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Accelerating Change in Your Organization Using Six Sigma

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Change Acceleration with Six Sigma. How do we use DMAIC for change? How do we control variation? ... DMAIC: Six Sigma Methodology. Identify customers. Define ... – PowerPoint PPT presentation

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Title: Accelerating Change in Your Organization Using Six Sigma


1
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

2
Outline
  • Change Acceleration with Six Sigma
  • How do we use DMAIC for change?
  • How do we control variation?
  • Conclusion

3
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

4
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

5
Accelerating Change - How
  • Building a shared vision
  • Making mental models explicit
  • Promoting skill mastery
  • Supporting team learning

6
How Do We Use DMAIC for Change?
7
DMAIC Six Sigma Methodology
8
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

9
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

10
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

11
Comparison of FI MFI - FTR
Kiviat Analysis
12
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.

13
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

14
Step 3 Identify the Process Variables
Fagan Inspections Average Effort per FI Process
Step
15
Step 3 Identify the Process Variables
  • Number of process steps
  • Overview effort
  • Planning effort
  • Meeting effort
  • Preparation effort
  • Preparation rate
  • Meeting Rate

16
Step 4 Select the Factor Levels and Ranges of
Factor Settings
17
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.

18
Step 6 7 Plan Execute the Experiment
19
Step 8 Analyze and Interpret the Results

20
Step 8 Analyze and Interpret the Results
21
Improve based on quantitative results
The results for each trial of the simulation are
plotted on the Kiviat Chart.
6 Point Kiviat Analysis
22
DMAIC Six Sigma Methodology
23
Control - Input Variables
U chart of Size/FTR
U Chart of Staff/FTR outliers removed
24
Control Process Variables
U chart of Defect Detection Effort/FTR
U Chart of Preparation Rate/FTR
25
Control Response Variables
U-chart of Fault Density/FTR
U Chart of Faults/FTR
26
How Do We Use SPC to Control Variation?
27
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.

28
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

29
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

30
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

31
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

32
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

33
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

34
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

35
Process Schematic
Controlled Process Variables
Process
Output Variables
Input Variables
Uncontrolled Process Variables
36
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
37
Control Charts
38
Control Chart Assumptions
39
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
40
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

41
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

42
Questions?
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