As airports recover from the global pandemic and rebuild their businesses, they will be looking to optimise their assets as much as possible, while also preparing for pandemics of the future or other operational disruptions. With data analytics, airports are in a position to optimise their operations without making any significant capital investments.
By Per Engelbrechtsen
In this article we look at how data analytics can optimise the baggage handling system (BHS) not just for present recovery but for future operations. With the volumes of data already available to airports, there really is no reason not to implement data analytics.
The aviation sector has suffered enormously as a result of COVID-19 and will be facing many post-COVID challenges.
In order to deal with the challenges and rebuild their businesses, airport managers will need to be thinking in terms of three core concepts:
So, airports will need to reshape to address the current crisis but also need to prepare to react to future disturbances in operations. As such, the need for the use of data has never been in sharper focus. The post-COVID airport will be about data-driven asset management and using existing data to work remotely with fewer people.
This is especially true for the airport’s BHS. Data is one of the most important elements for baggage flow and capacity and will be responsible for future-proofing BHS operations.
Let’s look at the role that data and data analytics can play in the BHS.
In the non-linear world of aviation, the ideal production of the BHS can be challenged on three levels:
Data is a way of dealing with these uncertainties. And even in the absence of these challenges, the ability to use data enables the airport organisation to replace today’s guesswork with informed decisions.
But how is data gathered in a BHS?
The raw data required for analysis can be sourced at any point throughout the BHS. It can be gathered from its alarm logs, sensors, event logs and the various data that the BSH captures. It is collected on-premise at the airport and live streamed to a data warehouse.
The data is then enriched, through the skills and know-how of the BHS provider and visualised through a cloud solution. In much the same way a crop is transformed into something edible, the data is presented in a way that makes sense to the user. The same data is shown in different visualisations to cater for the different types of jobs within the airport.
By capturing and analysing data, airports are able to use the insights the data gives through different tiers of data analytics.
With data analytics, airports can then start optimising their BHS maintenance, operations and management.
Data analytics can support predictive maintenance by highlighting the parts of the BHS most used and needing the most maintenance. Data can show real-time operational KPIs, such as the performance of different screening areas. And it can assist in seasonal benchmarking, forecasting of flow and resources planning. Data analytics can give insight into both the system’s general performance and specific performance areas, such as check-in and loading.
We can glean the possibilities data analytics can offer by looking at an international airport that has applied data analytics to optimise its BHS.
YYC Calgary International Airport (YYC) in Canada is a hub airport that saw traffic of more than 18 million passengers in 2019.
YYC has applied machine learning algorithms to monitor its BHS alarm log files, through which it is now able to predict possible future problems within the system. Issues are detected before they become problems, including microstops that the human eye can’t spot. Specific elements of the system needing attention are now identified, directing the focus of maintenance staff and saving time. This proactive approach prevents many unwanted stops and ensures the system runs 100% of the time.
Data is used to analyse baggage flow throughout the system and identify those areas requiring improvement. Specific data such as recirculating bags, manual encoding bags and mishandled bags are within 3D modelling, which enables a deeper understanding of how the flow and processes can be changed, improved and optimised.
Bottlenecks causing increased system time, unnecessary system wear and additional operator workload are identified and changed. The direct result? A significant reduction of bags requiring manual encoding and not reaching their flights in time.
The implementation of data-driven, condition-based monitoring tools has enabled better maintenance schedules and streamlining of teams. Preventative maintenance is executed with a lean approach and more flexible use of staff, without compromising the quality of the O&M operations.
For YYC, the use of data analytics has resulted in changes to how it addresses O&M, leading to significant reductions in its OPEX.
In addition to how data analytics can assist airports in the maintenance, operation and management of their BHS, there are further facets of data analytics that can help optimise the BHS. In brief, these are:
As airports recover from the global pandemic and rebuild their businesses, there will be increased interest in strategic KPIs and KPIs for overall equipment efficiency in their baggage handling. There will also be interest in ensuring systems can survive any future operational disruptions. Data analytics is the answer for airports looking to both optimise and future-proof their systems. It can provide insights in all parts of the BHS operations and processes that cannot be otherwise gleaned. From the analyses, BHS professionals can change, improve and optimise baggage flows and maintenance and resourcing schedules. With the future of data analytics increasingly moving into decision science, airports can look forward to their systems being able to recommend and make decisions for them.
