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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


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



OCCPeople



Technical System

Social System


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



OCCPeople



Technical System

Social System



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