Title: Six Sigma
1Six Sigma
- Beth Cudney
- Society of Manufacturing Engineers
- November 11, 2002
2What is Six Sigma
- Six Sigma is a customer focused continuous
improvement strategy and discipline that
minimizes defects and variation towards an
achievement of 3 defects per million
opportunities in product design, production, and
administrative processes. - Focused on customer satisfaction and results by
reducing variation in processes. - Methodology
- Metric based on standard deviation.
- Aggressive goals.
3Why Use Six Sigma?
- Increase capacity
- Reduce cost
- Improve yields
- Reduce the impact of a Hidden Factory
4The Hidden Factory
- Each defect must be detected, repaired and placed
back in the process. Each defect costs time and
money.
Inspect
First Time Yield
Pass
Inputs
Operation
Fail
Rework
Hidden Factory
Time, Cost, People
Scrap
Increased Cost - Lost Capacity
5Six Sigma Introduction
- Goals
- Benefits
- Roles
- Strategy
- Six Sigma Levels
6Agenda
- Six Sigma culture
- Six Sigma concepts for continuous improvement
- Role of statistics in process improvement
- Graphical tools and charting techniques
- Relationship between variation and the cost of
poor quality - Process control and process capability
- Measurement System Analysis
- Steps required for successful experimentation
7Six Sigma Goals
- Develop a world class culture
- Develop leaders
- Support long range objectives
8Six Sigma Benefits
- Stronger knowledge of products and processes
- Reduced defects
- Increased customer satisfaction - generates
business growth and improves profitability - Increased communication and teamwork
- Common set of tools
9Six Sigma Roles
- Champion
- Team
- Green Belt
- Black Belt
- Master Black Belt
10Champion
- Provide top-down commitment to the Six Sigma
goals - Selection and monitoring of projects
- Deliver support
- Assume a mentorship role
11Green Belt
- Fundamental Six Sigma tools
- Specific tools that relate to their project
12Black Belt
- Lead major projects
- Mentor, consult and teach Green Belts
- Promote out-of-the-box and critical thinking
- Challenge the old ways of doing business
- Approximate 1-2 of the population (10-20
per 1000 employees)
13Master Black Belt
- Drive major projects
- Train and mentor black belts
- Work with champions to select projects
- Typically 1 per business unit or site
(1 per 1000 employees)
14Six Sigma Strategy
- Measure
- Analyze
- Improve
- Control
15Measure
- What processes are involved?
- Who is the process owner?
- Who are the team members?
- Which processes are the highest priority to
improve? - What data supports the decision? (Metric)
- How is the process performed?
- How is the process performance measured?
- Is your measurement system accurate and precise?
16Measure (Cont.)
- What are the customer driven specifications for
the performance measures? - What are the improvement goals?
- What are the sources of variation in the process?
- What sources of variability are controlled and
how? - Tools Process Flow
- Process Failure Mode and Effects Analysis
(FMEA) - Measurement System Analysis
17Analyze
- What are the key variables affecting the average
and variation of the performance measures? - What are the relationships between the key
variables and the process output? - Is there interaction between any of the key
variables? - Tools Hypothesis Test
18Improve
- What are the key variable settings that optimize
the performance measures? - At the optimal setting for the key variables,
what variability is in the performance measure? - Tools Multiple Regression
- Design of Experiments
19Control
- How much improvement has the process shown?
- How much time and/or money was saved?
- Long term metric.
20Cost of Poor Quality
- Costs from
- Internal Failure Costs (Rework, Scrap)
- External Failure Costs (After Delivery)
- Appraisal Costs (Inspection, Test)
- Prevention Costs (Where you should spend)
- Lost Opportunity Costs (Sales, Competition)
21Process Flow Diagram Benefits
- Allows a team to identify the actual flow or
sequence of events. - Shows problem areas, redundancy, unnecessary
steps, and areas for simplification and
standardization. - Compares actual versus the ideal flow.
- Allows a team to identify activities that may
impact performance. - Identifies locations where additional data can be
collected and identified.
22Cause and Effect Diagram
- Structured approach to identifying root causes.
- Focuses the team on the content of the problem,
not the history of the problem. - Creates a collective knowledge and consensus of
the team around the problem. - Focuses the team on the causes, not the symptom.
- Identifies the factors to be held constant (C),
noise factors (N), and critical factors for
possible experimentation (X).
23Cause and Effect Diagram Layout
Person
Materials
Environment
Causes
Effect
Methods
Machines
24Pareto Diagram
- A bar chart whose bars are in descending order.
- Focuses efforts on the problems that offer the
greatest potential for improvement by showing
their relative frequency or size. - Based on the Pareto principle 20 of the sources
cause 80 of the problem. - Displays the relative importance of a problem in
a easily interpreted, visual format.
25Pareto Diagram Format
26Histogram
- Summarizes data from a process that has been
collected over time. - Graphically presents the frequency distribution
in bar form. - Displays large of amounts of data that are
difficult to interpret in tabular form. - Shows the relative frequency of occurrence of the
various data values. - Reveals centering, variation, and the shape of
the data. - Illustrates the distribution of the data.
- Helps indicate if there has been a change in the
process.
27Histogram Format
28Taguchi Loss Function
- Old way of looking at quality
- New way of looking at quality
Scrap and Rework
Scrap and Rework
No Loss
Lower Spec Limit
Upper Spec Limit
Large Loss
Medium Loss
No Loss
LSL
Target
USL
29Using the Data
- Location
- Variation
- Quality Measures
- Run Chart
- Control Chart
- Scatter Diagram
- Correlations
30Location
- Used to describe the middle of the data set.
- sample mean x x1x2x3 . . . xn
- n
- sample median x middle value, if n is odd
- the average of the two middle
values, if n is even
31Location Example
- Data set 8, 7, 3, 9, 4, 6, 4, 5, 3, 6, 7
- sample mean x 5.63
- sample median x 6
1 2 3 4 5 6 7
8 9 10 11
32Variation
- Used to describe the range of a data set.
- n
- sample variance s2 ? ( xi - x )2
- i1
- n-1
- sample standard deviation s sqrt (s2)
33Variation Example (Cont.)
x
1 2 3
4 5
y
1 2 3
4 5
z
1 2 3
4 5
34Quality Measures
- DPM (PPM) defects per million
- total number of defects x 1,000,000
- total number of units
- ?level number of standard deviations between
the center of the process and the nearest
specification limit - minimum USL - x, x - LSL
- ? ?
35Quality Measures (Cont.)
- ?capability worst case shift of ?level 1.5
- Cpk process capability index
- Cp process potential index
- FPY first pass yield
-
36Run Chart
- A graphical tool that records data collected over
time and displays trends.
37Run Chart Example
38Control Chart
- A run chart that includes statistically
determined upper and lower control limits and a
center line.
39Control Chart Example
40Scatter Diagram
- Used to display data that is associated with more
than one variable - Most commonly, bivariate data (data associated
with two variables) - Shown as ordered pairs
- first value is one variable
- second value is the second variable
- Purpose is to visually show the relationship
between the two variables
41Scatter Diagram Example
42Failure Mode and Effects Analysis
- A proactive, systematic method for identifying,
analyzing, prioritizing and documenting potential
failure modes, their respective effects and
potential causes of failures. - Recognize and evaluate potential failures in
processes and products. - Reduce or eliminate the potential of a failure.
43FMEA
- Spending time up-front can reduce costs.
- Product and process changes are easier to make
and less expensive to implement. - It is a continuous improvement tool applied
properly.
1 Design
10 Prototype
100 Production
1000 By the Customer
44Hypothesis Testing
- A statistical hypothesis is a statement or claim
about some unrealized true state of nature. - The actual hypothesis to be tested consists of
two complementary statements about the true state
of nature. - H0 null hypothesis
- H1 alternative hypothesis
- The true state of nature is rarely known with
100 accuracy. - Examples
- Average gas mileage differs depending on type A
or B gas. - Variability of machined thickness of a part
depends on they type of tool. - Type of aspirin determines the amount of pain
relief.
45Hypothesis Test Decision Table
- H0 Defendant is Innocent
- H1 Defendant is Guilty
- Probability of committing a Type 1 error is
defined as ? (0 lt ? lt 1). - Probability of committing a Type II error is
defined as ? (0 lt ? lt1).
46Gage Repeatability Reproducibility
- Variability in the way we measure product and
performance - The purpose is to asses how much variation is
associated with the measurement system and to
compare it to the total process variation - Three properties
- Accuracy - the ability to produce an average
measured value which agrees with the true value
or standard being used - Precision - the ability to repeatedly measure the
same product or service and obtain the same
results - Stability - the ability to repeatedly measure the
same product or service over time and obtain the
same average measured value
47Gage Repeatability Reproducibility
- Variability in the way we measure product and
performance - The purpose is to asses how much variation is
associated with the measurement system and to
compare it to the total process variation - Three properties
- Accuracy - the ability to produce an average
measured value which agrees with the true value
or standard being used - Precision - the ability to repeatedly measure the
same product or service and obtain the same
results - Stability - the ability to repeatedly measure the
same product or service over time and obtain the
same average measured value
48Definition of a Process
Inputs
Outputs
Process Various inputs blended together to
achieve a specified output
Material People Equipment Methods/Procedures Equip
ment Environment
Service Product Task completed
49Definition of DOE
- A scientific approach in which purposeful changes
of inputs (factors) to a process are made to
determine the corresponding changes in the
outputs (responses).
50Why Use DOE?
- Identify key input factors
- Gain an understanding between input factors and
responses - Build a mathematical model that relates response
to the input factors - Determine the settings for input factors that
optimize the response - Scientific method for setting tolerances
51Objectives
- To find factor(s) that improve a response
variable to some optimal value. - To find a less expensive way to provide
equivalent or improved performance. - To gain a better understanding of your process
- Define the magnitude of an effect
- Identify how factors react together
- To systematically demonstrate that the current
process is optimal.
52Steps In Designing an Experiment
Define the Objective
Developing a product or process / Product or
process problem
Select Testing Equipment
Lab or production
Select Response Variable
Max, Min, or Nominal / Quantitative vs.
Qualitative
Select Factors and Interactions
Brainstorming, Flowcharts, Review Existing Data
Select Factor Levels
Linear vs. Nonlinear
Linear graphs, Triangular tables, Assign factors
and interactions to columns of the array
Select Orthogonal Array
If not satisfied with confounding scheme, review
design for restructure
Determine Confounding Scheme
Randomize Test Order
Sequence concerns, Additional tests