Question

This assignment is from the perspective of Analytics of Marketing.

1)Challenges in adoption of AI and analytics in Aviation industry.
2) Impact on revenue and cost of Analytics for the aviation industry

please elaborate on it and gives example to show the impact of analytics in reference to aviation industry along with data also.

Data to showcase the example .

Try to lengthy as much as possible.

Answer 1

FLY AI: the role of artificial intelligence in aviation

The European Aviation High Level Group on AI has published a report assessing the benefits of using AI in aviation and air traffic management. Lead authors EUROCONTROL and SESAR JU discuss the report’s takeaways and where automation can help the sector take off in the coming years

British mathematician and scientist Alan Turing first looked into computing intelligence in 1950. In a paper called “Computing Machinery and Intelligence”, he suggested using a now-famous ‘Imitation Game’ to test a machine’s sentient capabilities, which eventually laid the groundwork for the development and discovery of artificial intelligence (AI).

Decades later, AI and its subsets – machine learning and deep learning – are set to influence the future of many sectors, including aviation. Over the last few years, AI has found a wide array of applications in the industry – from ground handling services to airport security and air traffic management (ATM) – and there is now scope for more.

This is the argument of the recently-published FLY AI report, which sets out key steps towards a stronger adoption of AI, machine learning and other digital tools in several areas of aviation. Released by the newly formed European Aviation High Level Group on AI, the paper draws upon expertise from key players in the sector. These include leader EUROCONTROL, the Single European Sky ATM Research (SESAR) Joint Undertaking, the International Air Transport Association, Airbus and Airport Council International’s European division.

Alongside debunking some myths around AI, the paper identifies the most promising areas for its uptake. It also features a ‘FLY AI Action Plan’ that outlines future measures to better integrate the technology in ATM and other segments of aviation.

The importance of demystifying artificial intelligence

“AI has been around for more than 60 years but has gained ground more recently, thanks to advances in computing and access to data,” comments SESAR JU executive director Florian Guillermet. “Machine learning and deep learning are helping to create applications that can learn autonomously and advise on complex problems. Aviation is no stranger to the virtues of AI.”

“The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications.”

In recent times the technology has gained traction in segments such as intelligent maintenance, engineering and prognostics tools, supply chains and customer services. The sector is now eager to find more applications for AI, with some European countries – particularly Ireland, Finland, Cyprus, Luxembourg, Sweden and the Netherlands – leading the way.

“The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications,” adds EUROCONTROL head of infrastructure division Paul Bosman. “Recently, significant effort has been put on adapting the current certification framework to the specific characteristics of AI applications. With AI, the industry’s focus on cybersecurity has also increased.”

Based on this assumption, the report aims to “demystify and accelerate the use of AI in aviation and ATM in particular”, says Guillermet. This field directly involves SESAR, which has been coordinating all EU research and development activities in ATM since 2007 and actively contributed to the production of the report. “We also looked at the future evolution in the use of AI,” he continues, “in particular with the development of joint human-machine cognitive systems.

On the other hand, EUROCONTROL has acted as leader of the project under the European Aviation High Level Group on AI. The group was formed during EUROCONTROL’s inaugural conference on AI that took place in May last year.

Where artificial intelligence can actually ‘fly’ in aviation

Guillermet explains that Europe has “a strong basis of expertise and knowledge to further develop AI for ATM”. Here, automation can help improve operational efficiency in different segments of aviation.

“For instance,” he says, “machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit to make the best possible decisions.”

Within this framework, the FLY AI report identified four areas where AI can help tackle current and future challenges. These are airspace capacity, which is rapidly running out in Europe, the climate change crisis, digital transformation and new levels of complexity in the integration of unmanned aircraft in an already overcrowded airspace.

“Machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit.”

“AI’s ability to identify patterns in complex real-world data that human and conventional computer-assisted analyses struggle to identify makes it extremely well-suited to the aviation sector,” says Bosman. “AI has the potential to transform aspects of the aviation sector, enabling ATM functions to be performed in entirely different ways in the future.”

Bosman also believes that automation can play a pivotal role in improving the industry’s environmental credentials. “By accelerating the digital transformation in terms of optimising trajectories, creating ‘green’ routes and increasing prediction accuracy,” he says, “AI could make a real difference to mitigating the environmental impacts of aviation, in addition to providing decision-makers and experts with new features that could transform the ATM paradigm in terms of new techniques and operating procedures.”

In addition, better use of data will help increase and improve predictions with more sophisticated tools, while also boosting the scalability, efficiency and resilience of the current ATM system. Lastly, the can enhance safety in segments such as cybersecurity, conflict detection, traffic advisory and resolution tools.

The FLY AI Action Plan and future steps

A key takeaway from the report is that stronger cooperation is needed to integrate AI in the existing aviation architecture. “The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders, in which all conditions are met to progress collectively on this,” says Guillermet. “No one entity can address it alone.”

In response to this need, the report’s FLY AI Action Plan looks at the whole value chain from research to implementation and provides stakeholders with a call for action.

According to Bosman, the plan identifies six accelerators that will help achieve this purpose. “It is now critical that the community gets together,” he adds. “A way forward would be to set up a community of practice.”

“The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders.”

The first step will be developing a “federated data foundation and AI-infrastructure” that will grant access to data and enable the creation of an AI aviation partnership. In addition, the European Aviation High Level Group on AI suggests launching specific aviation/ATM training, reskilling/upskilling programmes, change management, a knowledge-based toolbox, and European AI aviation/ATM master classes to share best practices. Awareness and demystification campaigns should also be included.

Meanwhile, the industry will also have to build an AI aviation/ATM community to attract future experts to the sector. This, Bosman concludes, will help consolidate community expertise.

Covid-19: can the ongoing crisis catalyse more automation in the future?

Both EUROCONTROL and SESAR believe that the ongoing coronavirus crisis could help foster automation in the industry despite the challenges that it is causing. “The crisis has shown the limits and significant efforts required when using the current manual approach or analytical tools to try to understand the impact of the crisis, predict and help business recovery,” says Bosman.

“The crisis has shown the limits and significant efforts required when using the current manual approach.”

Specifically, AI’s reliance on historical data sets on which to train neural networks means that in the event of a second wave of the pandemic, using these data sets will help improve crisis response. “As a precaution in the face of such a risk, it makes sense to gather and store all possible data related to the virus and its impact. This is so that they can be used to develop new AI applications that could support the aviation industry in dealing with any future waves of Covid-19 or other pandemics.”

In addition, Guillermet says that the pandemic is shining light on the importance of new technologies to help businesses through a crisis. “Mastering these and accelerating our plans for a digital Europe sky will deliver an aviation operating environment, which is more resilient, scalable and economically and environmentally sustainable in the long run,” he says.

“Because aviation is one of the industrial sectors most impacted by the coronavirus pandemic, it should further accelerate the adoption of AI,” Bosman concludes. “AI can really help transform the industry, provide better decision-making tools, and improve industrial and operational efficiency.”

II.

IMPACT ON REVENUE AND COST OF ANALYTICS FOR AVIATION INDUSTRY

What are the benefits of big data analytics?

The ultimate benefits of big data analytics include timely responses to current and future market demands, improved planning and strategically aligned decision making, as well as crystal clear comprehension and monitoring of all main performance drivers relevant to the airline industry.

When properly followed, the positive outcomes of the above mentioned benefits encompass lower operating costs, better customer service, market leading competitiveness and increased profit margin and shareholder value.

So, what are exactly the ways data analytics is transforming airlines?

1. Increase in airline revenue

Data analytics helps the industry to understand customers’ preferences and other maintenance issues. For instance, analysis of ticket booking helps the industry to target the customers with personalised offers while optimising the price in real-time using predictive analysis techniques. As a result, by gathering meaningful data, airlines can fetch more bookings in the given timeframe.

One should seriously consider revenue management based on the idea that customers perceive product value differently, so the price they are ready to pay for it depends on target groups they belong to and purchase time. Hence, revenue management specialists make good use of AI to define destinations and adjust prices for specific markets, find efficient distribution channels, and manage seats to keep the airline simultaneously competitive and customer-friendly.

2. Smart maintenance

Due to the use of smart data analytics, passengers will avoid many issues with baggage tracking. While radio-frequency identification prevents mishandling the baggage, predictive analysis assists in improving the predictability of fleet reliability. With the airport traffic increasing day by day, big data analytics will enable the airlines to keep on working on the optimization of the airspace use, especially when it comes to runway bandwidth, flight routes, types of aircraft, etc.

To illustrate, airlines bear high costs due to delays and cancellations that include expenses on maintenance and compensations to travellers stuck in airports. With nearly 30 % of the total delay time caused by unplanned maintenance, predictive analytics applied to fleet is a reasonable solution.

Carriers deploy predictive maintenance solutions to better manage data from aircraft health monitoring sensors. Usually, these systems are compatible with both desktop and mobile devices, granting technicians access to real-time and historical data from any location. Knowing an aircraft’s through alerts, notifications, and reports, employees can spot issues pointing at possible malfunction and replace parts proactively.

3. Cost reduction

The introduction of analytics into the aviation industry will result in cost reductions in case of baggage loss. As a rule, the damages are repaid by the industry, but when using real-time baggage tracking, data helps avoid losing, damaging, or delaying bags. On the other hand, when the fuel real-time consumption data is collected and analyzed, one can achieve an improved level of fuel use efficiency. According to IATA’s 2012, airlines spend 33% of their operating costs on fuel.

Airlines use AI systems with built-in machine learning algorithms to collect and analyze flight data regarding each route distance and altitudes, aircraft type and weight, weather, etc. Based on findings from data, systems estimate the optimal amount of fuel needed for a flight.

4. Customer satisfaction

With the help of predictive analysis, sentiment analysis, and travel journey analysis, the airline industry keeps its customers up-to-date in real-time, promoting special offers based on their needs, habits, and unique experiences. By collecting and crunching data about customers, airlines understand passengers’ tastes and behaviour well enough to offer them transportation options they prefer and, more important, are ready to spend money on.

Likewise, when a flight delay or baggage loss occur, travellers get nervous. And if customers don’t get a response or explanation of a problem from an airline representative in a timely manner, they likely won’t choose this airline for their next trip. The speed of response to customer queries matters as much as actual steps that are taken to solve an issue.

So, travel experience is getting extremely customized and customer-oriented.   

5. Digital transformation

With a purpose to deliver higher standard services to the passengers, big data and analytics considerably transform the commercial aviation industry. Some solutions have been launched to provide the perfect platform for the custom-made technology suppliers to showcase their products and services to airlines and airports in order to offer the passengers a more connected travel experience.

The emerging technologies are lifting the aviation industry into new heights by helping them in every possible way to meet the customer’s needs, real-time performance dashboards, and predictive maintenance.

6. Performance measurements

Airlines usually operate in a globally competitive environment and therefore require prompt and accurate enterprise performance measurements. Even more, airlines are volume-driven and small variations (passengers enjoying the services, fuel spent, load carried) can multiply into major effects – therefore appropriate and timely action is critical. They also suffer substantial difficulties to produce daily/weekly reliable performance measurements.

This is where big data analytics can automate production of daily activity reports such as number of passenger flown per flight/sector, distance flown which can be used to provide estimated performance measurements such as daily or weekly revenues for specific routes or sectors.

7. Risk management

The truth is that global airline industry has been subjected to major catastrophes over the past years. For this reason, it’s of utmost importance for the airlines to develop various risk management models and strategies to protect themselves from negative impact of these types of events. This is exactly where data analytics can be really of great assistance.

Some crew management solutions allow addressing fatigue risk that pilots are in danger of due to a constant change of time zones, long duty days, scheduling changes, etc. For example, developers of Crew Rostering solution from Jeppesen  started integrating bio-mathematical models of fatigue into flight crew scheduling software. Their goal is to provide schedulers with the ability to rely on data about predicted fatigue to reduce risks in the planning phase.

8. Control and verification

Airline carriers require a number of control and verification models to be able to control costs arising from its various operational activities. To enable this, airlines have a pressing need for a complete and integrated repository of flight information data gathered from all its disparate business units. This will enable computation of various efficiency analytics, such as planed fuel usage compared with actual fuel usage per aircraft, and crew utilization. Apart from that, these problems could also be fully addressed by consolidating and analyzing relevant flight and aircraft data. Thus, creation of a 360 ° view of each flight/aircraft will allow the airlines to considerably improve their control and verification systems.

9. Load forecasting

Airlines require the development of an effective and holistic forecasting model to regularly assess the impact of options, such as increasing aircraft seats available, adjusting fares, introducing new routes, etc. Forecasts should also take account of actual statistical trends and results.

Real-life examples from the world of aviation

Delta: introduction of self-service for better customer experience and predictive aircraft maintenance. This airline leverages AI to optimize operations and costs, as well as innovate customer service at every stage of a trip. With a great focus on the baggage safety technologies, in 2017, it announced the $600,000-worth investment in four self-service bag checking machines.

Delta Airlines also developed a novel application for baggage tracking. The app simply uses baggage check data which runs in the background and the staff members at Delta keep a track of the bags and send the tracker to the customers. The app has been a huge success and close to 11 million customers have downloaded it in their phones.

EasyJet:  British low-cost carrier easyJet has turned operational challenges into successful AI use cases. IT used data science to improve its pricing strategy and manage inventory. As a result, the company observed the increase in profits per seat almost 20 % between 2010-2014. The airline also uses a recognition tool that speeds up passenger information processing. It reads the numbers from a document and fills out the information at the airport, so the traveler doesn’t have to type anything.

Southwest: managed to find the “secret recipe” of fuel consumption optimization. It signed a contract with GE Aviation to use its flight analytics system to improve fuel consumption for its fleet of more than 700 Boeing 737s. The cloud-based system that runs on the Industrial Internet allows for collecting and analyzing data generated by aircraft during a flight. For instance, pilots can consider the information about wind speed, air humidity, plane weight and speed, maximum thrust, and altitude when planning the amount of fuel needed for the next flight to the same destination.

What’s next in the realm of AI and data in the airline industry?

Today, AI makes it possible to enhance customer experience with automation and self-service solutions, optimize employee workflow, and ensure higher air safety with predictive and prescriptive aircraft maintenance. It also allows airlines to make informed decisions about pricing and market positioning through the smart use of data.

No doubt, AI and big data analytics will transform the experience of customers and other engagements at airports. By 2020, numerous airlines are planning significant initiatives of artificial intelligence technology that include real-time predictive pricing offers, air travel experience to chatbots, etc.

There are many areas in the airline industry which can be tapped by big data solutions in a much better way. Marketing, crew and flight operations and aerial cargo are some of them. Big data solutions help the airlines to understand their customers individually, their preferences, their behavioural patterns and also predict the requests that might come up.

Today’s travellers are more happy with the fact that their airlines know where they are, what they would like to be served on board, and what climatic conditions they will be met with on arrival at their destination.

The airline industry has been keeping a tab on this information since long but it needs big data to help them analyse it and make it useful for the customer.