One of the most complicated systems in the world is the airline system as it has a lot of components or subsystems that need to be managed successfully. For example, the subsystem that’s responsible for measuring the flight delay, the system that manages the revenue, and more.
At each one of them, Data Science gives a lot of help along with Artificial Intelligence (AI). The system can run efficiently with the ability of making patterns of the huge amount of data in airline systems as well as predicting delays and calculating revenue.
So, let’s know more about the applications of Data Science in the airline system:
1- Revenue Management System (RMS)
In such a system, it’s vital to sell a product (flight ticket) at the right time using the right channel keeping in mind cost reduction and maximizing the revenue.
This system operates lots of data from seasonal data, flights’ conditions, customers feedback, costs, and more. At each moment, the system should be able to make patterns from this data using data science and AI in order to work fine.
As the airline company wants to serve a larger segment of customers, there should be an affordable plan for each segment. For example, a plan for the economic class that’s affordable. Another segment is the business class who want a high-quality service.
The RMS manages this process of adjusting prices to each class in order to market for each one with the right price at the right time in the right channel.
2- Air Safety and Airplane Maintenance
We heard a lot of cases that an airline company cancels its flights for some time because of the unstable weather conditions. This system is done in order to save the customers and the crew from the danger of flying in these conditions. But how is this done?
As we know before, data science helps scientists predict the future from current and past data for a long time. The data scientists do that through discovering hidden patterns in data and suggest some decisions according to what they came up with.
From the airline company’s perspective, the more time the plane spends in the ground, the more it costs for the company. That’s why the time delay for the flights is very expensive. And about 30% of the delays are caused by unplanned maintenance.
In such a case, the airline company wants to predict those delays whether they’re safety or maintenance delays so that they can take the right action. And this is what data science does.
3- Feedback Analysis
For travelers, some flights may take several hours till they reach their destination. In this travel time, travelers may feel a lot of feelings like depression, stress, or anxiety. The airline company goal is to make sure that their customers feel good about their flights and get feedback from them to improve their service.
In this situation, travelers may leave a feedback on their website, as a comment on their Facebook page, or as a review on Google reviews. With all of these ways and more, the company wants a way to analyze them all to get valuable information about them, get recommendations to get better, and learn more about customers’ pain points in order to avoid them.
That’s why AI and data science can make informed decisions and learn more about their customers’ persona and as the CEO of PureStrategy Inc said:
“AI systems can quickly allow airlines to determine if there is an opportunity to positively intervene in the customer journey and turn a poor experience into a delightful one. It also allows companies to react faster in a synchronized, aligned way that is on-brand and consistent with the business’s values.”
4- Messaging Automation
Many flights can be delayed due to some issues or conditions as we said before. In this situation, travellers may feel confused and disappointed as their flight is delayed and there’s no announcement about that. If we could automate the process of announcing delays for the airlines, they could be better a lot and a lot of customers will understand the situation.
As we said earlier, using data science can help in predicting delays in the airline flights. Using this opportunity to automate a system of messaging and announcements, it will save a lot of effort and time for the employees of the company explaining the case.
5- Food Supply and Sales System
How many times did you think about being in a flight and having a cup of tea while looking at the clouds? This is one of the dreams we think about when we talk about travelling. Well, the airlines don’t want to be wasteful when it comes to supplying a suitable amount of food and drinks in each flight.
After the plane takes off the airport, you may see the crew preparing lunch or breakfast according to the time and flight condition. Such a little process has data science behind it.
The reason is that the airlines want to predict how much food will be ordered in a flight and how many people will buy them. From another point of view, airlines may have a lot of food at the end of the day and this food has its own expiry date. That’s why the airlines want to balance between the two views to reach the optimal decision.
And data science can do a great job predicting those information for the huge number of flights the airline company has in order to save them a lot and make a lot of money.
But what about the plane crew? Aren’t they more important than food?
6- Crew Management
We can’t underestimate the importance of the crew in the plane for a lot of situations like serving the travellers and keeping them under control in any unusual situation. The crew should be trained to deal with various situations like these and more to be qualified for this position.
The thing is that the airline’s scheduling department can’t assign each crew to a flight for thousands of flights operated everyday. This process is very expensive and not practical at all. For example, imagine that each crew member has licensing, day-offs, route, working hours, and vacations. Other restrictions may be applied are the number of allowed flight hours and day offs. How hard will it be for the company to schedule flights with all of these factors?
With the help of big data and data science the employees can integrate the data from different sources to see the big picture as well as providing solutions and managing the schedule effectively.
7- Fuel Optimization
As for airlines, they want to optimize the efficiency of their planes. From an environmental perspective, global aviation produces about 2% of carbon dioxide (CO2) which is very bad. In today’s world, the problem of increasing earthworms has a lot of consequences that we don’t want them to happen.
Airlines are doing their best to improve their fuel efficiency from both environmental and cost perspectives. AI and data science provide solutions for this problem by collecting and analyzing data of each flight and calculating the distance to find the best route for the plane to take. Another thing that data science can do is to know how much weight should it carry in order to consume fuel as low as possible.
8- Risk Management
The flights do not always end happily. In lots of cases, airlines should make strategies to manage risks by enhancing a number of risk models or situations. As for the planes cost a lot and the people’s lives are not cheap, all of the airlines want to make their flights as safe as possible.
Using data science analytics can help a lot in this task by addressing fatigue risks in which the pilots are in risk due to a number of reasons. Those reasons may be the changes of the time zone frequently, the days that have heavy work, and more.
This solution with the help of the scheduling system explained in point (6) can be both integrated to avoid such a situation from happening.
9- Load Forecasting
For a lot of airlines, they want to create a good balance and optimize their techniques when they’re possible. To understand this more, let’s think about the number of seats at each plane. Will it be better for us to increase the number of seats in the same room of the plane? Is there any problem in the flying system? Will it be any costly action or fuel increase?
Well, the data science’s role in this situation is to manage the load and testing new techniques until reaching a good and profitable one. The optimization is run with a huge amount of data with lots of calculations for distances to discovering new routes.
Conclusion
Through new technologies like data science, airlines can do a lot of their work, optimizing their models, automating processes, data analyzation, and prediction. The good part is that the travellers are the winners at the end with the highest quality of service possible.
And the more competitive the airlines become, the more benefit we get as customers.
References
