Lessons From Using System Dynamics For Complex Decisions · 2020-01-11 · There are many lessons from using System Dynamics to help address difficult problems in complex organizations

Lessons From Using System Dynamics For Complex Decisions

Tom Mullen, PA Consulting Group Colonel Jim Baker, Joint Staff

25 July 2011

© PA Knowledge Limited 2011. Page 2

Context – lessons from work helping leaders on important challenges …

These are hard problems with many dynamics!

Analysis is only part of the challenge …

We face many big challenges with:• Overlapping objectives

• Complex histories and relationships

• Unintended consequences

• Long time delays

• Poor coordination across involved groups

• Difficulty understanding the full impacts of actions

Sophisticated non-state

extremist groups

Humanitarian disastersTransnational

criminal operations

Natural resource scarcity

Near peer trajectories & relationships

Rogue state aggression

Globalization & technology

Grievances & local

insurgenciesPopulation

growth

“Homegrown”terrorist threats

Partner relationships

& capacity

WMD proliferation

Economic dependencies

& trends

Formal alliances & agreements

Cyber attack

Susceptibility to radicalization

Introduction


There are many lessons from using System Dynamics to help address difficult problems in complex organizations

A few recurring challenges …

1. Match the method to the problem

2. Theories are different from reality

3. You will be a sound bite

4. Work in both the boardroom and the field

5. Use different formats to get your message out

6. Don’t rely only on System Dynamics

7. Don’t be discouraged by politics

Lessons Learned

These may be familiar to many of you …… we have some fresh examples to share!


Equipment

XXX

XXX

XXX

WORKING DRAFT

xxx

Retention xxx

Facilitiesxxx

xxx

Recruitingxxx

xxx

xxx

Training

xxx

Staffxxx

xxxxxx

Funding FY07FY08

FYXX

XXX

XXX

XXX

XXX

XXX

XXX

xxx

xxx

xxx

• Xxx• Xxx• Xxx







XXX

XXX

XXX

XXX

XXX

XXX

XXX

XXX

XXX

XXXXXX

XXX

XXX

XXX

XXX

XXX

XXX

WORKING DRAFT

FUNDING

EQUIPMENT

RECRUITING

TRAINING

RETENTION

STAFF

FACILITIES

Causal Dynamics Diagram

System Dynamics Model

WORKING DRAFT

Enterprise-Level Block Diagram

Benefit:• Create integrated view

of organization to elevate discussions

• Fill a gap – had no rigorous way to assess enterprise-wide impacts of key actions and decisions

Benefit:• Help identify

challenges and examine mitigation options qualitatively

• Identify potential unintended consequences to manage

Benefit:• Quantify impacts over

time of different options to manage growth

• Quantify factors ignored by more linear / narrower analyses

• Help those who want hard numbers

Lesson 1: Match the method to the problem

Lesson 1: Match the method to the problem

If you hear … “We want to model this” … consider what a pproach makes sense:


Lesson 2: Theories are different from reality

Lesson 2: Theories are different from reality

One expert’s theory for solving the challenges of getting disaster relief to victims of the 2005 tsun ami:

"People in [this country] are very musically oriented so to make a quick impact after a disaster it is important to use the popular music favored by the different classes…."

Field Manual 3-24 summarizes DoD approach to counterinsurgency

We developed a model to illustrate the dynamics of this theory

Reality has many complications …

Theory… … Reality

Horn of Africa (HOA)Field Manual 3-24

HOA Complexities:•Low literacy•High poverty •Poor border controls•Low government

‘legitimacy’, with military abuses against citizens

•Limited/no public services

•Ethnic, clan and regional conflicts and many insurgencies

• ...

#1 most unstable nation in

world

#2 most unstable nation


© PA Knowledge Limited 2009 Page 8

• The “base case” represents likely behavior given best assumptions / current model structure

• We can run many types of “what if” tests and compare outcomes against the base case to assess impacts

• Given the many sources of uncertainty and recognition of “90-day” model structure maturity, we first show:

1. Base Case behavior

2. “Optimistic” variation of Base Case

3. “Pessimistic” variation of Base CaseTime/Effort

Con

fiden

ce

Diminishing returns –well executed effort can get to 80% solution rapidly

“Max potential confidence” depends on the nature of the system – situations with many ‘soft’drivers such as human perceptions and limited data will be less certain than more ‘tangible’systems

We are here: COIN is inherently soft topic, but there is a lot of Afghan. info and data to draw from

Base case variations are based on different assumptions for key uncertainties including: • Utility of xxx• Number of xxx doing xxx• Xxx impact on xxx• Time to distribute xxx• Normal time to develop xxx• Sensitivity to xxx and xxx • Impact of xxx and xxx on xxx• Impact of xx and xxx on xxx, xxx, and xxx

Model Confidence “Status”(Illustrative)

Strong forward progress on model development, refin ement & preliminary ‘what if’ tests, but critical to recognize ’90-day’ status & view outputs without false precision

We are here


• The “base case” represents likely behavior given best assumptions / current model structure

• We can run many types of “what if” tests and compare outcomes against the base case to assess impacts

• Given the many sources of uncertainty and recognition of “90-day” model structure maturity, we first show:

1. Base Case behavior

2. “Optimistic” variation of Base Case

3. “Pessimistic” variation of Base CaseTime/Effort

Con

fiden

ce

Diminishing returns –well executed effort can get to 80% solution rapidly

“Max potential confidence” depends on the nature of the system – situations with many ‘soft’drivers such as human perceptions and limited data will be less certain than more ‘tangible’systems

We are here: COIN is inherently soft topic, but there is a lot of Afghan. info and data to draw from

Base case variations are based on different assumptions for key uncertainties including: • Utility of xxx• Number of xxx doing xxx• Xxx impact on xxx• Time to distribute xxx• Normal time to develop xxx• Sensitivity to xxx and xxx • Impact of xxx and xxx on xxx• Impact of xx and xxx on xxx, xxx, and xxx

Model Confidence “Status”(Illustrative)

Strong forward progress on model development, refin ement & preliminary ‘what if’ tests, but critical to recognize ’90-day’ status & view outputs without false precision

We are here

Lesson 3: You will be a sound bite

Lesson 3: You will be a sound bite

… is not what they’ll remember:

What you say:

“Our new policy won’t

work.”


Must recognize sources and range of uncertainty – un der more optimistic assumptions the same inputs yield a more positive outlook vs base case

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0Green Amber Red

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0xxx xxx xxx

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

Base

Optimistic

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

TimeTime

Time

Time TimePRELIMINARY ANALYSIS- NOT PRECISE / PREDICTIVE


Must recognize sources and range of uncertainty – un der more optimistic assumptions the same inputs yield a more positive outlook vs base case

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0Green Amber Red

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0xxx xxx xxx

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

Base

Optimistic

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

TimeTime

Time

Time TimePRELIMINARY ANALYSIS- NOT PRECISE / PREDICTIVE


But just as likely - more pessimistic assumptions yi eld more negative outcomes than base case

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxxxxxxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0Green Amber Red

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0xxx xxx xxx.

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

Base

Pessimistic

Preliminary “what if” analyses in Appendix

Time Time

Time TimeTime

PRELIMINARY ANALYSIS- NOT PRECISE / PREDICTIVE


But just as likely - more pessimistic assumptions yi eld more negative outcomes than base case

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxxxxxxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0Green Amber Red

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0xxx xxx xxx.

Comments: xxx

XXX

2002 2006 2010 2014 2018 2022 20260.0

0.2

0.4

0.6

0.8

1.0

Comments: xxx

Base

Pessimistic

Preliminary “what if” analyses in Appendix

Time Time

Time TimeTime

PRELIMINARY ANALYSIS- NOT PRECISE / PREDICTIVE


Lesson 4: Work in both the boardroom and the field

Ineffective or excessive security measures cause resentment —leading to an oscillating dynamic where clear progress is hard to see

population feels resentful/

fearful

DELAY

Fixes that fail #2: Resentment or fear grows

faster than security

insurgent attacks

COIN security measures

feelings of security

popular support for insurgents

+

+

+

-

DELAY

+

feelings of resentment


time

Popular support for insurgents

“ Insurgents will lead to sabotage, terror spreading insurrection. It will be a remarkable government that will not be driven to stern repressive measures – curfews, the suspension of civil liberties, a ban on assemble, illegal acts that can only deepen popular opposition, creating a vicious circle of rebellion and repression until he economy is undermined, the social fabric torn beyond question, and the regime tottering on the verge of collapse.”

- Taber, War of the Flea

Occupying forces are perceived as temporary stop-gaps or occupying forces apply inconsistent ROE

Fixes that fail #3: Security measures do not

appear permanent

security dependent on

transient conditions

insurgent attacks

COIN security measures


popular support for insurgents

+

+

+

-

DELAY

+“If the Americans keep doing it, they can make a difference. But they have to stay.

Otherwise, it will never work.”

-- Ali Muhahmed-Ice Cream shop owner

-16 Feb 07

Lesson 4: Work in both the boardroom and the field

Insurgency study used many sources:• Officers with field experience• Retired officers with COIN experience

in multiple places• Review of publicly available sources• What’s missing – Iraqi perspective

Fort Wainwright, Alaska

Meeting those in field is important to:• Test insights with those who will use it

and ensure messages hold up• Add real-world insight / experience

We got the full experience when

modeling insurgency …


© PA Knowledge Limited 2003. All rights reserved.-xxx- 25/03/2002- 9

Simplified ‘block view’ of LUL’s business and

interactions with key stakeholders

Market

Employees

Govt

Assets

Finances

SuppliersCapitalSpend

OtherCivils

Track

Stations

Power

Lifts & Escalators

Communications

Signalling

Rolling stock

LUL’s Capital Assetsxxxxxxxxxxxxxxxxxx

Financesxxxxxxxxxxxxxxx

Governmentxxxxxxxxxxxx

Taxis and Other

Private CarTrain Operating Companies

Bus

LUL

Marketxxx

xxxxxxxxxxxxxxx

XXXXXXXXXX

XXXXXXXXXX

Suppliersxxxxxxxxx

xxx

Train DriversStation Staff

Operations Support Staff

Corporate/Administrative StaffMaintenance Technicians

Project Management Staff

Capital Program Technicians

Professional Engineers

LUL’s Workforcexxxxxxxxxxxxxxx

XXXXXXXXXX

XXXXXXXXXX

XXXXXXXXXX

XXXXXXXXXX

XXXXXXXXXX

XXXXXXXXXX

XXXXX

XXXXX

XXXXX

XXXXXXXXXX

XXXXX

XXXXXXXXXX

XXXXX

XXXXXXXXXX

“Insidious Decline”

Lesson 5: Use different formats to get your message out

“Workforce Dynamics”

“Dynamics of Partnership”

“Winning Under the Performance

Regime”

High level block diagram for executive discussions

A Dynamic simulation model was used for analyses and included a user guide and interface so that non-experts could test scenarios

Lesson 5: Use different formats to get your message out

Written issue papers for potential bidders and internal LUL staff distilled results of simulation model analyses to explain the key elements of a successful partnership

Detailed diagrams for working sessions & analyses


A high level cause-effect view of key drivers of

Underground performance

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies

In-House Experience/ Competence

Desired LUL Employees

Prevailing Wages/Labour

Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks

Planned Service Level

Accident/ Threat

IncidenceNon-LUL Accidents

LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists

Employment Level in London Intermodal

Seamlessness

Ease of UsePerceived

Safety

Safety Standards

Network Seamlessness

Relative Attractiveness

Journey Time

Social Benefit

Ambience

Integrated Transport

PolicyGovernment

GrantPrivate Sources

Stability of Grant

Fare Policy/ Structure

Fare Level/ Structure

Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates

Performance Improvement Investment

InvestmentContractual Efficiency

Investment Efficiency

Utilization/ Crowding

Delivered Trains/Hour

Age/ Technology

Assets

Maintenance Spending

Condition

Facility Availability

Capacity

Available Capacity

Maintenance Backlog

Maintenance Required

Hires

MoraleLost Time

Calibre

HeadwayConsistency

Supplier Competition

Relative Supplier Cost

EfficiencyUse of Suppliers

Supplier ”Flux”

Supplier Experience/ Competence

Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies




Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks


Accident/ Threat


LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists


Seamlessness


Safety

Safety Standards



Journey Time

Social Benefit

Ambience


PolicyGovernment


Stability of Grant



Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates






Age/ Technology

Assets


Condition


Capacity

Available Capacity

Maintenance Backlog


Hires

MoraleLost Time

Calibre

HeadwayConsistency




Supplier ”Flux”


Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies




Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks


Accident/ Threat


LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists


Seamlessness


Safety

Safety Standards



Journey Time

Social Benefit

Ambience


PolicyGovernment

GrantPrivate

Sources

Stabil ity of Grant



Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates






Age/ Technology

Assets


Condition

Facil ity Availability

Capacity

Available Capacity

Maintenance Backlog


Hires

MoraleLost Time

Calibre

HeadwayConsistency




Supplier ”Flux”


Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies




Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks


Accident/ Threat


LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists


Seamlessness


Safety

Safety Standards



Journey Time

Social Benefit

Ambience


PolicyGovernment


Stability of Grant



Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates






Age/ Technology

Assets


Condition


Capacity

Available Capacity

Maintenance Backlog


Hires

MoraleLost Time

Calibre

HeadwayConsistency




Supplier ”Flux”


Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies




Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks


Accident/ Threat


LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists


Seamlessness


Safety

Safety Standards



Journey Time

Social Benefit

Ambience


PolicyGovernment


Stability of Grant



Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates






Age/ Technology

Assets


Condition


Capacity

Available Capacity

Maintenance Backlog


Hires

MoraleLost Time

Calibre

HeadwayConsistency




Supplier ”Flux”


Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Suppliers

Finance Assets

Natural Wastage

LUL Employees

Redund-ancies




Practices

Wages/ Labour

Practices

Staff Performance

Fraction of Desired

Staff Available

Productivity

Comparable Services

Benchmarks


Accident/ Threat


LUL Marketshare

LUL Ridership

Economy

Greater London

Transport Need

Tourists


Seamlessness


Safety

Safety Standards



Journey Time

Social Benefit

Ambience


PolicyGovernment


Stability of Grant



Margin

Passenger Revenue

Operating Costs

Cost Per Train

Kilometer

LUL Budget

Other Revenue

Interest Cost

Interest Rates






Age/ Technology

Assets


Condition


Capacity

Available Capacity

Maintenance Backlog


Hires

MoraleLost Time

Calibre

HeadwayConsistency




Supplier ”Flux”


Average Contract

Size/ Lifetimes

Market

GovernmentPolicy

Workforce

Finance Assets

Suppliers

Market

GovernmentPolicy

Workforce

Finance Assets

Suppliers


User’s Guide has detailed description of equation structures and lets users drill down to specific assumptions

Table of Contents:

LUL Dynamic Simulation Model Documentation

SECTION 1: A HIGH LEVEL VIEW OF THE MODEL .......... .......................... 1

SECTION 2: THE SECTORS OF THE MODEL ................ .............................. 2

SECTION 3: MARKET SECTOR ........................... ......................................... 3

SECTION 4: SERVICE DELIVERY SECTOR ................. ................................ 4

SECTION 5: LUL STAFF SECTOR ........................ ........................................ 5

SECTION 6: LUL FINANCE SECTOR...................... ...................................... 6

SECTION 7: GOVERNMENT POLICY SECTOR................ ............................ 7

SECTION 8: INFRACO STAFF SECTOR.................... ................................... 8

SECTION 9: INFRACO FINANCE SECTOR .................. ................................ 9

SECTION 10: INFRACO ASSETS SECTOR.................. .............................. 10

SECTION 11: PERFORMANCE REGIME SECTOR.............. ....................... 11

SECTION 12: MODEL DEVELOPMENT AND VALIDATION PROCES S .... 12

London UndergroundLondon Underground

DynamicSimulation ModelDocumentation

October 1999 PA-Consulting Group


Lesson 6: Don’t rely only on System Dynamics…

Lesson 6: Don’t rely only on System Dynamics

SD analysis identified a big delay in this large development program

Managers of the troubled area rejected the analysis, claiming they were on track

They attacked the SD analysis

To help reach agreement, we led a conventional analysis of the areas in question -- using their data -- and highlighted the hugely optimistic assumptions that were implicit

Change in Overall Mission Systems Productivity

0%

20%

40%

60%

80%

100%

120%

140%

2002 2003 2004 2005 2006 2007 2008 2009 2010


0%

20%

40%

60%

80%

100%

120%

140%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Cha

nge

in A

vera

ge M

S P

rodu

ctiv

ity

Base Red AggressiveRed Risk Blue

?

?

Red Base = Ave 30% Improvement

Blue = Ave 60% Improvement

Red Aggressive Red Base Red RiskBlue Outlook

(Average ∆Efficiency = 42%) (Ave ∆Efficiency = 30%) (Average ∆Efficiency = 25%) (Average ∆Efficiency = 60%)



~In the boxChange in Overall Mission Systems Productivity

0%

20%

40%

60%

80%

100%

120%

140%

2002 2003 2004 2005 2006 2007 2008 2009 2010


0%

20%

40%

60%

80%

100%

120%

140%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Cha

nge

in A

vera

ge M

S P

rodu

ctiv

ity

Base Red AggressiveRed Risk Blue

?

?

Red Base = Ave 30% Improvement

Blue = Ave 60% Improvement





~In the box

Change in Overall Software Productivity

Time

Cha

nge

in A

vera

ge P

rodu

ctiv

ity

© PA Knowledge Limited 2010.

Hours Expended & Progress Achieved - Spent ~34% of t otal hours and achieved ~27% complete

Hours Expended Progress Achieved

% of EAC Spent as of XXX Roll-up of detailed data fr om other reports and plans

At the time of April Study:

~20% = Overall Progress Achieved

Latest Study:

~27% = Overall Progress Achieved

At the time of April Study:

Total Hours Expended = 29%

Latest Study:

Total Hours Expended = 34%

Spent 5% budget and made 7% progress – progress in this block is catching up, but overall need to average

10% progress per 5% budget from here forward to meet plan … with no scope growth or unexpected rework!

2



We modeled the drivers of

regional stability

But some people find

expert opinion more credible

…

Use both approaches!


Disaster Relief Study

XxxXxxXxxXxxXxxXxxxxx

xxx xxx xxx xxx xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

Question 6: xxxxxxx

Extent, Type of involvement


Lesson 7: Don’t be discouraged by politics … find the art of the possible

Health System challenges are complex and high stakes with no easy answers

The highest-leverage actions are “non-starters” in this highly politicized arena

Having the “right” analysis is not enough to “fix the system”

Instead, find and implement improvements that arepossible given the politics


Healthcare supply, demand, and costs must be analyzed along

many dimensions (type, quantity, location, etc…)

Costs

• XXX

• XXX

• XXX

• XXX

Enterprise Impacts/Risks

• XXX

• XXX

• XXX

Health System Supply

XXX XXX

XXX XXX

Health System Demand

XXX XXX

XXX XXX

HEALTH

CARE

DELIVERED

Important to understand specific cost drivers, but must understand the broader “system” in order to rigorously evaluate options and avoid “local optimization”

Legal, regulatory, political, procedural, and cultural issues must also be reflected in the

framework.


Healthcare supply, demand, and costs must be analyzed along

many dimensions (type, quantity, location, etc…)

Costs

• XXX

• XXX

• XXX

• XXX

Enterprise Impacts/Risks

• XXX

• XXX

• XXX

Health System Supply

XXX XXX

XXX XXX

Health System Demand

XXX XXX

XXX XXX

HEALTH

CARE

DELIVERED

Important to understand specific cost drivers, but must understand the broader “system” in order to rigorously evaluate options and avoid “local optimization”

Legal, regulatory, political, procedural, and cultural issues must also be reflected in the

framework.

Lesson 7: Don’t be discouraged by politics

Persistence can also pay off … many important changes begin as ‘non-starters’ …


Moving Forward -- Our challenges are not getting any simpler!

• Sustainable Budgets / Reducing Long-Term Debts

• Energy Options for the Mid and Long Term

• Understanding Economic Interdependence

• National / International Security

• Healthcare

• Demographic Changes

• Rising and Falling Powers

• ….

System Dynamics can be a powerful approach to help analyze many of these issues – we hope these ‘lessons’ are of some h elp to those of you addressing these and similarly complex problems in the private sector!

Moving Forward

Documents

Lessons From Using System Dynamics For Complex Decisions · 2020-01-11 · There are many lessons from using System Dynamics to help address difficult problems in complex organizations