Business Case: London Heathrow Airport Launches BI and Machine Learning to Improve Airfield Management, Predict Passenger Flow, and Transform Airport Security
Heathrow airport in London is the second busiest international airport in the world, second only to Dubai international airport in number of airplanes landing and taking off each day and the seventh largest in terms of total passenger traffic. Managing over 215,000 passengers every day is a challenging task and requires a high degree of coordination to manage passenger traffic and give passengers a smooth airport experience. Any unexpected disruptions in the smooth workflow in operations at Heathrow such as damaged runways, storms, delayed or canceled flights, shifts in jet streams, etc. would disturb the entire functioning of the airport, passengers and airport employees.
Data analysts at London Heathrow were using Excel spreadsheets to analyze its airfield, passenger and flight data and sorely needed a centralized management system that would extract large volumes of data produced by airport operations and transform them into useful visual insights. Stuart Birrell, CIO at Heathrow was concerned that “We have tens of thousands of people who work around the airfield. Safety is critical. Adopting tools like Power BI makes life easier. It is the simple things. There is GPS in the airfield vehicles. If a driver finds a problem with the concrete, this can be recorded accurately.” Heathrow chose Microsoft Power BI as their BI solution. The reporting produced by its BI tool ensured airfield safety, allowed airport staff to function better and improved passenger management.
The key was moving from a paper-based, reactive operations model to a more predictive, proactive planning model in which staff were dealt fewer surprises on a day-to-day basis that enabled them to change their plans on-the-fly. The answer was BI reports and dashboards that were made available to airfield managers, security officers, transfers and customer service staff and a machine learning model that accurately predicts passenger flow in 15-minute increments into each terminal. Birrell says it's possible to mash up historical scheduling data and a feedback loop to provide more accurate forecasts. With insights from these data analytics tools managers could plan staff breaks, open and close security lanes as needed and schedule staff shifts to balance passenger flow across the airport in peak times. As Birrell said, “For passengers, it is all about getting them to aircraft on time.” The new system also helps manage arrivals. Under the old model, if several flights came into the airport an hour early because of tailwinds immigration and baggage staff would have to scramble to react to the sudden spike of arriving passengers. After the predictive model was deployed, the airport manager could share the insights with air traffic control and security staff to better schedule immigration and security lanes and teams by knowing where passengers are arriving, how many of them are arriving and at what terminal to ensure the right number of immigration/security lanes are open and reduce time and stress for both passengers and airport staff.
For Birrell, the biggest challenges were not technology-related but were about a culture and mindset shift to get people onboard. “It's easy to do a bit of data analysis with one or two experts. It's more about how you deploy this around your organization; how do you get that security team of 4,000 to start using that data and change the way they're working,” says Birrell. Now London Heathrow has people with its operations team who are deploying and building their own apps including one security officer who learned to build apps on his own with a little help from the IT department. So far, he has developed 12 apps to support his colleagues in security.
Questions:
Why did London Heathrow need to move up from Excel to a business intelligence platform?
What are the benefits that London Heathrow passengers experienced as a result of the new approach to data analytics?
Describe one specific way in which machine learning improved London Heathrow operations.
What was the biggest challenge that CIO Birrell faced in deploying BI and machine learning at London Heathrow?