How Optima is helping airlines evaluate billions of maintenance planning scenarios and make more informed lifecycle decisions
For many years, engine maintenance planning was built around a combination of engineering expertise, experience and increasingly sophisticated spreadsheets.
That approach worked because the variables while complex were still manageable, but today’s environment looks very different.
The Growing Complexity of Engine Planning
Powerplant teams are being asked to balance maintenance costs, engine availability, lease obligations, spare engine requirements, regulatory compliance and fleet strategy simultaneously, often against a backdrop of supply chain disruption, shop capacity constraints and changing operational demands.
A single decision around an engine induction can have implications for future maintenance requirements, lease compensation, spare engine availability and long-term fleet flexibility.
The challenge is not a lack of expertise. It is the sheer number of interconnected decisions that need to be evaluated at the same time. For a fleet of just 50 aircraft, there can be billions of possible engine maintenance planning permutations over a ten-year period. Assessing the downstream consequences of every option manually is becoming increasingly difficult, even for highly experienced teams.
From Reactive Planning to Scenario Modelling
As a result, a growing number of airlines are exploring whether advanced modelling techniques can complement traditional planning methods. By evaluating large numbers of potential maintenance scenarios, these tools help planners understand the trade-offs between cost, risk and operational resilience before key maintenance decisions are made.
One example is Optima, a collaborative initiative developed by PA Consulting, Decision Brain and TGIS Aviation. Designed to help airlines optimise engine maintenance planning and reduce total engine life costs by up to 20%, the system creates a digital representation of an airline’s engine planning environment and evaluates alternative maintenance, utilisation and retirement scenarios, allowing teams to understand the longer-term consequences of decisions before they are made.
The objective is not to replace engineering judgement. Rather, it is to provide planners with better visibility of the trade-offs that exist across increasingly complex fleets and maintenance programmes. By using advanced optimisation techniques, maintenance planning activities that historically took powerplant engineers several months to rework manually can now be generated in a matter of days.
The growing interest in this type of approach reflects a broader trend across aviation asset management. As fleets become more complex and maintenance costs continue to rise, the future of engine planning may be less about creating better spreadsheets and more about equipping experienced teams with the tools capable of evaluating a level of complexity that would previously have been simply impossible to analyse.
For airlines, lessors and investors alike, the question is no longer whether data-driven planning will become part of engine management but how quickly organisations will adapt to take advantage of it.
Read more about Optima here
