1. LSSG Green Belt Training
Measure: Finding and Measuring Potential Root Causes

2. 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

3. Agenda for Measure Types of Measures/Setting Targets
Data Collection and Prioritization, MSA
SPC, Control Charts
Process Capability

4. Measures Purpose of measurement:
Performance of a process vs. Expectations
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.
Select Measures
“SMART” Objectives
Clear operational definitions
E.g. Losing Weight

5. LSS Measurement Measurement vs. Control Causes/ Effects Measurement
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?
Measurement vs. Control
Causes/
Effects
Measurement
System
Control
System
Use: To identify important factors for data collection, to identify factors that influence the output
Control
Measurement Goal: to identify, collect and report key inputs and outputs of business processes
Control Goal: to proactively control and manage process performance rather then to simply measure the past
Historical data
Current data
Measurement is not control! So, what is it?

6. 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

7. Agenda for Measure Types of Measures/Setting Targets
Data Collection and Prioritization, MSA
SPC, Control Charts
Process Capability

8. 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

9. 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
Work Sampling
Data collected over a period of time (e.g., a shift, a day, etc.)
Assumption that breaks, delays, etc. occur normally
No need for special equipment or training
No need to make adjustments to observed times due to operator bias or allowances (lunch, breaks, etc.)

10. 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

11. 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:

12. 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.

13. Data Analysis Tools Scatter Diagram Run Chart Histogram Control Chart
Can be used to illustrate the relationships between factors such us quality and training
Can be used to identify when equipment or processes means are drifting away from specs
Histogram
Control Chart
Frequency
Data Ranges
Can be used to display the shape of variation in a set of data
Use to identify if the process is predictable (in control)

14. Cause and Effect Diagram
Material
Method
Environmental
Man
Machine
Effect

15. Pareto Charts Root Cause Analysis
80% of the problems may be attributed to 20% of the causes

16. Continuous Improvement Process
Orlando Remanufacturing
And
Distribution Center

17. Phase 1: Internal Kickbacks
Equipment To Be
Remanufactured
Tear Down
And Wash
Remanu-
facture
Reassembly
Final
Clean-up QA
Unit Not OK
To Customer
Five Most Common Reasons For Returns From QA
Missing/
Wrong Part
Dirt/Rust
Defective
Part
Leaks
Poor
Insulation
Impact of Reasons for Returns from QA - Weighted Average
Weighted Avg. = % Occurring X Defect Cost (0-10, Based on Time to Repair)
Leaks
Dirt/Rust
Stainless
Steel
Missing/
Wrong Part
Defective
Part
Poor
Insulation

18. Why Dirt? (Fishbone) Dirt Machines Best Tools for $$? Measurement
Lack of Communication
QA to IT
Rework
Rinse
Training
Attention to Detail
Poor Lighting
Dust/Humidity
Space Limitations
Tools for $$
Cleansing Compounds
Larger Wire Brushes
Environment
Dirt
Machinery
Materials
Methods
Man
Measurement
Machines
Best Tools for $$?
Measurement
QA Manager Fixes Some Things Without Informing the Technicians
Environment
Dust/Humidity
Poor Lighting
Space Limitations
Methods
Reworking Steel after Valves are Installed
Need to Rinse Parts off after Sandblasting
Materials
Cleansing Compounds
Need Larger WireBrushes
People
Need More Training
More Attention to Detail – Do it Right First Time

19. No Leak Testing Prior to QA Identify Most Occurrences
Why Leaks? (Fishbone)
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
Man
Measurement
Forget to Connect
Mishandle Units
Identify Most Occurrences
Environment
High Temperatures
Poor Lighting
Methods
Check Units for Ways They Could Leak
Does 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?

20. Variation Analysis Variation is Present in All Processes!
Most variation without “special” causes will be normally distributed
Environment
Machinery
Materials
Man
Measurement
Output
Variation is typically
classifiable into the 6 M’s
Methods
Variation is additive
Variation in the process inputs will generate more variation in the process output
Variation is Present in All Processes!

21. Measurement System Analysis (MSA)
Goal - To identify if the measurement system can distinguish between product variation and measurement variation
Some key dimensions
Accuracy
Precision
Bias
Key dimensions
Stability
Consistency; in statistical control; no special causes
Discrimination
Precision/ability to distinguish between values of a measure
Bias
Differences in measurements due to other variables, such as different instruments, different operators, different locations, time of day, day of week, etc.
Accuracy
Difference between observed value and a target (standard) value
Repeatability
Precision or test/retest error; the variation between successive measurements with no changes to other factors (man, machine, materials,…)
Reproducibility
Difference in measurement average by different operators measuring the same characteristic
Tools: Gage R&R, DOE, Control Charts

22. Agenda for Measure Types of Measures/Setting Targets
Data Collection and Prioritization, MSA
SPC, Control Charts
Process Capability

23. 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
LCL
UCL

24. Statistical Process Control
UCL
LCL
Process in Statistical Control
Process not in Statistical Control
Statistical process control is the use of statistics to measure the quality of an ongoing process
A Process is in control when all points are inside the control limits
A Process is not in control when one or more points is/are outside the control limits
Special Causes

25. When to Investigate
UCL
LCL
In Control
Even if in control the process should be investigated if any non random patterns are observed OVER TIME
UCL
LCL
Trend - Constant Increase/Decrease
UCL
LCL
Close to Control Limit
UCL
LCL
Cycles
UCL
LCL
Consecutive Points Below/Above Mean

26. Control Chart Development Steps
Collect Data 2
INPUTS
OUTPUT
X’s
Y’s
Identify Measurement 1
Improve Process 4 A B C D
Defects
Start
Eliminate Special Causes
Reduce Common Cause Variation
Improve Average
Determine Control Limits 3

27. 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

28. SPC Attribute Measurements-2 -1 1 2 3 -3
Z- VALUE is the number of Standard Deviations from the mean of the Normal Curve
Normal Distribution: Z-Value m Z
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

29. p-Chart Example Calculate the sample proportion, p, for each sample
Calculate the average of the sample proportions
Calculate the sample standard deviation
Calculate the control limits (where Z=3)
Plot the individual sample
proportions, the average
of the proportions, and the
control limits

30. SPC Continuous Measurements
X-bar, R Chart Control Limits
Chart Limits
R Chart Limits
Shewhart Table of
Control Chart Constants

31. SPC Continuous Measurements
UCL
X-bar
Chart
LCL
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 R
Chart

32. 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

33. Quarterly Audit Scores
Did something unusual happen? 1 2 3 4 5 6
Quarter
Score

34. Quarterly Audit Scores
What do these lines represent? 1 2 3 4 5 6
Quarter
Score

35. Quarterly Audit Scores
Now what do you think? 1 2 3 4 5 6
Quarter
Score

36. Agenda for Measure Types of Measures/Setting Targets
Data Collection and Prioritization
SPC, Control Charts
Process Capability

37. 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

38. Process Capability Scenarios
natural variation
specification A
specification
natural variation C
specification
natural variation B
specification
natural variation D

39. Process Capability Index, Cpk
Capability Index shows if the process is capable of meeting customer specifications
50.00 ± 4.00
Mean = Stdev = 1.5
Find the Cpk for the following:
A process has a mean of and a variance of The product has a specification of ± 4.00

40. Interpreting the Cpk Cpk > or = 0.33 Capable at 1 *
* Processes with Cpk < 1 are traditionally called “not capable”.
However, improving from 1 to 2, for example, is extremely valuable.

41. Calculating Yield Traditional Yield (TY)
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 rwk
100 units
Traditional Yield (TY)
Rolled Throughput Yield (RTY):
another way to get “Sigma” level
Some examples can be found at:
The Hidden Factory = TY - RTY
The Hidden Factory = =0.19
Traditional Yield assessments ignore the hidden factory!

42. 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).
Is Six Sigma Quality Possible?
IRS Tax Advice
DPMO
1,000,000
Doctor Prescription Writing
Restaurant Bills
93% good
100,000
Airline Baggage Handling
99.4% good
10,000
Payroll Processing
99.98% good
1,000
Link to:
Conference/presentations/safety IT.ppt
100
Domestic Airline Flight Fatality Rate (0.43PMM) 10 1
SIGMA
Source: Motorola Inc.

43. 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 and , or 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 *2 = .002 DPMO. Z
4.5
6.0
7.5
P(<Z)
1 - P(<Z)
* 1,000,000

44. Quality Levels and DPMO
Defects per million opportunities
Assumes 1.5 sigma shift of the mean
Sigma Level
DPMO (Defects per million opportunities)
Reduction from previous sigma level
1.0
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!

45. 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?