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Predicting human factors influence oneffectiveness of operational decision-making
in Airline Operation Control CentersNico Zimmer | Boeing Digital Aviation Research
NASA Airline Operations Workshop Aug 4 2016
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2Airline OCC goal: Maintain (together?) the schedule.When not possible, return to plan as fast as possible.
OperationControlCenter
How doesSuperman Culture
affect OCCs?
Source:DCComics
OperationControlCenter
Fleet Team
Crew Team
PassengerTeam
FlightDispatch
Team
MaintenanceTeam
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EU aircraft delayby irregular operational causes
3Source: CODA DIGEST - All-Causes Delay andCancellations to Air Transport in Europe – 2015
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US aircraft delayby irregular operational causes
74,90%
6,21%0,88%
6,61%0,03%
7,53%3,62% 0,22%
2015 % of Total Operations
On Time Air Carrier Delay Weather Delay
National Aviation System Delay Security Delay Aircraft Arriving Late
Cancelled Diverted
4Source:http://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp
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The devil is in the detail
5
HiddenCausesHiddenCauses
DisruptionCauses
DisruptionEvents
DisruptionCosts
A. Internal Issues
B. External Issues
1. Systems & Functions2. Management / People / Knowledge / Culture
I. Fast Decision Making issuesII. Knowledge and peopleIII. Cross-functional / interdepartmental
communicationIV. Airline Culture
3. Third Party damage & safety
1…100 IATA CODESTypes: Airline, Airport,EnRoute,ATC, Weather….
Source:Adapted after Beyond Airline DisruptionsJasenka Rapajic 2009
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Approach
“In general, management needs tounderstand that success of a company
is correlated to how the socio-technical system works –
it’s not just a technical system withindividuals you can replace and who
needs to adapt ”Emery, Thorsrud, Trist (1964) 6
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Open System Theory
7
Output
Throughput(Work)
Source: 1950 Ludwig vonBertalanffy
(Products &Services)
(Resources)
InputThe organization
Feedback loops
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Organizational Systems Model
8Source: 1964H. Leavitt
Structure
People
TechnologyTask
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Socio-Technical System
“Socio-technical design is anapproach that aims to give equal
weight to social and technical issueswhen new work systems are being
designed.”
Enid Mumford (2000)
9
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OCC as Socio-Technical System
10
OutputInput
Airline OCC as Throughput / Transformation
OCC OrganizationalModel
OCCPeople
Technology asAirline Maturity
OCC Workflows/Process
Technical System
Social System
Source: Adapted after1977 Nadler-Tushman1978 Katz & Kahn1985 Sydow
OCC Workflows/Process
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Workflow Model
11
DispatcherInterviews (internal)
Airline OperationManager Interviews(internal)
OCC Visits &Interviews
Internal OCCAnalysis Documents
Source: Zimmer, Jekaterina Balashova 2014
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Workflow Behavior Model
12
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OCC as Socio-Technical System
13
OutputInput
Airline OCC as Throughput / Transformation
OCC OrganizationalModel
OCCPeople
Technology asAirline Maturity
OCC Workflows/Process
Technical System
Social System
Source: Adapted after1977 Nadler-Tushman1978 Katz & Kahn1985 Sydow
OCCPeople
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Personality & Behavior
Personality traits can predict occupational behaviorand job effectiveness criteria
• Conscientiousness (C) is a valid predictor for job and trainingproficiency (Barrick & Mount, 1991)
• C has the highest validity for overall job effectiveness (Hurtz &Donovan, 2000)
• Neuroticism (N) is negatively correlated with individualproficiency (Neal et al., 2012)
• N and C are valid predictors for job effectiveness acrossoccupations (Salgado, 1997)
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OCC People ModelFive Factor Model of Personality after McCrae & Costa
15Source: boundless.com, Till Peters2015
Hypothesis1:OCC people
Higher C scorethan Norm ?
Hypothesis2:OCC people
Lower N scorethan Norm ?
Q1:Differences inoccupational
groups?
Q3:Differences in
differenttypes ofairlines?
Q2:Differences in
any othertrait?
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Significant Results
16Source: Till Peters 2015
Hypothesis1:OCC employees
showedsignifcantly
higher C scoremean than
Norm sample
Hypothesis2: Notsignificant. Need
to be rejected
Question2: OCCemployees
showedsignifcantly
lower A scoremean than
Norm sampleQuestion 3: Network Legacy Carrier employees showed asignificantly higher C-score mean than Low-Cost Carrieremployees in this sample
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Airline Workflow Simulator
17Source: Jekaterina Balashova 2014
AWS
Agent view
Simulation Input
Airline profile
Simulation Output
User
Flight view
Flight schedule
Fleet
Agents
Time estimates
Constants
Environment
adjusts
prepares analyzes
runs
Fleet type view
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Summary – First Simulation Results
18
Norm Five FactorProfile
OCC Five FactorModel (FFM)
Profile
OCC HighAgreeableness
Level
OCC HighConscientious
LevelScenario with NEO-FFI3 Norm Data based ongenerated sample data
Agreeableness: 69% ofNorm have tendencyto be agreeable
Conscientiousnesslevel 67% (100% is thehighest level)
OCC Baseline Scenariobased on Neo-FFI 3OCC Survey data
Agreeableness 7%lower than Norm
Conscientiousness 8%higher
All workflowperformanceincreased by 5.5%compared to Scenariowith norm Data
OCC Staff Scenariowith Neo-FFI 3 OCCSurvey Data with HighA- Factor
Agreeableness meanlevel 90%
Conscientiousnesssame as of OCC Profile
All workflowperformanceincreased by ~ 0,2%
OCC Staff Scenariowith Neo-FFI 3 OCCSurvey Data with HighC –Factor
Agreeableness same asOCC FFM Profile
Conscientiousnessmean level 90%
All workflowperformanceincreased by 6,3%
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19
Conscientious people tend to be purposeful, strong-willed, determined, and will to achieve!!
Be conscientious, but work together!Don’t be heroes!
Conclusion
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Outlook – More Research neededPeople & Teamwork Simulation
20
OutputInput
Airline OCC as Throughput / Transformation
OCC OrganizationalModel
OCCPeople
Technology asAirline Maturity
OCC Workflows/Process
Technical System
Social System
Source: Adapted after1977 Nadler-Tushman1978 Katz & Kahn1985 Sydow
OCC OrganizationalModel
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Outlook – Control Center Study
21
State of the art socio-technical assessment incommand and control
Supply Chain Control Center - DHLEnergy Supply Control Center - MainovaSignaling Control Center - DBAir Traffic Control Center – DFS
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THANK YOUFeedback | Ideas [email protected]
Nico Zimmer | Boeing Digital Aviation Research
NASA Airline Operations Workshop 2016