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Slide #1 Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling Safety, Jeppesen www.jeppesen.com/frm FRMS Forum, September.

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Presentation on theme: "Slide #1 Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling Safety, Jeppesen www.jeppesen.com/frm FRMS Forum, September."— Presentation transcript:

1 Slide #1 Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling Safety, Jeppesen www.jeppesen.com/frm FRMS Forum, September 2011, Montreal

2 Slide #2 Content 1.Background and Purpose 2.About BAM –Features / Capabilities / Limitations 3.Analysis of data provided 4.Upcoming functionality

3 Slide #3 Background and Purpose

4 Slide #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 Slide #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 cant 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 Slide #6 About BAM

7 Slide #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 Slide #8 Science (2) - Most recent and relevant references

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

12 Slide #12 Analysis of supplied data sets

13 Slide #13 Short haul Long haul PairingsRosters 1094 / 90 [ flights / chains] 3693 / 56 1006 / 64188 / 47 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). The task:

14 Slide #14 Tools used for the analysis Scenarios

15 Slide #15 Analysis data set A – short haul pairings Analysis data set A – short haul pairings

16 Slide #16 Overall solution statistics

17 Slide #17 Some of the worst flights *) *) From a fatigue risk perspective ignoring mission difficulty 615 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.

18 Slide #18 Some of the worst flights (2) 618 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.

19 Slide #19 Some of the worst flights (3) 769 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.

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

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

22 Slide #22 Data set A – low risk flights…

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

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

25 Slide #25 Data set A – worst and best pairings…

26 Slide #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 Slide #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. –Doesnt 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 Slide #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 Slide #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 Slide #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 Slide #31 Analysis data set B – short haul rosters Analysis data set B – short haul rosters

32 Slide #32 Overall solution statistics

33 Slide #33 Some of the worst flights (1) 392

34 Slide #34 Some of the worst flights (2) 405

35 Slide #35 Some of the worst flights (3) 425

36 Slide #36 Some of the worst flights (4) 578

37 Slide #37 Data set B – low risk flights…

38 Slide #38 >5000

39 Slide #39 >3000

40 Slide #40 >xxx Example that it does not have to be that bad

41 Slide #41 Data set B – worst and best rosters…

42 Slide #42...

43 Slide #43 Analysis data set C – long haul pairings Analysis data set C – long haul pairings

44 Slide #44 Overall solution statistics

45 Slide #45 Some of the worst flights (1) 842

46 Slide #46 Some of the worst flights (2) 1377

47 Slide #47 Some of the worst flights (3) 1402

48 Slide #48 Some of the worst flights (3) 1402

49 Slide #49 Data set C – low risk flights…

50 Slide #50 >5000

51 Slide #51 Data set C – worst and best pairings…

52 Slide #52...

53 Slide #53 Analysis data set D – long haul rosters Analysis data set D – long haul rosters

54 Slide #54 Overall solution statistics

55 Slide #55 Some of the worst flights (1) 487

56 Slide #56 Some of the worst flights (3) 926

57 Slide #57 Some of the worst flights (3) 956

58 Slide #58 Some of the worst flights (4) 971

59 Slide #59 Data set D – low risk flights…

60 Slide #60 >5000Best use of extra crew?

61 Slide #61 >5000

62 Slide #62 Data set D – worst and best rosters…

63 Slide #63 Summary of BAM capabilities

64 Slide #64 BAM – science Openly available for scrutiny Open sleep assumptions Open Science Data driven improvement

65 Slide #65 BAM - individualisation Diurnal type Commuting / transfer times Habitual sleep length Customizable sleep Openly available for scrutiny Open sleep assumptions Open Science Floating sleep predictions Acclimatisation Data driven improvement Augmentation

66 Slide #66 BAM - applicability >150 k p/s Paralell computing Diurnal type Commuting / transfer times Habitual sleep length Customizable sleep Pairing optimization Rostering optimization Integration Openly available for scrutiny Open sleep assumptions Open Science Floating sleep predictions Acclimatisation Risk layer Risk layer Customizable prediction point Data driven improvement Jeppesen Support and development Augmentation

67 Slide #67 Questions? www.jeppesen.com/frm

68 Slide #68 Backup slides...

69 Slide #69 BAM evolution

70 Slide #70 BAM and continous improvement Data driven improvement strategy - MUSIC –Manage –Use –Save –Improve –Compare

71 Slide #71

72 Slide #72

73 Slide #73

74 Slide #74 The crew management process

75 Slide #75 Where should fatigue be managed? Crew management processes * Today Correct data Salary events Recruit? Transition training? Base size? Qualification structure in cabin? Crew negotiations? Leave Promote instructors? Enough instructors? Leave Leave of absence? Move crew btw bases? Adjust the schedule? Productivity Real costs Robustness Quality of life Use reserves Trip trades Maintain productivity Maintain sby levels Crew quality Long term manpower Mid term manpower Planning Maintain planning Maintain planning FEBMARAPR...JUL…MAR10 * Passenger focus Legality / feasibility Secure revenue Day of operation Day of operation Today MAR Follow-up

76 Slide #76 Crew management processes * Today Long term manpower Mid term manpower Planning Maintain planning Maintain planning Follow-up Manpower Planning Applications: Crew Rostering Crew Pairing Crew Rostering Crew Pairing Day of operation Day of operation Today Crew Tracking Answer: Where its introduced. Time table planning FEBMARAPR...JUL…MAR10 * MAR Station, Departure time, Equipment, Augmentation, Choice of hotel, Deadheading,... The flight context: Surrounding activities/flights on the roster, Individual history and circumstances Maintain what has been planned...

77 Slide #77 Fatigue Model Predicted Alertness Sequence of flights + attr. The overall concept... Take fatigue / alertness into account while recombining the sequence of flights Crew Rostering Crew Pairing Flight Schedule Crew Rosters Rules Objectives Fatigue Model Predicted Alertness Sequence of flights + attr. CAPI Risk The Common Alertness Prediction Interface

78 Slide #78 The Jeppesen FRM portfolio

79 Slide #79 CrewAlert Get started using a fatigue model CrewAlert 1 www.jeppesen.com/crewalert Get aquainted with a model. Investigate individual patterns and see how science plays out on a roster. Collect fatigue data easily from your operation! Context sensitive mitigation advice (Jan/Feb 2012)

80 Slide #80 CrewAlert Get started using a fatigue model CrewAlert CFAS Extensive Fatigue Assessment with any solution CFAS 1 2 Fatigue Assessment on thousands of pairings or rosters in seconds through a web service. Show control and progress internally and to a regulator. Learn and improve from bad patterns. Use with any system! https://cfas.jeppesensystems.com

81 Slide #81 CAPI Integrate in your environment CAPI CrewAlert Get started using a fatigue model CrewAlert CFAS Extensive Fatigue Assessment with any solution CFAS 1 2 3 Your current system CAPI The CAPI 1 interface is available for licensing enabling a direct integration with BAM and other compliant fatigue models. Allows for adding direct decision support and visualization for planning / re-planning. Requires a system change. 1) The Common Alertness Prediction Interface BAM

82 Slide #82 CAPI Integrate in your environment CAPI Jeppesen Crew Solutions Optimize using a model Jeppesen Crew Solutions Optimize using a model CrewAlert Get started using a fatigue model CrewAlert CFAS Extensive Fatigue Assessment with any solution CFAS 1 2 3 4 Robustness Real Costs Productivity Quality Boost alertness while constructing your crew schedules with Jeppesen optimizers. Implement a minimum alertness level and/or introduce incentives to boost alertness in full control of the balance with other factors. Identify FTL/LBA loopholes and wise alleviations. Alertness

83 Slide #83 CAPI Integrate in your environment CAPI Jeppesen Crew Solutions Optimize using a model Jeppesen Crew Solutions Optimize using a model CrewAlert Get started using a fatigue model CrewAlert CFAS Extensive Fatigue Assessment with any solution CFAS 1 2 3 4 Fatigue Models BAM, Boeing FAID, Interdynamics SAFE, Qinetiq SAFTE, IBR Jeppesen strives to provide all the leading fatigue models throughout the portfolio. The CAPI specification has been shared and confirmed to fulfill the data provisioning needs of several leading models. Status Aug11: Only BAM fully compliant to CAPI 2.0

84 Slide #84 Fatigue Models Consultancy Impact assessments, finding relaxations, … Assess your current planned or actualized rosters. Investigate options. Re-run with world class optimization using also FRM capabilities. Sensitivity analysis…

85 Slide #85 Fatigue Models Consultancy Impact assessments, finding relaxations, … Training Three-day training course for planners, safety pilots and managers www.jeppesen.com/crewacademy

86 Slide #86 Fatigue Models Consultancy Impact assessments, finding relaxations, … Training Three-day training course for planners, safety pilots and managers Data collection surveys Using CrewAlert but also full surveys offered via Boeing Data collected with crewAlert is uploaded to Jeppesen and directly structured for further processing/analysis. Avoiding interpetation and formatting of paper work often taking weeks or months to complete…

87 Slide #87 Fatigue Models Consultancy Impact assessments, finding relaxations, … Training Three-day training course for planners, safety pilots and managers Data collection surveys Using CrewAlert but also full surveys offered via Boeing Service Bureau Remote Planning using FRM Often a continuation of a consultancy study allowing for quickly using the improved planning results – with Jeppesen staff doing the scheduling. Pairings and rosters.

88 Slide #88 Fatigue Models Consultancy Impact assessments, finding relaxations, … Training Three-day training course for planners, safety pilots and managers Data collection surveys Using CrewAlert but also full surveys offered via Boeing Service Bureau Remote Planning using FRM CAPI Integrate in your environment CAPI Jeppesen Crew Solutions Optimize using a model Jeppesen Crew Solutions Optimize using a model CrewAlert Get started using a fatigue model CrewAlert CFAS Extensive Fatigue Assessment with any solution CFAS 1 2 3 4 www.jeppesen.com/frm

89 Slide #89 Crew Alert

90 Slide #90 CrewAlert An evolving application to make sleep science more available. Currently meant primarily for crew schedulers and safety pilots to learn about sleep science (as represented by BAM) Also built for collecting operational fatigue data In work… –A schedule communication tool –Fatigue mitigation advice for crew Flight duties Predicted sleep Sleep journals with actual sleep/wake Predicted alertness average Predicted alertness 90% Self assessments

91 Slide #91 CrewAlert – collecting data Data collection has in the past been cumbersome, fragmented and quite expensive… Collected data is by design now: quality assured at entry well structured securely delivered back to the airline safeguarding personal integrity. Not a full scientific study, but a very cost effective alternative

92 Slide #92


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