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MIT OpenCourseWare http://ocw.mit.edu
16.660 / 16.853 / ESD.62J Introduction to Lean Six Sigma Methods January (IAP) 2008
For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
Quality Tools and Topics
Learning Objectives
At the end of this module, you will be able to: • Describe how quality is essential to lean in
achieving Customer Satisfaction
• Explain the relationship between product quality and process quality control
• Use seven basic tools of quality
• Explain why Design for Six Sigma (DFSS) and Six Sigma are valuable concepts in achieving customer satisfaction
Quality Tools and Topics V6.3- Slide 2 © 2008 Massachusetts Institute of Technology
Why Do We Care About Quality?
Hidden Costs of Non-Conformance = 6 to50X Measured Costs
• Direct Measured Costs:
Photo by Earll Murman
• Scrap/rework • Service calls • Warranties/concessions
Indirect/Hidden Costs: • Excess inventory • Overtime • Non-value added steps • Queues and delays • Reputation/image
Quality Tools and Topics V6.3- Slide 3 © 2008 Massachusetts Institute of Technology
Problems with Inspection Based Quality Control
• Inspection does not add value to the customer – it simply screens or detects (most of the time) defective products from leaving the factory.
• Inspection is subject to multiple errors • Inspector skill and attention
• Measurement capability
• Environment (Human Factors)
Quality Tools and Topics V6.3- Slide 4 © 2008 Massachusetts Institute of Technology
Inspection Exercise
1. This exercise will be timed.
2. Task: Circle all of the fs or Fs on a page of text.
3. After 30 seconds – “STOP” and count up thetotal number of fs and Fs that you found!
4. Take out the “F” Exercise from the student folder
5. When I say “GO” you may begin.
Quality Tools and Topics V6.3- Slide 5 © 2008 Massachusetts Institute of Technology
Inspection Exercise Results
23 22 15 34 36 21 28 29 23 28 29 29 30 32 34 34 31 26 19 26 23 34 38 23 34 12 19 22 16
38 36 34 34 34 34 34 32 31 30 29 29 29 28 28 26 26 23 23 23 23 22 22 22 21 19 19 16 15 12
Number 22 (Input Data)
Sorted Data
Qty 12 17 22 27 32 37 42
Histogram Data Freq 1 2 6 6 8 6 1
Inspection Results
1
2
6 6
8
6
1
0
1
2
3
4
5
6
7
8
9
12 17 22 27 32 37 42
Number
Freq
uenc
y
Number of Inspectors 30
Expected # 99
Total Expected 2970
Total Observed 792
Average Efficiency per Inspector
27%
Quality Tools and Topics V6.3- Slide 6 © 2008 Massachusetts Institute of Technology
Impact of Inspector Efficiency on Escaped Quality
Quality Tools and Topics V6.3- Slide 7© 2008 Massachusetts Institute of Technology
How can we assure Process Quality?
Product and Process Quality Defined
• Product Quality is the conformance to customer specifications within a tolerance band
• The upper and lower values that the customer is willing to accept are known as upper and lower specification limits (USL and LSL)
• Process Quality is a measure of the capability ofa process to produce to its expected capability• The upper and lower values between which the
process must be controlled are known as upper and lower control limits (UCL and LCL)
How can we assure Process Quality?
Quality Tools and Topics V6.3- Slide 8 © 2008 Massachusetts Institute of Technology
• Flow Charts
• Check Sheets
• Histograms
• Pareto Charts
•
The Seven Basic Quality Tools
Photograph of surgical checklist in use removed due to copyright restrictions.Source: http://seattletimes.nwsource.com/html/localnews/2008018070_checklist26m.html((second photograph).
Scatter Diagrams
• Control Charts
• Cause and Effect Diagrams
Quality Tools and Topics V6.3- Slide 9 © 2008 Massachusetts Institute of Technology
M&M Demonstration
• The Mars company is very aware of controlling variation
• Open your bag of M&Ms and dump them onto the paper provided
• Don’t eat them; it will “skew” the data
• Count the number of M&Ms in your bag • Record the number on your paper
• Sort the colors into a Pareto Diagram • Count the number of M&Ms in each group
• Record on your paper
Quality Tools and Topics V6.3- Slide 10 Suggested by Raytheon Corporation © 2008 Massachusetts Institute of Technology
0
52 54 56 58 60 62 64Quantity
Class Data: Total Bag Count
Raw Data 53 55 57 57 60 52 61 57 58 (Input Data) 52 55 54 53 57 58 59 59 61
Sorted Data 61 61 60 59 59 58 58 57 57 57 56 55 55 54 53 53 52 52
Histogram Qty Freq 52 2 54 3 56 3 58 6 60 3
Quality Tools and Topics V6.3- Slide 11 © 2008 Massachusetts Institute of Technology
M&M Quantity Histogram
2
3 3
6
3
2
0
1
2
3
4
5
6
7
Fre
qu
ency
62 2 64 0
Total 19
Average 56.5
Sorting data and arranging it from highest to lowest is apowerful, visual way to highlight areas of interest.
The Pareto Chart
A bar chart whose quantity sums to the total quantity.The bars are sorted by quantity from left to right.
16
14
12
10
8
6
4
2
0
M&Ms in a Bag
Red Blue Yellow Brown Green Aqua Orange
Color
Quantity
Sorting data and arranging it from highest to lowest is apowerful, visual way to highlight areas of interest.
Quality Tools and Topics V6.3- Slide 12 Suggested by Raytheon Corporation © 2008 Massachusetts Institute of Technology
Pareto Example - Discrepancies During Satellite System Integration & Test
Root Cause of Discrepancies for 229 Satellites tested from 1970-1999
30%
% o
f D
iscr
epan
cies
26% 24%25%
20% 18%
15% 12%
10%
6% 3%
8%
Satellite Causes
Non-Satellite Causes
5% 2%
Op
erat
or/
Em
plo
yee
Tes
tE
qu
ipm
ent
No
An
om
aly
Des
ign
So
ftw
are
Oth
er
Un
kno
wn
Mat
eria
l
Root Cause CategorySource: Weigel A. and Warmkessel, J., “Seeing The Spacecraft Testing Value Stream”, LAI Executive Board Briefing, June 2000 Ref: Weigel, A., “Spacecraft System Level Test Discrepancies: Characterizing Distributions and Costs”, MIT SM Thesis, May 2000
Quality Tools and Topics V6.3- Slide 13 © 2008 Massachusetts Institute of Technology
Heat Moisture
recision
eometry
Forming Shaping
Incentives
Precision Maintenance
Tools
Cause and Effect Diagram
Measurement Material Personnel
P
Calibration
Repetition
Inclusio
ns
Training
Certification
Properties
G The diagramprovides a
Defective resultscan be caused
structured methodby one or morefor identification
and corrective actionof these factors
Vibration Fastening
Environment Methods Machines Quality Tools and Topics V6.3- Slide 14
© 2008 Massachusetts Institute of Technology
Statistical Process Control
• Control charting is the primary tool of SPC
• Control charts provide information about the stability/predictability of the process, specifically with regard to its: • Central Tendency (to target value)
• Variation
• SPC charts are time-sequence charts of important process or product characteristics
Quality Tools and Topics V6.3- Slide 15 © 2008 Massachusetts Institute of Technology
Quality Tools and Topics Part II
Statistical Process Control
• Control charting is the primary tool of SPC
• Control charts provide information about the stability/predictability of the process, specifically with regard to its: • Central Tendency (to target value)
• Variation
• SPC charts are time-sequence charts of important process or product characteristics
Quality Tools and Topics V6.3- Slide 17 © 2008 Massachusetts Institute of Technology
Control Charts provide a means for distinguishing betweencommon cause variability and special cause variability
Types of Process Variation
• Common Cause Variation is the sum of many ‘chances causes,’ none traceable to a single major cause. Common cause variation is essentially the noise in the system. When a process is operating subject to common cause variation it is in a state of statistical control.
• Special Cause Variation is due to differences between people, machines, materials, methods, etc. The occurrence of a special (or assignable) cause results in an out of control condition.
Control Charts provide a means for distinguishing betweencommon cause variability and special cause variability
Quality Tools and Topics V6.3- Slide 18 © 2008 Massachusetts Institute of Technology
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
Standard Normal Distribution Curve
0.400
0.450
34.1%
13.6%
2.1%
0.1%
Some notable qualities of the normal distribution:
• The mean is also its mode and median.
• 68.27% of the area (green) is within one standard deviation of the mean.
• 95.45% of the area (green & yellow) is w/in two standard deviations.
• 99.73% of the area(green & yellow & red) is w/in three standard deviations
1σ
2σ
3σ
(6.00) (5.00) (4.00) (3.00) (2.00) (1.00) 0.00 1.00 2.00 3.00 4.00 5.00 6.00
Quality Tools and Topics V6.3- Slide 19 © 2008 Massachusetts Institute of Technology
Assessing Process Capability
Cp = 2
Cp = 1.33
Cp = 1
Cp = .67
USLLSL
BadBad
Cp, a term used to define process capability, is mathematically expressed by:
USL − LSLC = p 6σ
The figure shows centered distributions with various C levels. Note C s lessp pthan two have visible tails outside the acceptable limits.
Quality Tools and Topics V6.3- Slide 20 © 2008 Massachusetts Institute of Technology
Non-Centered Distributions
Cpk = 1.5
Cpk = .83
Cpk = .50
Cpk = .17
USLLSL
Very Bad
Center
If the distribution is off center, the probability of a bad result drastically increases. In this case Cpk is used. It is the smaller of
or
This figure shows the same distributions off-center by 1.5σ. The Cpks are smaller than the corresponding C s. This illustrates thepneed to both control variation and accurately hit the desired mean.
Quality Tools and Topics V6.3- Slide 21 © 2008 Massachusetts Institute of Technology
In this case, the Cp versus Cpk shooter (archer) has a bad eye – the shots are widely dispersed and slightly off-center
Cp is high
Cpk is high
In this case, the shooter
C is low (archer) has a good eye, p and has now adjusted the
Cpk is low gun (bow) sight to bring the shots on target
In this case, the shooter (archer) has a Cp is high
good eye, but all the Cpk is low shots are off-center
Quality Tools and Topics V6.3- Slide 22 © 2008 Massachusetts Institute of Technology
Process Capability
• Once we have a process in control then we can answer the question of whether the process is capable of meeting the customer’s specifications.
• “Process Capability is broadly defined as the ability of a process to meet customer expectations” (Bothe, 1997)
Quality Tools and Topics V6.3- Slide 23 © 2008 Massachusetts Institute of Technology
SPC Exercise
Obs ervation
Ind i
v idu
al V
alu
e
91 81 71 61 51 41 31 21 11 1
14
12
10
8
6
4
2
0
_ X
UCL
LCL
Sample Outcome
Quality Tools and Topics V6.3- Slide 24© 2008 Massachusetts Institute of Technology
Process Improvement and Control Charts
Process Input Output
Measurement System
Subgroup Sample Number
Av
era
ge
We
igh
t
25 23 21 19 17 15 13 11 97531
105
104
103
102
101
100
99
98
_ _ X=100.926
UCL=103.376
LCL=98.475
Weight of Bread Loaves
4.Verify and Monitor (Control) 1.Identify opportunities and measure inputs and outputs (Define & Measure)
3.Implement Corrective Action (Improve) 2.Identify Root Cause (Analyze)
Quality Tools and Topics V6.3- Slide 25 © 2008 Massachusetts Institute of Technology
Strategy for Using Control Charts
• In early stages, control charts (usually on output variables) are used to understand the behavior of the process
• After corrective actions, place charts on critical input variables
• If a chart has been implemented, remove it if it is not providing valuable and actionable information
• The goal: Monitor and control inputs and, over time, eliminate the need for SPC charts by having preventative measures in place
Quality Tools and Topics V6.3- Slide 26 © 2008 Massachusetts Institute of Technology
A lean enterprise requires robust engineeringdesigns and capable manufacturing processes!
Variability ReductionSpans The Enterprise
• Coordinated datums and tools
• Geometric dimensioning and tolerancing
• Process capability data • 3-D statistical modeling
• Focus on the significant few
• Focus on thesignificant few
• Key processes • Control charting • Process improvement • Feedback to design
Statistical Process Control in
Manufacturing
Dimensional Management in
Product Development Key
Characteristics Key
Characteristics
A lean enterprise requires robust engineeringdesigns and capable manufacturing processes!
Quality Tools and Topics V6.3- Slide 27 © 2008 Massachusetts Institute of Technology
Concentrate Dimensional Managementeffort where it matters - on KC’s
Dimensional Management Enabled by Key Characteristics
Key Characteristics: Critical few product features that significantly affect quality, performance, or cost
System KCs
Subassembly KCs
Feature KCs
Concentrate Dimensional Management effort where it matters - on KC’s
Source: Dr. Anna C.Thornton,Ref: Anna C.Thornton, Variation Risk Management, John Wiley & Sons, Inc. 2004 Quality Tools and Topics V6.3- Slide 28
© 2008 Massachusetts Institute of Technology
Height Angle Distance Angle
Skin Gap
AerodynamicsContour
Torque Box Contour
Figure by MIT OpenCourseWare.
747 777Assembly strategy Tooling ToollessHard tools 28 0Soft tools 2/part # 1/part #Major assembly steps 10 5Assembly hrs 100% 47%Process capability Cpk<1 (3.0σ ) Cpk>1.5 (4.5σ )Number of shims 18 0
Benefits of Variability Reduction: Floor Beams for Commercial Aircraft
Courtesy of Boeing. Used with permission.
747 777 Assembly strategy Tooling Toolless Hard tools 28 0 Soft tools 2/part # 1/part #Major assembly steps 10 5 Assembly hrs 100% 47% Process capability Cpk<1 (3.0σ ) Cpk>1.5 (4.5σ )Number of shims 18 0
Refs:J.P. Koonmen, “Implementing Precision Assembly Techniques in the Commercial Aircraft Industry”, Master’s thesis, MIT (1994), and J.C.Hopps, “Lean Manufacturing Practices in the Defense Aircraft Industry”, Master’s Thesis, MIT (1994) Quality Tools and Topics V6.3- Slide 29
© 2008 Massachusetts Institute of Technology
The goal of Six Sigma is to reduce process variation
What is Six Sigma?
Strategy… a data driven philosophy and process resulting in dramatic shifts in way a company behaves, treats its customers, and produces products/services. Measurement… Measurement of the variation of a process…standard deviation. Translates into process performance…defects per million (DPM).
The goal of Six Sigma is to reduce process variation
Quality Tools and Topics V6.3- Slide 30 © 2008 Massachusetts Institute of Technology
Defects
• Improving quality means reducing the defects per million opportunities (DPMO). There are two attributes to this metric that can be controlled: • Opportunities – reducing the number of parts,
joints, fasteners, and other “opportunities” will help improve quality – lean engineering
• Defects – reducing the number of defects by reducing the amount of process variation – Six Sigma
Quality Tools and Topics V6.3- Slide 31 © 2008 Massachusetts Institute of Technology
Six Sigma is defined as 3.4 defects per millionopportunities, or a first pass yield of 99.9997%
With a Six Sigma process even a significant shiftin the process mean results in very few defects
Implications of a Six Sigma Process
Six Sigma is defined as 3.4 defects per millionopportunities, or a first pass yield of 99.9997%
Process Mean Process Mean On-Target Shifted 1.5σ
Cp DPMO Cpk DPMO 2.00 0.00197 1.50 3.401.67 0.57330 1.17 233 1.33 63 0.83 6,2101.00 2,700 0.50 66,8110.67 45,500 0.17 308,7700.33 317,311 -0.17 697,672
With a Six Sigma process even a significant shiftin the process mean results in very few defects
Quality Tools and Topics V6.3- Slide 32 © 2008 Massachusetts Institute of Technology
99% GOOD (3.8 Sigma) 99.99966% GOOD (6 Sigma)
Six Sigma – Practical Meaning
99% GOOD (3.8 Sigma) 99.99966% GOOD (6 Sigma)
• 20,000 lost articles of mail per hour
• Unsafe drinking water for almost 15 minutes per day
• 5,000 incorrect surgical operations per week
• Two short or long landings at most major airports each day
• 200,000 wrong drug prescriptions each year
• No electricity for almost seven hours each month
• Seven articles of mail lost per hour
• One unsafe minute every seven months
• 1.7 incorrect operations per week
• One short or long landing every five years
• 68 wrong prescriptions each year
• One hour without electricity every 34 years
Quality Tools and Topics V6.3- Slide 33 © 2008 Massachusetts Institute of Technology
When Can Six Sigma Be Used?
• There are two major forms of Six Sigma, each of which apply to specific phases within a product’s lifecycle:
•Design for Six Sigma (DFSS) • Used to analyze and improve a product’s
(or service’s) key quality characteristics and to develop robust processes to produce the product (or service)
•“Traditional” Six Sigma Methodology • Typically used throughout the production
stages of a product’s lifecycle
Quality Tools and Topics V6.3- Slide 34 © 2008 Massachusetts Institute of Technology
Six Sigma Methodology
• There are five fundamental steps in the Six Sigma DMAIC process: • Define – • Who are the customers and what are their problems? • Identify key characteristics important to the customer.
• Measure – • Categorize key input and output characteristics, verify
measurement systems • Collect data and establish the baseline performance
• Analyze – • Convert raw data into information to provide insights into the
process
• Improve – • Develop solutions to the problem and compare the results to
the baseline performance
• Control – • Monitor the process to assure no unexpected changes occur
Quality Tools and Topics V6.3- Slide 35 © 2008 Massachusetts Institute of Technology
What is DFSS?
Design for Six Sigma • A methodology for designing new products
and/or processes
• A methodology for re-designing existingproducts and/or processes
• A way to implement Six Sigma methodology as early in the product or service life cycle
• A way to “design in” quality when costs are lowest
Quality Tools and Topics V6.3- Slide 36 © 2008 Massachusetts Institute of Technology
Timing of DFSS vs. Six SigmaR
elat
ive
Cos
t to
Mak
e a
Des
ign
Cha
nge 1000
100
10
1
Research Design Development Production
Design for Six Sigma Focuses Here
“Traditional” Six Sigma
Focuses Here
Product or Service Stage
Adapted from: “Using the Design for Six Sigma (DFSS) Approach to Design, Test, and Evaluate to Reduce Program Risk.” Dr. Mark J. Kiemele, Air Academy Associates. NDIA Test and Evaluation Summit, Victoria, British Columbia February 24-27, 2003.
Quality Tools and Topics V6.3- Slide 37 © 2008 Massachusetts Institute of Technology
DFSS Process & Tools
Identify Design Optimize Validate
• Integrated Product Team
• Team Charter • Voice of the
Customer • Benchmarking • Business Case • Critical-to-
Quality Variables
• Cause & Effect Analysis
• Computer Aided Design
• Computer Aided Engineering
• Pareto Analysis
• Histogram • Design for
Manufacturing and Assembly
• Error-Proofing
• Integrated product team
• Cause & Effect Analysis
• Capable process and product (Cp, Cpk)
LeadershipCommunication
Conflict resolution and consensus buildingOrganization
Note the overlap between Lean tools and Six Sigma tools! Quality Tools and Topics V6.3- Slide 38
© 2008 Massachusetts Institute of Technology
Comparison of Lean & Six Sigma Approaches
Program Six Sigma Lean
Theory Reduce variation Remove waste
Application Guidelines
1. Define 2. Measure 3. Analyze 4. Improve 5. Control
1. Identify Value 2. Identify Value Stream 3. Flow 4. Pull 5. Perfection
Focus Problem focused Flow focused
Assumptions
• A problem exists • Figures and numbers are
valued • System output improves
if variation in all processes inputs is reduced
• Waste removal will improve business performance • Many small
improvements are better than system analysis
Ref: Nave, Dave. “How to Compare Six Sigma, Lean, and the Quality Tools and Topics V6.3- Slide 39Theory of Constraints.” Quality Progress. March 2002 © 2008 Massachusetts Institute of Technology
Continuous flow cannot be achieved withouthigh quality processes
Lean and Six Sigma… Merging Process Improvement Approaches
• Lean And Six Sigma are merging into a unified framework for enterprise change
• Lean optimizes flow, reduces cycle time, and eliminates waste (including the waste of poor quality)
• Six Sigma provides a means for measuring quality • for improving individual processes • for measuring performance improvement across the
enterprise• Lean strives for speed through continuous “drum-
beat” flow… Six Sigma strives for quality through elimination of variation
Continuous flow cannot be achieved without high quality processes
Quality Tools and Topics V6.3- Slide 40 © 2008 Massachusetts Institute of Technology
Discussion and Take Aways
• Quality is essential to lean – not a competitor
Why?
• Process and product quality are linked
How?
• A variety of tools are available for determining and improving quality
What are some?
• Six sigma is a valuable concept in achieving customer satisfaction
Why?
Quality Tools and Topics V6.3- Slide 41 © 2008 Massachusetts Institute of Technology
Reading List
Bertels, T. Ed, Rath & Strong’s Six Sigma Leadership Handbook, John Wiley & Sons,2003.Bothe, D.R., Measuring Process Capability, 1997Deming, E., Out of Crisis, The MIT Press, Cambridge, MA, 2000
Gitlow, H.S. and Levine, D.M., Six Sigma for Green Belts and Champions, Foundations, DMAIC, Tools, Cases, and Certification, Prentice Hall (Pearson Education, Inc.) 2005
Harry, M. and Schroeder, R., Six Sigma, Currency Doubleday, New York, 2000
Henderson, G.R., Six Sigma Quality Improvement with Minitab, John Wiley & Sons, 2006.
Hopp, W.J. and Spearman, M.L., Factory Physics, 3rd Edition, McGraw-Hill/Irwin, 2007
Juran, J.M., Juran on Quality by Design, The Free Press, New York, 1992
Ledolter, J. and Burrill, C.W., Statistical Quality Control, Strategies and Tools for Continual Improvement, John Wiley & Sons, Inc., 1999
Montgomery, D.C., Design and Analysis of Experiments, 7th Edition, John Wiley & Sons, Inc., 2008
Thornton, Anna C., Variation Risk Management, John Wiley & Sons, Inc. 2004
Yang, K. and El-Haik, B., Design for Six Sigma, McGraw Hill, New York, 2005
Quality Tools and Topics V6.3- Slide 42 © 2008 Massachusetts Institute of Technology
Acknowledgements
• Tom Callarman – Arizona State University • Phil Farrington – University of Alabama in Huntsville • Al Haggerty - MIT, Boeing (ret.) • Trent Hancock – Rockwell Collins • Dick Lewis – Rolls-Royce (ret.) • Jose Macedo – Cal Poly, San Luis Obispo • Jan Martinson - Boeing, IDS • Hugh McManus, Metis Design • Earll Murman, MIT • Bo Oppenheim - Loyola Marymount University • Jack Reismiller – Rolls-Royce Six Sigma Master Black Belt • Steve Shade - Purdue University • Sue Siferd - ASU • Alexis Stanke - MIT • Barrett Thomas –University of Iowa • Ed Thoms - Boeing, IDS
Quality Tools and Topics V6.3- Slide 43 © 2008 Massachusetts Institute of Technology
Notes
Quality Tools and Topics V6.3- Slide 44© 2008 Massachusetts Institute of Technology