Slide #1 Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling...

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

Slide #2

Content

1. Background and Purpose

2. About BAM– Features / Capabilities /

Limitations

3. Analysis of data provided

4. Upcoming functionality

Slide #3

Background and PurposeBackground and Purpose

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

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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!

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About BAMAbout BAM

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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

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Science (2) - Most recent and relevant references

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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

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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

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BAM evolution

• BAM is built to self-tune in a closed-loop system to collected data

– Airline collections– Crowd sourcing (FDC 2011)

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Analysis of supplied data setsAnalysis of supplied data sets

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Short haul

Long haul

Pairings Rosters

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:

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Tools used for the analysis

Scenarios

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Analysis data set A – short haul pairingsAnalysis data set A – short haul pairings

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Overall solution statistics

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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.

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Some of the worst flights (2)

618


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769


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1154

Time of day. Sleep deprivation from falling asleep at 3AM. Many consecutive days and sectors adds a bit to the problem.

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1169

Time of day. Two-pilot operation through the WOCL. Sleep deprivation. 2h acclimatisation west – but small effect.

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Data set A – low risk flights…Data set A – low risk flights…

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>5000

Day time. Sensitive to early sleep in the evenings due to early starts.

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>5000

Day time. Long duties, but well placed.

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Data set A – worst and best pairings…

Data set A – worst and best pairings…

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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...

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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

Slide #28

flights

riskOp surescountermeathreatityvulnerabil.

The operational risk for an airline...

CP hour on typeCP hours totalCP airport recencyCP predicted alertnessFO hour on typeFO ...

Airport elevationRunway lengthLight conditionsWind direction/forceRain/hail/snowVisibilityAirport equipmentAirport terrainAirport trafficAircraft MEL items...

(More on this in the proceedings from IASS 2009)

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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

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.)

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Analysis data set B – short haul rostersAnalysis data set B – short haul rosters

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392

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405

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425

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578

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Data set B – low risk flights…Data set B – low risk flights…

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>5000

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>3000

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>xxx

Example that it does not have to be that bad

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Data set B – worst and best rosters…

Data set B – worst and best rosters…

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...

• <added later>

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Analysis data set C – long haul pairingsAnalysis data set C – long haul pairings

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842

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1377

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1402

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1402

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Data set C – low risk flights…Data set C – low risk flights…

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>5000

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Data set C – worst and best pairings…

Data set C – worst and best pairings…

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...

• <added later>

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Analysis data set D – long haul rosters

Analysis data set D – long haul rosters

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487

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926

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956

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971

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Data set D – low risk flights…Data set D – low risk flights…

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>5000 Best use of extra crew?

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>5000

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Data set D – worst and best rosters…

Data set D – worst and best rosters…

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Summary of BAM capabilitiesSummary of BAM capabilities

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BAM – science

Openly available for scrutiny


Open sleep assumptionsOpen sleep

assumptionsOpen ScienceOpen Science Data driven improvementData driven

improvement

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BAM - individualisation

Diurnal typeDiurnal type

Commuting / transfer timesCommuting / transfer times

Habitual sleep length


Customizable sleep

Customizable sleep




assumptionsOpen ScienceOpen Science

Floating sleep predictions


AcclimatisationAcclimatisation

Data driven improvementData driven

improvement

AugmentationAugmentation

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BAM - applicability

>150 k p/s>150 k p/s Paralell computing

Paralell computing

Diurnal typeDiurnal type

Commuting / transfer timesCommuting / transfer times



Customizable sleep

Customizable sleep

Pairing optimization

Pairing optimization

Rostering optimizationRostering

optimization IntegrationIntegration




assumptionsOpen ScienceOpen Science



AcclimatisationAcclimatisation

Risk layerRisk layer

Customizable prediction pointCustomizable

prediction point

Data driven improvementData driven

improvement

Jeppesen Support and development

Jeppesen Support and development

AugmentationAugmentation

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Questions?


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Backup slides...

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BAM evolutionBAM evolution

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BAM and continous improvement

• Data driven improvement strategy - MUSIC– Manage– Use– Save– Improve– Compare

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The crew management processThe crew management process

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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 manpowerLong term manpower

Mid term manpowerMid term manpowerPlanningPlanningMaintain

planningMaintainplanning

FEB MAR APR ...JUL …MAR‘10*

Passenger focus

Legality / feasibility

Secure revenue

Day ofoperation

Day ofoperation

Today

MAR

Follow-upFollow-up

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Crew management processes

*Today

Long term manpowerLong term manpower

Mid term manpowerMid term manpowerPlanningPlanningMaintain

planningMaintainplanningFollow-upFollow-up

Manpower PlanningManpower PlanningApplications:

Crew RosteringCrew Rostering

Crew PairingCrew Pairing



Day ofoperation

Day ofoperation

Today

Crew TrackingCrew Tracking

Answer: Where it’s introduced.

Time table planningTime table planning

FEB MAR APR ...JUL …MAR‘10*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...

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Fatigue ModelFatigue ModelFatigue Model

Predicted Alertness

Sequence of flights + attr.

The overall concept...

• Take fatigue / alertness into account while recombining the sequence of flights



Flight Schedule

Crew Rosters

RulesObjectives

Fatigue Model

Predicted Alertness

Sequence of flights + attr.CAPI

Risk

The Common Alertness Prediction Interface

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The Jeppesen FRM portfolioThe Jeppesen FRM portfolio

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CrewAlertCrewAlertGet started using a Get started using a

fatigue modelfatigue model


fatigue modelfatigue model 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)

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CFASCFASExtensive Fatigue Extensive Fatigue

Assessment with any solutionAssessment with any solution



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

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CAPICAPIIntegrate in your environmentIntegrate in your environment










1

2

3

Your current system

Your current system

CAPI

The CAPI1 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

BAMBAMBAM

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Jeppesen Crew SolutionsJeppesen Crew SolutionsOptimize using a modelOptimize using a model










1

2

3

4 RobustnessReal 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

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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 Aug’11: Only BAM fully

compliant to CAPI 2.0

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ConsultancyConsultancyImpact assessments, finding Impact assessments, finding

relaxations, … relaxations, …



Assess your current planned or actualized rosters. Investigate options. Re-run with world class optimization using also FRM capabilities. Sensitivity analysis…

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TrainingTrainingThree-day training course for Three-day training course for

planners, safety pilots and managersplanners, safety pilots and managers



www.jeppesen.com/crewacademy

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Data collection surveysData collection surveysUsing CrewAlert but also Using CrewAlert but also

“full surveys” offered via Boeing“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…

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Service BureauService BureauRemote Planning using FRMRemote 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.

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Slide #89

Crew AlertCrew Alert

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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

sleepSleep journals

with actual sleep/wake

Predicted alertness average

Predicted alertness

90%

Self assessments

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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

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Slide #1 Analysis of FRMS Forum data using BAM 2 September 2011 Tomas Klemets, Head of Scheduling...

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