If we look a bit further back into the past we see that for a very long time the costs for fuel were not of predominant importance for airlines. In fact, up until ten years ago the price for a barrel of oil had never exceeded $40. From the early 2000s onwards, the price of oil started to rise inexorably and has now passed the mark of $100 per barrel.

The common wisdom might well tell us that ‘no one believes forecasts, but everyone wants to hear them’: nevertheless, it is unlikely that the price of oil will ever again come down to where it was ten years ago. On the contrary, one can conclude that the age of cheap oil is over and that the fuel cost pressure on airlines will continue and possibly increase.

• The efficient gathering of large amounts of data from multiple sources;
• The deployment of appropriate techniques to extract the value from data;
• The cross-domain collaboration of several functional entities within the organization;
• The help to real-time decision making and reacting to operational incidents.

Aircraft raw data

The challenge arising from big data is not simply the volume of data, as the name might suggest. Volume, though, does play a major role, especially when one considers that much of this is historic data that cannot be accessed through classic queries because it is simply too large. The challenges are multi-layered. Having said that, fuel related data generated could be large volume once FDM, ACMS (Aircraft Condition Monitoring System) and FCPC (Flight Control Primary Computer) are considered. Traditionally, airlines have been storing FDM or ACMS data but seldom are they used to their full potential especially in co-relationship with each other. Also the velocity at which new data is produced and needs to be processed can pose issues for conventional techniques of data reception and treatment. Half of in-flight data is generated by engines in a six hour flight not to mention about the in-flight and on-ground/maintenance data generated by new generation aircraft. The airlines would need to adapt to new techniques to manage and make the most of these data. An advanced data management system, such as one required for fuel efficiency management, needs data from numerous sources and must be equipped to deal with a large variety of data, which comes from sources like the maintenance information system (MIS), flight planning, crew scheduling, in-flight, historical or other external sources such as weather, ATC (Air Traffic Control) and seasonal variations, to cite but a few. The data coming from these sources can range from being highly structured like application data to highly unstructured such as textual data.

Additionally, the accuracy of data must be assured. It is self-evident that a decision can only be as good as the information on which it is based. The source and information need to be trustworthy so that operations can confidently make data-driven decisions that rely on more than judgement alone. Leading airlines need to use ‘smarter’ technologies to harness big data to define fuel management strategy along with other departments such as maintenance, commercial, customer loyalty, in an effort to make more holistic and cost-‘wise’ decisions. This would lead to further opportunities that may, as yet, be difficult to comprehend or anticipate today.

Real time decision making assistance