Modeling Project Organizations: Virtual Prototypes and Virtual Experiments CEE214 Fall 1999 Raymond E. Levitt

Modeling Project Organizations:Virtual Prototypes

and Virtual Experiments

CEE214 Fall 1999Raymond E. Levitt

Elements of Model Development

Representation Reasoning User Interfaces System Interfaces Validation

OUTLINE

BackgroundWhy simulate organizations?Why start with project organizations?Why use “information processing” as the

central modeling framework? VDT Model Concepts and Evolution VDT Applications to Date

Using VDT models as “virtual prototypes”Using VDT models as “virtual experiments”

The Big Ideas

1. Validated analysis tools are central to design; they distinguish real design from trial-and-error experimentation!

2. In the same way that physical-science-based analysis tools help engineers design bridges, airplanes, semiconductors, pharmaceuticals, etc., social-science-based analysis tools can help managers design their organizations systematically

3. A small number of validated organizational analysis tools are already being used by managers to design organizations for projects, programs, and enterprises (SimVision, OrgCon… )

4. Validated organizational analysis tools also allow researchers to conduct new kinds of virtual computational experiments

Steps in a Formal Design Process

START… Set Design Goals

Design• Synthesize: Develop a candidate design solution

• Analyze: Predict candidate solution’s performance

• Evaluate: Compare predicted performance vs. goals

Iterate Designs

Relax Design Goals

…TERMINATE—Success or Failure?

“Project Org’n Design” vs. Trial and Error

0%

100%

Level of InfluenceLevel of Influence

Conceptual Design

Detailed Design and Implementation

Closeout &Operations

Expenditure of Funds Expenditure of Funds

OutcomeKnowledgeProject Design

OutcomePredictions

Modeling and Simulation of Organizations Bridges the Micro Macro Theory Gap

Organization macro-theory

Organization macro-experience

Sociology/Economics/Political Science

Organization micro-theory

Organizationmicro-experience

Cognitive andSocial Psychology

Agent micro-behavior

Agent-BasedSimu-lation




Emergent simulation macro-behavior

Raymond E. LevittJohn C. Kunz

Department of Civil EngineeringCenter for Integrated Facility Engineering

The Virtual Design Team (VDT):A language and tools for modeling and simulating “virtual organizations” executing work processes

SCOPE: Product Development Organizations? I. Practical Motivation—High Economic Importance

Product lifecycles and market windows are shrinking for many consumer and industrial products,

To accelerate time-to-market, complex, highly interdependent work processes must be executed concurrently

This causes an exponential increase in the amount of needed coordination work and rework

Insufficient “information processing capacity” emerges as a leading cause of “failure” in product development organizations

Extant theory and tools cannot help managers to predict whether, when and where their organizations will fail due to information processing overload

Why Study Product Development Organizations? II. Theoretical Motivation—Perfect fit for IP

frameworkTa

sk In

terd

epen

denc

e

Goal Incongruency/Ambiguity

Task Routineness

Fast Track Projects are Information-Intensive

High performance,complex product has high level of inter-

dependency betweenits subsystems

Fast-track schedule triggers unplanned coordination and rework for project

organization

Process OrganizationProduct

Project team must process large amount of information under

extremely tight time constraints

Using an “Information-Processing” Simulation Framework to Design Project Organizations

1. Model work process to determine “IP load” on organization arising from direct work, coordination and rework

2. Use simulation model as “virtual prototype” of real organization executing work process

3. Highlight predicted bottlenecks in IP capacity as likely loci of schedule and quality failures

4. Evaluate alternative “virtual prototypes” of work process and organization to test and select potential managerial interventions

What Affects Frequency of Exceptionsfor Workers and Managers in Projects?

For Workers:Task complexity relative to workers’ skillsTask interdependencyTask concurrency

For ManagersAbove factors, plus:

• Organizational span of control• Level of decentralization

OVERVIEW OF VDT REPRESENTATION & REASONING

VDT Info-Processing Abstraction Workers Process Information = “Direct Work”

TASK/ACTIVITY:(Volume of Information)

“ACTOR”:(Information Processor)

Coordination& Rework

Exceptions must be Processed by the Organization = “Hidden Work”

(Jay Galbraith, 1973)“Exception”

Direct Work is not Total Work! Total Work = Direct Work + Hidden Work

(Jay Galbraith, 1973)

Project Organization must also Coordinate and Supervise

+

“Exception”

Project ParticipantsPerform Assigned Tasks

Direct Work “Hidden Work”

Fast-Tracking Amplifies Hidden Work

REPRESENTATION

CPM to model processing of direct work Add two additional kinds of task

interdependence Add actor skill levels, application

experience Add organization structure, culture,

meetings …

Information Volume from Project Tasks & Dependencies:Direct Work, Communication Work and Rework

Start

Ready toExcavate

ChooseConstruction

Methods

Long LeadPurchasing

Apply Exc Permit

Seek ZoningVariance

Provide GMP

SelectSubconsultants

Arch Program

Select Key Subs

Estimate T ime

Define Scope

ProjectCoordination

Estimate Cost

Choose facadematerials

Choose Struct.System

GM PAccepted

DesignCoordination

Start

Ready toExcavate

ChooseConstruction

Methods

Long LeadPurchasing

Apply Exc Permit

Seek ZoningVariance

Provide GMP


Arch Program

Select Key Subs

Estimate T ime

Define Scope

ProjectCoordination

Estimate Cost



GM PAccepted

DesignCoordination

Start

Ready toExcavate

ChooseConstruction

Methods

Long LeadPurchasing

Apply Exc Permit

Seek ZoningVariance

Provide GMP


Arch Program

Select Key Subs

Estimate T ime

Define Scope

ProjectCoordination

Estimate Cost



GM PAccepted

DesignCoordination

Project Team Information Processing Capacity:# Actors, Skill Set, Experience, Structure, Policies

Client PM

Design PM Construction PM

ArchitecturalDesign Subteam

StructuralDesign Subteam

ProjectEngineers

ProcurementTeam

Client PM




ProjectEngineers

ProcurementTeam

VDT Information Processing Model:Matching IP Capacity to IP Demand

Client PM




ProjectEngineers

ProcurementTeam

Start

Ready toExcavate

ChooseConstruction

Methods

Long LeadPurchasing

Apply Exc Permit

Seek ZoningVariance

Provide GMP


Arch Program

Select Key Subs

Estimate T ime

Define Scope

ProjectCoordination

Estimate Cost


1

1

1


0.5

1

1.5

1

1.5

1

0.2

0.5

0.5

2

1

GM PAccepted

Design-Build Biotech Project

DesignCoordination

1

ProjectCoordination

M eeting

1

1 1

1

REASONING

Monte-Carlo discrete event simulation of information processing and communication

Used previously to model flow of physical work and materials through a supply chain

In VDT, direct work and hidden work are both simply quanta of information to be processed by humans and information processing/communication tools

Communicationsto other actors“Out tray”

Actor“In tray”

Communicationsfrom other actors

Direct Work

VDT Simulates Actors Working and Communicating

• Simulates:- every actor (team) & activity- work, errors, coordination, waiting, decisions, rework

• Produces:- “database” of actor and project behaviors/outcomes

• Simulates:- every actor (team) & activity- work, errors, coordination, waiting, decisions, rework

• Produces:- “database” of actor and project behaviors/outcomes

Performance Predictions Generated by VDT

Backlog

Quality

Schedule

Cost

ModelSimulation Results

StartFab Test& Deliver

DesignCoordination

DevelopSpecification

Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL

Sim GatesAssemble RTL

PhysVerifn

Verify B1RTL

Generate TestVectors

Synth_B1RTL

Activities


Project Lead

Marketing Team Chip ArchitectTest

Engineering StFoundry Lead

Logic DesignTeam 1

Foundry TestEngineer

Foundry LayoutEngineer

VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

Organization


Project Lead



Logic DesignTeam 1



VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

Communication


Project Lead



Logic DesignTeam 1



VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

Rework


Project Lead



Logic DesignTeam 1



VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

ManagementMeeting

Architecture TeamMeeting

Foundry TeamMeeting

1

1 1 1 1

1

1

1

1

1

1

Meetings


Project Lead



Logic DesignTeam 1



VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

0.8

4

4

4

4

1

1

1

1

1 1

1

1

1

1

1

Assignments 1


Project Lead



Logic DesignTeam 1



VerificationTeam

DesignCoordination


Insert Scan

Partition Chip

Gen Test Suite

PlaceRoute

FullChipSynth

Verify RTL

FloorPlanning

Write B1RTL


PhysVerifn

Verify B1RTL


Synth_B1RTL

0.8

4

4

4

4

1

1

1

1

1 1

1

1

1

1

ManagementMeeting

Architecture TeamMeeting

Foundry TeamMeeting

1

1 1 1 1

1

1

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Reasoning Representation Usefulness

OrganizationMicro-experience

SimulationMicro-behaviors

EmergentSimulation

Macro-behaviors

OrganizationMicro-theory

OrganizationMacro-experience

ValidationElements

OrganizationMacro-theory

Ethnography

Internal Validity

Toy Problems

Intellective Experiments

Authenticity

Generalizability

Reproducability

Retrospective

Prospectivewith

Intervention

Natural

Validation Trajectory

VDT as “Virtual Prototype”

forOrganization

Design

CASE STUDY:The Lockheed Martin

Launch Vehicle

Lockheed Martin Launch Vehicle Organization

ProgramManager

SE & IProject

Manager

AvionicsProject

Manager

AvionicsProject

Manager

PropulsionProject

Manager

NG & CProject

Manager

Facilities& SE

EngineeringManager

BusinessDevelopment

Manager

OperationsManager

TestEngineering

Manager

StructuresProject

Manager

Organization of Avionics PDT

ST SubTeam

PDT Product Development Team

SE & I Systems Engineering and Integration

NG & C Navigation, Guidance and Control

SE Support Equipment

(Actor Name)Actor Not Representedin Avionics Model

(Actor Name)(# FTEs) Actor Represented

in Avionics Model

Power Dist.Panel

Sub Team (3)

Firing UnitSubTeam (3)

OperationalInterlocks

Sub Team (4)

Flight Boxes SubTeam

ProgramManager

SE & IProject

Manager

PropulsionProject

Manager

NG & CProject

Manager

Facilities& SE

EngineeringManager

BusinessDevelopment

Manager

OperationsManager

TestEngineering

Manager

StructuresProject

Manager

StructuresProject

Manager

Total: (15)

Off-shelf Department

Cables Contractor

Packaging Department

Electronics Department

Flight BoxesDepartment

Processor forPackage

SubTeam (5)

CablesSubTeam

(3)

Off-shelfSubTeam

(5)

PackagingSubTeam

(1.5)

ElectronicsParts

SubTeam (6)

AvionicsProject

Manager

Functional Guidance

Project Oversight- Monitoring-Exception Handling

Predecessor - Successorrelationship

Activity

Total Hours

(start)milestone

milestone(finish)Vehicle

AvionicsConcept

SystemIntegration and

Test

Identify PartsRequired

Search forVendors

ProcurementSupport

PrepareDocumentation

Printed WiringBoard Design

Printed WiringAssembly

EnclosureDesign

Top Assembly

Procurement

DevelopingSub-contracts

TeamingAgreements

RangeRequirements

VehicleInterconnect

Layout

DefineInterfaces

DetailedCable Drw.

Fabricate andTest Cables

DefineRequirements

NewEngineering

(Cables)

ReengineeringExperiences

ProductionEnhancements

Build and TestFlight Units

Bread BoardAnd Physical

Mockup

ApplyingExisting

Applications

Offshelf SubTeam (ST) (5)

Cables ST (3)

Flight Boxes ST (15)

ElectronicParts ST (6)

Packaging ST (1.5)

Total Work Volume: 29,234 hours

Simulated Duration: 4,611 hours

Project Manager (1)

8 hours 584 hours

848 hours

400 hours

904 hours

344 hours

296 hours 2,056 hours

592 hours

504 hours

840 hours 800 hours

448 hours

504 hours

4,800 hours2,504 hours

888 hours

752 hours 752 hours 752 hours 752 hours

752 hours 752 hours 752 hours 752 hours

1,200 hours 8 hours

Cables SubTeam (3 FTE)

Flight Boxes ST (15 FTE)

Electronic Parts SubTeam (6 FTE)

Packaging SubTeam (1.5 FTE)

PhysicalMockup

368 hours

AvionicsDrawings

2,952 hours

Activity Model for Avionics PDT

Offshelf SubTeam (5 FTE)

Activity Interdependency Chart for LMLV Avionics PDT

Note: Predecessor-successor relationships between activities are not shown in this chart.

Cables ST

VehicleAvionicsConcept

SystemIntegr’n and

Test

Identify PartsRequired

Search forVendors

ProcurementSupport

PrepareDocument’n

Printed WiringBoard Design

Printed WiringAssembly

EnclosureDesign

Top Assembly

Procurement

DevelopingSub-contracts

TeamingAgreements

RangeRequirements

VehicleInterconnect

Layout

DefineInterfaces

DetailedCable Drw.

Fabricate andTest Cables

DefineRequirements

NewEngineering

(Cables)

ReengineeringExperiences

ProductionEnhancements

Build and TestFlight Units

Bread BoardAnd Physical

Mockup

ApplyingExisting

Applications

Offshelf ST

Electronic Parts ST

PhysicalMockup

AvionicsDrawings

Failure-Dependantrelationship

SubTeam

Info-Exchangerelationship (2-way)

ST

Flight Boxes ST

Packaging ST

Vité Vité Model of LMLV Avionics Team

P ro jec tM an a g em en t

F in is h

1

1 5

1 5

1 5

1 5

1 5 15

55

55

5

3

3

3

3 3

1

1 1 1

1

1.5

11

1

1

5

S ta rt

P ro cu re m e n tS u p p ort

T ea m in gA g re e m e nt s

P rint e d W ir in gB o ard De sig n

De fin eR eq u ire m en t s Fa b ric a te a n d

Te s t C a ble s

P rin te d W ir in gA s se m b ly

N e w E n g in ee rin g

P h ys ic a l M o c k u p

R an g eRe q u irem en tsD ef in e In t erf a ce s

De ve lo p in gSu b c on t rac ts

P ro d uc tio nE n ha n ce m e n ts

A vio n ics D raw in g s

P ro cu re m e n t

S e a rc h f o rV e n d ors

P rep a reDo c um en t at ion

Id en t ify P art sR eq u ire d

R e -e n g in ee rin gE xp e rie n c es

B u ild a n d T es tF ligh t U nit s

S ys te m In t eg ra tion& Te s t

V eh ic le A vio n icsC on c ep t

E nc lo su re d e sig n

B re a d B oa rd a n dP h ys ica l M oc ku p

V e h ic leI n te rco n n ec tLa yo u t

A p p ly E xis tin gA p p lic a tion s

To p A s s em b ly

D et aile d Ca b leD raw in g s

Av io n ics P ro jec tM an a g er

O ff S he lf S u bT ea mCa b les S u b -te am

E le ct ron ic P a rtsS u b -te a m

Flig h t B ox S u b -t ea m

P ac ka g in g S u b -t ea m

E n g in ee rin gM an a g er

Avion ic s Project - Sce nario 1

Notes: No meetings

Communicationsto other actors“Out tray”

Actor“In tray”

Communicationsfrom other actors

Direct Work

VDT Simulates Actors Working and Communicating

• Simulates:- every actor (team) & activity- work, coordination, errors, decisions, rework

• Produces:- “database” of project behaviors/outcomes

VitéVité Gantt Chart for LMLV Avionics Team

LMLV Project: Actors’ Backlogs

LMLV Project: Non-Completed Communications

Guiding Managerial Interventions: VDT “What-if Analysis” of LMLV Avionics Design Team

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

Increase Cable Subteam’s capacityfrom 3-5 engineers

Replace Cable Subteammembers with 3 moreexperienced engineers

Cost

Duration

Except's

Bet

ter

Wo

rse

Design Fast-Track Project Organizations: Some Example Applications

Reduced time to market for complex manufacturing facilities

Facilitated roll-out of new wireless telecom infrastructure across multiple regions

Developed best practices template toaccelerate factory start-ups

Identified & corrected subcontractor management problem that would have delayed project 4 mo.

Helped to meet ship milestone date required to close sale with largest customer

Aligned goals and accelerated rollout of innovative consumer product by 3 mo.

Identified and mitigated critical quality risks to accelerate rollout of new server product

Helped to define scope, schedule and organization for strategic IT projects

Chronology of VDT SimVision®

Steps in the Maturation of a COM&S FrameworkResearch at Stanford U. by Levitt, Jin, Kunz, et. al.Research at Stanford U. by Levitt, Jin, Kunz, et. al. • • •• • •

Concept/methodology development

Simulation tool development

30+ validating case studies

Mature technology

Mature technology

Exclusive License

Exclusive License

Commercial Product DevelopmentCommercial Product Development

1987 1997 1998 1999• • • • • •

1996

Vité Corp. Formed to Commercialize Technology

Vité Corp. Formed to Commercialize Technology

• • • • • •

Consulting & AnalysisConsulting & Analysis

Product OfferingsProduct Offerings

Ongoing VDT ResearchOngoing VDT Research • • •• • •

Maturation of Modeling User Interface

SYSTEM ELEMENTS/ INTERFACES OF VDT/SimVision® SOFTWARE

Vité Simulation Engine

Project Database

Analysis Tools

Model Builder/Viewer

S ta rt

Fa b, T e s ta n d D e liv er

D e v e lopS p ec i fic a tio n

W r ite _ B 1 R T L

V e ri fy _ B 1 R T L

Fu ll C hi pS y n th S im _ G a te s

F loo rp la nn in g P la c e _ R o ute P hy s V e r ifn

A s s em b le _ R T L

V e r ify _ R T L

In s e rt Sc a n

D e s i gn_ C oo rd ina ti on

P a rti t ion Ch ip

S y nth_ B 1 R T L

G en e ra te T e s tV e c tor s

G en _ Te s tS ui te

P r oje c t_ Le a d

C h ip_ A rc hi te c t

Lo gi cD e si gn Te a m 1

T es t_ E ng in e er in g_ S tM a r ke ti ng Te a m

Fo un dr y _ Le a d

Fo un dry _T e s t_ E ng rF oun dr y _ La y o ut_ E ng r

V e r if ic a ti on T ea m

4

1

4

1

1 1

1

4

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0 .9

1

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ASIC Developm ent ProjectNotes: Baseline Scenario - Project completes la te, wi th quality prob lems

0

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16

Jan Feb Mar Apr May Jun Jul Aug Sep

Bac

klo

g (

day

s)

Actor BacklogS001 - Baseline

Chip_ArchitectFoundry_Layout_EngrFoundry_LeadFoundry_Test_EngrLogicDesignTeam1MarketingTeamProject_LeadTest_Engineering_StVerification Team

XMLXM L

Virtual Organizational Experiments

Modeling and Simulation of Organizations Bridges the Micro Macro Theory Gap

Organization macro-theory

Organization macro-experience

Sociology/Economics/Political Science




Agent micro-behavior

Agent-BasedSimu-lation




Emergent simulation macro-behavior

Research Modalities in Engineering Science — (Pre-1960s)

EmpiricalData• Inputs• Outputs

Physical ScaleModels• Inputs• Outputs• Empirical scaling rules

Theory• Physics• Chemistry• Biology (generally expressed

as sets of linear or differential eq’s.)

Limitations of Physical Scale Models

Costly and time-consuming to build Required skilled physical model builders (often built by

model shop technicians—not scientists)

Slow and costly to modifyScientists could not adapt models rapidly to react to

surprising data or to test new insightsCalibration against real world data took decades

Results needed to be interpreted with care Many important effects do not scale linearly

Physical ScaleModels• Inputs• Outputs• Empirical scaling rules

Research Modalities in Engineering Science — (Post-1960s)

EmpiricalData• Inputs• Outputs

Computational Modeling & Simulation• Inputs• Outputs• Limiting modeling

assumptions

Theory• Physics• Chemistry• Biology

How CM&S Affected Engineering Science and Practice

Rapidly declining time & cost to build and change models Rapidly declining time & cost to build and change models Two orders of magnitude improvement Two orders of magnitude improvement “disruptive” changes “disruptive” changes

Progress of Engineering Science Dramatically AcceleratedProgress of Engineering Science Dramatically Accelerated Could rapidly modify models to test & refine theory iterativelyCould rapidly modify models to test & refine theory iteratively ““Regress” micro-modeling assumptions against meso/macro dataRegress” micro-modeling assumptions against meso/macro data

Engineering practice made huge leaps forwardEngineering practice made huge leaps forward ““Real-time” prediction now feasible for even complex problemsReal-time” prediction now feasible for even complex problems Wider range of mathematically indeterminate problems can be solvedWider range of mathematically indeterminate problems can be solved Computational modeling now part of standard BS/MS curriculaComputational modeling now part of standard BS/MS curricula

Model results still need to be interpreted with great care! Model results still need to be interpreted with great care! Violations of assumptions can be catastrophic —e.g. Sleipner platform)Violations of assumptions can be catastrophic —e.g. Sleipner platform)

Research Modalities in Organizational Science—Pre-1970s

Empirical Data from Natural Experiments• Micro/Meso/Macro Inputs• Micro/Meso/Macro Outputs• Span 1 or, at most 2, levels

Empirical Data from SyntheticExperiments• Micro/Meso Inputs• Micro/Meso Outputs

Theory• Sociology• Psychology• Economics (usually expressed in words & diagrams;

sometimes in mathematical or computational models)

Limitations of Synthetic Experiments

Modest time and cost to design and perform experiments

Validation, calibration against real world data is difficult Individual motivation and context are very difficult to replicate Many effects do not scale linearly

Ethical concerns nowadays preclude many kinds of experiments previously conducted on human subjects

No links between micro-behaviors and macro outcomes Micro inputs and outputs cannot generally be related to, or reconciled

with, macro data or even macro-theory Result: Discipline-Based “Islands of Theorizing”

Empirical Data from SyntheticExperiments• Micro/Meso Inputs• Micro/Meso Outputs

Research Modalities in Organizational Science — Post ~1970

Empirical Data from Natural Experiments• Micro/Meso/Macro Inputs• Micro/Meso/Macro Outputs

Computational Modeling & Simulation• Micro/Meso/Macro Inputs• Micro/Meso/Macro Outputs• Nested models link micro-

macro data and theories

Theory• Psychology• Sociology• Economics

Computational Virtual Experiments

CM&S of organizations now beginning to be used to replace some kinds of natural or synthetic social experiments

A validated “emulation” model can be viewed as an “organizational test bench” for theorem - proving experiments

CMOT Journal has already published several papers of this type(Wong & Burton, Carroll & Burton, …)

How Computational Modeling is Affecting Organizational Science and Mgt. Practice

Rapidly declining time and cost to start generating Rapidly declining time and cost to start generating validated predictions for a modeled systemvalidated predictions for a modeled system

Organization Science poised to make huge leaps forwardOrganization Science poised to make huge leaps forward Can now rapidly modify models to test & refine theory iterativelyCan now rapidly modify models to test & refine theory iteratively Can “regress” micro-modeling assumptions against meso/macro dataCan “regress” micro-modeling assumptions against meso/macro data Validated models beginning to serve as “virtual synthetic experiments”Validated models beginning to serve as “virtual synthetic experiments”

Org. Design practice starting to incorporate CM&SOrg. Design practice starting to incorporate CM&S Rapid feedback develops “management judgment” by inductionRapid feedback develops “management judgment” by induction Enabling “flight simulation of alternatives” and “extreme collaboration”Enabling “flight simulation of alternatives” and “extreme collaboration”

CM&S Entering Mainstream Education and ResearchCM&S Entering Mainstream Education and Research Computational modeling & simulation now taught as part of PhD/MS/(BS)Computational modeling & simulation now taught as part of PhD/MS/(BS) MIT launching a MIT launching a Center for Computational PoliticsCenter for Computational Politics

Model results still need to be interpreted with care Model results still need to be interpreted with care Contingency theory says Contingency theory says context matters greatlycontext matters greatly!! Differences in task, technology, … must be taken into accountDifferences in task, technology, … must be taken into account

Using a Calibrated “Emulation” Model to Conduct Virtual Experiments

StartFinish

Project Manager

Subteam Lead Subteam Lead

Task 4

Task 3

Task 2

Task 1

SL 1 Task

PM Task

SL 2 Task

Task 5

Task 6

Task 7

Task 8

Subteam 1 Subteam 2 Subteam 3 Subteam 4 Subteam 5 Subteam 6 Subteam 7 Subteam 8

0

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600

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Exception Probability

Indi

rect

Wor

k (Da

ys)

DR 1

DR 2

DR 3

DR 4

30

100

600

0.1 0.3

Error Rate

Ind

ire

ct

Wo

rk

(Da

ys

)

Laminar Transition Turbulent

Exception Rate

Commercialization of COM&S Tools

Progress of CM&S of Organizations

1950 1960 1970 1980 1990 2000

CM&S in Engineering ScienceCM&S in Engineering Science

CM&S in Organization ScienceCM&S in Organization Science

First use by leading edge consultants

First taught at MS level in multiple universities

Commercial SW — Routinely used in practice

2010

Where is COM&S Going? Games

From action games to Sims®

Analysis tools for many kinds of planningFrom military, intelligence, to other public agencies (e.g.,

building plans, health care, transportation)Commercial (department stores, arenas, …)

Analysis Tools for Corporate Decision MakingFrom project design to enterprise designOrganizational aspects of M&A evaluationOrganizational aspects of supply chain optimization

Analysis Tools for Personal Decision MakingEvaluating your fit with a prospective employer Evaluating compatibility with a marriage partner, …

Using VDT as “Virtual Experiment”

Example: Explore relationship between centralization of decision making and time taken to complete complex taskUse Contingent design and run multiple “virtual

experiments” varying centralization for:1. High Task Uncertainty

1.1 With High Skill for all Actors1.2 With Low Skill for all Actors

2. Low Task Uncertainty2.2 With High Skill for all Actors2.2 With Low Skill for all Actors

Current Research with UndergraduatesSimulate Virtual Organizations to Search for the

“Edge of Chaos”

00.05

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VFP external

VFP internal

Schedule Quality Ratio

3.5-4

3-3.5

2.5-3

2-2.5

1.5-2

1-1.5

Figure A. The parallel project with two dependency links; schedule quality ratio for different VFP values.

00.05

0.10.15

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0.1

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1

1.5

2

2.5

3

3.5

4

4.5

VFP external

VFP internal

Schedule Quality Ratio

4-4.5

3.5-4

3-3.5

2.5-3

2-2.5

1.5-2

1-1.5

Figure B. The parallel project with six dependency links; schedule quality ratio for different VFP values.

Closing in on an “Organizational Reynolds Number”

30

100

600

0.1 0.3

Error Rate

Laminar Transition Turbulent

0.2

E / C + 0.25 * Log(r) = 0.25

EVOLUTION OF SCOPE OF VDT

Trajectory of VDT Research Scope

Organizational Flexibility Low High

Predictable

Unpredictable

Task Predictability

Nonroutine Projects

Routine Projects

Service/Maintenance

Work

Communities of Practice

99-03: Lambert/ Buettner

Model More Complex Social BehaviorsModel More Complex Social Behaviors

Model More Innovative Tasks

Model More Innovative Tasks

Model More Dynamic Organizational FormsModel More Dynamic Organizational Forms

VDT Scope Trajectory: Routine Projects to Non-Routine Communities of Practice

Model More Effects of Communication/

Collaboration Tools

Model More Effects of Communication/

Collaboration Tools

97-01: Miller

96-03: Fridsma/Cheng

95-99: Thomsen/Kish

90-94: Cohen/ Christiansen

… behind the Virtual Design Team Faculty Collaborators

James March (SU: Ed., Sociology, GSB) John Kunz (SU: CIFE) Yan Jin (USC: ME) Clifford Nass (SU: Comm.) Richard Burton (Duke: Business) Martin Fischer (SU: CEE) Bernardo Huberman (Xerox PARC: Physics) Peter Glynn (SU: MS&E) Pam Hinds (SU: MS&E) Noah Mark (SU: Sociology) Dianne Bailey (SU: MS&E) Borge Obel (Odense U: Business School) Kathleen Carley (CMU: CS) Nosh Contractor (UIUC: Comm.) Andrea Hollingshead (UIUC: Psych) Janet Fulk (USC: Business School) Peter Monge (USC: Comm.) Douglass North (Wash. U: Econ., NL) Steve Barley (SU: MS&E) John Koza (SU: CS)

Students Geoff Cohen Tore Christiansen Jan Thomsen Douglas Fridsma Gaye Oralkan Yul Kwon John Chachere Per Björnsson William Hewlett, III Jolin Salazar Kish Carol Cheng-Cain Walid Nasrallah Roxanne Zolin Monique Lambert Archis Ghate Sam Miller Ray Buettner Mike Fyall Alfonso Pulido Ashwin Mahalingam Michael Murray Bijan Khosraviani Ryan Orr Tamaki Horii Laleh Haghshenass

The Real Team Behind the Virtual Design Team:Undergraduate Research Assistants to Date

Diane Newman BS German StudiesMS, Ph.D, MIT, Civil & Environmental Eng’g.

Professor of Geomicrobiology, Cal Tech

Yul Kwon BS Symbolic SystemsJ.D., Yale Law SchoolEnrolled in Ph.D. in Public Policy, Harvard

Corporate Attorney, Wilson Sonsini, Goodrich & Rosati

William C. Hewlett, III BS, Symbolic SystemsMS, Computer Science

PC game developer

Mike Fyall Senior, MSERecruited to Goldman Sachs

Summer internships with Vité management consultants

Jason Glickman Senior MSECoTerminal MS Student, MSE

Summer internships with MSD Investments

Jason Powers Senior MSECoTerminal MS Student, MSE

Summer internship with Vité management consultants

Tarmigan Casebolt Sophomore, MSE ?

Ongoing Research on Organization Design Institutional Complexity in Global Projects

Existing project organization modeling and simulation tools address engineering projects, whose participants have similar goals, values, culture, norms & technologies

Coordination Complexity

Production Costs

Coordi-nation CostsInstitutional

Complexity

Institutional Costs Global projects to develop

infrastructure, eco-sustainability, health care delivery and education encounter conflicting goals, values, norms, cultures and technologies

Documents

Modeling Project Organizations: Virtual Prototypes and Virtual Experiments CEE214 Fall 1999 Raymond E. Levitt