LSSG Green Belt Training Measure: Finding and Measuring Potential Root Causes
LSSG Green BeltTraining
Measure: Finding and Measuring Potential Root Causes
DMAIC Six Sigma - Measure
Objectives Identify Inputs and Outputs
Determine key inputs and outputs for the process and measures to be analyzed
Measure Process Capability Collect data and compare customer
requirements to process variation Revise Charter
Validate project opportunity and perform charter revision
Measure
Control
Analyze
Improve
Define
Agenda for Measure
1. Types of Measures/Setting Targets
2. Data Collection and Prioritization, MSA
3. SPC, Control Charts
4. Process Capability
Measures
Purpose of measurement:
Performance of a process vs. Expectations
Select Measures “SMART” Objectives Clear operational
definitionsE.g. Losing Weight
Objective Lose 13 Pounds in 3 months
Secondary Objective
Lose 1 Pound per week
Driver(s) Calories consumed less Calories burned
Critical Success Factors (Drivers
Run 4 miles/day and consume less than 1500 calories/day
Must measure both the result (Y) and the drivers (Xs). Measure daily – to determine if CSFs are met, and to make adjustments to plan.
LSS Measurement
Measurement is not control! So, what is it?
Causes/
Effects
Measurement
System
Control
System
Historical data Current data
Measurement vs. Control
Measurement Plan
Data Operational Definitions and Procedures
What data type?
How measured?
What conditions?
By who? Where measured?
What sample size?
How to ensure consistency of measurement?
What is the data collection plan?
Setting Targets
Set Targets Objective/Meaningful Management-employees collaboration Team goal compatible with value stream objective
Balanced Score Card Perspectives
Financial
Customer
Internal Process
Learning & Growth
Agenda for Measure
1. Types of Measures/Setting Targets
2. Data Collection and Prioritization, MSA
3. SPC, Control Charts
4. Process Capability
Data Collection and Prioritization
Some Collection Tools
Customer Survey
Work / Time Measurement
Check Sheet
Some Prioritization Tools
Pareto Analysis
Fishbone Diagram
Cause and Effect Matrix
Work Measurement
Goals of Work Measurement Scheduling work and allocating capacity Motivating workers / measuring performance Evaluating processes / creating a baseline Determining requirements of new processes
Time Studies
Typically using stop watches For infrequent information - estimates OK Measure person, machine, and delays independently Medium Duration - not too short; not too long Eliminate Bias - Compute Standard times from
observed times
Time Study: Calculations
Step 1: Collect Data (Observed Time) Step 2: Calculate Normal Time from Observed Time,
where:
Step 3: Calculate Standard Time from Normal Time, where:
normaln faster the orksoperator w when use
Rating) ePerformanc (1 *unit per Time Observed Time Normal
)Allowances (1 *unit per Time Normal Time Standard
Time Study: Numerical Example
A worker was observed and produced 40 units of product in 8 hours. The supervisor estimated the employee worked about 15 percent faster than normal during the observation. Allowances for the job represent 20 percent of the normal time for breaks, lunch and 5S.
Determine the Standard Time per unit.
Data Analysis Tools
02468
1012
0 10 20 30Hours of Training
De
fects
Scatter Diagram
0.46
0.5
0.54
0.58
1 2 3 4 5 6 7 8 9 10 11 12
Time
Dia
met
er
Run Chart
Can be used to identify when equipment or processes means are drifting away from specs
Can be used to illustrate the relationships between factors such us quality and training
Fre
quency
Data Ranges
Histogram
Use to identify if the process is predictable (in control)
Can be used to display the shape of variation in a set of data
400
420
440
460
480
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
LCL
UCL
400
420
440
460
480
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
LCL
UCL
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
LCLLCL
UCLUCL
Control Chart
Cause and Effect Diagram
MaterialMethod
Environmental
ManMachine
Effect
Pareto ChartsRoot Cause Analysis
Design Assy.Instruct.
Purch. Training Other
80% of theproblems may beattributed to 20%
of the causes
Continuous Improvement Process
Orlando RemanufacturingAnd
Distribution Center
Phase 1: Internal Kickbacks
Five Most Common Reasons For Returns From QA
Missing/Wrong Part
Dirt/Rust DefectivePart
Leaks PoorInsulation
Impact of Reasons for Returns from QA - Weighted Average
Weighted Avg. = % Occurring X Defect Cost (0-10, Based on Time to Repair)
Leaks Dirt/RustStainlessSteel
Missing/Wrong Part
DefectivePart
PoorInsulation
Equipment To BeRemanufactured
Tear DownAnd Wash
Remanu-facture
ReassemblyFinal
Clean-up
QA
Unit Not OKTo Customer
Why Dirt? (Fishbone)
EnvironmentDust/HumidityPoor LightingSpace Limitations
MethodsReworking Steel after
Valves are InstalledNeed to Rinse Parts off
after Sandblasting
Lack of CommunicationQA to IT
Rework
RinseTraining
Attention to Detail
Poor Lighting
Dust/Humidity
Space Limitations
Tools for $$Cleansing Compounds
Larger Wire Brushes
Environment
Dirt
Machinery Materials
Methods ManMeasurement
Materials
Cleansing Compounds
Need Larger WireBrushes
People
Need More Training
More Attention to Detail – Do it Right First Time
Machines
Best Tools for $$?
Measurement
QA Manager Fixes Some Things Without Informing the Technicians
Why Leaks? (Fishbone)
EnvironmentHigh Temperatures Poor Lighting
MethodsCheck Units for Ways
They Could LeakDoes Testing Create
Leaks?
Materials
Bad Tubing
“O” Rings Too Old (Dry)
People
Use Wrong Clamps
Don’t Crimp Properly
Forget to Connect
Machines
Need Rims That Make it Easier to Install Tubing
Measurement
No Testing for Leaks Prior to QA
Which Mfr./Model Leak the Most?
No Leak Testing Prior to QA Quality Check
Don’t Crimp Properly
Use Wrong Clamps
Poor Lighting
High Temperature
Reengineer Rims“O” Rings Old
Bad Tubing
Environment
Leaks
Machinery Materials
Methods ManMeasurement
Forget to Connect
Mishandle Units
Identify Most Occurrences
Variation Analysis
Most variation without “special” causes will be normally distributed
Variation is typically
classifiable into the 6 M’s
Variation is additive
Variation in the process inputs will generate more variation in the process output
Metho
ds
Environme
nt
Machinery
Materials
Man
Measureme
nt
Output
Variation is Present in All Processes!
Measurement System Analysis (MSA)
Goal - To identify if the measurement system can distinguish between product variation and measurement variation
222gageproductobseerved
Some key dimensions Accuracy Precision Bias
Tools: Gage R&R, DOE, Control Charts
Agenda for Measure
1. Types of Measures/Setting Targets
2. Data Collection and Prioritization, MSA
3. SPC, Control Charts
4. Process Capability
SPC vs. Acceptance Sampling
Acceptance Sampling: Used to inspect a batch prior to, or after the process
Take Sample
Receive Lot
Meet Criteria?
Accept
Reject Rework /Waste
Send to Customer
Yes
No
Statistical Process Control (SPC): Used to determine if process is within process control limits during the process and to take corrective action when out of control
400
420
440
460
480
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
LCL
UCL
Statistical Process Control
A Processis not in control when one or more points is/are outside the control limits
Special Causes
UCL
LCL
Process in Statistical Control
Process not in Statistical Control
Process not in Statistical ControlUCL
LCL
UCL
LCL
A Processis in control when all points are inside the control limits
Statistical process control is the use of statistics to measure the quality of an ongoing process
When to Investigate
Even if in control the process should be investigated if any non random patterns are observed OVER TIME
UCL
LCL
1 2 3 4 5 6
In Control
UCL
LCL
1 2 3 4 5
Close to Control Limit
UCL
LCL
1 2 3 4 5 6
Consecutive Points Below/Above Mean UCL
LCL
5 10 15 20
Cycles
1 2 3 4 5 6
UCL
LCL
Trend - Constant Increase/Decrease
Control Chart Development Steps
INPUTSOUTPUT
X’s Y’s
Identify Measurement1Sample
Sample Size Defective p
1 100 4 0.042 100 3 0.033 100 5 0.054 100 6 0.065 100 2 0.026 100 1 0.017 100 6 0.068 100 7 0.079 100 3 0.03
10 100 8 0.0811 100 1 0.0112 100 2 0.0213 100 1 0.0114 100 9 0.0915 100 1 0.01
Total 1500 59
Collect Data2
0
0.02
0.04
0.06
0.08
0.1
0 2 4 6 8 10 12 14 16 18
Determine Control Limits3Improve Process4
A B C D
Defects
Start
Eliminate Special Causes
Reduce Common Cause Variation Improve
Average
Frequently Used Control Charts
Attribute: Go/no-go Information, sample size of 50 to 100 Defectives
p-chart, np-chart Defects
c-chart, u-chart
Variable: Continuous data, usually measured by the mean and standard deviation, sample size of 2 to 10 X-charts for individuals (X-MR or I-MR) X-bar and R-charts X-bar and s-charts
SPC Attribute Measurements
p =Total Number of Defectives
Total Number of Observations
nS
)p-(1 p = p
p
p
Z- p = LCL
Z+ p = UCL
s
s
p-Chart Control Limits
percentage defects (mean)
Standard deviation of p
Z Number of standard deviations
n Number of observation per sample (i.e., sample size)
UCL Upper control limit
LCL Lower control limit
p
pS
-2 -1 0 1 2 3-3
Z- VALUE is the number of Standard Deviations from the mean of the Normal Curve
Normal Distribution: Z-Value
Z
p-Chart Example1. Calculate the sample proportion, p, for each sample2. Calculate the average of the sample proportions
3. Calculate the sample standard deviation
4. Calculate the control limits (where Z=3)
5. Plot the individual sample proportions, the average of the proportions, and the control limits
SampleSample
Size Defective p1 100 4 0.042 100 3 0.033 100 5 0.054 100 6 0.065 100 2 0.026 100 1 0.017 100 6 0.068 100 7 0.079 100 3 0.03
10 100 8 0.0811 100 1 0.0112 100 2 0.0213 100 1 0.0114 100 9 0.0915 100 1 0.01
Total 1500 59
0.0393=1500
59 = p
.0194= 100
.0393)-.0393(1=
)p-(1 p = p n
s
0 0.0189- = 3(.0194) - .0393 = Z- p = LCL
.0976 = 3(.0194) .0393 = Z+ p = UCL
p
p
s
s
0
0.02
0.04
0.06
0.08
0.1
0 2 4 6 8 10 12 14 16 18
SPC Continuous Measurements
n A2 D3 D4 2 1.88 0 3.27 3 1.02 0 2.57 4 0.73 0 2.28 5 0.58 0 2.11 6 0.48 0 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82
10 0.31 0.22 1.78
R A- x = LCL
R A+ x = UCL
2
2
RD = LCL
RD = UCL
3
4
x Chart Limits
R Chart Limits
X-bar, R Chart Control Limits
Shewhart Table of Control Chart Constants
SPC Continuous Measurements
1 2 3 4 5
1 10.6 10.7 10.5 10.9 10.9 10.7 0.4
2 10.4 11.0 10.4 10.7 10.7 10.6 0.6
3 10.8 10.8 10.8 10.2 10.5 10.6 0.6
4 10.3 10.2 10.3 10.4 11.0 10.4 0.8
5 11.0 10.7 10.9 10.6 10.8 10.8 0.4
6 10.9 10.0 10.4 10.1 10.5 10.4 0.8
7 10.8 10.4 10.5 10.7 10.7 10.6 0.4
8 10.1 10.3 10.9 10.2 10.4 10.4 0.8
9 11.0 10.5 10.7 10.8 10.7 10.7 0.5
10 10.8 10.9 10.4 10.3 10.4 10.6 0.6
11 10.5 11.0 10.5 10.8 10.8 10.7 0.5
12 10.2 10.1 10.7 10.8 10.2 10.4 0.7
13 10.8 10.6 10.3 10.4 11.0 10.6 0.7
14 10.1 10.3 10.3 10.3 10.8 10.3 0.7
15 10.1 10.1 10.3 10.2 10.1 10.2 0.2
10.54 0.58
Sample Range
Total Average
Sample
Observation Sample Mean
R
Chart
Sample Mean
10.10
10.20
10.30
10.40
10.50
10.60
10.70
10.80
10.90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Sample
Mean
s
LCL
UCL
Sample Range
-0.15
0.05
0.25
0.45
0.65
0.85
1.05
1.25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Sample
LCL
UCL
0
1
)58.0)(0(RD = LCL
22.)58.0)(11.2(RD = UCL
3
4
X-bar
Chart
10.19
10.87
=.58(0.58)-10.54R A- x = LCL
=.58(0.58)10.54R A+ x = UCL
2
2
Proper Assessment of Control Charts
Find special causes and eliminate If special causes treated like common causes,
opportunity to eliminate specific cause of variation is lost.
Leave common causes alone in the short term If common causes treated like special causes, you will
most likely end up increasing variation (called “tampering”)
Taking the right action improves the situation
Quarterly Audit Scores
0 1 2 3 4 5 6Quarter
Score
Did something unusual happen?
Quarterly Audit Scores
What do these lines represent?
0 1 2 3 4 5 6Quarter
Score
Quarterly Audit Scores
Now what do you think?
0 1 2 3 4 5 6
Quarter
Score
Agenda for Measure
1. Types of Measures/Setting Targets
2. Data Collection and Prioritization
3. SPC, Control Charts
4. Process Capability
Process Capability Introduction
“Voice of the Process” (The “Voice of the Data”)
Based on natural (common cause) variation
Tolerance limits (The “Voice of the Customer”) Customer requirements/Specs
Process Capability A measure of how “capable” the process is to meet customer requirements
Compares process limits to tolerance limits
Process Capability Scenarios
natural variation
specification
A
specification
natural variation
C
specification
natural variation
B
specification
natural variation
D
Process Capability Index, Cpk
Capability Index shows if the process is capable of meeting customer specifications
3
X-UTLor
3
LTLXmin=Cpk
Find the Cpk for the following:
A process has a mean of 50.50 and a variance of 2.25. The product has a specification of 50.00 ± 4.00
50.00 ± 4.00
Mean = 50.50 Stdev = 1.5
Interpreting the Cpk
Cpk > or = 0.33Capable at 1 *
Cpk > or = 0.67Capable at 2 *
Cpk > or = 1.00Capable at 3Cpk > or = 1.33Capable at 4Cpk > or = 1.67Capable at 5Cpk > or = 2.00Capable at 6
* * Processes with Cpk < 1 are traditionally called “not capable”.
However, improving from 1 to 2, for example, is extremely valuable. .
Calculating Yield
Task 1
Task 2
Task 3
Task 4
Task 5
96 units
4 rwk
98 units
2 rwk
95 units
5 rwk
90 units
10 rwk96 units100 units
Traditional Yield (TY)Started UnitsofNumber Total
Task Final theofOutput TotalTY 96.0
100
96TY
Rolled Throughput Yield (RTY):
another way to get “Sigma” level)
Started UnitsofNumber Total
work Without Re Produced Units(cumRTY
77.096.0*90.0*95.0*98.0*96.0 RTY
The Hidden Factory = TY - RTY The Hidden Factory = 0.96-0.77 =0.19
Traditional Yield assessments ignore the hidden factory!
37.099.0 90.099.0 10010
Six Sigma Quality Level
Six Sigma results in at most 3.4 DPMO - defects per million opportunities (allowing for up to 1.5 sigma shift).
1 2 3 4 5 6
1,000,000
10,000
1
100,000
1,000
100
10
DPMO
IRS Tax Advice
Doctor Prescription Writing
Airline Baggage Handling
Domestic Airline Flight Fatality Rate (0.43PMM)
93% good
99.4% good
99.98% good
Restaurant Bills
Payroll Processing
SIGMA
Is Six Sigma Quality Possible?
Source: Motorola Inc.
Six Sigma Quality
Six Sigma Shift The drift away from target mean over time
3.4 defects/million assumes an average shift of 1.5 standard deviations
With the 1.5 sigma shift, DPMO is the sum of 3.39767313373152 and 0.00000003, or 3.4. Instead of plus or minus 6 standard deviations, you must calculate defects based on 4.5 and 7.5 standard deviations from the mean! Without the shift, the number of defects is .00099*2 = .002 DPMO.
iesopportunit 1,000,000
DefectsofNumber TotalDPMO
Z 4.5 6.0 7.5
P(<Z) 0.99999660232687 0.99999999901341 0.99999999999997
1 - P(<Z) 0.00000339767313 0.00000000098659 0.00000000000003
* 1,000,000 3.39767313373152 0.00098658770042 0.00000003186340
Quality Levels and DPMO
Defects per million opportunitiesAssumes 1.5 sigma shift of the mean
Sigma LevelDPMO (Defects per
million opportunities)Reduction from previous
sigma level1.0 697672 2.0 308770 55.74%3.0 66811 78.36%4.0 6210 90.71%5.0 233 96.25%6.0 3.4 98.54%
Regardless of the current process sigma level, a very significant improvement in quality will be realized by a 1-sigma improvement!
Is Six Sigma Quality Desirable?
99% Quality means that 10,000 babies out of 1,000,000 will be given to the wrong
parents! One out of 100 flights would result in fatalities. Would you fly?
What is the quality level for Andruw Jones?