Model-Based Design of High-Performance Command & Control Organizations Daniel Serfaty Aptima, Inc....

Model-Based Design of High-Performance Model-Based Design of High-Performance Command & Control OrganizationsCommand & Control Organizations

Daniel SerfatyDaniel SerfatyAptima, Inc.

Modeling of C2 Decision Processes WorkshopVienna, VA, Jul 31-Aug 2, 2001

[email protected]

12 Gill Street, Suite 1400 Woburn, MA 01801

(781) 935-3966 Ext. 211

1030 15th St NW, Suite 400 Washington, DC 20005 (202) 842-1548 Ext. 211

Objective

Demonstrate the potential of advanced organizational and team modeling techniques and tools to support:– Model-based experimentation – C2 Design decisions

Outline of Presentation

Value of modeling C2 Organizations– Prescriptive vs. descriptive modeling– Model-Based Experimentation

TIDE modeling approachC2 Design ExampleHow to use TIDE products

Virtual,Virtual, Human-in-the-Human-in-the-

Loop ExperimentsLoop Experiments

ConstructiveConstructiveSimulationSimulation

FieldField Applications, Applications,

LiveLive Assessment Assessment

EVENTS

TEAM LEADER'SWORKSTATION

MULTI-CHANNELCOMMUNICATION LINK

"WORLD"EVENTS

JAOCJAOC

WOC AWOC A WOC BWOC B WOC CWOC C

ElectronicElectronicTriadTriad

ElectronicElectronicTriadTriad

DM0

Sea-Mines& General Defense (Sea + Ground):artillery+hostile air+frog-launchers+etc.

DDG-003S M C-007

DM5

Hill + Beach A + Port

IN F ( A A A V )CA S

INF (M V 22)

DM4

Beach B + Airport

INF (A A A V )CA S

IN F ( M V 2 2 )

DM3

Medevacuation

M E DM E D

LHA -004LPD-005

DM2

lead-vehicle+Bridge+ground mines+SAM sites

CA S

E NG

S OF

S A T

B A S E -008

DM1

Defend North& DefendSouth

V F

S D

CG-001V FV F

FFG-002

CV -000

A A A VA A A VM V 22

M V 22

M E D

Rigorous human modeling• Algorithm-based team design• Human decision maker models• Simulation-based assessment

Simulation-based experimentation

• Team-in-the-loop simulation• Partnerships with academia and

industry• Technologies support data

collection & analysis

Live Performance Assessment & Human Engineering

• Computer-based observer tools• Results inform training, performance,

display design

Understanding Command Teams: From the Lab to the Field... And Back…

Why Design C2 Teams?

Engineering the interplay between the command organization’s systems & procedures and its human decision-makers to optimize the quality of decisions

Complex human-system design issuesHow many operators/decision-makers?

How to partition command roles?

How to distribute tasks among operators?

What is the optimal team structure?

How should operators proceed within it?

How will new technology and missions impact an evolutionary team design?

Goal: Leverage C2 Research to Improve Teams & Technology

Assess/diagnose team performance

Design teams & procedures

Design systems & interfaces

Design team training

Team Research

The Challenge (JTF example)

How would you derive human requirements for the organization?

How would you evaluate its performance for this mission?

How would you design a command team organization for this mission?

AEW of ERS

ATTACK NAVAL BASES

ATTACK CDCM

ATTACK RED IADS

ISR Surveillance YSEA

CVBG penetrate ERSTAMD ERS

TAMD YSEA

TAMD Island O

MIW in TSUS Strait

ATTACK AIR BASES

MINE RED PORTS

DESTROY C2 NODES

ISR Surveillance ERS

USW ERS

ASuW ERS

USW TS area

Surveillance COAST

AEW O

Defend vs. CDCM

CONT

CONT

CONT

CONTCONT

CONT

CONT

CONT

45 DAYS

7 DAYS

7 DAYS

45 DAYS

30 DAYS

5 DAYS

24 HOURS15 DAYS

7 DAYS

30 DAYSAEW of SOG

TAMD Blue

MIW in TSUG StraitNegate RED subs

Defend vs. CDCMSurf Survellance SOG

ATTACK RED AIR BASES (incl. BDA)

ATTACK RED C2 NODES (incl. BDA)

ATTACK RED IADS (incl. BDA)

ATTACK RED MSL BASES (incl. BDA)

CVBG penetrate SOG

TAMD protect Green

sensorshigh flyers low flyers, incl self defense

USW sanitization in A

5 DAYS

CONT

10 DAYS

30 DAYSCONT

CONT

CONT

24 HRS

30 DAYS

45 DAYS

AREA A OPS

AREA B OPS

Phase II OPS

Model-Based Experimentation:Design-Model-Test-Model

Adaptive Architectures for Command and Control (A2C2) ProjectIntegration of Modeling, Simulation, and Experiments

A Paradigm for Future Joint Experimentation?A Paradigm for Future Joint Experimentation?

Define Mission,

Objectives, Resources

Organization Design Process Experiment

Design

Conduct Experiment

Analyze Data Results

Pre-Exp. Model

Post Exp.

Model

Design Model Test

Scenario

AAR

Propose Hypotheses for Next Experiment

Refine Design

Validate

Model

Key Learning Loops

Example: JTF Model-Based “Optimized” Command Teams

FLAG

SAT-006BAS-008• SOF on BASE• CAS on (GREENs)CV• ENG on (BLUEs)LPD

GREEN

CV-000• VF on CV• VF on CV• VF on CVCG-001FFG-002DDG-003SMC-007

BLUE

• CAS on (GREENs)CVLPD-005• MV22 on LPD• • INFh on MV22• AAAV on LPD• • INFa on AAAV• MED on LPD

PURPLE• CAS on (GREENs)CVLHA-004• MV22 on LHA• • INFh on MV22• AAAV on LHA• • INFa on AAAV• MED on LHA• MED on LHA

Net 1

FLAG

GREEN

CV-000• VF on CV• VF on CV• VF on CVCG-001FFG-002

DDG-003SMC-007

BLUE

SAT-006BAS-008• SOF on BASE• CAS on (GREENs)CV• ENG on (PURPLEs)LPD

LHA-004LPD-005• MED on LHA• MED on LHA• MED on LPD

PURPLE

RED

• CAS on (GREENs)CV• MV22 on (PURPLEs)LPD• • INFh on MV22• AAAV on (PURPLEs)LPD• • INFa on AAAV

ORANGE

• CAS on (GREENs)CV• MV22 on (PURPLEs)LHA• • INFh on MV22• AAAV on (PURPLEs)LHA• • INFa on AAAV

Net 1

Net 2

A1-6: 6 nodes

A1-4: 4 nodes

Can Model-Based Designs Can Model-Based Designs Improve on Common Sense? Improve on Common Sense?

Experiments validate model-based team organizational design approach

Model-reduced

50 10060 70 80 9050 10060 70 80 90

Model-based

85.1 78.1

Ad-hoc79.7 59.7

76.2 68.5

Ove

rall

Mis

sio

n O

utc

om

e

Ob

serv

er’s

Ove

rall

Rat

ing

Design

Improved Team Process Performance

Findings-- Model-based architectures required less communication-- Engineered capabilities at each command node reduced wasteful inter-node coordination-- Better and more timely use of communication channels supported anticipatory behavior (a performance predictor)

Ad-hoc Model-based Model- reduced0

1

2

3

4

5

6

7

8

Co

mm

Rat

e [m

sgs/

min

]

0

0.5

1

1.5

2

2.5

3

Co

ord

inat

ion

Act

ion

s [/

min

]

Model- reduced Model-basedAd-hoc0

1

2

3

4A

nti

cip

atio

n R

atio

Model-reduced Model-basedAd-hoc

Limited-Objective, Model-Based Experimentation (A2C2/GLOBAL ’99)

Feedback, Learning

Field Test Org. Designs

“Bridge”“Bridge” GLOBALWargame

GLOBALWargame

Adaptive Architectures for Command and

Control (A2C2)

Adaptive Architectures for Command and

Control (A2C2)

Network Centric Warfare


Design of Command Orgs.

Model-Based Experimentation Opportunity to

“Port” A2C2 Innovation to Warfighters

Innovative Organization to

Test NCW Concepts Effects-Based

Operations


Info. Tech: IT21

Model-Based Team Architectures

ALPHA CHARLIE BRAVO

FLAG

Phase I

ALPHA

CHARLIE BRAVO

FLAG

Phase II

ALPHA, BRAVO, and CHARLIE cells are multi-functional, multi-service sub-teams

coordinationcommand or supported/supporting

Adaptation

“Bridge” Example: Phase I Architecture

FLAG

CHARLIEALPHA

BRAVO

Assets1 CVN(X)1 DDG1 CG1 JSTARS2 MH-531 MCM24 P3C3 SSNRC-135s…

Area of OperationsYSEA , ERS , I.O

CVN penetration, ASuW, MIW + Attack

Mission Tasks• Surface Surveillance of ERS, YSEA, and island O• Defense vs. CDCM Attack• USW in ERS, YSEA, and island O• ASuW in TSUS area• MIW in TSUS Strait• CVBG penetrates ERS

In addition, Bravo is/can be involved in the following tasks:

• Attack Naval Bases from ERS• Attack Red C2 Nodes from ERS (together with Charlie)• Attack Red IADS from ERS (together with Charlie)• Attack Red Missile Bases from ERS (together with Charlie)

Team Modeling for C2 Organizations

to support

• Planning• Direction• Control • Coordination

• Joint operations• New technology• Rapid response• Unpredictability

CurrentResponsibilities

OperationalChallenges

require

• Adaptability • Flexible structures• Inter-operability• Optimized communication

OrganizationalNeeds

Team/Organizational Modeling produces organizationalstructures that are “congruent” with mission needs

Questions

Designing an Organization

Who does what?Who controls what?Who sees what?Who knows what?Who talks to whom?Who gives orders?Who makes decisions? Who overrides decisions?

Str

uc

ture

Who is responsible for what?Who is tasked with what?Who backs-up whom?Who talks with whom? Who coordinates with whom?P

roc

ess

Allocation- Resources- Information- Communication- Command

Scheduling- Functionality- Tasks- Coordination- Back-up

Method Outcomes

OptimalProcess

OptimalStructure

Optimal Structure

Optimal Process

Can the structure sustain the C2 process?

What effect the process has on the organization?

Mission Structure

OrganizationalConstraints

Problem Formulation as Multi-Objective Structural and Process Optimization

Defining Organizational Design

What can be replaced by what

Team Organizational Design

Who talks to whom

Who does what

Resources

What it takesto complete

HumanDecision-MakersWho owns what &

Who knows what

Tasks

Mission

RESOURCEALLOCATION

TEAM STRUCTURALENGINEERING

FUNCTIONALLOCATION

MISSION ANALYSIS

Mission-Based Methodology for Modeling Organizations

FE

ED

-BA

CK

FE

ED

-BA

CK

Iter

ativ

e D

esig

n P

roce

ss

Organizational Design Process1) Task to Resource Allocation2) DM to Resource Allocation3) Organizational Structure

Organizational Constraints

Team/System Capabilities

Quantitative Mission Structure

Task Requirements

Multi-Dimensional Task DecompositionMissionMission

Organizational Structure1) Taskwork Strategy2) Teamwork Strategy3) Physical Lay-Out Performance

Measures

T

R DM



TIDE modeling approach

C2 Design Example

How to use TIDE products

TIDE: Organizational EngineeringTeam Integrated Design Environment

Mission-driven organizational design process

A novel, formal, and quantitative way to model teams and organizations– Prescriptive methodology– New technology/automation tradeoffs– Cost and risk reduction

Multiple optimization criteria– Error minimization, workload balance, speed, etc…

TIDE is capable of supporting the design of both revolutionary and evolutionary organizational structures

to support optimized mission performance

Examples of C2 Organizational Design Objectives

Speed of Command – Organization must meet mission objectives at maximum

speed of command (--> op tempo)

Staff Reduction – Reduce Command Staffing by X%

Acceptable Workload – Minimize Peak Workload/Balance Workload– Total Workload Accumulation

Effective Team Coordination – Optimize inter-node synchronization– Minimize communication message queues

TIDE Five-Phase Process

Phase A:Mission

RepresentationPhase B:

Task-ResourceMapping

Phase E:Organizational

Structuring

Phase C:Clustering Tasks

into Roles

Event-Task Mapping

Optimized TaskScheduling

Operator Role Definition/Info Requirements

Team-LevelAssignments

Team DesignStructures

+ Processes

Design Objectives,

Criteria,Constraints

Phase D:Design Team Interactions

Phase A: Mission Representation

Phase A:Mission

Representation

Tasks Tasks (Required (Required

responses)responses)

Scenario Scenario EventsEvents

Response Response CriteriaCriteria

Stochastic Stochastic Mission Mission ModelModel

Event to Event to Task Task

MappingMapping

CategorizationCategorization

Representing the Mission

Task interdependencies

External triggering events

Multiple scenarios that represent extremes for mission performance

Resources required for each task & effectiveness of resource packages

Duration and workload associated with tasks

air-defenseNorth

AirportAirport

suppress airportSAM-sites

North roadground mine PortPort

suppress portSAM-sites

suppress submarines

air-defenseSouth

Hill Hill hill

sea-minesholdhill

Beach BBeach Bbeach B

sea-mineshold

beach B

Beach ABeach Abeach A

sea-mineshold

beach A

detect & eliminate

lead vehicle BridgeBridge

South roadground mine

medicalevacuation

generaldefense

artillerytanks

hind-hellosfrog-launchers

hostile air silk-wormspatrol boats

* Navy/USMC J oint scenario (North Africa circa 2005)

Subject matter experts define the mission

Note: In this phase, TIDE can take advantage of an existing Mission model (IDEF, Task Network, Petri Net, Simulation model, etc…)

Event-to-Task Mapping

Issue_Level_1_Query

Conduct_Engagement_with_Birds

Respond_to Air_Threat

Conduct_Threat_Assessment

UAE_within_40_NM_of_USN_Surface_Ships

UEV_within_40_NM_that_prompts_self_defence

Air_Warfare_Commander_order_to_issue_Level_II_warning

Issue_DMZ_Violation_ReportMonitor_Airspace_Compliance

Track_approaching_DMZ

Track_in_violation_of_DMZ

Plan/Configure_for_Air_Defense_MissionConfigure_Watchstation

Review_Systems_Status

Monitor_Air_Situation

Log_in

Delouse_Aircraft_RTF

Need_to_delause_A/C

UAE/UEV_closing_within_60_NM_of_USN_Surface_Ships

UEV_within_40_NM_of_USN_Surface_Ships

Review_and_Respond_to_ESM_Information

Correlated_ESM

Correlated_95%_ESM

Uncorrelated_ESM

Review_ID_Indicators

UEV_closing/is_within_75_NM_of_CV

Issue_New/Update_Track_Verbal_Report

UEV

UAE

Maintain_Ownship_SA

External_Comm

Clear_Aircraft_Departing_CV

A/C_requests_permission_to_proceed_to_RTF

Monitor_Team_Workload

Workload_of_a_watchstander_too_high

Conduct_DCA_Intercept_&_Escort

Control DCA

UAE_closing_within_75_NM_of_CV

UAE_is_within_75_NM_of_CV

New_Air_Contact_Radar_Detection(Com_Air_track)

New_Air_Contact_Radar_Detection(Enemy_track)

New_Air_Contact_Radar_Detection(Enemy_track)

Phase B: Optimal Task Scheduling

Meet time constraints

Maximize effectiveness

Resolve resource contentions

Mission Schedule

Multi-objective optimization algorithms to develop optimal schedule:

Note: Roles for individuals not yet considered

1. Optimal branch-and-bound algorithm

2. Dynamic Programming algorithm

3. Dynamic List Scheduling (DLS)

4. Pair-wise task exchange

Phase C: Cluster Tasks Into Roles

Typically optimize for:– balanced workload across

individuals

– minimize need for coordination and communication

Constrain using individual workload ceilings

Results are fed back to task scheduling

Multi-dimensional multi-objective clustering algorithms leading to task-resource pairs:

Team size is either given or optimized

Multi-dimensional Clustering AnalysisEx: Cluster on Information

Location(A/C)

Conduct_DCA_Intercept_&_Escort

Issue_Level_1_Query

Clear_Aircraft_Departing_CV

ID(Track)

Range(Track,USN_Ship)IFF(A/C)

Engagement_solution

Engagement_order

IFF(Track)

Location(Track)

Review_ID_Indicators

Respond_to Air_ThreatConduct_Threat_Assessment

Issue_DMZ_Violation_ReportControl DCA

Conduct_Threat_ Assessment

Conduct_Engagement _with_Birds

Control_ DCA

Review_ID_ Indicators

Location(A/C) 0 0 1 0Location(Track) 1 1 1 1Range(Track,USN_ships) 1 1 0 0Engagement_solution 1 1 0 0Engagement_order 0 1 0 0IFF(A/C) 0 0 1 0IFF(Track) 0 0 0 1ID(Track) 1 0 0 1

Conduct_Engagement_with_Birds

Info

rmat

ion

feat

ures

• Goal: Maximize information within watchstanders; minimize info overlap when unnecessary• Conduct_DCA & Control_DCA use similar info; Review_ID & Conduct_engage-ment do not.

InfoTask

Phase D: Engineering Team Interactions

Uniquely assign tasks when possible to minimize routine communications between team members

May need to split tasks if individuals are overloaded

Detailed Modeling Tool:

Note: Splitting tasks introduces new communication workload

Information Variables

Decision Variables

Action Variables

Outcome Variables

Initiate/Confirm Engage Order

Engage_with_Birds

In-boundmissile msg

ROE

Track’s failure torespond to Level II Warning

Track’s failure torespond to Illumination

System Status

Track Verbal Report

Engagement Status

Weapons Away (Y/N?)

Engagement OrderKill Evaluation (Y/N?)

‘Initiate/ConfirmEngagement’Msg

‘Produce EngagementSolution’Msg

‘Weapons Ring’Msg

‘Issue Track Report’Msg

‘Kill Evaluation’Msg

Release Missiles

Track’s visualdisplay profile Track is covered (Y/N?)

System’s movingtwds releasing Weapons (Y/N?)

Firing Solutionstill a go (Y/N?)

In-boundMissile (Y/N?)

Track’s meetingROE (Y/N?)

Track’s approachingits weapons range (Y/N?)

Initiate Engage Orders (Y/N?)

Engagement Solution

WeaponsAway Yes

Kill Yes

Track is covered

System’s movingtwds releasing Weapons

Firing Solutionstill a go Yes

Engage OrdersInitiated

Firing Solutionno longer a go

System’s failed inmovingtwds releasingWeapons

Engage OrdersNOT Initiated

Track is NOT covered

WeaponsAway No

OUTCOME Variables

DECISION Variables

INFORMATION Variables

ACTION Variables

OUTCOME Variables

DECISION VariablesINFORMATION Variables

ACTION Variables

Respond_to_air_threat

Evaluate_threat

Intra- and inter-task analyses suggest opportunities to combine tasks into roles

3

2

Intra- and Inter-Task Analysis

Example Objective: Balancing Workload in the Team

Instant Workload

0

Threshold

)()(max0;

maxargmax tWtW iDMiDM

ttAEGISiDMMMWSiDM

Objective 1:

min)(

)(

max

max

0

0

minarg

maxarg

tt

iDM

tt

iDM

tW

tW

MMWSiDM

MMWSiDM

Workload AccumulationBalance

DM1 DM2 DM3 DM4 DM5

1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Team Leader

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Air Coordinator-

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Watchdog

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Information Coordinator

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Battle Manager

Notional Workload Analysis




C2 Design Example: AWACS


AWACS Design Challenge

TIDE Design Approach Mission analysis

What needs to be done What information is available

Task analysis How is it done What information is used

Organizational analysis How is information shared Who does what

How do you design an optimal command & control teams for complex, variable AWACS missions to take advantage of advanced information fusion technology?

Example: AWACS Crew Optimization

Human-Centered Re-Engineering of AWACS Command and Control Teams (REAC2T)– Phase III SBIR Project funded by AWACS System Program

Office ESC, Hanscom AFB

Demonstrate proven, scientific approach to C2 team design in AWACS domain– Team Integrated Design Environment (TIDE)

Present ACC/Wing with proof-of-concept for crew optimization– Evaluate impact of information fusion on mission performance

and operator functions– Introduce optimized team structures to enhance mission

performance

AWACS Example: Inputs to Mission Model

Mission decomposition and evaluation– Work with operational community to define current approach to

mission completion• CONOPS, tactics, roles, and responsibilities

Red Flag Spin-Up Training– Tinker AFB

Live Fly Red Flag Exercises– Five flights, Nellis AFB

Cognitive Task Analysis– Wing Tactics Office, Tinker AFB– SD instructors Fighter Weapons School, Nellis AFB

TIDE Prototype Software

Mission & task graphs are converted into data tables to serve as input for optimization algorithms

Preliminary Results: Baseline 14 Operator Task Distribution

Colors represent unique operational tasks

Max Workload = 1400

Impact of Technology (MSI) Insertion:Non-Optimized 14 Operator Configuration


Max Workload = 950

Impact of Technology (MSI) Insertion:Optimized 14 Operator Configuration


Max Workload = 750

Impact of Technology (MSI) Insertion:Optimized 12 Operator Configuration


Max Workload = 800

Internal Communication: Outgoing Messages

MSS W1S W2 W3 W4 W5 W6 S1S S2 S3 S4 S5 0

20

40

60

80

100

120

140

DMs

Out

goin

g M

essa

ges

MCC SD STK OCA Chk_In HVAA STK_Ast OCA_Ast ASO ECO AAST AST_1 AST_2 AST_3 0

20

40

60

80

100

120

DMs

Out

goin

g M

essa

ges

Baseline


50

100

150

200

250

300

350

400

450

500

DMs

Out

goin

g M

essa

ges

MSI Non-Optimized 14

MSI Optimized 14 MSI Optimized 12


20

40

60

80

100

120

140

160

DMs

Out

goin

g M

essa

ges1. Technology1. Technology

InsertionInsertion

500 160

3. Manning 3. Manning OptimizationOptimization

2. Optim

al

2. Optim

al

TeamTeam

Summary: Model-based Re-Engineering of AWACS Command & Control Teams (REAC2T)

T91 -Recordkeep - Significant

events

T76 - Provide C2for workaroundsto appropriate

players

T75 - Coordinatewith senior C2

elements

T74 - Define/recommend

missionmodifications

T71 - Define whatis needed to

continue/complete mission

T62 - Send anarrow

T60 - Broadcastnew information

T57 - Requestsupport fromsupervisorT47 - Distribute

mission status

T46 - Providevector information(i.e. refueling, flight

join, airspace)

T43 - Respond torequests for

information (RFI)

T42 - Makepicture calls

T26 - Identify airthreats

T24 - Provide allavailable amplifyinginformation (tracks)

T20 - Utilizeorganic radar

data

T19 - Fuse datafrom multiple

sources to locateobjects (detect)

T7 - Reconfigureradar for

changing missionobjectives

T102 - Receiveinformation from

pilots

T45 - Monitorairborne assets

missionreadiness

T48 - Assessfighter fuel level

T103 - Pullinformation from

pilots

ANDOR

AND

OR

AND

AND

OR

AND

A17 - Fuse data frommultiple sources toensure tracks are

correct

A12 - Detectnew track

D28 - Determinewhere you shouldanticipate a threat

D16 - Decidewhat is a valid

air object (track)

I17 -Intelligence

dataI4 - ATO

I64 - RadarData

I65 - IFFData

I66 - ESMData

I67 - OtherSensor

A2 - InitiateTrack

SymbologySwitch Action

AND

O39 - Tracksymbology

I56 - Trackposition

Re-engineering based on AWACS mission task models Scientific C2 team design approach to AWACS domain: TIDE

-Team Integrated Design Environment Org. design for new technology insertion and optimized manning

0

2

4

6

8

10

12

Baseline New Tech Only

New Tech + Optimized 1

New Tech + Optimized 2

Crew Configuration

Ave

rag

e T

ask

Del

ay

(se

co

nd

s)

Task Model

Faster Teams




C2 Design Example


TIDE Model Products

Mapping of Tasks to Team Members

Task Cluster Output

DM1 DM2 DM3 DM4 DM5

Configure_Watchstation 1 1 1 1 1

Plan/Configure_for_Air_Defense_Mission 1 1 1 1 1

Review_Systems_Status 1 1 1 1 1

Issue_Level_1_Query 0 0 1 0 0

Respond_to_Air_Threat 1 0 1 1 0

Conduct_Engagement_with_Birds 1 0 0 1 0

Review_ID_Indicators 3 1 2 2 4

Issue_Update_Track_Verbal_Report 0 0 0 1 0

Conduct_DCA_Intercept_&_Escort 1 0 0 1 1

Issue_DMZ_Violation_Report 2 1 1 1 1

Clear_Aircraft_Departing_CV 0 0 0 0 1

Team Descriptions

4.0

4.1

4.2.1 4.2.2

4.3

CAS (8)

CV (1)

TARP (1)

Rifle Co (3)

Stinger (1)

4.2

Cobra (2)

Rifle Co (3)

Cobra (2)

Stinger (1)

Eng . (1)

MED (1)

MCM (1)

CG(1), DDG(2)

Eng . (1)

MED (1)

MCM (1)

VF(8), FFG(2)

Performance Predictions

1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Team Leader

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

AIC-REDCROWN

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Watchdog

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Information Coordinator

101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601

Battle Manager

Detailed Specification of Team Roles:

When Tasks are performed

How Decision are made

What Resources are used

What Information is used

What Communications are required

Multiple Applications of TIDE Model

EVENTS

TEAM LEADER'S WORKSTATION

MULTI-CHANNEL COMMUNICATION LINK

"WORLD" EVENTS

Synthetic Task Environments

TIDE Organizational Design Process

Phase A:Mission

RepresentationPhase B:

Task-ResourceMapping

Phase E:Organizational

Structuring

Phase C:Clustering Tasks

into Roles

Event-Task Mapping

Optimized TaskScheduling

Operator Role Definition/Info Requirements

Team-LevelAssignments

Team Design(Structures

+ Processes)

Design Objectives,


Design Objectives,


Phase D:Decompositionof Role Overlap

Organizational Design

Intelligent Agents

Training Programs

Tide Input Model

IDAO Decomposition

A31 - Target thethreat

D4 - Decidehow to use

available airand groundresources

O14 - Commit pairfighters

I49 - Fighterweapons

status

I19 - Missionrequirementsand priorities

I41 - Fighterfuel status

I51 - Threat/target

location

ANDI17 -

Intelligencedata

A22 - Monitor theair picture

IDAO Decomposition

Event-Task Mapping

Time on Target (ToT)

T36 - Maintainaccurate track /

symbologycorrelation

T19 - Fuse datafrom multiple

sources to locateobjects (detect)

T22 - Interpret /Filter information

T20 - Utilizeorganic radar data

T95 - Monitorairspace

T42 - Make picturecalls

T86 - Providetarget location

T43 - Respond torequests for

information (RFI)

T24 - Provide allavailable amplifyinginformation (tracks)

OR

T102 - Receiveinformation from

pilots

T56 - Receivemission effectiveness

/ BDA

T47 - Distributemission status

AND

Event-Task Mapping

Task Decomposition

ID/track air objects

Respond to threats

Record keeping

ASK FOR INPUTto ATSO (self-preservation)

Identify Threat type

Identify ThreatLocations

Direct weaponsto target/threat

Analyze system resourcesto locate jammers whileMonitoring system for jamming

Execute electronic countermeasures while Monitoringsystem for jamming

Broadcast jammer locationwhile Monitoring systemfor jamming

TCTC Mission

Define Threats

Decide to engage or not

Operate MCS systems

Surveillance/detectair objects

Create/maintain recognizable& integrated air picturePass/receive information

Manage ATO execution

ATSO (self preservation)Airborne (C2 of airborne assets)

Respond to Threats Respond to pop-up targets

Monitor system for jamming


Respond to threats

Record keeping








TCTC Mission

Define Threats


Operate MCS systems







Mission Decomposition


Respond to threats

Record keeping








TCTC Mission

Define Threats


Operate MCS systems








Respond to threats

Record keeping








TCTC Mission

Define Threats


Operate MCS systems







0 200 400 600 800 1000

Line1

T18T68T28 T20 T66 T70 T67 T69 T71 T28 T83 T83 T83

Line2

T18T18T28 T20 T37 T67 T69 T71 T95 T44 T71 T83

Line3

T18T20 T20 T67 T69 T71 T83

Line4

T18T18T18T18T20 T66 T69 T71 T44 T98 T70

Line5

T28T18T20 T20 T90 T67

Line6

T28 T20 T20 T92 T28 T83 T1

Line7

T28T18T20 T92 T28 T9 T83 T44 T83 T87

Line8

T18T18T18T20 T20 T65 T64 T69

Line9

T4T18 T66 T77 T70 T81 T69

Line10

T28 T20 T97 T83 T83 T83

Line11

T18T18 T16 T9 T83

Line12

T18T18 T16 T27

Line13

T18T28 T20 T2 T83

Line14

T20 T20 T37

Line15

T18T18 T20

Line16

T18T18 T20

Line17

T18T18 T20

Line18

T28 T20

Line19

T89

Line20

T20 T20

Line21

T16

Line22

T20 T20

Line23

T20

Time

C2 Process Re-engineering

Interface Design

Model-driven Measurement ProcessModel-driven Measurement Process

Learning Objectivesin JTF Environment

Competencies:Knowledge, Skills,and Abilities

Tasks

Scenario

Stimulated or Trained by...

Improve by X%

Put together into vignettes...

IndividualTeam

Team-of-Teams

Theories ofPerformance LinkSkills to Behaviors toTasks

Stories andevents

MOPs &Measurement

Tools

MeasurementChallenges

Performance Measures by TaskHow well did learners perform?

KSA AssessmentWhat KSAs do learners have/lack?Diagnose individuals’ needs for additional training

Success in Meeting Training ObjectivesHow well are training objectives met?Success at JTF; Certification

Subject Matter Experts

TIDE Model

TIDE Integrated Toolset

Task Network SimulationOrganizational Structure

Task Hierarchy

Assess platformneeded

Receiveplatform

status

Assess need torequest

Task

Platform, DM Platform

Task, Platform

Task, Platform

Task, Platform, DM

Decision-Maker Model

DESIGN

SIMULATION-BASEDEVALUATION

ANALYSIS

Team Optimal Design

(TOD)(TOD)T

DMR

User/SME input and review

MissionAnalysis

S u b 3

S u b 1 S u b 2

L ead

Sub 2Sub 3Sub 1

Leader

Computational Organization Model

50 10060708090

85.179.7

76.2

Mis

sio

n O

utc

om

e

Some Current Military Applications of the TIDE Modeling Methods and Tools

Joint Task Force Adaptive Architectures for Command and Control (A2C2)Next Generation Navy Surface Ships (SC-21/DD-21)Human-Centered Re-Engineering of AWACS Command and Control Teams (REAC2T)Uninhabited Combat Air Vehicle (UCAV) Control CenterKwajalein Radar/Missile Control Center (ATIDS)Air Operations for Time Critical Targets (JFACC)Time Critical Targeting Cell in Air operations (CAOC)Effects-Based Operations in Operations Center (EBO)Army Future Combat Systems (FCS)Global Wargame JTF Org. Design and Assessment….

Summary: Why Model C2?

TIDE is a method to optimize decision-making organizations to capitalize on advanced technology

Model-based organizational structures are “congruent” with mission needs

Modeling guides experimentation and performance assessment

Analysis and design tool for system designers– Cost and risk reduction

– New technology payoffs

Mission/organization model serves multiple purposes – Organizational design: Provide alternative, optimized organizations

– Team training: Highlight areas for team training

– Synthetic tasks: Develop environments to train and evaluate teams

– Interface design: Functional definition of GUI

Model-Based Design of High-Performance Command & Control Organizations Daniel Serfaty Aptima, Inc....

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