The 7 basic quality tools through minitab 18

THE 7 BASIC QUALITY TOOLS
through
by
Bs Ram

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Introduction to MINITAB
The Minitab user interface
Projects and worksheets
Data types
Open and examine a worksheet

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The Minitab user interface
Start > All Programs > Minitab > Minitab 18.

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Projects
Project (.MPJ) files store the following items:
In a project, you can manipulate data, perform analyses, and generate graphs. Projects contain one or more worksheets.
 Worksheets
 Graphs
 Session window output
 Session command history
 Dialog box settings
 Window layout
 Options

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worksheets
In a project, you can manipulate data, perform analyses, and generate graphs. Projects contain one or more worksheets.
 Columns of data
 Constants
 Matrices
 Models for response variables
 Design objects
 Column descriptions
 Worksheet descriptions
Worksheet (.MTW) files store the following items:

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Numeric data
Numbers, such as 264 or 5.28125.
Text data
Letters, numbers, spaces, and special characters,
such as Test #4 or North America.
Date/time data
Dates, such as Mar-17-2013, 17-Mar-2013, 3/17/13, or 17/03/13. Times,
such as 08:25:22 AM. Date/time, such as 3/17/13 08:25:22 AM or 17/03/13 08:25:22.
Data types

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Open and examine a worksheet

1. Cause-and-Effect (Fishbone
Diagram)
2. Control Chart
3. Histogram
4. Pareto Chart
5. Run Chart
6. Scatter Plot
7. Process Map / Flow Chart
Target verticals
Control
Chart
Fishbone
QC Histogram
Pareto
Chart
Run Char
Process
Map
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Cause-and-Effect / Fishbone Diagram
Before you start

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Overview
• To organize brainstorming information about the potential causes of a
problem.
• To compare the relative importance of different causes.
• It is also called a C&E diagram, a fishbone diagram, or an Ishikawa diagram.
For example, a hotel manager wants to investigate why guest rooms are not ready for check-in at 4:00 PM. The manager
brainstorms reasons with the improvement team and creates a cause-and-effect diagram to categorize the reasons. The
team can then prioritize problem areas and develop improvement ideas.

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Where to find this analysis
To create a cause-and-effect diagram,
choose
Stat > Quality Tools > Cause-and-Effect

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Data considerations
• The effect must be a dependent variable
For example, the response variable might be the wait time for service. In a cause-and-effect
diagram, the dependent variable is the variable (or effect) that is affected or measured.

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The engineers creates a cause-and-effect diagram
1.Open the sample data, SurfaceFlaws.MTW.
2.Choose Stat > Quality Tools > Cause-and-Effect.
3.Under Causes for Branch 1, select In column from the drop-down menu.
4.In the blank field under Causes for Branch 1, enter Man.
5.Repeat steps 3 and 4 for the next 5 branches.
Enter Machine, Material, Method, Measure, and Enviro in branches 2–6.
6.In Effect, type Surface Flaws.
7.Click OK.

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Sub-Branches
1.Open the sample data, SurfaceFlaws.MTW.
2. Choose Stat > Quality Tools > Cause-and-Effect.
3.Under Causes for Branch 1, select In column from the drop-down menu. In the blank field
under Causes for Branch 1, enter Man.
3. Repeat steps 3 and 4 for the next 5 branches. Enter Machine, Material, Method, Measure,
and Enviro in branches 2–6.
5. Click Sub for Branch 1. Under Causes for Sub-Branch 3, select In column from the drop-
down menu.
6.Click Sub for Branch 1. Under Causes for Sub-Branch 3, select In column from the drop-
down menu.

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Sub-Branches
8. Click Sub for Branch 2. Under Causes for Sub-Branch 4, select In column from the drop-
down menu.
9. In the blank field under Causes for Sub-Branch 4, enter Speed. Click OK.
10. Click Sub for Branch 5. Under Causes for Sub-Branch 1 select In column from the drop-
down menu.
11. In the blank field under Causes for Sub-Branch 1, enter Micrometers. Click OK.
12. In Effect, type Surface Flaws.
13. Click OK.

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Surface Flaws
Environment
Measurements
Methods
Material
Machines
Personnel
Operators
Training
Supervisors
Shifts
Speed
Lathes
Bits
Sockets
Suppliers
Lubricants
Alloys
Brake
Engager
Angle
Inspectors
Microscopes
Micrometers
Condensation
Moisture% Testing
M
entors
Erratic
Too
slow
Condition
Accuracy
Cause-and-Effect Diagram with sub-branches

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Cause-and-Effect / Fishbone Diagram
Perform the analysis

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Enter and label the causes
NOTE
You can change or add branches after you create a cause-and-effect diagram. Right click on the cause-
and-effect diagram and choose Graph Options.
1. Under Causes, select In column or Constants from the drop-down menu.
•In column: Enter the column that contains the causes. Values in the columns can contain up to 72
characters.
•Constants: Type the list of causes directly into the dialog box. Use a blank space between causes.
If a cause contains multiple words, enclose the cause in double quotation marks. For example,
"Operator training".
NOTE
Odd branches are on top (5, 3, then 1, left to right), and even branches are on the bottom (6, 4, then 2).
To display a diagram with the main branches but no causes, do not enter any columns or type any
causes.

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Understanding control charts
What is a control chart?
A control chart indicates when your process is out of control and
helps you identify the presence of special-cause variation. When
special-cause variation is present, your process is not stable and
corrective action is necessary.

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When to use a control chart
Communicate the performance of your process during a specific
period of time.

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I-MR Chart
1. Open the sample data, DetergentpH.MTW.
2. Choose Stat > Control Charts > Variables Charts for Individuals > I-MR.
3. In Variables, enter pH.
4. Click I-MR Options.
5. On the Tests tab, select 1 point > K standard deviations from center
line (Test 1) and K points in a row on same side of center line.If you are not
sure which tests apply in your specific situation, use Tests 1 and 2.
6. Click OK in each dialog box.

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Scale for I-MR Chart
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Time
Index
Label the x-axis with the number of the subgroup.
Stamp
Label the x-axis with values from a column. In Stamp
columns (1-3, innermost first), enter up to three columns
that contain date/time, numeric, or text values for the scale.

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Axes and ticks
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Axes and Ticks
A: Y-scale axis lines
B: Y-scale major tick
C: Y-scale major tick label
D: Y-scale minor tick

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Gridlines
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Gridlines
A: Horizontal gridline at Y major tick
B: Horizontal gridline at Y minor tick
C: Vertical gridline at X major tick

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Reference lines
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Reference lines
Show reference lines at Y values
Enter the y-scale values to display reference lines at.
Show reference lines at time scale positions
Enter the x-axis values to display reference lines at.

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Overview
Use Xbar-R Chart to monitor the mean and variation of a process
when you have continuous data and subgroup sizes of 8 or less. Use
this control chart to monitor process stability over time so that you can
identify and correct instabilities in a process.

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Data considerations
• The data should be continuous
• The data should be in time order
• The data should be collected at appropriate time intervals
• The data should be in rational subgroups
• The subgroup size should be 8 or fewer observations
• Collect an appropriate amount of data
• The data do not need to be normally distributed
• The observations within each subgroup should not be correlated with
each other

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Xbar-R.
1. Open the sample data, CamshaftLength.MTW.
2. Choose Stat > Control Charts > Variables Charts for Subgroups > Xbar-R.
3. From the drop-down list, select All observations for a chart are in one column and enter Machine 1 Machine
2 Machine 3.
4. In Subgroup sizes, enter Subgroup ID.
5. Click Xbar-R Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1), K points in a row on
same side of center line (Test 2), and K points in a row within 1 standard deviation of center line (either
side) (Test 7).If you are not sure which tests apply in your specific situation, use Tests 1, 2, and 7 when you first
establish the control limits based on your data. After the control limits are established, you can use the known
values of those limits and Test 7 is no longer needed.

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Labels
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Labels
Specify a title, subtitles, and footnotes.

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Multiple graphs
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Multiple Graphs
Default
Same Y
If you enter more than one column, Minitab
creates a separate control chart for each
column. Select Same Y to use the same y-scale
for each control chart.

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Xbar-R Options : Historical Parameters
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Parameters
Mean
Standard deviation
You can enter a single mean for the entire chart, or you can enter a mean for each stage. Minitab uses the
mean to calculate the center line on the Xbar chart.
You can enter a single standard deviation for the entire chart, or you can enter a standard deviation for
each stage. The standard deviation is used to calculate the control limits on both charts and the center line
on the R chart.

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Specify how to estimate the parameters
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Estimate
Specify subgroups to estimate parameters
1. From the drop-down list, specify whether you want to list the subgroups to omit or the subgroups to
include.
2. List the subgroups. Use a colon to indicate a range of subgroups. Leave a space between each subgroup
or range of subgroups.
Method for estimating standard deviation
Subgroup size > 1
1. Rbar: Rbar is the average of the subgroup ranges. The Rbar method is a common estimate of the
standard deviation and works best with subgroup sizes from 2 to 8.
2. Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup
variances, which gives larger subgroups more influence on the overall estimate.

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Modify the control limits
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Limits
Display additional σ limits at
1. From the drop-down list, specify whether you want to list the subgroups to omit or the subgroups to
include.
2. List the subgroups. Use a colon to indicate a range of subgroups. Leave a space between each subgroup
or range of subgroups.
Method for estimating standard deviation
Subgroup size > 1
1. Rbar: Rbar is the average of the subgroup ranges. The Rbar method is a common estimate of the
standard deviation and works best with subgroup sizes from 2 to 8.
2. Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup
variances, which gives larger subgroups more influence on the overall estimate.

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Xbar-S Chart
1. Open the sample data, CanWeight.MTW.
2. Choose Stat > Control Charts > Variables Charts for Subgroups > Xbar-S.
3. From the drop-down list, select All observations for a chart are in one column, enter Weight.
4. In Subgroup sizes, enter Subgroup ID.
5. Click Xbar-S Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1), K points in a row
on same side of center line (Test 2), and K points in a row within 1 standard deviation of center
line (either side) (Test 7).If you are not sure which tests apply in your specific situation, use Tests 1, 2,
and 7 when you first establish the control limits based on your data. After the control limits are
established, you can use the known values of those limits and Test 7 is no longer needed.

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Xbar-S Chart of Weight
Test Results for Xbar Chart of Weight
TEST 1. One point more than 3.00 standard deviations from center line. Test Failed
at points: 3 * WARNING * If graph is updated with new data, the results above may no
longer be correct.

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The supervisor for a call center wants to evaluate the process for answering
customer phone calls. The supervisor records the total number of incoming
calls and the number of unanswered calls for 21 days. The supervisor
creates a P chart to monitor the proportion of unanswered calls.

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P Chart
1. Open the sample data, UnansweredCalls.MTW.
2. Choose Stat > Control Charts > Attributes Charts > P.
3. In Variables, enter Unanswered Calls.
4. In Subgroup sizes, enter Total Calls.
5. Click P Chart Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1) and K points in a
row on same side of center line (Test 2).If you are not sure which tests apply in your specific situation,
use Tests 1 and 2 when you first establish the control limits based on your data.

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The director of quality for a group of hospitals wants to assess the medication
error rate. Examples of errors include delivering medication at the wrong time,
delivering the wrong dose, and delivering the wrong medication. The director
records the number of patients and the number of medication errors each week for
32 weeks.

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1. Open the sample data, MedicationErrors.MTW.
2. Choose Stat > Control Charts > Attributes Charts > U Chart
Diagnostic.
3. In Variables, enter Errors.
4. In Subgroup sizes, enter Patients.
5. Click OK.

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Overview
Use Histogram to examine the shape and spread of your
data. A histogram divides sample values into many
intervals and represents the frequency of data values in
each interval with a bar.

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Data considerations
The sample size should be approximately 20 or greater
A histogram works best when the sample size is at least 20. If the sample size is too small, each bar on
the histogram may not contain enough data points to accurately show the distribution of the data. If the
sample size is less than 20, consider using an Individual value plot instead.
The sample data should be selected randomly
In statistics, random samples are used to make generalizations, or inferences, about a population. If your
data were not collected randomly, your results may not represent the population

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Histogram
A quality control engineer needs to ensure that the caps on shampoo bottles are fastened correctly. If the
caps are fastened too loosely, they may fall off during shipping. If they are fastened too tightly, they may be
too difficult to remove. The target torque value for fastening the caps is 18. The engineer collects a random
sample of 68 bottles and tests the amount of torque that is needed to remove the caps..

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1. Open the sample data, CapTorque.MTW.
2. Choose Graph > Histogram > Simple.
3. In Graph variables, enter Torque.
4. Click OK.

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Histogram with fit lines and groups
1. Open the sample data, PistonLength.MTW.
2. Choose Graph > Histogram > With Fit and Groups.
3. In Graph variables, enter Length.
4. In Categorical variables for grouping (0-3), enter Supplier.
5. Click OK.

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Select a histogram
1.Simple
2.With Fit
3.With Groups
4.With Fit and Groups

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A Simple histogram
Graph > Histogram > Simple

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Create a histogram with a fitted distribution line
Graph > Histogram > With Fit

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Create a histogram with groups
Graph > Histogram > With Groups
1. In Graph variables, enter the numeric or date/time
column that you want to graph.
2. In Categorical variables for grouping (0-3), enter up
to three columns that define the groups.
1. In Graph variables, enter multiple numeric or
date/time columns that you want to graph.
2. Select Graph variables form groups.
Groups are graph variables
Groups are defined by values in categorical variables

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Create a histogram with a fitted distribution line and groups
Graph > Histogram > With Fit and Groups

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Overview
Use Pareto Chart to identify the most frequent defects, the most
common causes of defects, or the most frequent causes of
customer complaints. Pareto charts can help to focus improvement
efforts on areas where the largest gains can be made.

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Data considerations
Collect summary data or raw data

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Pareto Chart
1. Open the sample data, ClothingDefect.MTW.
2. Choose Stat > Quality Tools > Pareto Chart.
3. In Defects or attribute data in, enter Defect.
4. In Frequencies in, enter Count.
5. Select Combine remaining defects into one category after
this percent, and enter 95.
6. Click OK.

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A weighted Pareto Chart
1.Open the sample data, ClothingDefect.MTW.
2.Choose Stat > Quality Tools > Pareto Chart.
3.In Defects or attribute data in, enter Defect.
4.In Frequencies in, enter Count*Cost.
5.Select Combine remaining defects into one category after this percent, and enter 95.
6.Click OK.

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A weighted Pareto Chart

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Pareto Chart with a By variable
1.Open the sample data, PaintFlaws.MTW.
2.Choose Stat > Quality Tools > Pareto Chart.
3.In Defects or attribute data in, enter Flaws.
4.In BY variable in, enter Shift.
5.Select Default (all on one graph, same ordering of bars).
6.Select Combine remaining defects into one category after this percent, and enter 95.
7.Click OK.

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Pareto Chart with a By variable

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Overview
A run chart represents your process data over time. Use a run chart to look for evidence of
special-cause variation in your process.
Run Chart to assess differences in measurements
between different operators and different parts

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Data considerations
Operators should measure parts in a random order
Operator and Part factors must be crossed
To ensure that the data collection order does not influence the results, each operator should
measure all parts randomly within a replicate. After all operators measure all parts one time, you
repeat the process for all replicates.
Two factors are crossed when each level of one factor occurs in combination with each level of the
other factor.

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Run Chart
1. Open the sample data, GageData.MTW.
2. Choose Stat > Quality Tools > Gage Study > Gage Run Chart.
3. In Part numbers, enter Part.
4. In Operators, enter Operator.
5. In Measurement data, enter Measurement.
6. Click OK.

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Overview
Use Scatterplot to investigate the relationship between a pair of continuous variables. A
scatterplot displays ordered pairs of x and y variables in a coordinate plane.

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Scatterplot
A medical researcher studies obesity in adolescent girls. Because body fat percentage is difficult
and expensive to measure directly, the researcher wants to determine whether the body mass
index (BMI)—a measurement that is easy to take—is a good predictor of body fat percentage.
The researcher collects BMI, body fat percentage, and other personal variables of 92 adolescent
girls.

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Scatterplot
1. Open the sample data, BodyFatPercentage.MTW.
2. Choose Graph > Scatterplot > Simple.
3. Under Y variables, enter %Fat.
4. Under X variables, enter BMI.
5. Click OK.

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Scatterplot with regression and groups
1. Open the sample data, FlashRecoveryTime.MTW.
2. Choose Graph > Scatterplot > With Regression
and Groups.
3. Under Y variables, enter Flash Recovery.
4. Under X variables, enter Volts After.
5. In Categorical variables for grouping (0-3),
enter Formulation.
6. Click OK.

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Process Flow Chart

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Value stream map

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Value stream map
Use a value stream map to illustrate the flow of materials and information as a
product or service moves through the value stream.

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Create a value stream map
Insert a value stream map
1. Choose Insert > Value Stream Map.
2. Click a value stream map template, and then click Create. Some templates start with blank shapes and
some do not.

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Add shapes and connectors
In the Shapes gallery, click a shape, and then click the workspace.

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Add shapes and connectors
To connect the shapes, click Connector, and then choose a connector. To ensure
that shapes stay connected even when you move them, connect the shapes at
their anchor points. Anchor points turn green when they are connected to the
shape.
Start point
End point

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Format shapes and connectors
Option Description
Text Change the font type, font style, font size, and color of the text on a shape or
a connector.
Paragraph Change the alignment or apply bullets or numbering to the text on a shape or
a connector.
Lines Change the color, type, and size of a connector or of a shape's outline.
Fill Change the color, pattern, pattern color, or shadow color of the shape or the
connector.

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Add the same shape or connector multiple times
Choose Map > Multi-Insert, and then, in the Shapes gallery, select the shape or connector to add.

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