Accelerating Change in Your Organization Using Six Sigma

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Accelerating Change in Your Organization Using
Six Sigma

• Prepared for CQAA February 19, 2004
• by
• Dr. Nancy Eickelmann
• Team Contributors Dr. Jongmoon Baik, Animesh
  Anant
• Software and System Engineering Research
  Laboratory
• MOTOROLA LABS

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Outline

• Change Acceleration with Six Sigma
• How do we use DMAIC for change?
• How do we control variation?
• Conclusion

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What is Six Sigma?

• Six Sigma is a 4-step high performance system to
  execute business strategy. Matt Barney, Motorola
  Inc.
• Align executives to the right objectives and
  targets
• Mobilize improvement teams
• Accelerate results
• Govern sustained improvement
• http//www.asq.org/pub/sixsigma/motorolafigs.html

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Accelerating Change - What

• Create a Community of Practice
• Create a Community of Practicioners, i.e.,
• MBB, BB, GB
• Foster a Quality Culture that Institutionalizes
  Best Practices and Change for Improvement
• Maintain Strategic Focus

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Accelerating Change - How

• Building a shared vision
• Making mental models explicit
• Promoting skill mastery
• Supporting team learning

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How Do We Use DMAIC for Change?
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DMAIC Six Sigma Methodology
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Project Charter

• Project Name
• Project Information
• Leader
• Master Black Belt
• Project Start
• Project End
• Cost of Poor Quality
• Team Members
• Executive Sponsor
• Black Belt
• Master Black Belt
• Subject Matter Experts
• Process Start/Stop
• Start Point
• Stop Point

• Process Importance
• n.
• Process Problem
• n.
• Project Goals
• n.
• Process Measurements
• n.

• Project Time-Frame
• Milestone(s)
  2001
  iSixSigmaLLC
• Date(s)
  http//www.iSixSigma.
  com

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Applying DOE

• State the Problem with Clarity
• Select the Output or Response Variables
• Identify the Process Variables
• Select the Factor Levels and Ranges of Factor
  Settings
• Select the Appropriate Experimental Design
• Plan the Experiment
• Execute the Experiment
• Analyze and Interpret the Results

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Step 1State the Problem with Clarity

• Defect Prevention is the most cost-effective
  means of producing high quality software.
• Fagan Inspections (FIs) have been shown to be an
  effective technique of defect prevention.
• Motorola has modified this process and performs
  Modified Fagan Inspections (MFIs) and Formal
  Technical Reviews (FTRs)
• Does it make a difference whether we use
• Fagan Inspections
• Modified Fagan Inspections
• Formal Technical Reviews

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Comparison of FI MFI - FTR
Kiviat Analysis
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Comparison of MFI - FTR

• Summary of measures
• Number of Staff per Inspection/FTR
• Staff effort expended in
• preparation and meeting time
• Preparation Rate per Page
• Preparation Rate per LOC
• Average Defects Found
• Defects per KSLOC

• The p-value (Prob gt F) for every measure is
  statistically significant. The means for
  Inspections versus FTRs are significantly
  different for every measure.

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Step 2 Select the Output or Response Variables

• The output variables selected were
• staff hours of effort per Inspection/FTR
• staff hours of effort per sub-activity
• defects found per Inspection/FTR

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Step 3 Identify the Process Variables
Fagan Inspections Average Effort per FI Process
Step
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Step 3 Identify the Process Variables

• Number of process steps
• Overview effort
• Planning effort
• Meeting effort
• Preparation effort
• Preparation rate
• Meeting Rate

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Step 4 Select the Factor Levels and Ranges of
Factor Settings
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Step 5 Select the Experimental Design

• The design selected was a 23 Full Factorial that
  evaluates 3 input factors for inspections
• Team Size
• Product Size
• Estimated Fault Density
• Two of these factors (team size and product size
  inspected) are both measurable and controllable
  by management.
• The third factor number of initial defects is
  considered uncontrolled but measurable and a key
  factor.

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Step 6 7 Plan Execute the Experiment
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Step 8 Analyze and Interpret the Results

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Step 8 Analyze and Interpret the Results
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Improve based on quantitative results
The results for each trial of the simulation are
plotted on the Kiviat Chart.
6 Point Kiviat Analysis
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DMAIC Six Sigma Methodology
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Control - Input Variables
U chart of Size/FTR
U Chart of Staff/FTR outliers removed
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Control Process Variables
U chart of Defect Detection Effort/FTR
U Chart of Preparation Rate/FTR
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Control Response Variables
U-chart of Fault Density/FTR
U Chart of Faults/FTR
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How Do We Use SPC to Control Variation?
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SPC and Predictability

• Shewhart (1931-1980) defined control as
• A phenomenon will be said to be controlled when,
  through the use of past experience, we can
  predict, at least within limits, how the
  phenomenon will vary in the future. Here it is
  understood that prediction within limits means
  that we can state, at least approximately, the
  probability that the observed phenomenon will
  fall within given limits.

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SPC and Variation

• Processes are executed with inherent variation
• Measurements or counts collected on a process
  will also vary
• Quantifying the process variation is key to
  improvement
• Understanding causes of variation dictates the
  appropriate action in response to that variation

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SPC and Variation

• Common Causes of Variation
• Any unknown or random cause of variation is a
  common cause
• Common cause variation within predictable limits
  is a controlled system or constant system
• Common cause variation is addressed through
  long-term process improvement efforts
• Special Causes of Variation
• Variation that is not part of the constant system
  is an assignable or special cause of variation
• Special cause variation is an uncontrolled or
  unstable system
• SPC specifically addresses the identification and
  elimination of special causes of variation

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SPC Data Visualization Tools

• Time plots or run charts
• 20 or more points plotted against the median

• Run charts show trends or patterns
• Provide visibility into process variation
• Compare before and after a change
• Detect trends, shifts, and cycles in the process

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SPC Data Visualization Tools

• Frequency plot or histogram

• Frequency plot or histogram graphically depicts
  the distribution
• Height of the column indicates the frequency a
  value occurs
• Reveals the centering, spread, and variation of
  the data

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SPC Data Visualization Tools

• Pareto charts depict categorical data
• Height or (length) of each bar represents
  relative importance
• Bars are arraqnged in descending order left to
  right (top to bottom)
• Bar for the biggest problem is on the left or the
  (top)
• Vertical axis height (length) is the sum of all
  bars

• Pareto charts
• The pareto principle implies that we can attack
  problems by focusing on a vital few sources

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SPC Data Visualization Tools

• Control charts plot time-ordered data with
  statistically determined control limits
• Statistical control limits establish process
  capability
• Differentiates common from special causes
• Useful with all data types
• Provides a common language for process
  performance

• Control charts
• Centerline calculation uses the mean not the
  median
• Limits for individual charts require 24 data
  points

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How to Construct a Control Chart

• Select the process to be charted
• Determine the sampling method and plan
• Initiate the data collection
• Calculate the appropriate statistics
• Plot the data values on the first chart(mean,
  median or individuals)
• Plot the range or standard deviation of the data
  on the second chart (only for continuous data)
• Interpret the control chart and determine if the
  process is in control

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Process Schematic
Controlled Process Variables
Process
Output Variables
Input Variables
Uncontrolled Process Variables
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Data Sampling
X X X X X X X X X X X X X
X X X X X
X X X X X X X X X X X X X
X XX X X X
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Control Charts
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Control Chart Assumptions
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Control Chart Selection
Start
Type of data
discrete
continuous
Item with attribute or counting
Detect small shifts quickly
Equal sample sizes
Equal opportunity
Individual or subgroup
p
c
u
np
individual
Xbar,R
Do the limits look right?
EWMA
Try individual chart
Transform data
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Key Issues for Software

• First, software engineering has a large number of
  key variables that have different degrees of
  significance depending on the process lifecycle,
  organizational maturity, degree of process
  automation, level of expertise in the domain,
  computational constraints on the product,
  required properties of the product.
• Second, the individual key variables required to
  mirror the real world context have the potential
  property of extreme variance in the set of known
  values within the same context or across multiple
  contexts. For instance, programmer productivity a
  key variable in most empirical studies has been
  documented at 101 and 251 variances in the same
  context.
• Third, software engineering domain variables, in
  combination, may create a critical mass or
  contextual threshold not present when studied in
  isolation.
• 1986 IEEE TSE, Basili, Selby and Hutchins

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Why is SPC different for software?

• Contributing causes for extreme variation in
  software measurement include
• People comprise most of the software production
  process
• Software measurement may introduce more variation
  than the software process
• Size metrics do not count discrete, identical
  units

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Questions?