systems thinking overview - john dubuc 2010

8/6/2019 Systems Thinking Overview - John Dubuc 2010

1/59

Lean-6Sigma Directed Evolution

Science of Success

(Utilizing Human Potential)

Science of Innovation

(I-TRIZ & Systems Thinking)

Mfg & Service

Process Improvement

(Lean-6Sigma)

John Dubuc ROI Creations, LLC Scottsdale, AZ (480) 283-7014


2/59

Systems Thinkingfor Teams, Leaders & Policy-Makers



3/59



KEY OUTCOMES:

Understand what Systems Thinking is and its purpose

Know the difference between Analytic Thinking and

Systems Thinking and the roles they play

Use Causal Loop Diagrams (CLDs) to capture and interpret

the Voice of the System (VOS)

Use DMAIC for Systemic Problem-Solving

Understand the connection to modern TRIZ innovation

methods


4/59

Ever wonder

What causes the economy to cycle and crash?

Is global warming all bad?

Would there be less crime if drugs were legal?

Why do profits rise and fall? What will healthcare reform or tax reform bring?

Why change programs fail?



5/59

Tough Questions!

How would you begin

to answer these

questions?

Would others agree?

How would you get

other people TO

AGREE?



6/59

Systems Thinking

Systems Thinking is any process of foreseeing howlocal policies and actions influence the state of theuniverse

We all have different views of how the world works(how this affects that why events happen)

To some degree were already Systems Thinkers



7/59

Systems Thinking

Systems Thinking is any process of foreseeing howlocal policies and actions influence the state of theuniverse

We all have different views of how the world works(how this affects that why events happen)

To some degree were already Systems Thinkers



8/59

Systems Thinking


But we didnt start out

as Systems Thinkers

Our young INFINITE

minds were wide open

to accepting the way

our teachers thought


9/59

What do children do with new objects?

New Object



10/59

Analytic Thinking

1. Take apart

2. Try to understand what the parts do

3. Assemble understanding of the parts into anunderstanding of the whole

Whole = Parts


?


11/59

Analytic Thinking

We use analytic thinking to try and understand

people as well


President = George + Bush + Republican


12/59

Sales Service Quality R&D

Analytic Thinking

We use analytic thinking when our business is

not meeting its goals


Sales


13/59

From a very early age we are taught to break apart

problems; to fragment the world. This apparentlymakes complex tasks and subjects more manageable,

but we pay an enormous hidden price. We can no

longer see the consequences of our actions. We lose

our intrinsic sense of connection to a larger whole.


When we then try to see the big picture we try to

reassemble the fragments in our minds, to list and

organize the pieces, but the task is futile. Its similar to

trying to reassemble the fragments of a broken mirror

to see a true reflection. So after a while we give up

trying to see the whole all together.

Peter Senge


14/59

Analytic Thinking

Formal education is based on analytic thinking


FinanceEconomics

Production

Personnel

Marketing

Management

Supply Chain

MBA


15/59

Analytic Thinking

In many ways its like trying to understand life

by studying the dead

Our entire culture

is based on

Analytic Thinking



16/59

Assumption:

If you know what the parts do youll be able to

integrate that knowledge into an understanding of

the WHOLE

Analytic Thinking

Finance

Economics

Production

Personnel

Marketing

Management

Supply Chain



17/59

Assumption:

If you know what the parts do youll be able to

integrate that knowledge into an understanding of

the WHOLEBusiness is the

product of the

interactions

between the parts

Analytic Thinking

Finance

Economics

Production

Personnel

Marketing

Management

Supply Chain



18/59

Analytic Thinking

Its not surprising that corporations, governments and

organizations are created and managed by parts

ProductionSales

R&D HR

Customer

Service Quality

IT

Corporate System


How is problem-solving approached in companies?


19/59

Analytic Thinking

Analytic Thinking cant tell us why were getting theresults were getting because the parts lose their

meaning when separated from the whole

This sets up learning disorders in organizations

because cause and effect are often separated in space

and time

System dynamics and delays make it impossible to

judge or measure the effectiveness of our actions


Click me to

play YouTube video
http://www.youtube.com/watch?v=7NCpdLKhY04


20/59

Analytic vs. Systems Thinking

Analytic Thinking

1. Take apart

2. Try to understand

what the parts do

3. Assemble your

understanding of the

parts into anunderstanding of the

whole

Systems Thinking

1. Determine what its a

part of

2. Try to understand thebehavior of the whole

3. See the functional

interdependencies ofthe parts with respect

to the whole



21/59


Analytic Thinking

1. Take apart


what the parts do

3. Assemble



whole

Systems Thinking


part of




to the whole

Whole =

Sum of Parts

Whole =

Product of Part

Interactions



22/59


Analytic Thinking

1. Take apart


what the parts do

3. Assemble



whole

Systems Thinking


part of




to the whole

How parts work

& the role they

play

Why events occur



23/59

Systemic Problem-Solving

Non-Systems

Thinking

Systems

Thinking


Todays solutions

tomorrows problems


24/59

Tools for Systems Thinking

Measurement Material

ManMother

NatureMachine

Method

Problem

Cause-Effect Diagram


Analytic Thinking

Measurement

Material

ManMother

Nature

Machine Method

Causal Loop Diagram

Systems Thinking

Mostly Cause Theories All Causal Facts


25/59

Tools for Systems Thinking

Measurement Material

ManMother

NatureMachine

Method

Problem

Cause-Effect Diagram


Analytic Thinking

Measurement

Material

ManMother

Nature

Machine Method

Causal Loop Diagram

Systems Thinking

Mostly Cause Theories All Causal Facts


26/59

Itching & Scratching


Itching Scratching++

-Connection Matrix

(Destination)

(Source)

Biological System


27/59



-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T2 3 T24 T25 T26 T27 T2 8 T29 T30

St

andardizedScale


Itching

Scratching

Scratching

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 0 T 11 T1 2 T 13 T1 4 T 15 T1 6 T 17 T1 8 T 19 T2 0 T 21 T2 2 T 23 T2 4 T 25 T2 6 T 27 T2 8 T 29 T3 0

Sta

ndardizedScale

No Scratching

Itching

No Scratching

Pattern =

Voice of the

System


28/59

Voice of the System

Answers are not interms of how much orwhen

X-Y scales are unit less

Answers are systembehavior patterns

Pattern = Vibration(Voice of the System)

Experienced asrecurring or isolatedevents in space & time


-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T1 4 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T2 8 T29 T30

St

andardizedScale


Itching

Scratching

Scratching


29/59

Voice of the System

Vibration is a universal

language

Hear, see, smell, taste,

touch is all vibrationalenergy

System vibration also

tells us what to expect


-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T1 4 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T2 8 T29 T30

StandardizedScale


Itching

Scratching

Scratching


30/59

Highway Accident Rate


2

Accident

Rate

3

Patrol

Frequency

1Vehicle

Speed +

+

-+

-

Connection Matrix

Destination

1 Speed 2 Accident Rate 3 Patrol Freq.

1 Speed 0 1 1

Source 2 Accident Rate 0 0 1

3 Patrol Freq. -1 -1 0

Opinion of

an Expert


31/59



-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12

StandardizedSca

le

Accident Rate

1 Speed

2 Accident Rate

3 Patrol Freq.


32/59



5

Traffic

Congestion

6Bad

Weather

4

Raise

Driver Age

7

Drivers Risk

Aversion

2

Accident

Rate

3

Patrol

Frequency

1

Vehicle

Speed +

+-

-+

8

Drivers

Education


33/59


2 Accident

Rate

3 Patrol

Frequency

1 Vehicle

Speed+

+

--

+

5 Traffic

Congestion

4 Raise

Driver

Age

7 Drivers Risk

Aversion+

8 Drivers

Education

+

+

+

+

+

+

+

-

6 Bad

Weather

Policy not in

our control

Policy in our

control


Expert Opinion


34/59


-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T2 T4 T6 T8 T10 T12 T14 T16 T18 T20 T22 T24 T26 T28 T30

StandardizedScale

Accident Rate (Raise Driver Age & Driver's Education are Active)

1 Speed

2 Accident Rate

3 Patrol Freq.

4 Raise Driver Age

5 Traffic Congestion

6 Bad Weather

7 Driver's Risk Aversion

8 Driver's Education


Vehicle

Speed

Drivers Risk

Aversion

< Accident

Rate


35/59

Product Line Profit


1 Production

2 Costs

3 Inventory

Growth

4 UnmetDemand

5 Competitor

Sales

6 Sales7 Demand

8 Price

9 Revenue

10 Profit 11 Advertising

+

+

+

+

+

+

+

+

+

+

-

-

-

-

-

-

+ +

+

+

-


36/59

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9T10T11T12T13T14T15T16T17T18T19T20T21T22T23T24T25T26T27T28T29T30

Standardized

Scale

Product Line Profit

(Present State Policy)1 Production

2 Costs

3 Inventory Growth

4 Unmet Demand

5 Competitor Sales

6 Sales

7 Demand

8 Price

9 Revenue

10 Profit

11 Advertising

Product Line Profit


Run 3 to 1 6 to 11 6 to 8 MScore(SS) VScore

1 -1 -1 -1 4.3 4.28

2 -1 -1 0 -1.6 1.04

3 -1 -1 1 0 1

4 -1 0 -1 8.2 4.64 < Optimal

5 -1 0 0 4 4.1

6 -1 0 1 -0.1 1.29

7 -1 1 -1 16.8 1.08

8 -1 1 0 6.8 3.33

9 -1 1 1 4 4.1

Target Variable = ProfitConnection Policy

Profit CLD Simulation Experiment

< Baseline

0

1

2

3

4

5

6

7

-20 -15 -10 -5 0 5 10 15 20

Vscore

Mscore

Profit Policy Scores

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


Standardized

Scale

Product Line Profit(Future State Policy)

1 Production

2 Costs

3 Inventory Growth

4 Unmet Demand

5 Competitor Sales

6 Sales

7 Demand

8 Price

9 Revenue

10 Profit

11 Advertising


37/59

Sick Call-Ins


12 Family

Time

1 Employees

In Training2 Sick

Call-Ins

3 Late

Employees

4 Employees

Temporarily

Reassigned

5 Available

Employees6 Productivity

7 Perceived

Training Rqmts.

8 Overtime

Utilized

9 Employee

Morale

10 Mgrs Doing

Employees Duties

11 Supervision

Quality

+

---

-

--+

-

+ +

-

-

13 Delay

-

-

- +

14 Return Early

if Possible

17 Evaluate ROI

15 Borrow Employees

From Other Depts. 16 Remind Employees ofProper Call-In Policy-

+

-


38/59

Sick Call-Ins


-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

T21

T22

T23

T24

T25

T26

T27

T28

T29

T30

Standardized

Scale

Sick Call-In Issue (Present State)

1 Employees In Training

2 Sick Call-Ins

3 Late Employees

4 Employees temporarily Reassigned

5 Available Employees

6 Productivity

7 Perceived Training Rqmts.

8 Overtime Utilized

9 Employee Morale

10 Mgrs. Doing Employees Duties

11 Supervision Quality

12 Family Time

14 Return Early If Possible

15 Barrow Employees From Other Depts.

16 Remind Employees of Proper Call-In Policy

17 Evaluate ROI


39/59

Sick Call-Ins


-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

T0

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

T21

T22

T23

T24

T25

T26

T27

T28

T29

T30

Standardized

Scale

Sick Call-In Issue (Active Training ROI Evaluation)

1 Employees In Training

2 Sick Call-Ins

3 Late Employees

4 Employees temporarily Reassigned

5 Available Employees

6 Produc tivity

7 Perceived Training Rqmts.

8 Overtime Utilized

9 Employee Morale

10 Mgrs. Doing Employees Duties

11 Supervision Quality

12 Family Time

14 Return Early If Possible

15 Barrow Employees From Other Depts.

16 Remind Employees of Proper Call-In Policy

17 Evaluate ROI

Sick Call-Ins

Employee

Morale

Late

Employees

Mgrs. Doing

Employees Duties


40/59

Cash for Clunkers


5 Fund

Growth

6 Interest

Income

8 Dirty

Cars

3 Buy BackWithdrawals

9 Car

Emissions

11 Delay

12 Delay

-

-

+

+

+

++

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


Standardized

Scale

Cash for Clunkers

Clean Car Rebates

Dirty Car Sales Fees

Dirty Car Buy Back

1-Time Funding

Fund Growth

Interest Income

Clean Cars

Dirty Cars

Car Emissons


41/59

Cash for Clunkerswith FeeBate Structure


5 Fund

Growth

6 Interest

Income

7 Clean

Cars

8 Dirty

Cars

3 Buy Back

Withdrawals1 Clean Car

Rebates

2 Dirty Car

Sales Fees

9Car

Emissions

10 Delay

11 Delay

12 Delay

-

-

+

+

+

+

++

+

+

-

+ --1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


Standa

rdized

Scale

FeeBate Policy

Clean Car Rebates

Dirty Car Sales Fees

Dirty Car Buy Back

1-Time Funding

Fund Growth

Interest Income

Clean Cars

Dirty Cars

Car Emissons


42/59

Modeling Hierarchy


Time & Effort

Value

(Util

ity)

+ Causal Magnitude

+ Functional Form

+ Constraints

+ Learning new language & software

Seeking Structure Instead of Blame

Modeling

Hierarchy+ Noise uncertainty with all the above

+ CLD (Key Variables, Causal Arrows & Signs)

VOS


43/59

Google CLDs


Click image to Google
http://images.google.com/images?sourceid=navclient&rlz=1T4ACAW_enUS301US302&q=causal%20loop%20diagrams&um=1&ie=UTF-8&hl=en&sa=N&tab=vihttp://images.google.com/images?sourceid=navclient&rlz=1T4ACAW_enUS301US302&q=causal%20loop%20diagrams&um=1&ie=UTF-8&hl=en&sa=N&tab=vihttp://images.google.com/images?sourceid=navclient&rlz=1T4ACAW_enUS301US302&q=causal%20loop%20diagrams&um=1&ie=UTF-8&hl=en&sa=N&tab=vi


44/59

How Can Experts Agree?Brooksly BornAlan Greenspan vs.



45/59

How Can Experts Agree?Brooksly BornAlan Greenspan


A.G. & B.B.


46/59

1 Production

2 Costs

3 Inventory

4 Unmet

Demand

5 Competitor

Sales

6 Sales7 Demand

8 Price

9 Revenue


+

+

+

+

+

+

+

+

+

+

-

-

-

-

-12 Family

Time

1 Employees

In Training2 Sick

Call-Ins

3 Late

Employees

4 Employees

Temporarily

Reassigned

5 Available


7 Perceived

Training Rqmts.8 Overtime

Utilized

9 Employee

Morale

10 Mgrs Doing

Employee's Duties

11 Supervision

Quality

+

- --

-

--+

-

+ +

-

-

Delay

-

-

- +

Company

Performance

Organization

Performance

Individual

Morale

Thoughts

& Emotions

Commitment

& Action

Team

Performance

+

+

+

+

+

+

+

Goal

Alignment

Mgt.

Support

Needs Met

++

+

2

Accident

Rate

3

Patrol

Frequency

1

Vehicle

Speed +

+

--

+

2 Accident

Rate

3 Patrol

Frequency

1 Vehicle

Speed+

+

--

+

5 TrafficCongestion

6 Bad

Weather

4 Driver

Age

7 Drivers Risk

Aversion+

8 Driver

Training

+

+

+

+

+

+

+

-

+ = Proportional

- = Inversely Proportional

Knowledge Networking(Linked Expert CLDs)

Expert CLD 1 Expert CLD 2

Expert CLD K

Expert CLD 4Expert CLD 3

TT

T

T

T

T

T

T = Target Variable



47/59

1 Production

2 Costs

3 Inventory

4 Unmet

Demand

5 Competitor

Sales

6 Sales7 Demand

8 Price

9 Revenue


+

+

+

+

+

+

+

+

+

+

-

-

-

-

-12 Family

Time

1 Employees

In Training2 Sick

Call-Ins

3 Late

Employees

4 Employees

Temporarily

Reassigned

5 Available


7 Perceived

Training Rqmts.8 Overtime

Utilized

9 Employee

Morale

10 Mgrs Doing

Employee's Duties

11 Supervision

Quality

+

- --

-

--+

-

+ +

-

-

Delay

-

-

- +

Company

Performance

Organization

Performance

Individual

Morale

Thoughts

& Emotions

Commitment

& Action

Team

Performance

+

+

+

+

+

+

+

Goal

Alignment

Mgt.

Support

Needs Met

++

+

2

Accident

Rate

3

Patrol

Frequency

1

Vehicle

Speed +

+

--

+

2 Accident

Rate

3 Patrol

Frequency

1 Vehicle

Speed+

+

--

+

5 TrafficCongestion

6 Bad

Weather

4 Driver

Age

7 Drivers Risk

Aversion+

8 Driver

Training

+

+

+

+

+

+

+

-

+ = Proportional

- = Inversely Proportional

Knowledge Networking(Linked Expert CLDs)

Expert CLD 1 Expert CLD 2

Expert CLD K

Expert CLD 4Expert CLD 3

TT

T

T

T

T

T

T = Target Variable



48/59


L

V LV

T

L

V

$


49/59


DefineCreate problem specification:

Who (Who cares and why VOC)

Extent (Scope, COPQ, ROI)

What, Where, When (Is and Is Not for each)

Create teamDefine target metrics

MeasureGather baseline data for target metrics

AnalyzeResearch & development (operational thinking)

See Six Sigma tools that facilitate creation of CLDs

Brainstorming system variables using 5-WhysOrganize using cause-effect (fishbone) diagram

Separate causal facts from theories

Create expert causal loop diagram (CLD)

Expert mental model mixing

Simulate (event pattern inference)

Determine leverage variables & policy types

Experiment & optimize target variable scores

Improve Share with al l stakeholders and adjust as needed

Execute policy design projects at optimal leverage points

Assess impact relative to baseline data

ControlMonitor target metrics, communicate and audit

Systemic Problem-Solving

using DMAIC

Expert 1

CLD

Expert 6

CLD

Expert 3

CLD

Expert 4

CLD

Expert 5

CLD

Expert 2

CLD

Expert CLD

Expert K

CLD.

.

.

Knowledge Network


50/59

Systems vs. Statistical Thinking

Statistical Thinking

Detect and react to

signals (not noise)

Prescribes appropriate

inaction

Systems Thinking

How to react to signals

Prescribes appropriate

action


Skills leaders, policy-makers and change

agents need to cure corporate arrhythmias


51/59

TRIZ Systems Thinking


Patents represent the best definition of what constitutes

INNOVATION

Inventive

Principles

(operators)

All Patents (Inventors)

Inventive

Patents

All levels & patterns of invention

and technological evolution

AnalogousKnown Problem

>

New Problem

Trial & Error

DELAYS

Best Solutions

>

I-TRIZ

> ExhaustiveSolution Set

AvailableResources

Solution?

Structured

Knowledge

Bank Deadline!


52/59

I-TRIZ Knowledge Bank

Knowledge within Company

Knowledge within Industry

Knowledge throughout Society

All that is knowable

Personal

Knowledge


I-TRIZ BEAM

Knowledge

Bank

Intranet


53/59

I-TRIZ InnogramSystem Cause-Effect Diagram

All

Harmful

All Useful

Ideality =

Automatically

generates directions

for systemimprovement (37

directions for this

diagram)


I-TRIZ

Inventive

Principles

(operators)


54/59

Summary

PROBLEMS? Dont just do something, SIT THERE! Take time to seek VOS not blame

Its possible to agree on answers to tough questions

We can create and simulate our mental models and

use them to test policies

CLDs are true cause-effect diagrams

Expert opinion is better than opinion of an expert

Sustainability requires knowledge of the parts andhow they interact and function as a whole

I-TRIZ is Systems Thinking & Systems Doing!



55/59

I dont worry about the future;

I know what will happen

managers will either learn how to

optimize the system or they will goout of business.

W.EdwardsDeming


Click me to

play YouTube video
http://en.wikipedia.org/wiki/File:W._Edwards_Deming.gifhttp://www.youtube.com/watch?v=iebuYMYMTrg&feature=related


56/59

Team Workshops

Create and simulate CLDs using CLD SIM


1 Production

2 Costs

3 Inventory

Growth

4 Unmet

Demand

5 Competitor

Sales

6 Sales7 Demand

8 Price

9 Revenue


+

+

+

+

+

+

+

+

+

+

-

-

-

-

-

-

++

+

+

-


57/59


CRC Press

Optimizing the Health of Your Company

with Systems Thinking
http://www.amazon.com/gp/product/images/1439803943/sr=1-1/qid=1251324128/ref=dp_image_0?ie=UTF8&n=283155&s=books&qid=1251324128&sr=1-1http://www.amazon.com/gp/product/images/1439803943/sr=1-1/qid=1251324128/ref=dp_image_0?ie=UTF8&n=283155&s=books&qid=1251324128&sr=1-1http://www.amazon.com/gp/product/images/1439803943/sr=1-1/qid=1251324128/ref=dp_image_0?ie=UTF8&n=283155&s=books&qid=1251324128&sr=1-1http://www.amazon.com/gp/product/images/1439803943/sr=1-1/qid=1251324128/ref=dp_image_0?ie=UTF8&n=283155&s=books&qid=1251324128&sr=1-1


58/59

Belief Systems

Do our results create our beliefs or do our

beliefs create our results?

Yes,both!


+

Vision

Results+

Action

++

Belief Potential+

S t Thi ki


59/59


systems thinking overview - john dubuc 2010

Documents