Improving Project Management Skills with Lean Six Sigma

©2010 International Institute for Learning, Inc., All rights reserved. 1Intelligence, Integrity and Innovation©2010 International Institute for Learning, Inc., All rights reserved.
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Improving Project Management Skills
with Lean Six Sigma

©2010 International Institute for Learning, Inc., All rights reserved. 2Intelligence, Integrity and Innovation
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The goal of this webinar is
to provide participants with
three important
concepts/techniques from
Lean Six Sigma that every
project manager should
know and apply to improve
project and product quality.
Webinar Goal

At the end of this session, you should be able to:
Define Lean Six Sigma from a high level.
Identify vital inputs to improve outputs.
Determine the difference between Common Cause and Special
Cause Variation.
Error proof project processes using Poka Yoke.
Webinar Objectives

Lean Six Sigma Quality
TopicTopic

Take a minute to think of a
product or service that you
have purchased and
considered to be “high
quality.”
What are the attributes that
led you to that conclusion?
Please answer in Text Chat.
What is Quality?

Improvement of Results starts with improvement
of Quality.
Dr. Deming was a quality and process
improvement pioneer credited with helping the
Japanese industry recover after World War II.
1. Lean Six Sigma is about improving results by
improving the quality of products and services.
2. Improvement comes from managing the
process, not the results.
Deming’s Chain Reaction

Both methodologies are geared towards reducing costs.
Lean Focus – Speed
Cycle Time Reduction
Cleanliness and Orderliness
Elimination of Waste
Rapid Project Execution
Error Proofing
Six Sigma Focus – Quality
Customer-focused (customer pain)
Financially-focused (financial Impact)
Methodology (DMAIC)
Support Infrastructure (Project Framework)
Defect Reduction
Data-based (management by fact)
What is Lean Six Sigma?
Six Sigma
Lean

Improving Outcomes –
Management by Fact
TopicTopic

The Mathematical Model
Defined processes enables you to control processes using the model.
Input
Variables
(Xs)
Outputs
(Ys)
Process Variables (Xs)
Process
)X,...,X,f(XY n21=

Output to ImproveOutput to Improve Input and Process Variables to Investigate (X’s)Input and Process Variables to Investigate (X’s)
Profits Market price, costs, volume, etc.
Costs Materials, labor, cycle-time, scrap, waste, rework,
etc.
Cycle Time Lead time, bottle necks, number of approvals
needed, incentives paid to workers for production,
etc.
Business Examples for Y = f(X)
In order to improve the Y’s, you must improve the key X’s.

The Concept of Variation
TopicTopic

No two things are exactly
alike. There is variation in
every process or product.
Even things that appear
consistent or identical are
not (e.g., steel plate).
Variation is the enemy of
Quality.
Variation is Reality
_
x
Time
LoanCT
654321
9
8
7
6
5
4
3

Special Cause and
Common Cause Variation
TopicTopic

Common Cause – events
happen sometimes to
everyone – part of the
process (indicates process
needs improvement)
Special Cause – events only
happen sometimes to some
people/processes – out of
the ordinary
Two Causes of Variation
This happens
all the time
This happens
all the time
Why does this
only happen to
me?
Why does this
only happen to
me?

Common Cause Factors Special Cause Factors
Example: Work Commute
When you commute to work – the time it takes varies from day to day. Why?

Why it matters
Variation exists in all processes.
There are two fundamental causes of variation:
Special Cause & Common Cause.
Knowing the factors and the type of cause (Common versus
Special) will determine the action you should take.
Understanding Variation

Example: Responding to Variation
SituationSituation
All clerks make some mistakes from time
to time
Only one clerk is making mistakes
Type of VariationType of Variation Common Cause Special Cause
Appropriate ResponseAppropriate Response
Look at all the data and find out why
mistakes are being make (e.g. old clerks
not trained on new system). Make
fundamental process change (e.g., re-
train old clerks on new system).
Look at that one clerk and find out
what is different (e.g., sticky key on
keyboard). Address that specific
instance (e.g., buy new keyboard for
that clerk).
InappropriateInappropriate
ResponseResponse
Investigate each occurrence of a mistake
and try to change the process so it won’t
happen again.
Buy new keyboards for entire
department.
“Tampering” – treating Common Cause like
Special Cause – increases process variation,
wastes time trying to investigate and explain
random events, and frustrates workers
Treating Special Cause like Common –
wastes resources and may frustrate
workers

TopicTopic
Introduction to Control Charts
Distinguishing Common & Special Cause Variation
Example of Standard Business Reporting

Business Performance Report: Sales
Please assess our recent performance
1. Last month’s performance (108) is better than this month’s (101).
2. This month’s performance (101) isn’t even as good as YTD’s (102).
3. But this month’s performance (101) is better than the performance the
same month last year (98).
Let’s see if our interpretation changes when we plot our data over
time, where variation can be seen and taken into account…

Mean CL: 30.48
-2.68
63.65
-7.21
2.79
12.79
22.79
32.79
42.79
52.79
62.79
72.79
82.79
1
2
3
4
5
6
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8
9
10
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13
14
15
16
17
18
19
20
21
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23
24
25
26
27
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33
Individuals-InstallationTime
Control Chart
A picture of a process over time (e.g., Time Series Plot)
With Control Limits that help distinguish between Special Cause
variation (abnormal) and Common cause variation (normal or
systemic)
The control limits are based on the data, not customer or internal
requirements
Control Limits

A Control Chart helps distinguish
between Common Cause & Special
Cause Variation.
It is like a “pipeline” representing
typical, or Common Cause process
variation.
Points outside the “pipeline”, or control
limits, represent Special Cause
Variation.
The Control Limits are calculated from
the data. They do not come from the
customer, or internal requirements.
Control Charts: 50,000 Foot View
Mont h
IndividualValue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_
X=97.61
UCL=104.96
LCL=90.26
1
I Char t of Scenar io 1
Mont h
IndividualValue
115
110
105
100
95
90
85
80
_
X=97.61
UCL=104.96
LCL=90.26
1
I Chart of Scenario 1
Common
Cause
Special
Cause

Scenario 1 – Last Month Result Was Special
Cause
This chart supports
an interpretation of
a significant
change last month
– a special cause.
Mont h
Scenario1
110
105
100
95
90
97.6197.61
Time Series Plot of Scenario 1
This
Month
Last
Month
Year-
To-
Date
Same
Month
Last
Year
101 108 102 98

Scenario 2 – Last Month Result Was Common
Cause
Last month’s result
doesn’t appear
unusual – just
common cause
variation.
Mont h
Scenario2
110
105
100
95
90
97.6197.61
Time Series Plot of Scenario 2
This
Month
Last
Month
Year-
To-
Date
Same
Month
Last
Year
101 108 102 98

Two radically different processes, requiring
different management approaches, both
produce the same standard management
report … this should concern you!
Charting data over time gives context.
Can see patterns and variation in the data
Control Charts plot data over time and use
Control Limits to detect Special Cause
variation so appropriate action can be taken.
Do managers and workers in your company
understand the difference between common
and special cause variation? If not, then
tampering is occurring.
Conclusions: Standard Business
Reporting
Mont h
IndividualValue
115
110
105
100
95
90
85
80
_
X=97.61
UCL=114.49
LCL=80.73
I Chart of Scenar io 2
Mont h
IndividualValue
115
110
105
100
95
90
85
80
_
X=97.61
UCL=104.96
LCL=90.26
1
I Chart of Scenario 1
This
Month
Last
Month
Year-
To-
Date
Same
Month
Last
Year
101 108 102 98

Management takes a big step forward when it stops asking
workers to explain randomness.
Something to Think About…

TopicTopic
Poka Yoke (Error Proofing)

“It is good to do it right the first time. It is even better to
make it impossible to do it wrong the first time.”
Yokeru (to avoid) Poka (inadvertent errors)
A technique for eliminating errors
Three principles of Poka-Yoke:
1. Make wrong actions more difficult or impossible.
2. Make mistakes obvious to the person so that the mistake can be
corrected.
3. Detect errors so that downstream consequences can be
prevented by stopping the flow or other corrective action.
What is Poka-Yoke?

Auto shutoff on electric coffee pots
Child-proof caps on medications
Ground fault circuit breakers
Auto shutoff on the lawn mower
Seat belts; air bags
Child-proof door latches
Car engine warning lights
Spell check in software
Auto-save documents
Poka-Yoke in Everyday Examples

Use colors and color-coding
Credit card receipts (the customer gets the yellow copy)
Use shapes (symmetrical and asymmetrical)
Store different types of parts in different shaped bins
Notching a stack of forms
Make it easy to do right
Checklists and effective data collection formats
Workflows with fewer hand-offs; SOP
Control plans
Other Methods
Where can you error proof your processes?

Good management is about your ability to predict and improve
outcomes.
Predicting outcomes is very difficult without measurement – Y
= f x
Understanding the causes of variation will help you to make
better decisions on what to improve.
Quality comes from well defined, simple and standardized
processes with low margins for error.
In Summary

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