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A Bird’s Eye-View On Use Of AI & ML In Airlines Industry

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The advent of many low-cost airlines across the world in the early 2000s fuelled the business transformation of the overall industry, said Rahul Chogle, Head of Data and Analytics at SpiceJet, during his talk at the third edition of MLDS 2021.

The keynote speaker started the session by providing a quick overview of the airline industry and how SpiceJet started using emerging technologies like AI and machine learning almost three years ago. The session gave an insider’s perspective on airline business and the role of AI and machine learning in facilitating a smooth travel experience. 

The  airline industry has embraced data science and machine learning-based decision making with a full heart. He said, “At SpiceJet, our journey started with a focus on maximising revenue. A collaborative effort with key stakeholders, and with the guidance of executive management, we focused on multiple facets of commercial initiatives to improve decision making.” He added, “Currently, we are also looking into potential cost initiatives that could lead to significant savings for the internal value chain.”


Chogle also shed light on the “under the hood” factors shaping the industry, such as compliance, ATF costs, etc. He then gave a detailed explanation on the functional map of the airline that includes two categories- revenue and operations along with other support functions, such as financial, legal, admin, training, etc. While revenue is all about growing in the network, selling seats at the highest possible price,etc, operations include everything related to the airports.

The speaker mentioned some of the core airline functions to maximise revenues, minimise costs and provide a differentiated customer experience. The core functions include:

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  • Revenue Management: Generate, sustain, match demand for optimal revenue with relevant pricing
  • Marketing & Loyalty: Branding, product planning and improved customer understanding, etc.
  • Flight Ops: Aircraft performance monitoring, technical route planning, minimising the impact of disruptions, etc.
  • Engineering: Optimal inventory and minimal costs towards maintenance

Talking about the industry and analysis, Chogle detailed the role of AI and machine learning in airlines industry. According to him, AI and machine learning can help by:

  • Enabling scenario planning: Machine learning can help bring richer perspectives to scenario planning by deciphering the trends in competitive capacity/ fare strategy, etc.
  • Quicken the pace of analytics: Delay, predictions, forecasting congestions at airports can provide a critical lead time for the airline to minimise disruptions. 
  • Deal with the complexity of datasets as well as complex business challenges: AI and ML are useful for forecasting demand, passenger no-show or cancellation and access revenue impact of the course of action.
  • Encourage being “Future Ready”: Support functions such as HR, financial, legal, have been increasingly looking into AI-led tools for data-driven decision making.

Before wrapping up, Chogle stressed on the need to invest more in training and delivering solutions that create a measurable impact on business outcomes.

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