Both Are Models. One Is Built for the Track.

Fifteen years ago this month, Finnair became the first airline operator in the world to influence crew pairings and rosters using a bio-mathematical fatigue model directly during optimization. Instead of only evaluating fatigue risk after the roster had already been built, the optimizer continuously used BAM to nudge the results toward a part of the solution space better aligned with human physiology - creating work patterns with much lower overall risk exposure.

Today, 15 years later, more than 70 operators use BAM in their crew management process, and there is still not a single other model capable of coping with real-time interaction with industry-strength optimizers. Why is that? Well, the performance requirements are brutal. Some operators require fatigue predictions to keep pace with optimization engines processing up to 250,000 rostered days per second, for many hours. At that scale, the bio-mathematical model itself - and the way the model and application communicate - must be engineered from the outset to survive and perform under extreme computational pressure. BAM is engineered to do exactly that.

So the question is: are you considering truly proactive crew management that moves the needle on overall operational risk exposure? Or are you content “fixing” the ten worst rosters at the end of the process with your current model - essentially playing for optics?

Because in the end, both are fatigue models. But one was built for the track. Compare your current model to BAM using this matrix, and let us know when you are ready to buckle up for a test run.