NG BB 21 Intro to Minitab

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National GuardBlack Belt Training

Module 21

Intro to Minitab

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CPI Roadmap – Measure

Note: Activities and tools vary by project. Lists provided here are not necessarily all-inclusive.

TOOLS

•Process Mapping

•Process Cycle Efficiency/TOC

•Little’s Law

•Operational Definitions

•Data Collection Plan

•Statistical Sampling

•Measurement System Analysis

•TPM

•Generic Pull

•Setup Reduction

•Control Charts

•Histograms

•Constraint Identification

•Process Capability

ACTIVITIES• Map Current Process / Go & See

• Identify Key Input, Process, Output Metrics

• Develop Operational Definitions

• Develop Data Collection Plan

• Validate Measurement System

• Collect Baseline Data

• Identify Performance Gaps

• Estimate Financial/Operational Benefits

• Determine Process Stability/Capability

• Complete Measure Tollgate

1.Validate the

Problem

4. Determine Root

Cause

3. Set Improvement

Targets

5. Develop Counter-

Measures

6. See Counter-MeasuresThrough

2. IdentifyPerformance

Gaps

7. Confirm Results

& Process

8. StandardizeSuccessfulProcesses

Define Measure Analyze ControlImprove

8-STEP PROCESS

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Learning Objectives

Become familiar with the Minitab statistical software

Learn some of the most widely used tools within Minitab

Learn how to navigate within Minitab

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Overview

Minitab is a good tool for beginners, as well as established statisticians

This is just an introduction – Minitab is a powerful tool that is capable of much more than what we will have time to cover here

Suggestion: For now, do not be concerned with all the bells and whistles

Minitab Help function is always available and easy to use

FEAR NOT!

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What Will We Cover?

We are going to cover just a few of Minitab tools, including:

The Minitab layout

An overview of the menus

The Minitab Calculator

Some BASIC statistical tools

Some BASIC graphing tools

The HELP menu

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Minitab Layout

The overall lay out of Minitab is similar to Excel, with some key differences:

The worksheet looks similar to a spreadsheet, but Minitab prefers data to be in columns. An easy way to think of it is to think of each column as a variable: Time, speed, date, etc.

The Session Window is a dialog window. It is where the non-graphical results of your calculations will be displayed.

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What Does It Look Like?

Session Window

Worksheet

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Minitab Characteristics

Although not a Microsoft product, Minitab shares some characteristics of other programs running on the Windows platform. In particular, the menu bar running across the top of the page, and the button bar below that. We will briefly go through some of the key functionality in the menu bar. The button bar contains shortcuts to many of the same functions.

Also, you can cut and paste data from Excel. You can do this by simply using the Ctrl-C and Ctrl-V functions you might already be familiar with.

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Minitab Characteristics

One of the key differences in Minitab is the distinction between a Project and a Worksheet

Worksheets contain your data and nothing else. It is where you can manipulate the data and store your data. Minitab will let you have several worksheets open simultaneously. In addition, worksheets can be saved independently and shared between projects.

Projects are more like an entire notebook, containing not only one or more worksheets, but also your session window (containing any analysis you have already performed) and any graphs you have created

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File Menu

Project Commands

WorksheetCommands

Recently Opened

Worksheets

Select File

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Edit Menu

Select Edit

Select Edit Last Dialog

The most frequently used feature found under the Edit drop down menu is the Edit Last Dialogwhich can (and should) be quickly accessed by using the Control-Equick key combination.

This feature will allow you to quickly access the last dialog box you used and either re-run, change, or review the last analysis you performed.

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Data Menu

Select Data

Select

Unstack Columns

or Stack Columns

The Data menu contains several Column manipulation features. Two examples are the Column Stack and Unstack features.

These features allow you to combine multiple columns (or rows) into one, or break one column (or row) into multiple columns (or rows).

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Pizza Example

Let‟s work through an example of this. Open the Minitab worksheet called Pizza Exercise.mtw.

We are going to stack the columns with the different defect types so that we can make a Pareto chart

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Pizza Example (Cont.)

Step One:

Open data set Pizza Exercise.mtw

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First, let's add a couple of headers for the columns we are about to stack

The first column we will call “Defect Type” and it will be where the defect types will be listed. We will put that header over column C11. The other column we will call “Count” and that is where we will put the count for each defect type. We will place that over column C12.

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Step 2:

Type in the two new column

headers

C11 = Defect Type

C12 = Count

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Now, let‟s go to the Data menu:

Data>Stack>Columns

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Data> Stack>Columns

Step 3:

Select Data,

choose Stack

and finally,

select Columns

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We are going to stack all the columns that list the different defect types. That would include Late, Wrong Ingredients, Wrong Order, Wrong Address, and Damaged. Highlight all these columns and select them for the “Stack the following columns” box.

We have a few choices for where to put the new stacked columns. Remember we created two headers for the new columns: Defect Type and Count. Since these columns are on the current worksheet, we select the radio button labeled Column of current worksheet. This will open up two more fields for us to place the stacked columns.

In the first field, next to Column of current worksheet, select the “Count” column. Next, in the field labeled Store subscripts in, select the “Defect Type” column. Make sure that the box labeled Use variable names in subscript column is selected. Remember, Minitab often refers to columns as variables.

When done, click OK

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Data>Stack Columns

Step 4

Highlight the columns you want stacked, then click “Select” to cause

them to appear here

Step 5

We want the data

stored in the current

worksheet, so select

this Radio Button,

enter data and

click on OK

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There you go!

We will come back to this worksheet a little later and use these stacked columns to create a Pareto chart

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Data>Stack>Columns

Your Stacked Data

are now

in columns

C11-T & C12

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Calc Menu

The Calc menu contains a wealth of tools that you will be using on a regular basis. We will touch on them only briefly here.

Two such items are the Column Statistics and Row Statistics features. These items allow you to run simple calculations such as the sum, mean, median, and standard deviation on a row or column of data. The result will be displayed in the session window.

Two other commonly used items are the Make Patterned Data, and Random Data functions

Make Patterned Data will allow creating series, such as 1,2,3,1,2,3 etc. This is good for coding items such as days of the week.

Random Data can do a couple of things for you. Not only can you create a set of random numbers, but this function can also be used to randomize a column of data.

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Calc Menu (Cont.)

Select Calc

To Get The

Following

Drop-Down Menu

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Calc Menu (Cont.)

The Calculator is a very commonly used tool. The power behind the calculator is that it allows us to perform a wide range of mathematical and statistical functions not only on a column of data, but between columns of data.

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The Calculator

The Calculator

Dialog Box

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The Calculator – Example

Let’s Look at an Example:

Anthony‟s Pizza delivery company was interested in customer service problems with pizzas that they delivered. Over a 12-day period, they recorded how many pizzas they delivered, and how many had defects that fell into the following categories: Late, Wrong Ingredients, Wrong Order, Wrong Address, and Damaged. In addition, they recorded the “Pizza Stylist” who was making the pizzas each day.

When they were done collecting the data, they keyed it into the Minitab worksheet in Columns C4 thru C9.

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The Calculator – Example (Cont.)

For this example, let‟s say that we want to know how many total defects we had each day. Since we know how many of each type of defect we have, all we need to do is add all of the defect columns together.

We will use the Calculator to calculate this result, and put the result in column C10

Let‟s start by typing the header for column C10 and calling it “Total Defects”

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Type in the new

column header for

“Total Defects”

in column C-10

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The Calculator – Example (Cont.)Now we can have some fun!

We want to store the result in our new variable “Total Defects”. We can select this variable in two ways. We can select the column from the list on the left, then click the Select button. Or we can simply double click the column from the list.

It is important to note that to select an item for a particular function, whether it be the Store the Result in Variable box, or Expression box, the cursor MUST be in the box you are selecting (or else you might not see the variables you want to choose).

Next, we want to create the equation. We would do this exactly the same as we would using a calculator.

In the expression box, select Late, then the “+” sign, then Wrong Ingredients, “+” Wrong Order, etc., until you have selected all the variables you want to add together. We have already told Minitab where we want the result stored in the previous step. When you are done, hit OK.

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Tell Minitab

where to store the results

C10 or Total Defects

Put the formula

to use here:

Late + Wrong Ingr.

+ Wrong Order + Wrong Address

+ Damaged

Click On OK

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Voila!

Column C10 (Total Defects) is now populated with the data you wanted

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Stat Menu

The Stat menu is where the majority of Minitab‟s functionality resides (such as Basic Statistics, Regression, and Control Charts)

We are not going to get into a lot of this at this time, but we will be coming back to visit Basic Statistics and Pareto charts shortly

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Stat Menu (Cont.)

Select Stat

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Graph Menu

The Graph menu contains many, many graphs that can be quite helpful, including Time Series plots, Histograms, and Boxplots. It should be noted however, that these graphs are also available in other places while you are using other tools.

After selecting a graph type, for many of the graphs, you will get a Dialog Box which illustrates the various styles available for the specific graph you have chosen. (There are some types of graphs that have only a single style.)

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Graph Menu (Cont.)

Select Graph drop down menu

If you choose Dotplots you would then get another dialog box showing the various types

of Dotplots

Select Cancel

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Editor Menu

The Editor menu contains many features you might already be familiar with due to your exposure with Excel and other related products. Such features include Find and Replace.

Selecting Editor

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Window Menu

The Window menu covers several features that are common to other Windows-based programs, such as Tile, and Cascade

Selecting Window

This area

shows currently

open Windows

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Help Menu

The Help menu is similar to other help menus you have probably seen

The main difference is that (as you may have noticed) Help is also available from all the dialog boxes

In addition, Minitab has a built in Stat Guide. The Stat Guide is more centered around the how and why you use a particular tool and assistance in interpreting the results.

The traditional Help menu is more centered around how to use the software

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Help Menu (Cont.)

Selecting Help

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Basic Statistics

Let‟s work through our Pizza example to see how to use Minitab to get some basic information about a process

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Basic Statistics (Cont.)

Under Stat>Basic Statistics, there is a small feature that can provide a wealth of information. We use it often to get a quick snapshot of the data underlying the process we are looking at.

Let‟s say we want to examine the number of defects our pizza company has been experiencing over the past 12 days. Now that we are back at our desk, we are excited to do something with this data to give us an idea how the process is doing. We might start by going to: Stat>Basic Statistics>Graphical Summary

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Stat>Basic Statistics> Graphical Summary

We want the

Graphical Summary

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We select the variable we wish to examine – in this case we are looking at “Total Defects”

Now, since a picture is worth a hundred words, let‟s have Minitab show us some graphs along with our data analysis

Click the OK button

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Double Click on“Total Defects”

to add it to the Variables Box

Click on OK

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To get a good statistical overview of the data, let‟s take a look at the Graphical Summary. This summary will include several graphs along with summary statistics and other useful information.

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252015105

Median

Mean

15.012.510.07.55.0

A nderson-Darling Normality Test

V ariance 37.333

Skewness 1.00674

Kurtosis 0.40899

N 12

Minimum 4.000

A -Squared

1st Q uartile 6.000

Median 8.500

3rd Q uartile 14.750

Maximum 24.000

95% C onfidence Interv al for Mean

6.784

0.49

14.549

95% C onfidence Interv al for Median

6.000 14.737

95% C onfidence Interv al for StDev

4.328 10.374

P-V alue 0.174

Mean 10.667

StDev 6.110

95% Confidence Intervals

Summary for Total Defects

Stat>Basic Statistics>Graphical Summary

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First, let‟s look at the graphs that Minitab gives us

The left side of the graph includes the “pictures” including (from top to bottom):

Histogram – Sorts the data into groups, then plots the frequency of each group along an X-axis and overlays a „Normal‟ distribution

Boxplot of the data – Breaks the data into quartiles (i.e., 50% of our data is inside the box)

95% Confidence Intervals – For our Mean and Median

The right side of the page contains the calculated data. The main items you will be using include:

Mean Median Standard DeviationMinimum Maximum

In addition there is also a Normality Test and Quartile Information

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Pizza Example Analysis

But what does this mean for our Pizza example? Let’s look at it piece by piece:

Histogram – This is showing us that our number of defects vary between 4 and 24 per day. This tells us that there is a great deal of variation in our process.

Boxplot of the Data – This shows us the same variation, but with some additional information. For example, we can see that 50% of the pizzas examined in this time period had between 6 and 14.75 defects, but with an additional 25% having more than 14.75 defects.

The right side of the page contains some detail about our process as well. For example:

The Mean (or average) number of defects on a given day is 10.7 defects, but the standard deviation is over 6 defects. In other words, on average we create over 10 defects per day, and the variation (as measured by the Standard Deviation) further amplifies the problem.

Our Median (the middle value in our data set) is 8.5 defects and the range of our data (another measure of variation calculated as Max - Min) is a whopping 20 defects!

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Pizza Example Analysis (Cont.)

What if you do not need the graphs and you just want the data analysis? You can choose just the data analysis by selecting Display Descriptive Statistics. This analysis will show up in your session window. Highlights include:

N = Sample size

Mean = Statistical average of the sample

Median = The “middle point” of the sample

Standard Deviation = The “average” amount that each data point deviates from the mean

Maximum = The largest data point in the sample

Minimum = The smallest data point in the sample

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Stat>Basic Statistics> Display Descriptive Statistics

Select Display Descriptive Statistics

for data analysis without the graphs

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Click on OK to get

your analysis in

the Session Window

Double click on C-10,

“Total Defects” to add

it to the Variables box

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Stat>Basic Statistics>Display Descriptive Statistics

Here is the analysis in the

Session Window

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You can choose which statistics are included by selecting the statistics tab

and checking just the statistics you want

Select Cancel

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Now let's say we want to compare Total Defects by Pizza Stylist

We can do this in the same way we ran Basic Statistics> Graphical Summary on one variable, but now we will be examining Multiple Variables all in one step

The only difference here is that we need is to select a “By Variable” that we would like to compare by. In this case, that would be by “Pizza Stylist.”

Once again, click OK

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Select “Total Defects”from the list and double click on it to place it in

the Variables box

Select “Pizza Stylist” for the

By Variables box

Click On OK

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Stat>Basic Statistics>Graphical Summary

This gives you the

Graphical Summary

for all three Pizza Stylists

in One Analysis

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Minitab will give us the same graphs we requested before, but this time, we will get one for each pizza stylist (which we have tiled for this screen shot). This allows us the opportunity to compare each stylist side by side.

Again, by looking at the graphs of the pizza stylists, we learn a lot very quickly, such as: Pizza Stylist Bob has much less variation in his process. This can be seen

in the histogram (which is not as spread out as the other stylists), box plot (which is also much tighter than the other stylists), and the range of the data (8 defects for Bob, 10 Defects for Sally, and 18 defects for Frank).

At this point, we can tell that stylist Bob appears to be more consistent in minimizing defects. But one thing that is important to note is that we do not know why. Are they following the same process? Is Bob cutting corners?

Also, it should be noted here, that our sample size for this example is too small to draw statistically significant conclusions

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Similar to the Graphical Summaries, Minitab will layout the results for each Pizza Stylist side by side in the session window. For this we have to select the option Display Descriptive Statistics. Once again, we can compare the means, standard deviations, and ranges for these two associates in fairly short order.

In addition to the difference in the ranges between the stylists, one sure sign that Bob has less variation in his process is his standard deviation – it is much less than that of the other stylists.

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Multiple Group Data Output

In Session Window

Note: Remember, you get to choose what statistics are included in this analysis

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Boxplots

Another useful item that we can have Minitab create are Boxplots

In addition to being a part of several of Minitab's other tools, you can find Boxplots under the Graph menu

The next slide illustrates the Nomenclature and Various Parts of a Boxplot

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Boxplot Terminology

1 2

1.5

2.5

3.5

4.5

Third Quartile Line

Median Line

First Quartile Line

Outlier

Whisker shows range for the

lower 25% of the data points.

Whisker shows range for the

top 25% of the data points.

Inter Quartile Range(IQR or Box Length)

Inter Quartile Range (IQR) = The distance between the Third Quartile Line and the First Quartile Line. This includes 50% of your data. Simply stated: IQR = Q3 – Q1.

Outlier = A data point is considered an outlier if it is more than 1.5 x IQR above the Third Quartile Line or below the First Quartile Line

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Boxplots (Cont.)

Once again, Boxplot is an area with lots of bells and whistles, we are just going to create a simple boxplot

We do this by first selecting Graph>Boxplot

For the type of Boxplot, select One Y and With Groups, since we have three groups (or boxes) on the same Y axis

In the final dialog box select “Total Defects” for the Graph Variable and choose “Pizza Stylist” for the By Variable

Click OK when done

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Boxplots (Cont.)

Select Graph

Select Boxplot

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From Next Dialog Box

Select One Y and With Groups

Boxplots (Cont.)

Click On OK

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Boxplots (Cont.)

Select “Total Defects”for the Group Variable

Use “Pizza Stylist” for

the By Variable

Click On OK

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Boxplots (Cont.)

Note: There are no Outliers (asterisks) in this data set

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Boxplots (Cont.)

A boxplot is a very good tool for graphically representing variation between items you are trying to compare

The Box Portion of the graph represents where 50% of the data for that item lies, with the line in the box representing the median for that item

The lines on either side of the box are called Whiskers, and represent the range for the Upper 25% and Lower 25% of the data. An asterisk outside of the whiskers is commonly referred to as an Outlier. An outlier is a data point flagged as unusual, usually because it is significantly outside the range that Minitab would be expecting to see your data.

As we can see from these boxplots, stylists Sally and Frank have significantly more variation in their process, as well as a larger median

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Time Series Plot

Another useful chart we often run is called a Time SeriesPlot (also commonly referred to as a run chart). As the name would suggest, this is a useful tool for displaying how a process is operating over time.

We find this tool under the Graph menu, labeled “Time Series Plot”

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Time Series Plots (Cont.)

Select Graph

Select

Time Series Plot

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With Time Series Plots there are many places for us to tweak the format of the output and add enhancements to the graph, but for our example, we just want a plain and simple Run Chart

We do this by simply selecting “Total Defects” as the Series Variable. In addition, if we have a Date column (as we do here), we can place that under Time/ Scale.

The steps needed for a Time Series Plot are shown on the next few slides

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1. Select Graph and

Time Series Plot

2. Select Simple and

Click on OK

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3. Add “Total Defects”

in the Series box

4. Click on Time/Scale

to add Dates and then

click on OK

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5. Select Radio Button

for Stamp

6. Add “Date” to

Stamp Columns box

and click on OK

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And here we have it!

This shows what is happening to our process as time goes on. Of course, in order for this graph to be of any value, the data must be entered in time order.

In our pizza example here, what seems to beg questioning is “what happened in the period from 9/4 to 9/7 – particularly 9/6 & 9/7?”

Can you see any other patterns in this data? Hint: 9/1 is a Monday.

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If you hold your Mouse Cursor over any data point, you will get the

corresponding X and Y data values for that point. You can still use the Brush feature to highlight several data points to see what Rows the data come from.

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Pareto Charts

Another useful tool for deciding what areas for improvement should be addressed first are Pareto Charts

We can find this tool under Stat>Quality Tools>Pareto Chart

The steps needed to do a Pareto Chart for the pizza data are shown on the next few slides

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Pareto Charts (Cont.)

1. Select Stat> Quality Tools> Pareto Chart

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Remember, we stacked our data into the columns “Defect Type” and “Count.” These two columns make up a Defects table that can now be turned into a Pareto Chart.

To do this, we click the radio button labeled Chart defects table and fill in the two fields that are now available to us. For the first one, our Labels are stored in the “Defect Type” column. Once you have selected this for the Labels field, we need to tell Minitab where our frequency data resides. In this case it is in the column we called “Count” so select that column. When you have done that, click OK.

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3. Put “Defects Type”in Labels in box

and put “Count” in theFrequencies in box and

click on OK

2. Select Radio Button for Chart defects table

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Given the limited resources available for us to address process improvements, it is important that we concentrate those resources on the largest problem areas first

From our Pareto Chart, we can see in short order that Late

Deliveries make up 43.0% of the total defects we are

experiencing, with Wrong Ingredients making up an

additional 29.7%

This is telling me that the biggest impact on defects will be made by addressing the portion of the process resulting in Late Deliveries and (resources and scope permitting) Wrong Ingredients

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Saving as a Project will save the worksheet, session window,

report pad, and all graphs

Saving as a Worksheetwill save only the worksheet

(Data Set)

Saving and Exiting

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Takeaways

Minitab is a powerful tool that, with just a few simple mouse clicks, can tell you a great deal about your data including:

Graphical

Histogram

Box Plots

Time Series Plots

Run Charts

Pareto Charts

Numerical

Mean

Median

Minimum

Maximum

Range

Standard Variation

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What other comments or questions

do you have?

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APPENDIX

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Minitab Exercise

This exercise concerns a Customer Service Center. It utilizes the basic Minitab tools that you have learned and allows you to play with some basic options.

AHT = Average Handle Time (The average number of seconds required to complete a call)

Occupancy = The percentage of time that Customer Service Reps are on the phone or completing after call work relative to the amount of time they are logged into the system. Generally the target is 80-85%.

Speed of Answer = The average number of seconds it takes to answer an inbound call. Targets vary depending on the required service level but generally 30 seconds is standard.

Call Quality Score = “Your call may be recorded for quality purposes.” This really happens. Calls are recorded for each rep (usually 10-15 per month) and scored by a dedicated call monitoring team. It is a critical measure for individual reps.

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Minitab Exercise

Using the dataset Call Center Data.xls:

1.Copy the data into Minitab

2.Display the distribution of AHT (Average Handle Time) for the business. What is the mean?

3.On a single graph, display the AHT for sites A, B, and C. Which site has the least variation?

4.Create the appropriate graph which shows the frequency of call types. Which call type occurs most often and what percentage of the overall call volume does it represent?

5.Display the relationship between Occupancy (X) and Speed Of Answer (Y). What is the nature of the relationship?

6.On a single graph, display the monthly Average Call Quality Score for Sites A, B, and C. Overall, which site has the highest scores?

NG BB 21 Intro to Minitab

Education

Transcript of NG BB 21 Intro to Minitab