Lecture 11 - Six-Sigma Management and Tools

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Lecture 11 - Six-Sigma Management and Tools

• 6S Organization, DMAIC, Taguchi Method, Robust
  Design, Design of Experiments, Design for Six
  Sigma, Reasons for 6S Failure

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Topics
What is Six-Sigma? Organizing Six-Sigma DMAIC overview DMAIC phases The Taguchi method Design for Six-Sigma Using Six-Sigma from a contingency perspective

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Six Sigma Evolution

• Started as a simple quality metric at Motorola in
  1986 (Bill Smith)
• Concept migrated to Allied Signal
• (acquired Honeywell and took its name)
• Picked up by General Electric
• Commitment by CEO Jack Welch in 1995
• Grown to be an integrated strategy for attaining
  extremely high levels of quality

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What is Six-Sigma?

Sigma (?) is a Greek letter used to designate a standard deviation (SD) in statistics Six refers to the number of SDs from the specialized limit to the mean. Six sigma is a fairly recent umbrella approach to achieve quality

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Percent Not Meeting Specifications

• 1S 32
• 2S 4.5
• 3S 0.3
• 6S 0.00034

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Six-Sigma Levels
Sigma Level Long-term ppm defects
1 691,462
2 308,538
3 66,807
4 6,210
5 233
6 3.4

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Statistics - DPU

• Defect
• Six Sigma any mistake or error passed on to the
  customer ???
• General view any variation from specifications
• DPU (defects per unit)
• Number of defects per unit of work
• Ex 3 lost bags 8,000 customers
• .000375

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Statistics dpmo (defects per million
opportunities)

• Process may have more than one opportunity for
  error (e.g., airline baggage)
• dpmo (DPU 1,000,000)
• opportunities for error
• Ex (.000375)(1,000,000) 1.6 234.375
• or (3 lost bags 1,000,000) (8,000
  customers 1.6 average bags)
• 234.375

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Statistics dpmo (contd)

• May extend the concept to include higher level
  processes
• E.g., may consider all opportunities for errors
  for a flight (from ticketing to baggage claim)

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Statistics - Off-Centering

• Represents a shift in the process mean
• Impossible to always keep the process mean the
  same (this WOULD be perfection)
• Does NOT represent a change in specifications
• Control of shift within 1.5 s of the target
  mean keeps defects to a maximum of 3.4 per
  million

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Statistics - Off-Centering (contd)Source Evans
Lindsay, The Management and Control of Quality,
Southwestern, 2005
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k-Sigma Quality Levels

• Number of defects per million opportunities
• For a specified off-centering and
• a desired quality level

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k-Sigma Quality Levels Source Evans
Lindsay, The Management and Control of Quality,
Southwestern, 2005
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Six Sigma and Other Techniques

Six-Sigma is designed to handle the most difficult quality problems.

Quality Problems Techniques
90 Basic tools of Quality
lt 10 Six-Sigma
lt 1 Outside specialists
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Organizing Six Sigma

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Key Players
Champion. Work with black belts to identify possible projects Master Black Belts. Work with and train new black belts Black Belts. Committed full time to completing cost-reduction projects Green Belts. Trained in basic quality tools

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Distribution of Six Sigma Trained Employees
In a company with 100 employees there might be One black belt Sixty green belts Some companies have yellow belts, employees familiar with improvement processes

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Six Sigma Tools

• DMAIC, Taguchi Method, Design for Six Sigma

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DMAIC

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DMAICDMAIC Overview

Stands for the six phases Define Measure Analyze Improve Control

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DMAIC Define (1)

Four Sub-Phases Develop the business case Project evaluation Pareto analysis Project definition

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DMAIC Define (2)

Business Case Project objectives, measurables, justification Developing the Business Case Identify a group of possible projects Writing the business case Stratifying the business case into problem statement and objective statement

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DMAIC Define (3)

RUMBA is a device used to check the efficacy of the business case Realistic Understandable Measurable Believable Actionable

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DMAIC Measure (1)

Two major steps Selecting process outcomes Verifying measurements

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DMAIC Measure (2)
Selecting process outcomes (step 1) Tools Used Process map (flowchart) XY matrix (like QFD) FMEA (Failure Modes and Effects Analysis) (aka DFMEA) Gauge RR (Repeatability and Reproducibility) Capability Assessment (cp or cpk)

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DMAIC Measure (3)
Verifying measurements (step 2) Tools Used Gauges, calipers and other tools. Management System Analysis (MSA) is used to determine if measurements are consistent

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DMAIC Measure (4)

Gauge RR Most commonly used MSA Determine the accuracy and precision of your measurements

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DMAIC Repeatability Reproducibility

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Measurement System DMAIC Evaluation

• Variation can be due to
• Process variation
• Measurement system error
• Random
• Systematic (bias)
• A combination of the two

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DMAIC Metrology - 1

• Definition The Science of Measurement
• Accuracy
• How close an observation is to a standard
• Precision
• How close random individual measurements are to
  each other

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DMAIC Metrology - 2

• Repeatability
• Instrument variation
• Variation in measurements using same instrument
  and same individual
• Reproducibility
• Operator variation
• Variation in measurements using same instrument
  and different individual

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DMAIC RR Studies

• Select m operators and n parts
• Calibrate the measuring instrument
• Randomly measure each part by each operator for r
  trials
• Compute key statistics to quantify repeatability
  and reproducibility

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DMAIC RR Spreadsheet Template
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DMAIC RR Evaluation

• Repeatability and/or reproducibility error as a
  percent of the tolerance
• Acceptable lt 10
• Unacceptable gt 30
• Questionable 10-30
• Decision based on criticality of the quality
  characteristic being measured and cost factors

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DMAIC Calibration

• Compare 2 instruments or systems
• 1 with known relationship to national standards
• 1 with unknown relationship to national standards

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DMAIC Analyze (1)

Three major steps Define your performance objectives (Xs) Identify independent variables Analyze sources of variability

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DMAIC Analyze (2)
Define your performance objectives (Xs) (step 1)

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DMAIC Analyze (3)
Identify the independent variables where data will be gathered (step 2) Process maps (flowcharts), XY matrices, brainstorming, and FMEAs are the tools used

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DMAIC Analyze (4)
Analyze sources of variability (step 3) Use visual and statistical tools to better understand the relationships between dependent and independent variables

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DMAIC Improve
Off-line experimentation Analysis of variance (ANOVA) Determines whether independent variable affect variation in dependent variables Taguchi method or approach

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DMAIC Control Phase

Manage the improved processes using control charts covered in Variables Attributes

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The Taguchi Method

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The Taguchi Method provides

A basis for determining the functional relationship between controllable factors A method for adjusting a mean of a process by optimizing controllable variables. A procedure for examining the relationship between random noise and product or service variability

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Design of Experiments (DOE)

Robust design designed so that products are inherently defect free Concept Design considers process design and technology choices Parameter Design selection of control factors and optimal levels Tolerance Design specification limits

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The Taguchi Process
Problem identification Brainstorming session Experimental design Experimentation Analysis Confirming experiment

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Taguchi Quality Loss Function

• Traditional view anything within specification
  limits is OK, with no loss
• Taguchi
• Any variation from the target mean represents a
  potential loss
• The greater the distance from the target mean the
  greater the potential loss

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Design for Six Sigma

• DFSS

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Design for Six-Sigma (DFSS)
Used in designing new products with high performance, instead of DMAIC DMADV (see next slide) IDOV (see 2 slides ahead) Focuses on final engineering design optimization Relates to new processes and products

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DMADV
Design Measure Analyze Design Verify

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IDOV
Identify Design Optimize Verify

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Reasons for Six Sigma Failure

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Reasons for Six-Sigma Failure - (1)
Lack of leadership by champions Misunderstood roles and responsibility Lack of appropriate culture for improvement

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Reasons for Six-Sigma Failure - (2)
Resistance to change and the Six-Sigma structure Faulty strategies for deployment Lack of data

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Summary
The process for Six-Sigma is define, measure, analyze, improve and control Keys to Six-Sigma success are skilled management, leadership and long-term commitment