easyjet geography case study

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Geography Unit 1, Going Global Case Study 3 - easyJet

Description.

• going global

Resource summary

The easyJet airline was founded in 1995 by Sir Stelios Haji-Ioannou. It began as a small venture, running flights solely within the UK. Most of Europe's major cities are now interconnected via easyJet's cheap flight network.At the start, the airline had just two aircraft. Inaugural flights from Luton to Edinburgh and Glasgow were supported by the advertising slogan 'making flying as affordable as a pair of jeans - £29 one way'. In 1996, flights to Barcelona commenced and thereafter the company expanded at breakneck speed. Now it has around 300 flight routes in the EU. Shortly after its 100 millionth passenger flight in 2005, easyJet announced another massive expansion, this time into new markets outside the EU (to Marrakech, Istanbul and Rijeka).Clear signs of the firm evolving into a major TNC came early on in 1998 when easyJet acquired 40% of the Swiss air company TEA Basel AG, allowing it to establish its first European base in Geneva.Technology has helped to build the easyJet global network. It was one of the first airlines to embrace the opportunity of the internet, and the company's first online sale was made in April 1998. Now approximately 95% of flights are purchased in this way, making easyJet one of Europe's biggest internet retailers. By 2006, the company owned 122 aeroplanes (both Airbus and Boeing), carrying 33 million people to their destinations that year and bringing revenues of nearly £2 billion.Places that easyJet adds to its flight network become more 'switched on'. For instance, Tallinn in Estonia is home to 400 000 people. On 31 October 2004, easyJet started to fly British tourists there for just £40 each. Suddenly, the city became an affordable destination for UK citizens, especially groups of young men and women seeking a cheap but interesting destination for 'stag' and 'hen' weekends. There have been complaints of bad behaviour, and a rise in sexually transmitted diseases has been reported. However, the new route has brought more money to Tallinn and boosted trade for its hotels, restaurants, bars and nightclubs.

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Shrinking World

The 21st century has been dominated by rapid development in ICT and mobile connections, as shown by this map. This has meant that globalisation is deepening its connections at an unprecedented rate. The proliferation of the internet and mass consumption of mobile phones has caused space-time compression, meaning we can call people anywhere in the world, or book a flight to most places at the click of a button. This is happening at a much greater speed than previously, accelerating globalisation hugely.

Factors of Acceleration

The factors that have accelerated globalisation are wide and varied. These are the main factors in the 20th and 21st Century that have accelerated globalisation.

Colonialism

Containerisation

International Monetary Fund/World Bank

Increased Trade

Financial Market Trading

Information Exchange

This table shows a timeline of events that have accelerated globalisation.

The easyJet airline was founded in 1995 by Sir Stelios Haji-Ioannou. It began as a small business, but now most of Europe’s major cities are connected via easyJet’s network.

At the start, the airline had just two aircraft flying within the UK. In 1996, flights to Barcelona commenced, now it has around 300 flight routes in the EU. Shortly after its 100 millionth passenger flight in 2005, easyJet announced another massive expansion, this time into new markets outside the EU (to Marrakech, Istanbul and Rijeka).

In 1998 easyJet acquired 40% of the Swiss air company TEA Basel AG, allowing it to establish its first European base in Geneva.

Technology has helped to build the easyJet network. It was one of the first airlines to embrace the opportunity of the internet, and the company’s first online sale was made in 1998. Now approximately 95% of flights are purchased in this way, making easyJet one of Europe’s biggest internet retailers . By 2006, the company owned 122 aeroplanes, carrying__ 33 million__ people to their destinations that year and bringing revenues of nearly__ £2 billion__.

Places that easyJet adds to its flight network become more ‘switched on’. For instance, Tallinn in Estonia is home to 400 000 people. In 2004, easyJet started to fly British tourists there for just £40 each. Suddenly, the city became an affordable destination for UK citizens. The new route has brought more money to Tallinn and boosted trade for its hotels, restaurants, bars and nightclubs.

Case study : How easyJet is delivering on its mission to become the world's most data-driven airline

During the job interview, I think the interviewers (who are data scientists themselves) didn’t know about some of the things I mentioned, but that just tells you how cutting edge the information Cambridge Spark provides is. Overall, I would say the course is a great future-proofer."

Edgar Tse, Data Scientist at easyJet

easyJet is one of the largest airlines globally and one of Europe’s leading low-cost point-to-point airlines. Back in 2018, the company announced a new mission to become the “most data-driven airline in the world”, setting plans in motion to use data to improve passenger experiences, customer loyalty, operational efficiency and revenue. In this case study, we catch up with Edgar Tse, a Data Scientist at easyJet, and recent graduate of our Level 7 AI and Data Science apprenticeship to find out more about how he is applying his new skills at work.

Building the world’s most data-driven airline

Since the onset of the COVID-19 pandemic in early 2020, it’s been no secret that the aviation industry has been among those hardest hit amid international lockdowns, travel restrictions, and consumer unease keeping would-be travellers from flying. easyJet realised that it was going to have to double-down in its data-driven pledge in order to become more nimble in response to rapidly shifting demands. 

Ben Dias , easyJet’s Director of Data Science and Analytics recently shared how the pandemic has transformed the way the company uses data , including increasing the use of automation to support increased workloads; using external data to signal market changes and evolving customer needs and, finally; using reinforcement learning to learn from the new data sets in real-time so the organisation can respond to shifts in demand much faster.

Equipping staff with the data skills to succeed

In order to achieve easyJet’s data-driven goals, Ben knew he needed a team that had the cutting-edge skills to remain one step ahead of the curve. To find the right members for this team, Mr Dias looked inwards as well as outwards to fill the skills gap.

“One day I was in a meeting with Ben Dias, the Head of Data Science, and he says ‘hey, do you want to be the first guinea pig of the Level 7 AI and Data Science course?’ And I’m excited and say ‘Okay, I'll give it a go!’”  

This was the meeting that kicked off Edgar’s upskilling journey with Cambridge Spark. But of course, it wasn’t just the chance to test out the programme that drew Edgar to our apprenticeship. Taking time out from work to pursue expensive, inflexible full-time education again was not something that appealed to Edgar. However, he was keen to advance his data science knowledge on a time and value scale that made sense to him.

My previous study had covered a lot of the stuff I needed to know except for the coding part, so an apprenticeship made perfect sense to me. You're not only able to learn new theories and techniques, you're also able to apply them to your daily role. That's a really big positive. With a traditional degree, you more than likely have to quit your job to study. Plus, an apprenticeship is sponsored by the government. In my opinion, there’s no better deal out there!”

Ultimately, to Edgar the most appealing aspect of the Level 7 AI and Data Science apprenticeship   was that he recognised it as a way to move and fast-track his career from data analytics into data science , which is where he saw his future moving in the long term;

“One thing that really appealed to me about this apprenticeship is that it provides a high level view on every single data science topic. It’s great to get a macro perspective. I saw my existing role of data analyst slowly transforming into a data science role. So in my mind, I thought, why don't I learn it now and future-proof myself?”

12 months into his apprenticeship, Edgar took the plunge and put himself forward for a new role in data science ;

"During the job interview, I think the interviewers (who are data scientists themselves) didn’t know about some of the things I mentioned, but that just tells you how cutting edge the information you guys are providing is. Overall, I would say the course is a great future-proofer, and it also allowed me to move from one place to another."

The best parts of learning with Cambridge Spark

We asked Edgar to tell us which parts of the Level 7 AI and Data Science programme he found to be the most useful and interesting to him. Here’s what he had to say;

I enjoyed the module covering software best practice for designs the most because that type of stuff doesn't get mentioned too often. If you search ‘data science’ you always find information about this or that model, but you don’t really find information about best practices. For example, I learned about simple things like how to increase speeds, or reduce memory size. I also found it really useful learning about how to architect your data, project structures, and your whole pipeline. Again, it's the less glamorous stuff that people don't care about but it’s critical!”

Applying new advanced data science skills at easyJet

Coming out at the other end of the apprenticeship, we asked Edgar if he could give us some insight into the kinds of things he has been able to apply and achieve at easyJet, armed with the new knowledge and skills he learned on the course. 

He took us through a number of examples of practical solutions he had been working on, starting with improvements he had made to a statistical model using a Bayesian optimisation approach. Explaining it in layman terms, here’s what he did;

“Within a statistical model, you can tune different variables to make them behave differently. Previously, I would have used methods called the grid search, and random search, right? But those methods tend to waste a lot of time. Later on during the course, I learned about the Bayesian optimisation approach. 

On a high level, what it does is speed up the whole process. I found that really, really useful because easyJet’s data platform is still not at its best status just yet. The memory, the CPU, the GPU is not fully optimised, if you like. So speeding up the machine learning model in itself, is very important for easyJet. And by using Bayesian optimisation tuning techniques for this particular model, I was able to execute the predictions, which is fantastic.

In one example, using this approach yielded a x5 increase in speed ” 

In the same area of Bayesian optimisation, he helped to solve an important query for easyJet;

"One of the operational analysts came and asked me whether we are able to use Bayesian techniques or use a probability approach to predict which airport has the highest level of flight delays. I looked through my Cambridge Spark resources and tried to formulate the problem through this Bayesian formula but surprisingly, the solution only came to me later when I was playing tennis. It’s usually the most unexpected time when the aha moment comes! I was playing and thought, ‘Ah, yes, I could do that!’ and worked out the solution. It’s a good example of how I used what I learned from Cambridge Spark to try to formulate a problem, to give it a framework within which to solve it.

The result is that Ground Operations are now able to ramp-up the crew standby based on airports that have the highest delays” 

As a final example, the following describes the system Edgar is working on to predict the correct number of standby cabin crew replacements; 

"For every flight there's always a chance of pilot or cabin crew absences. And when that's the case, you always want to have a standby replacement on hand. Trying to estimate how many crew members should be on standby can be tricky. We don't want to overestimate the number because if you do that, we are wasting money. But if the standby number is underestimated, it risks flights being cancelled. So there's a fine balance. As a solution, we've been trying to use a machine learning model to predict the actual standby usage needed . This new model is currently in its testing phase.”

Closing thoughts

Finally, we asked Edgar if he would recommend Cambridge Spark to others looking to upskill in data science and here’s what he had to say;

I feel I've become ambassador of Cambridge Spark in easyJet, to be honest! I have a lot of people asking about my experience, and I always recommend you guys.”

Ready to start your own data science journey?

Cambridge Spark offers both entry level data science apprenticeships as well as the more advanced Level 7 AI and Data Science apprenticeship referenced in this case study. 

And if your company doesn’t meet apprenticeship requirements, we also offer corporate training courses such as our Data Fluency for Leaders course, our Data Analysis Foundations course and our Digital Leader Executive programme. 

What to find out more? Fill out the form below and one of our consultants will contact you directly to answer any questions you may have.

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Fill out the following form and we’ll email you within the next two business days to arrange a quick call to help with any questions about the programme. We look forward to speaking with you.

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An Analysis and Assessment of EasyJet's Strategy and Options: A Case Study

Student thesis : Master thesis

This analysis describes, analyzes, and assesses easyJet and the market it operates in. It proves a statistically relevant and strong relationship between the highly regulated and capital-intensive airline industry’s (and its individual companies’) top-line (i.e. revenues) growth and the condition of the overall economy (i.e. the GDP growth rate). It also shows how in an extremely competitive market, that nevertheless has a tendency for oligopolies on a specific route, an individual carrier’s bottom-line (i.e. the profit margin) is largely a function of (i) finding the right place in the value chain, (ii) differentiating services, and (iii) building comparative (cost) advantages. The analysis of easyJet’s strategy and options, furthermore, reveals that its business has relative to peers to cope with (i) weak non-seat revenues and (ii) higher costs, meaning easyJet is facing issues with respect to both top- and bottom-line. The assessment of easyJet’s theoretical value follows a present value approach, based on models looking at discounted cash flows and the economic value added. The results being challenged by multiple sensitivity analyses. As per reference date (November 15, 2016) and based on the analyses and assessment of strategy and options, the theoretical value of easyJet comes in at 1,766.44 pence per share, indicating a premium of 62.5% relative to the corresponding London Stock Exchange closing price (1,087 pence), however, it is approximately only 14% above easyJet’s pre-BREXIT level, and 6.6% below the shares’ all-time high (April 13, 2015). Consequently, the analysis and assessment of easyJet’s strategy and options c.p. indicate: easyJet is better suited relative to its peers than market price suggests and/or that investors on average assume higher uncertainty and/or make more conservative assumptions regarding its prospective top- and bottom-line development.

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Case Study: Urban Change in Liverpool

An overview of liverpool.

Liverpool is a port on the north-west coast of England. It is built upon the estuary of the River Mersey and is close to Manchester.

What is the importance of Liverpool?

• Liverpool's port was a hub for trade with the United States of America and was an important shipping port in the slave trade.
• Cotton prices in England were benchmarked by the prices that were available in the market by Liverpool's port.
• Liverpool is home to Liverpool Football Club (and Everton).

Impacts of national migration on Liverpool

• Liverpool has populations with strong origins from Wales and Ireland.
• The Irish potato famine of 1845-1849 led many to sail across the Irish Sea to Liverpool.
• Liverpool is on the border of Wales, so there is also a strong Welsh contingent in Liverpool.
• In 2019, there were estimates that 3/4 of Liverpool's population had Irish heritage of some kind.

Impacts of international migration on Liverpool

• Liverpool's port has been a trading hub with the world for many years.
• People who come from Liverpool are known as Scousers and have a strong accent, distinctive to many other parts of the UK.
• The decline of the UK's physical manufacturing industries has hurt Liverpool's economic wellbeing as there are fewer physical goods to export via ship.
• International migration has helped Liverpool to end their Premier League title drought, with Mohammed Salah, Sadio Mane, and Roberto Firmino migrating from Egypt, Senegal, and Brazil to improve a weak strike force.

Opportunities Created by Urban Change in Liverpool

Liverpool's transition from a thriving port and manufacturing hub towards a tourist destination and creative industry hub has led to the following opportunities:

Social opportunities

• The migration has created more ethnic diversity and more diversity of opinion in Liverpool.
• Recreation and entertainment have improved and international migration now allows Liverpool Football Club to beat everyone, instead of losing with Daniel Sturridge and Christian Benteke playing up front.
• The Albert Dock in Liverpool has become a cultural hub, home to museums, docks, restaurants, and bars.

Economic opportunities

• Liverpool John Lennon Airport has become a transport hub for the north of England and Easyjet has lots of cheap flights flying out of the airport every day.
• Merseytravel is a single business that operates all of Liverpool's transport infrastructure, making it very convenient for customers.
• In 2018, 494,000 people lived in Liverpool and only 4.2% of people were unemployed.

Environmental opportunities

• Urban greening involves turning brown belt sites or wasteland in cities into green spaces like parks.
• The Liverpool ONE development included a brand new park in Liverpool City Centre.

Challenges Created by Urban Change in Liverpool

Liverpool's decline from a thriving port in the 1800s, to a struggling area from 1950 to a new tourist destination with creative industries has created new problems for the city.

Social and economic challenges

• As with many cities, inequality grows as the city gets larger.
• Parts of Liverpool like Croxteth and Anfield are dominated by council housing and low income residents.
• There is great inequality between people living in these areas and those living in brand new developments in the new Beetham Tower.
• People in the less advantaged areas often perform worse in school, have a lower life expectancy and are more likely to become unemployed in the future.

Environmental challenges

• In 2019, 22 green belt sites in the Wirral were protected from development. Although the projects did not go ahead, this shows the pressure to build on green belt land when a city is growing quickly.
• Newsham Park Asylum and the Liverpool Fruit Exchange are now derelict, unused buildings. However, these buildings are also opportunities for redevelopment.
• In 2014, a local newspaper report claimed that Liverpool did not have enough capacity to recycle all the relevant materials, so lots of things that could be recycled were put in landfill sites instead.

1 The Challenge of Natural Hazards

1.1 Natural Hazards

1.1.1 Types of Natural Hazards

1.1.2 Hazard Risk

1.1.3 Consequences of Natural Hazards

1.1.4 End of Topic Test - Natural Hazards

1.1.5 Exam-Style Questions - Natural Hazards

1.2 Tectonic Hazards

1.2.1 Tectonic Plates

1.2.2 Tectonic Plates & Convection Currents

1.2.3 Plate Margins

1.2.4 Volcanoes

1.2.5 Effects of Volcanoes

1.2.6 Responses to Volcanic Eruptions

1.2.7 Earthquakes

1.2.8 Earthquakes 2

1.2.9 Responses to Earthquakes

1.2.10 Case Studies: The L'Aquila & Kashmir Earthquakes

1.2.11 Earthquake Case Study: Chile 2010

1.2.12 Earthquake Case Study: Nepal 2015

1.2.13 Living with Tectonic Hazards 1

1.2.14 Living with Tectonic Hazards 2

1.2.15 End of Topic Test - Tectonic Hazards

1.2.16 Exam-Style Questions - Tectonic Hazards

1.2.17 Tectonic Hazards - Statistical Skills

1.3 Weather Hazards

1.3.1 Global Atmospheric Circulation

1.3.2 Surface Winds

1.3.3 UK Weather Hazards

1.3.4 Tropical Storms

1.3.5 Features of Tropical Storms

1.3.6 Impact of Tropical Storms 1

1.3.7 Impact of Tropical Storms 2

1.3.8 Tropical Storms Case Study: Katrina

1.3.9 Tropical Storms Case Study: Haiyan

1.3.10 UK Weather Hazards Case Study: Somerset 2014

1.3.11 End of Topic Test - Weather Hazards

1.3.12 Exam-Style Questions - Weather Hazards

1.3.13 Weather Hazards - Statistical Skills

1.4 Climate Change

1.4.1 Evidence for Climate Change

1.4.2 Causes of Climate Change

1.4.3 Effects of Climate Change

1.4.4 Managing Climate Change

1.4.5 End of Topic Test - Climate Change

1.4.6 Exam-Style Questions - Climate Change

1.4.7 Climate Change - Statistical Skills

2 The Living World

2.1 Ecosystems

2.1.1 Ecosystems

2.1.2 Ecosystem Cascades & Global Ecosystems

2.1.3 Ecosystem Case Study: Freshwater Ponds

2.2 Tropical Rainforests

2.2.1 Tropical Rainforests - Intro & Interdependence

2.2.2 Adaptations

2.2.3 Biodiversity of Tropical Rainforests

2.2.4 Deforestation

2.2.5 Case Study: Deforestation in the Amazon Rainforest

2.2.6 Sustainable Management of Rainforests

2.2.7 Case Study: Malaysian Rainforest

2.2.8 End of Topic Test - Tropical Rainforests

2.2.9 Exam-Style Questions - Tropical Rainforests

2.2.10 Deforestation - Statistical Skills

2.3 Hot Deserts

2.3.1 Overview of Hot Deserts

2.3.2 Biodiversity & Adaptation to Hot Deserts

2.3.3 Case Study: Sahara Desert

2.3.4 Desertification

2.3.5 Case Study: Thar Desert

2.3.6 End of Topic Test - Hot Deserts

2.3.7 Exam-Style Questions - Hot Deserts

2.4 Tundra & Polar Environments

2.4.1 Overview of Cold Environments

2.4.2 Adaptations in Cold Environments

2.4.3 Biodiversity in Cold Environments

2.4.4 Case Study: Alaska

2.4.5 Sustainable Management

2.4.6 Case Study: Svalbard

2.4.7 End of Topic Test - Tundra & Polar Environments

2.4.8 Exam-Style Questions - Cold Environments

3 Physical Landscapes in the UK

3.1 The UK Physical Landscape

3.1.1 The UK Physical Landscape

3.2 Coastal Landscapes in the UK

3.2.1 Types of Wave

3.2.2 Weathering & Mass Movement

3.2.3 Processes of Erosion & Wave-Cut Platforms

3.2.4 Headlands, Bays, Caves, Arches & Stacks

3.2.5 Transportation

3.2.6 Deposition

3.2.7 Spits, Bars & Sand Dunes

3.2.8 Case Study: Landforms on the Dorset Coast

3.2.9 Types of Coastal Management 1

3.2.10 Types of Coastal Management 2

3.2.11 Coastal Management Case Study - Holderness

3.2.12 Coastal Management Case Study: Swanage

3.2.13 Coastal Management Case Study - Lyme Regis

3.2.14 End of Topic Test - Coastal Landscapes in the UK

3.2.15 Exam-Style Questions - Coasts

3.3 River Landscapes in the UK

3.3.1 The River Valley

3.3.2 River Valley Case Study - River Tees

3.3.3 Erosion

3.3.4 Transportation & Deposition

3.3.5 Waterfalls, Gorges & Interlocking Spurs

3.3.6 Meanders & Oxbow Lakes

3.3.7 Floodplains & Levees

3.3.8 Estuaries

3.3.9 Case Study: The River Clyde

3.3.10 River Management

3.3.11 Hard & Soft Flood Defences

3.3.12 River Management Case Study - Boscastle

3.3.13 River Management Case Study - Banbury

3.3.14 End of Topic Test - River Landscapes in the UK

3.3.15 Exam-Style Questions - Rivers

3.4 Glacial Landscapes in the UK

3.4.1 Erosion

3.4.2 Landforms Caused by Erosion

3.4.3 Landforms Caused by Transportation & Deposition

3.4.4 Snowdonia

3.4.5 Land Use in Glaciated Areas

3.4.6 Tourism in Glacial Landscapes

3.4.7 Case Study - Lake District

3.4.8 End of Topic Test - Glacial Landscapes in the UK

3.4.9 Exam-Style Questions - Glacial Landscapes

4 Urban Issues & Challenges

4.1 Urban Issues & Challenges

4.1.1 Urbanisation

4.1.2 Urbanisation Case Study: Lagos

4.1.3 Urbanisation Case Study: Rio de Janeiro

4.1.4 UK Cities

4.1.5 Case Study: Urban Regen Projects - Manchester

4.1.6 Case Study: Urban Change in Liverpool

4.1.7 Case Study: Urban Change in Bristol

4.1.8 Sustainable Urban Life

4.1.9 End of Topic Test - Urban Issues & Challenges

4.1.10 Exam-Style Questions - Urban Issues & Challenges

4.1.11 Urban Issues -Statistical Skills

5 The Changing Economic World

5.1 The Changing Economic World

5.1.1 Measuring Development

5.1.2 Classifying Countries Based on Wealth

5.1.3 The Demographic Transition Model

5.1.4 Physical & Historical Causes of Uneven Development

5.1.5 Economic Causes of Uneven Development

5.1.6 How Can We Reduce the Global Development Gap?

5.1.7 Case Study: Tourism in Kenya

5.1.8 Case Study: Tourism in Jamaica

5.1.9 Case Study: Economic Development in India

5.1.10 Case Study: Aid & Development in India

5.1.11 Case Study: Economic Development in Nigeria

5.1.12 Case Study: Aid & Development in Nigeria

5.1.13 Economic Development in the UK

5.1.14 Economic Development UK: Industry & Rural

5.1.15 Economic Development UK: Transport & North-South

5.1.16 Economic Development UK: Regional & Global

5.1.17 End of Topic Test - The Changing Economic World

5.1.18 Exam-Style Questions - The Changing Economic World

5.1.19 Changing Economic World - Statistical Skills

6 The Challenge of Resource Management

6.1 Resource Management

6.1.1 Global Distribution of Resources

6.1.2 Food in the UK

6.1.3 Water in the UK 1

6.1.4 Water in the UK 2

6.1.5 Energy in the UK

6.1.6 Resource Management - Statistical Skills

6.2.1 Areas of Food Surplus & Food Deficit

6.2.2 Food Supply & Food Insecurity

6.2.3 Increasing Food Supply

6.2.4 Case Study: Thanet Earth

6.2.5 Creating a Sustainable Food Supply

6.2.6 Case Study: Agroforestry in Mali

6.2.7 End of Topic Test - Food

6.2.8 Exam-Style Questions - Food

6.2.9 Food - Statistical Skills

6.3.1 The Global Demand for Water

6.3.2 What Affects the Availability of Water?

6.3.3 Increasing Water Supplies

6.3.4 Case Study: Water Transfer in China

6.3.5 Sustainable Water Supply

6.3.6 Case Study: Kenya's Sand Dams

6.3.7 Case Study: Lesotho Highland Water Project

6.3.8 Case Study: Wakel River Basin Project

6.3.9 Exam-Style Questions - Water

6.3.10 Water - Statistical Skills

6.4.1 Global Demand for Energy

6.4.2 Factors Affecting Energy Supply

6.4.3 Increasing Energy Supply: Renewables

6.4.4 Increasing Energy Supply: Non-Renewables

6.4.5 Carbon Footprints & Energy Conservation

6.4.6 Case Study: Rice Husks in Bihar

6.4.7 Exam-Style Questions - Energy

6.4.8 Energy - Statistical Skills

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