image: Jordan Tan/