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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
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
EU aircraft delayby irregular operational causes
3Source: CODA DIGEST - All-Causes Delay andCancellations to Air Transport in Europe – 2015
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
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
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
Open System Theory
7
Output
Throughput(Work)
Source: 1950 Ludwig vonBertalanffy
(Products &Services)
(Resources)
InputThe organization
Feedback loops
Organizational Systems Model
8Source: 1964H. Leavitt
Structure
People
TechnologyTask
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
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
Workflow Model
11
DispatcherInterviews (internal)
Airline OperationManager Interviews(internal)
OCC Visits &Interviews
Internal OCCAnalysis Documents
Source: Zimmer, Jekaterina Balashova 2014
Workflow Behavior Model
12
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
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)
14
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?
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
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
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%
19
Conscientious people tend to be purposeful, strong-willed, determined, and will to achieve!!
Be conscientious, but work together!Don’t be heroes!
Conclusion
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
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
THANK YOUFeedback | Ideas [email protected]
Nico Zimmer | Boeing Digital Aviation Research
NASA Airline Operations Workshop 2016