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Analysis of FRMS Forum data using BAM 2 September 2011

FRMS Forum, September 2011, Montreal. DRAFT, not for distribution!. Analysis of FRMS Forum data using BAM 2 September 2011. Tomas Klemets, Head of Scheduling Safety, Jeppesen. www.jeppesen.com/frm. Content. Background and Purpose About BAM Features / Capabilities / Limitations

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Analysis of FRMS Forum data using BAM 2 September 2011

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  1. FRMS Forum, September 2011, Montreal DRAFT, not for distribution! Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling Safety, Jeppesen www.jeppesen.com/frm

  2. Content • Background and Purpose • About BAM • Features / Capabilities / Limitations • Analysis of data provided • Upcoming functionality

  3. Background and Purpose

  4. Jeppesen and Jeppesen Crew Solutions • 3,000 employees • Denver, Frankfurt, Gothenburg, Montreal, Singapore, New York, Brisbane... • Navigation, Flight planning, and: • Crew Solutions: 500 people focused entirely on crew management. • Affecting some 250,000 crew daily. • Mostly crew planning, but also day-of-ops solutions

  5. Why a need for models and BAM? • Regulatory rule sets, as well as union/pilot agreements are really binary fatigue models • Perfectly safe / Perfectly un-safe • Alignment with current science is so-so... • What you can’t measure... • Mathematical prediction models, even if not perfect, provides a continous metric... • ...to be used for influencing, to push, an overall collection of crew schedules away from unneccessary fatigue • Crew scheduling with a metric for human physiology!

  6. About BAM

  7. The science behind BAM • Based on the Three Process Model of Alertness by Åkerstedt / Folkard • Predicts sleepiness • Sleep prediction enhanced to better reflect flight operation and take sleep oppurtunity into account • No published validation studies to date on airline crew but straightforward for an airline to check applicability • Returns continous predictions on a scale 0-10,000 • High resolution a need for optimization • KSS , easy to close the loop The Karolinska Sleepiness Scale - KSS The Common Alertness Scale - CAS

  8. Science (2) - Most recent and relevant references

  9. BAM features • BAM is built to support the complex crew management processes for airlines of all sizes. • Integration with industry strength optimizers generating up to 6000 rosters per second over many hours • Initial state assumptions for pairing construction • Augmentation, acclimatisation... • Customizability • Habitual sleep length, Diurnal type,Transfer times • Sleep/wake overrides • Prediction point

  10. BAM features (2) • CAPI compliant – easy to connect and exchange • Usable in a more comprehensive risk layer taking mission context into account: weather, airport properties, crew experience, light conditions, etc... • Limitations • Predicts the average of a population • Does not take actual light conditions into account, approximates via time zone • Sleep inertia is not implemented • BAM (as any model?) should primarily be used to rank relative fatigue between flights

  11. BAM evolution • BAM is built to self-tune in a closed-loop system to collected data • Airline collections • Crowd sourcing (FDC 2011)

  12. Analysis of supplied data sets

  13. The task: Pairings Rosters A B 1094 / 90 [flights / chains] 3693 / 56 Short haul C D 188 / 47 1006 / 64 Long haul 1. Which are the worst flights and why? Example also of good ones. 2. Which are the worst pairings/rosters and why? Examples also of good ones. (Ignore mission context – all flights are equally ”difficult”).

  14. Tools used for the analysis CFAS CrewAlert Scenarios

  15. Analysis data set A – short haul pairings

  16. Overall solution statistics A

  17. Some of the worst flights *) A *) From a fatigue risk perspective ignoring mission difficulty Time of day. Slight sleep deprivation previous night, no/little chance for afternoon sleep when departing home 15:46. Many consecutive days and sectors adds a bit to the problem. 615

  18. Some of the worst flights (2) A Time of day. Slight sleep deprivation previous night, no/little chance for afternoon sleep when departing home 15:46. Many consecutive days and sectors adds a bit to the problem. 618

  19. Some of the worst flights (3) A Time of day. Slight sleep deprivation previous night, no/little chance for afternoon sleep when departing home 15:46. Many consecutive days and sectors adds a bit to the problem. 769

  20. Some of the worst flights (4) A Time of day. Sleep deprivation from falling asleep at 3AM. Many consecutive days and sectors adds a bit to the problem. 1154

  21. Some of the worst flights (5) A Time of day. Two-pilot operation through the WOCL. Sleep deprivation. 2h acclimatisation west – but small effect. 1169

  22. Data set A – low risk flights…

  23. A Day time. Sensitive to early sleep in the evenings due to early starts. >5000

  24. A Day time. Long duties, but well placed. >5000

  25. Data set A – worst and best pairings…

  26. Risk... • The operational risk we try to adress is for a flight to suffer an ”adverse event” with crew fatigue as a contributor or a direct cause • A flight! • The total operational risk (of this type) for the airline is the sum over all flights. • Most likely a weighted sum... • A pairing or a roster can rarely be modified in isolation! • All flights in a crew scheduling problem need to be assessed at once...

  27. Risk for a flight vs. risk for a pairing or a roster... • Lowest point during a flight, top of descent, average, or time below treshold. • Doesn’t really matter when reshuffling a sequence of flights! • What is worst? (recall low is bad...) • Pairing 1; active flights on 600, 3000, 3300, 2700 • Pairing 2; active flights on 700, 700, 700, 700

  28. The operational risk for an airline... CP hour on type CP hours total CP airport recency CP predicted alertness FO hour on type FO ... Airport elevation Runway length Light conditions Wind direction/force Rain/hail/snow Visibility Airport equipment Airport terrain Airport traffic Aircraft MEL items ... (More on this in the proceedings from IASS 2009)

  29. BAM and operational risk • There is no sharp threshold on predicted alertness where risk suddenly goes from non-existant to non-acceptable • Risk grows exponentially when approaching 0 • BAM is built to adress also the total risk • All flights in the lower tail of the alertness distribution makes sense to improve R A

  30. BAM and operational risk (2) • When constructing pairings and rosters – one alertness value per flight is sufficient • BAM is configurable and supports using either: • Lowest point during flight • Lowest point during a customizable part of the flight • Prediction at a certain point in the flight – like TOD • (True fatigue risk management takes mission difficulty into account when prioritising crew assignments.)

  31. Analysis data set B – short haul rosters

  32. Overall solution statistics B

  33. Some of the worst flights (1) B 392

  34. Some of the worst flights (2) B 405

  35. Some of the worst flights (3) B 425

  36. Some of the worst flights (4) B 578

  37. Data set B – low risk flights…

  38. B >5000

  39. B >3000

  40. B Example that it does not have to be that bad >xxx

  41. Data set B – worst and best rosters…

  42. ... • <added later>

  43. Analysis data set C – long haul pairings

  44. Overall solution statistics C

  45. Some of the worst flights (1) C 842

  46. Some of the worst flights (2) C 1377

  47. Some of the worst flights (3) C 1402

  48. Some of the worst flights (3) C 1402

  49. Data set C – low risk flights…

  50. C >5000

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