Efficiency and agility for supporting operational decision-making at the airports operated by Zurich Airport Brasil

Even though information can be quickly and easily accessed nowadays, knowledge has always been the starting point for making the best decisions. Therefore, optimisation is the keyword for assertive management, which in turn leads to good results. Conscious of this fact, Zurich Airport Brasil implemented a programme that compiles data from operations in an agile, efficient, and objective manner to facilitate better operational decision-making daily.

Even in the dynamic routine of airports with situations mirrored in terminals around the world, where flights can be cancelled or rescheduled, the travelling experience should not be a stressful time for passengers. The challenge of maintaining a quality standard for services in such scenarios is even greater.

Decisions must be assertive and consistent. This was one of the lessons learned. By creating a new information management model, decisions gain agility through a complete mapping of data that is linked to operational intelligence, which in turn, allows for cost optimisation, service quality, and above all, maintenance of an optimal standard of customer service.

The principles of the project

At first, the aim of the project was to generate information for the operational areas, which would have data input for the passenger demand and the main peak hours of passenger traffic at the airport. This information used to be made available once a month, which created a problem as it was not dynamic enough for the needs of the operational agents and generated information bottlenecks for operational decision-making.

Once the first phase was implemented, a need for information to be available 24/7 to all people involved in the operational plan became evident, and to supply this demand, an automated platform that works as an information hub was created. This platform collects data from different systems, processes the information and automatically makes it available to those who require specific information.

As soon as information is made available and automatically delivered to the right people, the reactive mode of operational management becomes more predictive, and operational agents can prepare and foresee changes, which enables them to make better allocations of resources.

This programme was based on three main pillars:

People: Every type of data analysis starts with questions and hypotheses from the people who need answers to make decisions in their daily routines. Therefore, all the information available on the platform is created from questions asked by the people involved in the operational plan and, thus, they become the catalysts of the entire process.

Technology: Technology is the means through which information flows, starting from the data source and going on to the devices of the people who need it. Thus, to implement this platform, technological tools are required so that the data generated by the company’s internal and external systems are automatically collected, processed, calculated, and delivered to the right people.

Process: Three stages were created to bring consistency to the platform so that all the information generated would fit into a simple, effective, and reliable management process. These stages are called: ‘Plan, Do, and Review’. At the planning stage, detailed information about the operational plan is supplied, based on the forecast demand. At the do stage, the management of the flow of passengers, aircraft and resources is carried out and the information is delivered to maintain communication and general awareness of operational situations, where all agents receive the same information at the same time. Finally, for the review stage, the information generated is used to detect improvement actions, review performance, and provide input for a new plan cycle.

Solution for all airport components

Embracing the considerations above, the Plan, Do, Review process can be applied to different components of the airport infrastructure, where each aspect has its particularities and operates in a synchronised way, providing the possibility for extracting an overview of the points and moments that put yet further stress on the infrastructure. The application of each of the components can be detailed as follows:

Curbside

This component is highly critical as this is the first impression passengers have of the airport. A scenario where passengers who are about to catch a flight find the curbside overcrowded, will most certainly have a negative impact at the beginning of their journey. This engenders the need for appropriate planning actions to improve curb flow, which means reducing the waiting time each vehicle requires at this component. To monitor this flow, data is collected from all vehicles that go through the checkpoints placed at the entrance and exit of the drop-off and pick-up area. This data is used to measure the average time each vehicle remains on the curbside and is then cross-checked with data from the scheduled flights. This analysis makes it possible to calculate the number of expected vehicles at certain times and allows for action planning for increased vehicle flow during periods of heavy traffic.

Terminal cleaning

A relevant factor for providing an excellent service for passengers is the cleanliness of the terminal area. Therefore, ensuring clean floors, windows, and restrooms, among others, becomes a part of a complete and an excellent passenger experience, and to achieve this we have cleaning teams that are organised according to the volume of passenger traffic and the service curve required during the day. Thus, whenever there are changes in flight demands, the model automatically calculates the appropriate size of the required cleaning teams. In addition, the system provides detailed information for creating cleaning plans, including which restrooms and which times of the day are likely to have higher volumes of people. This information, in turn, offers insights to the employees who manage contracts and allows them to carry out actions that will optimise the resource allocation.

Security checkpoint

It is known that queues without appropriate control at security checkpoints is the most critical aspect of this component, and if there is no adequate planning in force for the equipment required to manage the curve demand and process the passenger flow at each time of the day, disorganised queues tend to form. Such a scenario will have a major impact on the passenger experience as it decreases the quality of services provided by the airport. At the same time, an excess of equipment in operation during the times of lower passenger flow is counterproductive for the airport as it tends to generate idleness and unnecessary costs. The developed programme allows for quick actions for determining whether operating equipment needs to be added or removed, according to changes in passenger demand. This means that whenever there are changes to the flight demand, the model will automatically calculate the real need for equipment, providing our employees with the necessary information to optimise the use and allocation of resources.

Boarding bridges

Once slots are approved to operate at the airport, flights must be allocated to boarding bridges in each terminal. This allocation process becomes very important, as it directs the distribution of passengers within the departure lounge, which itself has an impact on consumer buying, use of restrooms, seat availability and equipment maintenance, among others. With this in mind, the platform was parameterised to be able to give an alert whenever a boarding bridge is becoming overcrowded during a given period so that there is a balanced distribution of passengers in the departure lounge.

Next steps

Once the main components of the airport are organised and parameterised in relation to the level of service we want to offer our passengers, the next step is to increase the volume and points for data collection. This can enable us to keep improving the calculation models, raising opportunities for the development of machine learning algorithms and the implementation of artificial intelligence to increase automation between data collection and delivery of information for decision-making processes.