As per IATA Resolution 753, which was approved in 2013, from Jun-2018 IATA member airlines will be required to keep track of every item of luggage from start to finish.
Nevertheless, a SITA survey has found that just 60% of airlines have predicted that they would be able to track bags across more than three quarters of their networks by Jun-2018, let alone their full networks. The majority of airlines are seemingly working towards achieving compliance by the end of the year.
As exhibited by IATA, aviation passenger demand is forecast to soar from around 4 billion to 7.8 billion passengers in 2036. As a consequence of this growth, huge pressure can be expected on all aspects of air transport infrastructure, including baggage operations.
SITA's report notes that 4.5 billion bags are handled globally by the air transport industry, with the figure forecast to double in the next 20 years. But with adoption of the new resolution, the bag tracking documentation that is generated will provide the air transport industry with a new, rich stream of data.
The question is: what is the future technology differentiator for baggage? Can this data be used effectively to optimise operations and improve customer satisfaction?
The case for AI in baggage handling:
In the white paper SITA explores whether artificial intelligence (AI) could be the game changer required to transform the baggage-handling industry. AI tools such as machine learning, robotics, and predictive analytics are still in the early stages of evolution but have a chance to revolutionise the management of baggage, and could one day make the issue of mishandled bags near obsolete.
Looking to Amazon's cloud based voice service Alexa, a clear sample of AI and machine learning emerges.
Several airlines, such as Air Canada, have developed skills or apps for Alexa. In Nov-2017 Air Canada integrated general travel questions into the device. Air Canada workers can simply request any Alexa enabled device to deliver customer information, which can then be relayed to the passenger. The information includes (yet is not limited to) flight status, receiving fare quotes, and finding at which baggage carousel to pick up baggage.
SITA also cites another example in Korean Air, which has been working with IBM's AI platform, 'Watson'.
Specifically, the carrier worked with Watson to make historical maintenance records for its fleet accessible and searchable. According to SITA, Watson ingested structured and unstructured data from multiple sources such as technicians' notes, technical guidelines and inflight incident histories. Then Watson Explorer, IBM's cognitive exploration and content analysis platform, was deployed to locate previously hidden connections that helped maintenance crews diagnose and solve problems more quickly.
The combination of AI and tracking data could lead to more "cognitive solutions" for baggage handling, such as those mentioned above. An example could be labour scheduling and forecasting.
On a day that is disrupted by cancellations due to bad weather conditions, an airline could use AI with the new mandated baggage-tracking data to have full visibility of all cancellation data, using it to put together a labour schedule that can help gather available ground crew, maintenance staff, flight attendants, pilots, available gates and flight take-off/landing slots. Essentially, an AI system would help to highlight patterns of delays and errors and enable steps to be taken to remove the identified issues.
Such increased levels of connectivity and collaboration stand to benefit all parties in the long term, and would be the driver of a new baggage handling and travel experience for passengers.