Efficiency and agility in supporting operational decision-making

Knowledge has always been the basis for making the best decisions. Even with all the speed and ease of information available today. Thus, optimization is the keyword of assertive management, which leads to good results. Aware of this, Zurich Airport Brasil implemented a program that organizes data from operations with agility, efficiency and objectivity, for better operational decision-making on a daily basis.

Even in the dynamic routine of airports, with situations commonly seen in terminals around the world, where flights are canceled or schedules are changed, the trip should not be a stressful time for the passenger. The challenge of maintaining a standard of service quality in these scenarios is even greater.

That was one of the lessons learned. Decisions must be assertive and consistent. With the creation of the new information management model, it is possible to obtain agility in decisions, through a complete mapping of the data, linked to operational intelligence, allowing cost optimization, quality of services, and above all, maintaining an optimal standard. of the service level.

The foundations of the project

At first, the project started with the objective of generating information for the operational sectors, which would know the passenger demand and the main peak hours of movement at the airport. This transfer of information occurred on a monthly basis, which created a problem because it was passively positioned in relation to the transfer of information, generating a slowness and information bottlenecks for operational decision-making.

With this phase implemented, there was a need for information to be available 24/7 to all people involved in the operational plan. In this way, an automated platform was created that works as an information hub that collects data from different systems, processes them and automatically makes available and informs who needs each specific information.

From the moment that information is unlocked and automatically delivered to the right people, it moves from a reactive mode of operational management to a more predictive mode, where it is possible to prepare operational agents to anticipate changes and allow them to make better allocations. of resources.

The foundations of this program were based on 3 pillars, which are:
 

People

All types of data analysis arise through questions and hypotheses from people who need answers to make some kind of decision in their daily lives. In this way, all the information available on the platform arises from questions from the people involved in the operational plan and thus they become the catalysts of the entire process.

Technology

 Technology is the way information flows from the source of the data to the devices of the people who need it. Thus, to implement this platform, technological tools are needed so that the data generated by the company's internal and external systems are automatically collected, processed, calculated and delivered to the right people.
 

Production

In order to generate consistency for the platform, it was identified the need to fit all the information generated, in different stages of a simple and effective management process. Thus, it uses the Planning, Execution and Review stages. In the planning phase, detailed information about the operational plan is delivered based on the forecast demand. In the execution phase, the management of the flow of passengers, aircraft and resources is carried out and the information is delivered in order to maintain communication and awareness of operational situations in a common way, where all agents have the same information in a synchronized way. Finally, for the review stage, information is generated that is used to raise improvement actions, review performance and provide inputs for a new planning cycle.

Solution for all airport components

From this, it is possible to carry out the application of this planning, execution and review process in different components of the airport infrastructure, where each one has its particularities and operates in a synchronized way, giving the possibility to extract an overview of the points and moments that further stress the infrastructure. With this, we can detail the applications in each of the components:

               Half wire

              The criticality of this component ends up being high, due to being one of the first contacts that the passenger has with the airport. In a scenario where a passenger who is boarding and finds the curb congested, it generates a negative impact at the beginning of the passenger's journey. This brings the need for adequate planning of actions to increase the flow on the curb, which translates into a reduction in the downtime of each vehicle that accesses this component. To meet this aspect, through gates positioned at the entrance and exit, data are collected from each vehicle that passes through the component in order to be able to measure the average time that each vehicle remains on the curb. Through these data, crossed with data from the aerial network, it is possible to calculate the number of cars expected and measure the moments with a tendency of overload and congestion, thus bringing important information for the planning of actions to increase the flow of vehicles to be carried out in the component.

                   Terminal cleaning

              A relevant factor to provide an excellent service to passengers is the cleanliness of the terminal environment, be it bathrooms, floors, windows, among others. To offer an excellent experience in a complete way, we have cleaning teams that are sized according to the volume of movement and the curve of passengers that occurs during the day. Thus, the model automatically calculates, whenever there are changes in flight demand, the necessary dimensioning of cleaning teams. In addition, it provides detailed information for drawing up cleaning plans, showing which bathrooms and times of day are likely to be overloaded. This information generates insights for the employees who manage the contracts, allowing them to carry out actions that optimize the allocation of resources. 

                inspection channel

              It is known that the formation of uncontrolled queues is the most critical situation of this component and if there is no adequate planning of the amount of equipment operating at each moment of the day, in a way that optimally follows the arrival curve of passengers, it tends to the formation of uncontrolled queues. This type of situation has a major impact on the passenger experience, reducing the quality of services provided by the airport. At the same time, it is not interesting for the airport to have an oversizing of operating equipment, which generates idleness and unnecessary costs. In this way, the developed program allows for quick action regarding the inclusion or removal of operating equipment at each moment of the day, according to the variation in passenger demand. That is, whenever there are changes in the demand for flights, the model automatically calculates the real need for equipment, giving our employees the chance to make the decision in advance and plan the allocation of this resource in an optimized way.

              boarding bridges  

              Once slots are approved to operate at the airport, flights must be allocated at boarding bridges in each terminal. This allocation process becomes very important, as it dictates the distribution of passengers inside the departure lounge, which impacts commercial consumption, use of restrooms, availability of seats, maintenance of equipment, among others. With this in mind, the platform was parameterized to always indicate that a boarding bridge is being overloaded in a certain period, aiming at a balanced distribution of passengers in the departure lounge.      

Next Steps

From the moment we have the main airport components dimensioned and parameterized in relation to the level of service we want to offer our passengers, the next step we see is to increase the volume and data collection points, in order to improve each time more the calculation models, opening opportunities for the development of Machine Learning algorithms and the implementation of Artificial Intelligence to increase the automation between data collection and the delivery of information for decision making.