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The Proof is in the Data: Real-World Results of Tail Assignment Optimization


In the final blog in the series on tail assignment we explore how the application can drive significant benefits by evaluating a use case.

This is the final blog post in our series on Tail Assignment Optimization. If you have not had the chance to read the previous blogs , we invite you to check them out:

In this fourth and final blog, we will provide some real-live examples that illustrate the benefits that good tail assignment optimization can bring to an airline.

As we have discussed in the previous blogs, tail assignment optimization is a complex process, but the positive impacts on airlines can be substantial. By optimizing tail assignments effectively, airlines can improve efficiency, reduce costs and improve sustainability and robustness of operations.

Great tail assignment can deliver efficiency, robustness and sustainability benefits to an airline

Let us explore these benefits in more detail, i.e. by optimizing tail assignments effectively, airlines can

  • Improve Efficiency: airlines can ensure that their aircraft are being used in the most efficient way possible. This can lead to a reduction in delays and cancellations, as well as a decrease in the amount of time that aircraft spend on the ground (i.e. reduce the overall Turnaround Time by minimizing idle time).
  • Reduce costs: airlines can minimize the costs across various areas, such as fuel, maintenance, crew, and more. This can lead to significant cost savings over time.
  • Improve sustainability: tail assignment optimization plays a crucial role in improving sustainability within the airline industry. By reducing fuel consumption through optimal aircraft assignments, airlines can lower their carbon footprint and contribute to environmental conservation efforts. This does not only benefit the environment but also enhances the reputation of airlines as socially responsible organizations.
  • Improve robustness of operations: airlines can create more robust schedules, which are more resilient to disruptions and changes. This means airlines can manage better unexpected events such as aircraft maintenance issues or airport issues (e.g. due to weather conditions, industrial action…​). This minimizes delays and cancellations, thus also reducing costs for paying out passenger compensations (in case of delayed & cancelled flights), but also improves the overall experience of the passengers, which leads to increased loyalty and repeat business.

In a use case savings of 4% could be identified using optimization

Now let us look at a real-life example of tail assignment optimization, executed by Motulus for a carrier of approximately fifty aircraft flying a combination of short and medium-haul routes. This gave following observations:

  • By playing with the parameters of the cost model (i.e. adjusting weights), the benefits in above four areas can be adapted to meet the airline’s specific priorities. For example by giving more weight to fuel costs, the tail assignment will result in more sustainability improvements, but potentially less in the other three domains. It is up to the management team of the airline to decide where the priorities should be put.
  • The tail assignment will be driven by a set of constraints (e.g. minimal turnaround times, maximum number of legs, maintenance windows and maintenance bases…​), which are implemented in the form of soft or hard constraints. These limits will give guidance to the tail assignment tool, as they considerably reduce the number of potential solutions to investigate. The choice of those constraints is therefore crucial in the tail assignment exercise.
  • scenario focused on minimizing the overall cost demonstrated an average saving of just over 4%. This saving was a combination of landing, overflying and fuel costs. The saving was delivered by allocating the most efficient aircraft (in terms of fuel and MTOW) to specific routes. This scenario gave a fuel reduction of just above 0.5%.
  • The same tail assignment, but with a scenario parameterized to put the focus on minimizing fuel consumption only (i.e. maximizing sustainability) gave a fuel reduction of close to 1%, but an overall cost reduction of less than 4%. This shows that a different focus shifts the optimal result.

Small percentage changes in fuel consumption will have significant CO2 and cost benefits

A cost saving of 4% may not seem significant at first, but when applied to an airline’s total expenses, it can translate into millions. For example in 2021 the total fuel cost for Air France-KLM and Lufthansa amounted to 2.7 billion euros and 2.4 billion euros respectively. This means a 1% fuel reduction means a cost saving of 27 and 24 million euros.

As good Tail Assignment Optimization only requires a small number of resources and a good and reliable software tool to effectively execute this complex optimization, the business case becomes compelling. Many examples show that advanced technology to optimize airline operations is a must for frontrunners nowadays, airlines not pursuing technology and digitization will possibly be left behind.

If you are interested in exploring tail assignment optimization further, we encourage you to reach out to us to discuss your operation in more detail or click this link to schedule a free tail assignment proof-of-concept. Let us demonstrate firsthand the benefits we can bring to your airline.

Thank you for joining us on this journey through Tail Assignment Optimization. We hope this series has provided valuable insights into how this powerful optimization process can benefit any airline.



Photo by Reza Rostampisheh on Unsplash