LEAN SIX SIGMA
IE Elective 2
TOPIC 4: ANALYZE PHASE
OUTLINE
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Visually Displaying Data (Histogram, Run Chart, Pareto Chart, Scatter
Diagram)
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Detailed (Lower Level) Process Mapping of Critical Areas
Value-Added Analysis
Cause and Effect Analysis (a.k.a. Fishbone, Ishikawa)
Affinity Diagram
Data Segmentation & Stratification
Verification of Root Causes
Determining Opportunity (Defects & Financial) for Improvement
Analyze Phase Review
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Analyze Phase
Analyze Phase is the statistical analysis of the problem
statement.
The goal of this phase is to find and validate the root
causes of business problems and ensure that
improvement is focused on causes, rather than symptoms.
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1.
Visually Displaying Data
Histogram, Run Chart, Pareto Chart, Scatter Diagram, etc.
Visually Displaying Data
Variation
Classification
collecting
data
After
for analysis, charts and graphs are effective
Data Type
tools
to provide visual clues to process problems.
Transforming spreadsheets of numbers into revealing visuals allows
the LSS team to easily communicate and interpret findings.
Selecting the right charts and graphs provides the LSS team with
valuable insights about the causes of process issues.
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Process Variation &
Representation Tools
Data Type
Variation
Classification
Discrete Data
Continuous Data
Variation for a period
of time
Bar Diagram
Pie Chart
Pareto Chart
Histogram
Box Plot
Scatter Plot
Variation over
time
Run Chart
Control Chart
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Bar Diagram
A bar diagram is a
graphical representation
of attribute data.
It is constructed by
placing
the
attribute
values on the horizontal
axis of a graph and the
counts on the vertical
axis.
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Pie Chart
A pie chart is also a graphical
representation of attribute data.
The
“pieces”
represent
proportions of count categories
in the overall situation.
Pie charts show the relationship
among quantities by dividing
the whole pie (100%) into
wedges or smaller percentages.
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Pareto Chart
Pareto Chart is a data
display tool for numerical
data that breaks down
discrete observations into
separate categories for the
purpose of identifying the
"vital few".
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Histogram
A histogram is a graphical
representation
of
numerical data.
It is constructed by placing
the class intervals on the
horizontal axis of a graph
and the frequencies on
the vertical axis.
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Box Plot
A box plot summarizes information about the shape, dispersion, center of process
data and also helps spot outliers in the data.
The box plot can be interpreted as follows:
Box – represents the middle 50% values of the process data.
Median – represents the point for which 50% of the data points are
above and 50% are below the line.
Q1 – represents the point for which 25% of the data points are above
and 75% are below the line
Q3 – represents the point for which 75% of the data are above and
25% are below in the line
Aestrix – represents an outlier and is a point which is more than 1.5
times the inter-quartile range (Q3-Q1) in the data.
Lines – These vertical lines represent a whisker which joins Q1 or Q3
with the farthest data-point but other than an outlier.
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Box Plot Example
A call center process where Average Handle Time (AHT) of the calls is compared between
Team Leads of the process
The variation is highest for TL1
and for the rest it is much
smaller.
This
indicates
that
the
associates working under TL1
need training or some other
help which will reduce the
variation and bring the overall
AHT under control.
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Scatter Plot
A scatter plot is often employed to
identify potential associations between
two variables, where one may be
considered to be an explanatory variable
(such as years of education) and another
may be considered a response variable
(such as annual income).
Scatter plots are similar to line graphs in
that they use horizontal and vertical
axes to plot, large body of, data points.
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Scatter Plot
Scatter plots have a very specific purpose.
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They show how much one variable is affected by another variable and
this relationship is called as their correlation.
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The closer the data points come when plotted to making a straight
line, higher is the correlation between variables.
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If the data points make a straight line going from the origin out to high
x- and y-values, then the variables are said to have a positive
correlation.
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If the line goes from a high-value on y-axis down to a high-value on xaxis, the variables have negative correlation.
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Run Chart
A run chart is a line graph of
data plotted over time.
By collecting and charting data
over time, trends or patterns in
the process can be identified.
Because they do not use control
limits, run charts cannot tell you
if a process is stable.
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Control Chart
Control chart is a graph used to study how a
process changes over time. Data are plotted
in time order.
It always has a central line for the average,
an upper line for the upper control limit, and
a lower line for the lower control limit. By
comparing current data to these lines, you
can draw conclusions about whether the
process variation is consistent (in control)
or is unpredictable (out of control, affected
by special causes of variation).
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2.
Detailed (Lower Level)
Process Mapping of Critical
Areas
Detailed (Lower Level) Process
Mapping of Critical Areas
Variation
Upon the identification
of critical
Classification
Data Type
areas
in a process, it is important to
map
the
process
in
detail
compared to the process mapping
done in the Measure Phase.
In this way, the critical areas of the
process are seen at the micro level.
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3.
Value-Added Analysis
Value-Added Analysis
Variation
Classification
Value-Added
Analysis is a technique for improving a process
Data Type
by enhancing attributes that are important to a customer.
It adds another dimension of discovery by looking at the
process through the eyes of the customer to uncover nonvalue-adding steps and the cost of doing business.
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Value Stream Mapping
Variation
A Value Stream
Map (VSM) visually displays the flow of steps, delays and
Classification
information required
to deliver a product or service to the customer.
Data Type
It allows analysis of the Current State Map in terms of identifying barriers
to flow and waste, calculating Total Lead Time and Process Time and
understanding Work-In-Process, Changeover Time, and Percent Complete &
Accurate for each step.
It combines process data with a map of the value-adding steps to help
determine where waste can be removed.
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Value Stream Map
Data Type
Variation
Classification
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VSM Example
Data Type
Variation
Classification
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4.
Cause and Effect Analysis
(Fishbone/Ishikawa Diagram, Why Analysis, etc.)
Cause and Effect Diagram
Cause and Variation
Effect Diagram is also known as the Fishbone/Ishikawa
Classification
Diagram.
Data Type
This is a visual tool used to brainstorm the probable causes for a
particular effect to occur.
The causes for this effect or problem is generated through team
brainstorming and are captured along the bones of the fish.
The causes generated in the brainstorming exercises by the team will depend on
how closely the team is related to the problem.
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Cause and Effect Diagram
Typically the causes are captured under
predetermined categories such as 6M’s or
5M’s and a P asVariation
given below:
Classification
Data
Type
Machine/Equipment:
Tools
used
to
execute the process
Material: Information and forms needed to
execute the process
Nature/Environment: Work environment,
market conditions, and regulatory issues
Measure: Process measurement
Method/Process: Procedures, hand-offs,
input-output issues
People/Management:
People
and
organizations
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Cause and Effect Diagram Example
Capturing the root causes of High Turn Around Time (TAT)
Data Type
Variation
Classification
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Why Analysis
Variation
Classification
analysis
is an iterative
Why
Data Type
activity.
process or a simple question asking
The purpose behind why analysis is to get the right people in the
room discussing all of the possible root causes of a given defect
in a process.
Many times teams will stop once a reason for a defect has been identified.
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Why Analysis Example
▪
Why does the Home finance loan application process take more than 10 working days
to arrive at the decision of “Credit Worthy”?
Because many application received especially by Post have fields that are either
not clear or left blank.
▪
Variation
Why do we have
applications that have blank fields?
Classification
Because
Data
Type the customer did not fill out the details.
▪
Why did the customer not fill the details?
Because they were not clear.
▪
Why were they not clear?
Because the direction was not clear.
▪
Why were the direction not clear?
Because many of the customers never read them.
▪
Why did they not read them?
Because the print was too small.
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5.
Affinity Diagram
Affinity Diagram
Variation
An Affinity Diagram
is an analytical tool used to organize many ideas into
Classification
subgroups
Data Typewith common themes or common relationships.
The method is reported to have been developed by Jiro Kawakita and so is
sometimes referred to as the K-J method.
By organizing the ideas into “affinity groups,” it is much easier to visualize
the commonality and plan for and address the challenges to the Six Sigma
approach.
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Affinity Diagram Example 1
Several members of a small company have just returned from a workshop on
the methods of Six Sigma. On the trip back from the seminar, the group
engaged in a vigorous discussion of the challenges they would confront if they
attempted toVariation
implement the Six Sigma approach. One person quickly jotted
down the listClassification
of challenges they generated. The list of brainstormed
Data Type
challenges
is given below.
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Affinity Diagram Example 1
An affinity diagram organizes the previous list based upon common themes or
relationships.
For example, an affinity diagram for this example might look as follows.
Data Type
Variation
Classification
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Affinity Diagram Example 2
Step 1: First, write down the problem. Then quietly put ideas, data, etc. on cards, pieces
of paper, or Post-it notes. The operative word is quietly. This is not like a typical
brainstorming session where people are very vocal about their ideas. We want this to be
a quiet exercise so that no one person(s) biases the other team member’s ideas.
Data Type
Variation
Classification
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Affinity Diagram Example 2
Step 2: Quietly put into homogeneous groupings.
Data Type
Variation
Classification
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Affinity Diagram Example 2
Step 3: Affinity Heading
Develop affinity heading cards. For example, there is a homogeneous grouping for human
resources related items. There is another grouping for the training department. Another
grouping deals with general processing. One grouping has to do with billing. And, the last
Variation
grouping addresses
employee empowerment. The heading cards will be placed on top of
Classification
each of the homogeneous
groupings.
Data Type
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Affinity Diagram Example 2
Step 4: Put the groupings into the order of the process.
Variation
For instance, Classification
when employees get hired, they first start off with human
resources.
Data TypeThe human resources department deals with employee
empowerment. And you have the process itself – that goes in the middle.
Billing usually comes late in the game. And finally, training is something that
involves all employees on an ongoing basis so the team chose to put it in
last position.
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5.
Data Segmentation &
Stratification
Data Segmentation
Data segmentation
is a process used to divide a large group into
Variation
Classification
smaller, logical
categories for analysis. Some commonly
Data Type
segmented entities are customers, data sets, or markets.
For example, you may collect the cause of defects of a process
and place the data into a pareto chart. The pareto chart then
displays the segmentation: type A defects are 50%, type B defects
are 30% and type C defects are 10%. These are possible ways to
segment the data.
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Data Stratification
Variation
Data stratification
is a technique used to analyze/divide a universe
Classification
Type
ofData
data
into homogeneous groups (strata) often data collected
about a problem or event represents multiple sources that need to
treated separately.
It involves looking at process data, splitting it into distinct layers
(almost like rock is stratified) and doing analysis to possibly see a
different process.
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Data Stratification
For instance,Variation
you may process loans at your company. Once you stratify by loan
size (e.g. less Classification
than 10 million, greater than 10 million), you may see that the
Data Type
central
tendency metrics are completely different which would indicate that you
have two entirely different processes. Maybe only one of the processes is
broken.
Stratification is related to, but different from, Segmentation.
A stratifying factor, also referred to as stratification or a stratifier, is a factor
that can be used to separate data into subgroups. This is done to investigate
whether that factor is a significant special cause factor.
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6.
Verification of Root
Causes
?
How do we verify root
causes?
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Hypothesis Testing
HypothesisVariation
testing tells us whether there exists statistically
Classification between the data sets for us to consider
significant difference
Data Type
that they represent different distributions.
For continuous data, hypothesis testing can detect difference in
average and difference in variance.
For discrete data, hypothesis testing can detect difference in
proportion defective.
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Hypothesis Testing
Steps in Hypothesis Testing:
Step 1: Determine
appropriate Hypothesis test
Variation
Step 2: State Classification
the Null Hypothesis Ho and Alternate Hypothesis Ha
Data3:Type
Step
Calculate Test Statistics / P-value against table value of test
statistic
Step 4: Interpret results – Accept or reject Ho
Mechanism:
Ho = Null Hypothesis – There is No statistically significant difference
between the two groups
Ha = Alternate Hypothesis – There is statistically significant difference
between the two groups
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Types of Hypothesis Testing
Data Type
Variation
Classification
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Types of Hypothesis Testing Examples
Normal Continuous Y and Discrete X
Data Type
Variation
Classification
Non-normal Continuous Y and Discrete X
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Types of Hypothesis Testing Examples
Variation
Continuous Y and
Continuous X
Data Type
Classification
Discrete Y and Discrete X
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7.
Determining Opportunity
(Defects & Financial) for
Improvement
Determining Opportunity of
Improvement
Variation
Classification
Data root
Typecauses are validated, the team at this point must be able
After
to determine and pinpoint critical area of focus and take into
account the opportunities for defect reduction and potential
savings/cost avoidance.
The LSS team must not proceed to the Improve phase empty
handed.
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8.
Analyze Phase Review
Analyze Phase Overview: https://bit.ly/2SC0vSF
https://bit.ly/3jJtujd
Videos
Value Stream Mapping:
https://www.youtube.com/watch?v=fkk0hkunfcE
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References
Six Sigma Study Guide
Six Sigma Daily
https://goleansixsigma.com/
https://www.isixsigma.com/
https://www.sixsigma-institute.org/
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