Lean Six Sigma Black Belt Training

1
Lean Six Sigma Black Belt Training
Measure How are we doing today?
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

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

• Select Measures
• SMART Objectives
• Clear operational definitions
• E.g. Losing Weight

5
LSS Measurement
Measurement vs. Control
Causes/ Effects
Measurement System
Control System
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
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

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
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
Histogram
Control Chart
Use to identify if the process is predictable (in
control)
Can be used to display the shape of variation in
a set of data
14
Cause and Effect Diagram
15
Pareto ChartsRoot Cause Analysis
80 of theproblems may beattributed to 20of
the causes
16
Continuous Improvement Process
Orlando Remanufacturing And Distribution Center
17
Phase 1 Internal Kickbacks
18
Why Dirt? (Fishbone)
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
Why Leaks? (Fishbone)

• 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 Dont 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
Most variation without special causes will be
normally distributed
Variation is typically classifiable into the 6
Ms
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

• Key dimensions
• Stability
• Discrimination
• Bias
• Accuracy
• Repeatability
• Reproducibility

Tools Gage RR, 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
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
24
Statistical Process Control
Statistical process control is the use of
statistics to measure the quality of an ongoing
process
A Processis in control when all points are
inside the control limits
A Processis not in control when one or more
points is/are outside the control limits
Special Causes
25
When to Investigate
Even if in control the process should be
investigated if any non random patterns are
observed OVER TIME
1 2 3 4 5
6
26
Types of Variation
Special cause (unexpected) variation

• Caused by factors that can be clearly identified
  and possibly managed assignable causes evident,
  not in statistical control
• Short-term objective - to eliminate unexpected
  variation Inherent in the process
• Normal variation only, stable, predictable, in
  statistical control
• Long-term objective - to
• reduce expected variation

Common cause (expected) variation
27
Control Chart Development Steps
28
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-bar and R-charts
• X-bar and s-charts
• X-charts for individuals

29
SPC Attribute Measurements
p-Chart Control Limits
percentage defects (mean)
Standard deviation of p Z Number of
standard deviations UCL Upper control
limit LCL Lower control limit
30
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 Z3)
• Plot the individual sample
• proportions, the average
• of the proportions, and the
• control limits

31
SPC Continuous Measurements
X-bar, R Chart Control Limits
Chart Limits
R Chart Limits
Shewhart Table of Control Chart Constants
32
SPC Continuous Measurements
UCL
X-bar Chart
LCL
R Chart
33
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

34
Quarterly Audit Scores
Did something unusual happen?
35
Quarterly Audit Scores
What do these lines represent?
36
Quarterly Audit Scores
Now what do you think?
37
Agenda for Measure

• Types of Measures/Setting Targets
• Data Collection and Prioritization
• SPC, Control Charts
• Process Capability

38
Process Capability Introduction

39
Process Capability Scenarios
40
Process Capability Index, Cpk
Capability Index shows if the process is capable
of meeting customer specifications
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
41
Interpreting the Cpk

• Cpk lt 1 Not Capable
• Cpk gt 1 Capable at 3?
• Cpk gt 1.33 Capable at 4?
• Cpk gt 1.67 Capable at 5?
• Cpk gt 2 Capable at 6?

42
Calculating Yield
Traditional Yield (TY)
First Pass Yield (FPY)
Rolled Throughput Yield (RTY) another way to
get Sigma level
The Hidden Factory TY - RTY
The Hidden Factory 0.96-0.77 0.19
Traditional Yield assessments ignore the hidden
factory!
43
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
Doctor Prescription Writing
1,000,000
Restaurant Bills
93 good
100,000
Airline Baggage Handling
99.4 good
Payroll Processing
10,000
99.98 good
1,000
100
Domestic Airline Flight Fatality Rate (0.43PMM)
10
1
1 2 3 4 5
6
SIGMA
44
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 .000992 .002 DPMO.

45
Quality Levels and DPMO
46
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?