Reference no: EM132252098

Air travelers are accustomed to long flight delays and cancellations for any number of reasons. Few customers realize that airlines themselves are not particularly accurate at predicting when a fl ight will arrive at its destination even when it is ready to leave the gate. Making a pinpoint-accurate prediction on gate arrival times is notoriously tricky, because many factors alter fl ight times. Weather and wind are the most common, but there are also ground issues, such as the passenger who neglects to board his fl ight on time, forcing the airline to delay departure while they offl oad his luggage. As a result, airline predictions are off by an average of seven minutes across the industry. Flights normally operate according to a fl ight plan put together a few hours in advance of a fl ight’s scheduled departure. After the fl ight takes off it is tracked by a dispatcher, who may be monitoring 15 fl ights simultaneously. So, for example, if headwinds increase, then the pilot must talk to a dispatcher, who may decide to reprogram the “cost index” of the fl ight, revise the fl ight plan, and give permission to the pilot to pick up speed (and therefore use more fuel) to arrive on time. Airlines have been looking to automate these kinds of processes to save costs and also to provide travelers with a better fl ying experience. Gary Beck, vice president of fl ight operations for Alaska Airlines, maintains that airlines need to eliminate the human part of these communications in favor of automation. To encourage this process, Alaska Airlines (www.alaskaair .com) and General Electric (GE) (www.ge.com) sponsored a Flight Quest contest aimed at developing an algorithm that could help airlines better predict fl ight arrival times and reduce passenger delays. The contest, which was set up on the contest Web site Kaggle (www.kaggle.com), provided contestants with two months of fl ight data, such as arrivals, departures, weather, and latitudes and longitudes along the routes. Such data are typically not available to the public because they are owned by the airlines and manufacturers. A team from Singapore won the contest and the $100,000 prize. The winning algorithm produced fl ight arrival estimates that were nearly 40 percent more accurate than existing estimates. The algorithm could help airlines reduce gate congestion, manage crews more effi ciently, and save travelers up to fi ve minutes at the gate. Each minute saved in a fl ight saves $1.2 million in annual crew costs and $5 million in annual fuel savings for a midsized airline. A second Flight Quest contest, with a $250,000 prize, challenged data scientists to determine the most effi cient fl ight routes, speeds, and altitudes at any moment, taking into account variables such as weather, wind, and airspace constraints. The winning model proved to be up to 12 percent more effi cient when compared with data from past actual fl ights. GE plans to develop software and services that incorporate the results of the two Flight Quest contests. It is important to note that GE’s goal is not to replace pilot decisions, but to create smart assistants for pilots. It may take some time, however, before software can be used to fundamentally change how commercial fl ights operate. For example, Alaska Airlines, which frequently lands planes under diffi cult weather conditions, has pioneered the use of satellite navigation, as opposed to relying on ground-based instruments. The use of satellite navigation lowers the standard minimum elevations for a plane’s approach upon landing. The airline is working with the U.S. Federal Aviation Administration to spread the technique, which also saves fuel, to the lower 48 states. Beck claims that the greatest challenge has been to alter the practices and offi - cial handbooks of air traffi c controllers, who would no longer need to tell planes where and when to turn. He maintains that satellite navigation systems essentially transform air traffi c controllers into air traffi c monitors.

1. Do you think that satellite-based navigation will meet resistance among air traffi c controllers? Why or why not?

2. Do you think that pilots will object to having “smart assistants” help them make decisions? Why or why not?

3. Do you think the overall response of the airlines to satellitebased navigation and smart assistants for pilots will be positive or negative? Support your answer.

4. What is the relationship between analytics and smart assistants for pilots?