Transcript P Q Lect_30.pptx

Productivity and Quality
Management
Lecture 30
SIX SIGMA BACKGROUND
LAST LECTURE SUMMARY
What is Six Sigma?
• A goal of near perfection in meeting customer
requirements
• A sweeping culture change effort to position a company for
greater customer satisfaction, profitability and
competitiveness
• A comprehensive and flexible system for achieving,
sustaining and maximizing business success; uniquely driven
by close understanding of customer needs, disciplined use of
facts, data, and statistical analysis, and diligent attention to
managing, improving and reinventing business processes
(Source:The Six Sigma Way by Pande, Neuman and Cavanagh)
KEY ROLES FOR SIX SIGMA
Six Sigma identifies several key roles for its
successful implementation:
Top
• Executive leadership
• Champions
• Master Black Belts (Identify projects& functions)
• Black Belts (Identify non value added activities)
• Green Belts ( works on small projects )
Bottom
7
SIX SIGMA MANAGEMENT
When practiced as a management system, Six Sigma is a
high performance system for executing business
strategy.
Six Sigma is a top down solution to help organizations:
• Align their business strategy to critical improvement
efforts
• Mobilize teams to attack high impact projects
• Accelerate improved business results
• Govern efforts to ensure improvements are sustained
8
SIX SIGMA METHODOLOGIES
SIX SIGMA METHODOLOGY
(It takes money to save money)
• BPMS
Business Process Management System
• DMAIC
Six Sigma Improvement Methodology
• DMADV
Creating new process which will perform at Six
Sigma
10
SIX SIGMA METHODOLOGIES
BPMS
BUSINESS PROCESS MANAGEMENT
SYSTEM:
• BPM strategies emphasize on process
improvement and automation to derive
performance
• Combining BPM strategies with sigma six is
most powerful way to improve performance
• Both strategies are not mutually exclusive but
some companies produced dramatic results by
combining them.
12
SIX SIGMA METHODOLOGIES
BPMS
BUSINESS PROCESS MANAGEMENT
SYSTEM:
• BPM strategies emphasize on process
improvement and automation to derive
performance
• Combining BPM strategies with sigma six is
most powerful way to improve performance
• Both strategies are not mutually exclusive but
some companies produced dramatic results by
combining them.
14
SIX SIGMA METHODOLOGIES
DMAIC
PHASES
Phases of Six Sigma are:
– Define specific goals to achieve
outcomes, consistent with customers
demand and business strategy
– Measure reduction of defects
– Analyze problems ,cause and effects
must be considered
– Improve process on bases of
measurements and analysis
– Control process to minimize defects
16
WHAT IS DMAIC?
(Define,Measure,Analyse,Improve.Control)
• A logical and structured
approach to problem solving
and process improvement.
• An iterative process
(continuous improvement)
• A quality tool which focus on
change management style.
17
DMAIC – The Improvement
Methodology
Define
Measure Analyze Improve Control
Objective:
DEFINE the
opportunity
Objective:
Objective:
Objective:
MEASURE current ANALYZE the root IMPROVE the
performance
causes of problems process to
eliminate root
causes
Objective:
CONTROL the
process
to sustain the gains.
Key Define Tools:
• Cost of Poor
Quality (COPQ)
• Voice of the
Stakeholder
(VOS)
• Project Charter
• As-Is Process
Map(s)
• Primary Metric
(Y)
Key Measure
Tools:
• Critical to Quality
Requirements
(CTQs)
• Sample Plan
• Capability
Analysis
• Failure Modes
and Effect
Analysis (FMEA)
Key Control
Tools:
• Control Charts
• Contingency
and/or Action
Plan(s)
Key Analyze
Tools:
• Histograms,
Boxplots, MultiVari Charts, etc.
• Hypothesis Tests
• Regression
Analysis
Key Improve
Tools:
• Solution
Selection Matrix
• To-Be Process
Map(s)
Define – DMAIC Project
What is the project?
Project
Charte
r
$
Cost of
Poor
Quality
Stakeholders
Voice of
the
Stakeholde
r
Six Sigma
• What is the problem? The “problem” is the Output (a “Y”
in a math equation Y=f(x1,x2,x3) etc).
• What is the cost of this problem
• Who are the stake holders / decision makers
• Align resources and expectations
Define – As-Is Process
How does our existing process work?
Move-It! Courier Package Handling
Process
Courier
Mail Clerk
In-SortClerk
In-SortSupervisor DistanceFeeClerk
WeightFeeClerk
Accounts
ReceivableClerk
Accounts
Supervisor
Out-SortClerk
Out-SortSupervisor
Observ e package
weight (1 or 2) on
back of package
Look up
appropriate
Weight Fee and
write in top middle
box on package
back
Add Distance &
Weight Fees
together and write
in top right box on
package back
Accounting
Take packages
f rom Weight Fee
Clerk Outbox to
A/R Clerk Inbox.
Take packages
f rom A/R Clerk
Outbox to
Accounts
Superv isorInbox.
Does EVERYONE agree
how the current
process works?
Circle Total Fee
and Draw Arrow
f rom total to
sender code
Write Total Fee
f rom package in
appropriate
Sender column on
Accts. Supv .’s log
Take packages
f rom Accounts
Superv isor
Outbox to OutSort Clerk Inbox.
Delivery
Finalizing
Take packages
f rom Out-Sort
Clerk Outbox to
Out-Sort
Superv isorInbox.
Draw 5-point Star
in upper right
corner of package
f ront
Define the Non Value
Add steps
Sort packages in
order of Sender
Code bef ore
placing in outbox
Add up Total # of
Packages and
Total Fees f rom
log and create
client inv oice
Observ e sender
and receiv er
codes and make
entry in Out-Sort
Superv isor’s log
Deliv erPackages
to customers
according to N, S,
E, W route
Deliv er inv oiceto
client
Submit log to
General Manager
at end of round
Submit log to
General Manager
at conclusion of
round.
Submit log to
General Manager
at end of round
Define – Customer Requirements
Listening Posts Industry Intel
What are the CTQs? What motivates the customer?
SECONDARY RESEARCH
Market
Data
Customer
Service
Industry
Benchmarking
Customer
Correspondence
PRIMARY RESEARCH
Survey
s
OTM
Focus Groups
Observations
Voice of the Customer
Key Customer Issue
Critical to Quality
Measure – Baselines and Capability
What is our current level of performance?
•
•
•
Descriptive Statistics
Sample some data / not all data
Current Process actuals measured
against the Customer expectation
What is the chance that we will succeed
at this level every time?
Variable: 2003 Output
Anderson-Darling Normality Test
A-Squared:
P-Value:
0
10
20
30
40
50
95% Confidence Interval for Mu
0.211
0.854
Mean
StDev
Variance
Skewness
Kurtosis
N
23.1692
10.2152
104.349
0.238483
0.240771
100
Minimum
1st Quartile
Median
3rd Quartile
Maximum
0.2156
16.4134
23.1475
29.6100
55.2907
95% Confidence Interval for Mu
21.1423
19.5
20.5
21.5
22.5
23.5
24.5
25.5
26.5
25.1961
95% Confidence Interval for Sigma
8.9690
11.8667
95% Confidence Interval for Median
95% Confidence Interval for Median
19.7313
26.0572
Measure – Failures and Risks
Where does our process fail and why?
Subjective opinion mapped into an “objective” risk profile number
Failure Modes and Effects Analysis (FMEA)
Process/Product
Process or
Product Name:
Prepared by:
Responsible:
FMEA Date (Orig) ______________ (Rev) _____________
Process
Step/Part
Number
Potential Failure Mode
Potential Failure Effects
S
E
V
Potential Causes
X1
X2
X3
X4
etc
O
C
C
Current Controls
Page ____ of ____
D
E
T
R
P
N
Actions
Recommended
Resp.
Actions Taken
S
E
V
O
C
C
D
E
T
R
P
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Analyze – Potential Root Causes
What affects our process?
Ishikawa Diagram
(Fishbone)
Six Sigma
y = f (x1, x2, x3 . . . xn)
Analyze – Validated Root Causes
What are the key root causes?
Experimental Design
Data
Stratification
Regression
Analysis
Process
Simulatio
n
Six Sigma
y = f (x1, x2, x3 . . . xn)
Critical Xs
Improve – Potential Solutions
How can we address the root causes we identified?
• Address the causes, not the symptoms.
Evaluate
Clarify
Generate
y = f (x1, x2, x3 . . . xn)
Critical Xs
Divergent | Convergent
Decision
Improve – Solution Selection
How do we choose the best solution?
Solution Selection Matrix
Qualit
y
Time
Solution
Sigma
Cost
Six Sigma
Implementation
Good
Bad
Solution
Right
Wrong
☺
Nice
Try
Nice
Idea
X
Solution
Implementatio
n Plan
Time
CBA
Other
Score
Control – Sustainable Benefits
How do we ”hold the gains” of our new process?
•
•
•
Some variation is normal and OK
How High and Low can an “X” go yet not materially impact the “Y”
Pre-plan approach for control exceptions
Process Control System (Business Process Framework)
Process Owner:
Process Description:
Direct Process Customer:
CCR:
Date:
Flowchart
Customer
Sales
Specs
&/or
Targets
Measures
(Tools)
Responsibility Contingency
Where &
(Who)
(Quick Fix)
Frequency
Remarks
35
Review
appliation for
completeness
No
Application
Complete?
P2 - # of
incomplete
loan
applications
UCL=33.48
Individual Value
1.1
Application & Review
1.2
Processing
1.3
Credit review
1.4
Review
1.5
Disclosure
Branch Manager
Key
Measure
ments
P1 - activity
duration,
min.
Apply for
loan
Complete
meeting
information
Processing
Measuring and Monitoring
Loan Service
Manager
25
Mean=24.35
LCL=15.21
15
0
10
20
Observation Number
30
SIX SIGMA METHODOLOGIES
DMADV
WHAT IS DMADV?
• Acronym for:
 Define the project
 Measure the opportunity
 Analyze the process
options
 Design the process
 Verify the performance
30
DMADV – The Design Methodology
Design for Six Sigma
Define
Measure Analyze
Develop
Verify
• Uses
– Design new processes, products, and/or services from scratch
– Replace old processes where improvement will not suffice
• Differences between DMADV and DMAIC
– Projects typically longer than 4-6 months
– Extensive definition of Customer Requirements (CTQs)
– Heavy emphasis on benchmarking and simulation; less emphasis on
baselining
• Key Tools
– Multi-Generational Planning (MGP)
– Quality Function Deployment (QFD)
SPECIAL ISSUES IN SIX SIGMA
METHODOLOGIES
Topics for Detailed Discussion





Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding
Improvement
Problem Identification
“If it ain’t broke, why fix it
“This is the way we’ve always done it…”
Problem Identification
•First Pass Yield
•Roll Throughput Yield
•Histogram
•Pareto
Problem Identification
First Pass Yield (FPY):
The probability that
any given unit can go
through a system
defect-free without
rework.
100 Units
Step 1
Outputs / Inputs
100 / 100 = 1
100
Scrap 10 Units
Step 2
90 / 100 = .90
90
Scrap 3 Units
Step 3
87 / 90 = .96
87
Scrap 2 Units
At first glance, the yield would seem to be
85% (85/100 but….)
Step 4
85 / 87 = .97
When in fact the FPY is (1 x .90 x .96 x .97 = .838)
85
Problem Identification
Rolled
Throughput
Yield (RTY):
The yield of
individual
process steps
multiplied
together.
Reflects the
hidden factory
rework issues
associated with
a process.
Re-Work
10 Units
100 Units
Outputs / Inputs
Step 1
90 / 100 = .90
100 Units
97 / 100 = .97
Step 2
Re-Work
3 Units
100 Units
98 / 100 = .98
Step 3
Re-Work
2 Units
100 Units
Step 4
100 Units
.90 x .97 x .98 = .855
Problem Identification
RTY Examples - Widgets
50
Roll Throughput Yield
Function 1
50/50 = 1
(50-5)/50 = .90
50
Function 2
(50-10)/50 = .80
5
(50-5)/50 = .90
50
Function 3
10
1 x .90 x .80 x .90 = .65
50
Function 4
5
50
Put another way, this process is
operating a 65% efficiency
Problem Identification
RTY Example - Loan Underwriting
50
Roll Throughput Yield
50/50 = 1
Application
2
50
Underwrite
6
7
Fails
Underwriting
(50-7-2)/50 = .82
(43-6)/43 = .86
(43-1-2)/43 = .93
43
Complete Full
Paperwork
2
43
Close
42
1 x .82 x .86 x .93 = .66
1
Decide not to
borrow
Put another way, this process is
operating a 66% efficiency
Problem Identification
Histogram – A histogram is a basic graphing tool that displays the
relative frequency or occurrence of continuous data values showing
which values occur most and least frequently. A histogram illustrates the
shape, centering, and spread of data distribution and indicates whether
there are any outliers.
Histogram of Cycle Time
40
Frequency
30
20
10
0
0
100
300
200
C8
400
500
Problem Identification
Histogram – Can also help us graphically understand the data
Descriptive Statistics
Variable: CT
Anderson-Darling Normality Test
A-Squared:
P-Value:
25
100
175
250
325
400
95% Confidence Interval for Mu
6.261
0.000
Mean
StDev
Variance
Skewness
Kurtosis
N
80.1824
67.6003
4569.81
2.31712
8.26356
170
Minimum
1st Quartile
Median
3rd Quartile
Maximum
1.000
31.000
66.000
105.000
444.000
95% Confidence Interval for Mu
69.947
54
64
74
84
94
90.417
95% Confidence Interval for Sigma
61.098
75.664
95% Confidence Interval for Median
95% Confidence Interval for Median
55.753
84.494
Problem Identification
Pareto – The Pareto principle states that 80% of the impact of the
problem will show up in 20% of the causes. A bar chart that displays by
frequency, in descending order, the most important defects.
Pareto Chart for WEB
100
100
60
50
40
20
0
Defect
Count
Percent
Cum %
0
n-W
No
96
86.5
86.5
EB
ers
Oth eb)
W
(
15
13.5
100.0
Percent
Count
80
Topics (Session 2)





Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding
Improvement
Cost of Poor Quality
COPQ - The cost involved in fulfilling the gap between the desired and
actual product/service quality. It also includes the cost of lost opportunity
due to the loss of resources used in rectifying the defect.
Hard Savings - Six Sigma project benefits that allow you to do the same
amount of business with less employees (cost savings) or handle more
business without adding people (cost avoidance).
Soft Savings - Six Sigma project benefits such as reduced time to market,
cost avoidance, lost profit avoidance, improved employee morale,
enhanced image for the organization and other intangibles may result in
additional savings to your organization, but are harder to quantify.
Examples / Buckets–
Roll Throughput Yield Inefficiencies (GAP between desired result and
current result multiplied by direct costs AND indirect costs in the process).
Cycle Time GAP (stated as a percentage between current results and
desired results) multiplied by direct and indirect costs in the process.
Square Footage opportunity cost, advertising costs, overhead costs, etc…
Topics





Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding
Improvement
Problem Refinement
Multi Level Pareto – Logically Break down initial Pareto data into subsets (to help refine area of focus)
Pareto Chart for WEB
100
100
60
50
40
Percent
Count
80
20
Pareto Chart for Type
0
Defect
nNo
Count
Percent
Cum %
96
86.5
86.5
ers
Oth eb)
(W
100
100
15
13.5
100.0
80
Count
B
WE
60
50
40
20
0
Defect
Count
Percent
Cum %
al
nu
An
45
41.3
41.3
e
On
e
Tim
35
32.1
73.4
e
On
g
oi n
nG
dO
n
ea
Tim
13
11.9
85.3
0
ers
Oth
16
14.7
100.0
Percent
0
Problem Refinement
Fish Bone Diagram - A tool used to solve quality problems by
brainstorming causes and logically organizing them by branches. Also
called the Cause & Effect diagram and Ishikawa diagram
Provides tool for exploring cause / effect and 5 whys
Topics (Session 2)





Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding
Improvement
Process Understanding
SIPOC – Suppliers, Inputs, Process, Outputs, Customers
You obtain inputs from suppliers, add value through your process, and
provide an output that meets or exceeds your customer's requirements.
Process Understanding
Process Map – should allow people unfamiliar with the process to
understand the interaction of causes during the work-flow. Should outline
Value Added (VA) steps and non-value add (NVA) steps.
Receipt /
Extract
Start
Size Sorts
Control
Docs
Full Form
Open
Pull & Sort
Ck / Vouch
Verify
Perfection
Requal Group
No
Remit
Yes
Pass 1
Pass 2
Rulrs
Prep cks,
route
vouch
Prep cks
Ship to IP
Vouchers
Data Cap
Key from
image
Balance
Vouch
OK
Inventory
No
Yes
Prep
Folders /
Box
Full Form
QCReview
Ship to
Cust
Topics (Session 2)





Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding
Improvement
Improvement
•Once we know the degree to which inputs (X)
affect our output (Y), we can explore
improvement ideas, focusing on the cost
benefit of a given improvement as it relates to
the degree it will affect the output.
•In other words, we generally will not attempt
to fix every X, only those that give us the
greatest impact and are financially or
customer justified.
Control
Once improvements are made, the question becomes, are the
improvement consistent with predicted Design of Experiment
results (ie – are they what we expected) and, are they statistically
different than pre-improvement results.
Process Capability Analysis for Sept
Process Data
USL
0.23000
Target
*
LSL
-1.00000
Mean
-0.02391
Sample N
23
StDev (Within) 0.166425
StDev (Overall) 0.221880
LSL
USL
Within
Overall
Potential (Within) Capability
Z.Bench
1.53
Z.USL
1.53
Z.LSL
5.87
Cpk
0.51
Cpm
*
Overall Capability
Z.Bench
1.14
Z.USL
1.14
Z.LSL
4.40
Ppk
0.38
-1.0
-0.5
Observed Performance
% < LSL
0.00
% > USL
13.04
% Total
13.04
0.0
0.5
Exp. "Within" Performance
% < LSL
0.00
% > USL
6.35
% Total
6.35
1.0
Exp. "Overall" Performance
% < LSL
0.00
% > USL
12.62
% Total
12.62
Control
Control Chart - A graphical tool for monitoring changes that occur
within a process, by distinguishing variation that is inherent in the
process(common cause) from variation that yields a change to the
process(special cause). This change may be a single point or a
series of points in time - each is a signal that something is different
from what was previously observed and measured.
Individual Value
I and MR Chart for Sept
1
UCL=0.5293
0.5
Mean=0.03
0.0
2
LCL=-0.4693
-0.5
Sept 13
Sept 20
Date
9/13
9/25
Moving Range
Subgroup
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
UCL=0.6134
R=0.1877
LCL=0
Six Sigma Quality
The objective of Six Sigma quality is 3.4 defects per
million opportunities!
(Number of Standard Deviations)
3 Sigma
4 Sigma
5 Sigma
6 Sigma
0.0
2700
63
0.57
0.002
0.5
6440
236
3.4
0.019
1.0
22832
1350
32
0.019
1.5
66803
6200
233
3.4
2.0
158,700
22800
1300
32
But is Six Sigma Realistic?
·
IRS – Tax Advice (phone-in)
(66810 ppm)
·
Defects Per Million Opportunities (DPMO)
100K
10K
41
Average
Company
1K
31
Restaurant Bills
Doctor Prescription Writing
··· ·
···
Payroll Processing
Order Write-up
Journal Vouchers
Wire Transfers
Air Line Baggage Handling
·
Purchased Material
Lot Reject Rate
100
21
(233 ppm)
10
11
Best in Class
Domestic Airline
Flight Fatality Rate
1
1
(3.4 ppm)
2
3
3
4
4
SIGMA
5
5
6
6
7
(0.43 ppm)
7
TOOLS & TECHNIQUES
• 7QC tools
 Check Sheets (collect data to make improvements)
 Pareto Charts( define problem and frequency)
 Cause and effect diagram (Identify possible causes
to solve problem)
 Histogram (Bar charts of accumulated data to
evaluate distribution of data)
 Scatter diagram (plots many data points and
pattern between two variables)
 Flow Chart (Identify unwanted steps)
 Control charts (Control limits around mean value)57
Data Collected
From Check Sheet
• Time Range (in secs)
• Frequency
44-50
51-57
58-64
65-71
72-78
79-85
86-92
93-99
100-106
107-113
1
4
17
12
14
19
18
11
3
1
A Histogram
89
96
103
110
20
18
11
3
1
47
18
54
16
61
14
68
12
75
10
82
8
89
6
96
4
2
103
0
110
47
54
61
68
75
82
89
96
103
110
Be careful of Cell Size
40
30
20
10
0
47
54
61
68
75
82
89
96
50103 64
110
78
1
4
17
12
14
19
18
11
923
1
50
64
78
5092
106
64
78
92
106
106
Pareto Diagrams
 Purpose:
◦ helps organize data to show major factors
◦ displays data in the order of importance
◦ organize based on fact rather than perception
 To construct:
◦ use data from a check sheet or similar instrument
◦ analyze data to determine frequency
◦ identify the vital few
◦ calculate percentages
◦ add percentages to find vital few (80%)
◦ draw cumulative curve
 Typical Application:
◦ display relative importance of different factors
 choose starting point for problem solving
 monitor success
 identify basic cause of a problem
◦ use a selling tool to gain support
Pareto Chart (80-20 Rule)
100
80
60
40
20
0
1
2
3
47
54
61
68
75
82
89
96
103
110
4
5
6
1
4
17
12
14
19
18
11
3
1
120%
100%
80%
60%
Series2
40%
Series1
20%
0%
7
85
Cause and Effect Diagram
“Fishbone Diagram”
 Purpose:
◦ visual display of information to identify root causes rather than symptoms.
 To construct:
◦ determine the issue and write problem statement in a box to the right of
diagram
◦ find the main causes and write them on branches flowing to the main branch
(method, equipment, people, material, environment, customer expectations,
money, management, govt. regulations)
◦ identify all possible causes and write them on the diagram as sub-causes in
each category
 Typical Application:
◦ determine the real cause of the problem
◦ check the potential effects of a solution
Fishbone Diagrams Explained
5 Why’s problem solving technique
Teller
Processes
Sequence
of activities
Fatigue
Training
Too
many
steps
Control
functions
Attitude
Processing
Delays
Too much
downtime
Not user
friendly
Slow
response
time
Computers
Fishbone Diagram aka
Cause & Effect Diagram
Mizenboushi and GD3 Concepts
Robust Design
Good
Design
Prevent Problems
Find Problems
GD3
Good
Discussion
DRBFM
- keep Good Designs
- minimize change
Good
Dissection
DRBTR
Address any potential issues up stream at Design Phase
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Quality Focus At the Design Stage
Quality from the start –
–
Directs attention to “Change”
–
Directs attention to “Interfaces”
Change = potential to have problems
Most defects occur at the “interface”
Focus on
Change Points & Interface Points
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No change – No Problem
Examples:
• Design change
• Packaging environment
change
• Usage environment
change
• New manufacturing
process
• New supplier
Change Points have the highest potential to
introduce defects
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Design Review By Failure Modes (DRBFM)
Basic Concepts
•
•
•
•
Before and After – Description of the Change Point
Describe the Potential failure modes
Describe the Design Countermeasures
Target Testing of the change points and Countermeasures
Only
Design techniques to uncover defects at the
design stage – Up stream
DRBFM – Example
• Tire Pressure Monitoring System –
• Changing the sensor from Aluminum Valve to Rubber Valve.
• Purely for cost reduction purposes... System Performance is the same.
Simple change – What could go wrong?
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Interfaces
Interfaces – (Interfaces where issues can brew and
surface later)
– Customer to Supplier
– Department to Department
– System Interfaces
• The Crash sensor failure on Honda Minivans
Interface Points have the highest potential to
introduce defects
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Where do failures occur
• Design Phase (Suppliers are Up Stream)
• Production
• In the field
• Where is it cheapest to detect failures?
• Example:
Replacing a four crash sensors by a single one ..
When Failures Occur!
• Why did the failure happen?
– Symptoms vs. Root Causes
– Root Causes (Investigate the whole chain):
•
•
•
•
Suppliers/Component failure
Design
Manufacturing
Change management
• Why were not able to detect it?
Rootcause Analysis:
•Why Occurred?
•Why Not Detected?
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Failure Detection 5Ws-2Hs
 Who
 Where
 When
 What
 Why
 How was the problem found?
 How can we isolate it? Turn On / Turn Off
Rootcause Analysis Methodology
Finding the root causes of a problem is not Fault
Finding/Criticism.
• To find problems is not fault finding/criticism.
• To find problems is a creative act, same as innovation.
• We should never stop at only finding problems, but also develop
a systemic corrective action plan... FIX THE PROCESS that
created the problem & identify detection algorithms
• We never forget that every job should relate directly to
improving a product. Other jobs are nothing but waste, e.g.,
only to check, to inspect, etc.
• Everyone should readily accept help from review participants.
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WHEN SHOULD SIX SIGMA BE USED?
Its usage depends on the type of business. In general,
“If there are processes that generate a lot of negative
customer feedback, whether that customer is internal
or external, the components of Six Sigma should be
considered as a means to study and rectify the
problem.”
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BENEFITS OF SIX SIGMA
•
•
•
•
•
•
Generates sustained success
Sets performance goal for everyone
Enhances value for customers
Accelerates rate of improvement
Promotes learning across boundaries
Executes strategic change
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USAGE OF SIX SIGMA
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Summary
Six Sigma Methodologies
• BPMS
Business Process Management System
• DMAIC
Six Sigma Improvement Methodology
• DMADV
Creating new process which will perform at Six Sigma
Seven Basic Quality Tools To improve
Process Quality
 Scatter Diagrams: Plot data on a chart – no attempt is made
to classify the data or massage it
 Pareto Charts: Organize data on a histogram based on
frequency from most prevalent to least. Help identify major
causes or occurrences (80:20 rule)
 Check Sheets: Easy way to count frequency of occurrence by
front line workers
 Histograms: Categorize data is cells and plot (see if any
patterns emerge)
 Run Charts: Plot data as a function of time
 Cause and effects Charts: fishbone diagrams are used to
identify the root causes of a problem
 Control Charts: are statistical tools used to determine if the
variation in results is caused by common or special events
Improving Productivity