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Basic Quality Tools

1. Check Sheets

2. Histograms

3. Pareto Charts

4. Cause and Effect Diagram

5. Flowcharts

6. Control Charts

7. Scatter Diagrams

Many people only consider 7 basic tools. ASQ does not list Flowcharts

as one of the basic tools. However, in order to improve processes and

know where to measure, you also need to know how the process is

designed, what are the variables that influence the process, and how

they interact.

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Check Sheets

• A structured form for collecting and analyzing data.

• A generic tool that can be adapted for a wide variety of purposes.

• Used by teams to record and compile data

• Easy to understand

• Builds with each observation based on facts

• Forces agreement on each definition

• Generates patterns

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When to Use a Check Sheet

• When data can be observed and collected repeatedly by the same person or at the same location.

• When collecting data on the frequency or patterns of events, problems, defects, defect location, defect causes, etc.

• When collecting data from a production process.

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Check Sheet Procedure 1. Decide what event or problem will be observed.

Develop operational definitions. 2. Decide when data will be collected and for how

long. 3. Design form. Set it up so data can be recorded

simply by making check marks or Xs or similar symbols and so that data do not have to be recopied for analysis.

4. Label all spaces on the form. 5. Test the check sheet for a short trial period to be

sure it collects the appropriate data and is easy to use.

6. Each time the targeted event or problem occurs, record data on the check sheet.

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Check Sheets

Keyboard Errors in Class Assignments

1 2 3 Total

Centering || ||| ||| 8

Spelling |||| || |||| |||| | |||| 23

Punctuation |||| |||| |||| |||| |||| |||| |||| |||| 40

Missed Paragraph || | | 4

Wrong Numbers ||| |||| ||| 10

Wrong Page Numbers | | || 4

Tables |||| |||| |||| 13

Totals 34 35 33 102

September-01Mistakes

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Histograms• A frequency distribution shows how often each

different value in a set of data occurs.

• It is the most commonly used graph to show frequency distributions.

• It looks very much like a bar chart, but there are important differences between them.

• It summarizes large amount of data

• It shows relative frequency of occurrence and process centralization with relative distribution of data

• Shows capability of the process

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When to Use a Histogram • When the data are numerical and you want to

see the shape of the data’s distribution, especially when determining whether the output of a process is distributed approximately normally.

• When analyzing whether a process can meet the customer’s requirements or when analyzing the output from a supplier’s process.

• When seeing whether a process change has occurred from one time period to another.

• When determining whether the outputs of two or more processes are different.

• When you wish to communicate the distribution of data quickly and easily to others.

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Histogram Analysis

• Before drawing any conclusions from your histogram, satisfy yourself that the process was operating normally during the time period being studied. If any unusual events affected the process during the time period of the histogram, your analysis of the histogram shape probably cannot be generalized to all time periods.

• Analyze the meaning of your histogram’s shape.

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Histograms

1 2 3 4 5 6 7 8 9 10 ClassBoundariesMid PointFrequency

5.5 5.0 5.6 5.5 6.1 7.2 6.8 4.9 8.0 7.1 1 0 - .99 0.5 0

5.3 8.0 4.8 6.2 5.0 8.0 5.2 5.3 5.2 4.9 2 1 - 1.99 1.5 0

8.0 5.2 4.6 4.2 4.2 3.1 5.0 3.1 5.0 8.0 3 2 -2.99 2.5 0

3.1 5.0 7.5 6.2 7.1 6.2 5.3 3.8 4.2 5.2 4 3 -3.99 3.5 8

8.8 4.2 6.3 7.0 6.8 5.3 6.2 7.1 8.0 5.2 5 4 - 4.99 4.5 12

7.0 6.7 6.2 7.1 8.2 5.0 7.0 4.1 3.1 5.0 6 5 - 5.99 5.5 29

6.3 3.4 7.0 5.5 7.8 4.2 7.1 5.6 6.2 7.0 7 6 - 6.99 6.5 19

7.2 6.8 7.1 5.6 6.2 8.0 5.2 5.0 6.7 7.1 8 7 - 7.99 7.5 21

8.1 7.1 6.0 7.1 7.0 3.8 5.0 4.2 3.8 6.7 9 8 - 8.99 8.5 11

8.5 5.8 6.5 5.0 7.1 5.0 6.9 5.5 4.8 7.5 10 9 - 9.99 9.5 0

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Histograms

Histogram

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Typical Histogram Shapes and What They Mean

• Normal

• Bi-Modal

• Multi-Modal

• Skewed

Normal. A common pattern is the bell-shaped curve known as the

“normal distribution.” In a normal distribution, points are as likely to

occur on one side of the average as on the other. Be aware, however,

that other distributions look similar to the normal distribution. Statistical

calculations must be used to prove a normal distribution.

Don’t let the name “normal” confuse you. The outputs of many

processes—perhaps even a majority of them—do not form normal

distributions , but that does not mean anything is wrong with those

processes. For example, many processes have a natural limit on one side

and will produce skewed distributions. This is normal — meaning

typical — for those processes, even if the distribution isn’t called

“normal”!

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Histograms Interpretation

Normal Distribution Bi-Modal Distribution Multi-Modal Distribution

Positively Skewed Negatively Skewed

Normal. A common pattern is the bell-shaped curve known as the “normal distribution.” In a normal distribution, points are as likely to occur on one side of the average as on the other. Be aware, however, that other distributions look similar to the normal distribution. Statistical calculations must be used to prove a normal distribution.

Don’t let the name “normal” confuse you. The outputs of many processes—perhaps even a majority of them—do not form normal distributions , but that does not mean anything is wrong with those processes. For example, many processes have a natural limit on one side and will produce skewed distributions. This is normal — meaning typical — for those processes, even if the distribution isn’t called “normal”!

Skewed. The skewed distribution is asymmetrical because a natural limit prevents outcomes on one side. The distribution’s peak is off center toward the limit and a tail stretches away from it. For example, a distribution of analyses of a very pure product would be skewed, because the product cannot be more than 100 percent pure. Other examples of natural limits are holes that cannot be smaller than the diameter of the drill bit or call-handling times that cannot be less than zero. These distributions are called right- or left-skewed according to the direction of the tail.

Double-peaked or bimodal. The bimodal distribution looks like the back of a two-humped camel. The outcomes of two processes with different distributions are combined in one set of data. For example, a distribution of production data from a two-shift operation might be bimodal, if each shift produces a different distribution of results. Stratification often reveals this problem.

Plateau. The plateau might be called a “multimodal distribution.” Several processes with normal distributions are combined. Because there are many peaks close together, the top of the distribution resembles a plateau.

Edge peak. The edge peak distribution looks like the normal distribution except that it has a large peak at one tail. Usually this is caused by faulty construction of the histogram, with data lumped together into a group labeled “greater than…”

Truncated or heart-cut. The truncated distribution looks like a normal distribution with the tails cut off. The supplier might be producing a normal distribution of material and then relying on inspection to separate what is within specification limits from what is out of spec. The resulting shipments to the customer from inside the specifications are the heart cut.

Dog food. The dog food distribution is missing something—results near the average. If a customer receives this kind of distribution, someone else is receiving a heart cut, and the customer is left with the “dog food,” the odds and ends left over after the master’s meal. Even though what the customer receives is within specifications, the product falls into two clusters: one near the upper specification limit and one near the lower specification limit. This variation often causes problems in the customer’s process.

Excerpted from Nancy R. Tague’s The Quality Toolbox, Second Edition, ASQ Quality Press, 2004, pages 292-299.

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Histograms

LCL TARGET LCL LCL TARGET LCL

LCL TARGET LCLLCL TARGET LCL

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Pareto Charts

• A bar graph where the lengths of the bars represent frequency or cost (time or money), and are arranged with longest bars on the left and the shortest to the right.

• The chart visually depicts which situations are more significant.

• Used by teams to concentrate on greatest problems

• Based on 80-20 Rule

• Easy display of problems in order of importance

• Helps prevents “shifting of problems”

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When to Use a Pareto Chart

• When analyzing data about the frequency of problems or causes in a process.

• When there are many problems or causes and you want to focus on the most significant.

• When analyzing broad causes by looking at their specific components.

• When communicating with others about your data.

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Mistakes Tally % Cum %

Punctuation 40 39% 39%

Spelling 23 23% 62%

Tables 13 13% 75%

Wrong Numbers 10 10% 84%

Centering 8 8% 92%

Missed Paragraph 4 4% 96%

Wrong Page Numbers 4 4% 100%

Total 102 100%

Keyboard Errors in Class Asignments

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40

23

4810

13

62%

75%84%

92% 96% 100%

39%

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Pareto Charts

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Pareto Chart Analysis

As any Pareto is constructed, you need to look at the criticality of each category and weigh its value to the improvement effort. Just because a category is the highest, it does not necessarily mean that it is the costliest. As you analyze the graph, look into cost, value, impact, recovery time, etc. on the product, process, operation, organization, etc. You will then apply same rules once you break down each category.

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Pareto Chart with Weights

Mistakes Points Tally Weight % Cum %

Spelling 4 23 92 23% 23%

Punctuation 2 40 80 39% 62%

Centering 3 8 24 8% 70%

Missed Paragraph 4 4 16 4% 74%

Tables 1 13 13 13% 86%

Wrong Numbers 1 10 10 10% 96%

Wrong Page Numbers 1 4 4 4% 100%

Total 102 100%

Keyboard Errors in Class Asignments

4

92

80

10131624

62%70% 74%

86%

96%100%

23%

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Spe

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Cause and Effect Diagram

• Also called Ishikawa or Fishbone Diagram

• It identifies many possible causes for an effect or problem. It can be used to structure a brainstorming session. It immediately sorts ideas into useful categories.

• Used by teams to identify and explore possible causes to problems

• Concentrates on causes, not symptoms

• Involves everyone’s knowledge of process

• Great brainstorming tool

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When to Use a Fishbone Diagram

• When identifying possible causes for a problem

• When causes can come from many different areas

• Especially when a team’s thinking tends to fall into ruts

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Cause and Effect Diagram

Excessive Punctuation

Errors

People

MaterialsMethods

MachineryMachine gets stuck

Too old

Needs Maintenance

No $ for repairs

Improper training

Chairs uncomfortable

Damaged ribbonsTeachers don’t have training in new methods

Too many people taking tests at same time

Not enough typewriters

Students not used to typewriters

Students use computers

Spell check built into programs.

Students attitude about typewriters

Material to type is too hard

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Cause and Effect Analysis

Once you have narrowed down to one or two possible causes, then you need to assemble a team, assign task, or just direct the correction for the situation.

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Flow Charts

• Shows the steps in a process to aid in its understanding

• Identifies locations and steps where data can be collected

• Examines which activities may impact process improvements and streamlines

• Easy to understand and follow

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Flowcharts (Process Flow Diagram)

• Is a generic, pictorial tool that can be adapted for a wide variety of purposes by separating steps of a process in sequential order.

• Elements that may be included are: sequence of actions, materials or services entering or leaving the process (inputs and outputs), decisions that must be made, people who become involved, time involved at each step and/or process measurements.

• The process described can be: a manufacturing process, an administrative or service process, a project plan.

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When to Use a Flowchart

• To develop understanding and to communicate to others of how a process is done.

• To study a process for improvement.

• When better communication is needed between people involved with the same process.

• To document a process. When planning a project.

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Flow Chart Sample

PrepareTest

Materials

PrepareTest

Site

Verify

Number ofStudents

Taking Test

EnoughMaterials

for

Students?

NO

YES

Open Room,Sit Students, and

Distribute Test

Materials

Record

Attendanceand test scores

Collect Testsafter test is over

Read Instructions

andStart Test

ProcureMaterials for

Students Taking Testor Re-schedule Test

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Flowchart Considerations

• The best Flowchart – Do not worry. • Identify and involve all key people

involved with the process. • Do not assign a “technical expert”. • Computer software is available for

drawing flowcharts.

•Don’t worry too much about drawing the flowchart the “right way.”

The right way is the way that helps those involved understand the

process.

•Identify and involve in the flowcharting process all key people

involved with the process. This includes those who do the work in the

process: suppliers, customers and supervisors. Involve them in the

actual flowcharting sessions by interviewing them before the sessions

and/or by showing them the developing flowchart between work

sessions and obtaining their feedback.

•Do not assign a “technical expert” to draw the flowchart. People who

actually perform the process should do it.

•Computer software is available for drawing flowcharts. Software is

useful for drawing a neat final diagram, but the method given here

works better for the messy initial stages of creating the flowchart.

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Control Charts

• Used to monitor, control and improve process performance over time

• Aid in detecting and monitoring variation

• Separate special and common causes of variation

• Helps in controlling and improving the process

The control chart is a graph used to study how a process changes over

time. Data are plotted in time order. A control chart 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. These lines are determined from

historical data. 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).

Control charts for variable data are used in pairs. The top chart monitors

the average, or the centering of the distribution of data from the process.

The bottom chart monitors the range, or the width of the distribution. If

your data were shots in target practice, the average is where the shots

are clustering, and the range is how tightly they are clustered. Control

charts for attribute data are used singly.

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When to Use Control Charts

• To control ongoing processes by finding and correcting problems as they occur.

• To predict the expected range of outcomes from a process.

• To determine whether a process is stable (in statistical control).

• To analyze patterns of process variation from special causes (non-routine events) or common causes (built into the process).

• To determine whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

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Control Charts

• Types of Data

– Variable: Data that can be measured

• Distance, time, volume, concentration, etc.

– Attribute: Data that is subject to two outcomes

• Pass/fail, Go-NoGo, etc.

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Out-of-control signals

• A single point outside the control limits. In Figure 1, point sixteen is above the UCL (upper control limit).

• Two out of three successive points are on the same side of the centerline and farther than 2 σ from it. In Figure 1, point 4 sends that signal.

• Four out of five successive points are on the same side of the centerline and farther than 1 σ from it. In Figure 1, point 11 sends that signal.

• A run of eight in a row are on the same side of the centerline. Or 10 out of 11, 12 out of 14 or 16 out of 20. In Figure 1, point 21 is eighth in a row above the centerline.

• Obvious consistent or persistent patterns that suggest something unusual about your data and your process.

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Types of Control Charts• Variable Data

– Average and Range (X & R) - Small Sample Size

– Average and Variance (X & s) - Large Sample Size

– Individual and Moving Range (IX-MR) - Sample = 1

• Attribute Data

– Defect Data

• c Chart - Constant Sample Size (c > 5)

• u Chart - Variable Sample Size

– Defective Data

• np Chart - Constant Sample Size (≥ 50)

• p Chart - Variable sample Size (≥ 50)

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X-Bar Char

2.9

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Week No.

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UCL

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Week No.

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UCL

UCL

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Scatter Diagrams

• Studies and identifies possible relationship between changes of two variables

• Provides visual and statistical means to test the strength of a potential relationship

• Is a good follow-up to a Cause & Effect Diagram

The scatter diagram graphs pairs of numerical data, with one variable on

each axis, to look for a relationship between them. If the variables are

correlated, the points will fall along a line or curve. The better the

correlation, the tighter the points will hug the line.

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When to Use a Scatter Diagram

• When you have paired numerical data.

• When your dependent variable may have multiple values for each value of your independent variable.

• When trying to determine whether the two variables are related, such as…– When trying to identify potential root causes of problems.

– After brainstorming causes and effects using a fishbone diagram, to determine objectively whether a particular cause andeffect are related.

– When determining whether two effects that appear to be related both occur with the same cause.

– When testing for autocorrelation before constructing a control chart.

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Scatter Diagram Example

The ZZ-400 manufacturing team suspects a relationship between

product purity (percent purity) and the amount of iron (measured in

parts per million or ppm). Purity and iron are plotted against each other

as a scatter diagram, as shown in the figure below.

There are 24 data points. Median lines are drawn so that 12 points fall

on each side for both percent purity and ppm iron.

To test for a relationship, they calculate:

A = points in upper left + points in lower right = 9 + 9 = 18

B = points in upper right + points in lower left = 3 + 3 = 6

Q = the smaller of A and B = the smaller of 18 and 6 = 6

N = A + B = 18 + 6 = 24

Then they look up the limit for N on the trend test table. For N = 24, the

limit is 6.

Q is equal to the limit. Therefore, the pattern could have occurred from

random chance, and no relationship is demonstrated.

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Scatter Diagrams Interpretation

Positive Correlation

Negative Correlation

No Correlation

Possible Positive Correlation

Possible Negative Correlation

•Here are some examples of situations in which might you use a scatter diagram:

•Variable A is the temperature of a reaction after 15 minutes. Variable B measures the color of the product. You suspect higher temperature makes the product darker. Plot temperature and color on a scatter diagram.

•Variable A is the number of employees trained on new software, and variable B is the number of calls to the computer help line. You suspect that more training reduces the number of calls. Plot number of people trained versus number of calls.

•To test for autocorrelation of a measurement being monitored on a control chart, plot this pair of variables: Variable A is the measurement at a given time. Variable B is the same measurement, but at the previous time. If the scatter diagram shows correlation, do another diagram where variable B is the measurement two times previously. Keep increasing the separation between the two times until the scatter diagram shows no correlation.

•Even if the scatter diagram shows a relationship, do not assume that one variable caused the other. Both may be influenced by a third variable.

•When the data are plotted, the more the diagram resembles a straight line, the stronger the relationship.

•If a line is not clear, statistics (N and Q) determine whether there is reasonable certainty that a relationship exists.

•If the statistics say that no relationship exists, the pattern could have occurred by random chance.

•If the scatter diagram shows no relationship between the variables, consider whether the data might be stratified. If the diagram shows no relationship, consider whether the independent (x-axis) variable has been varied widely. Sometimes a relationship is not apparent because the data don’t cover a wide enough range.

•Think creatively about how to use scatter diagrams to discover a root cause.

•Drawing a scatter diagram is the first step in looking for a relationship between variables.

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ASQ

• A global community of the best quality resources and experts in all fields, organizations, and industries.

• People passionate about quality.

• Bringing together the people, ideas, and tools that make our world work better.

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ASQ Offerings

• ASQ Membership

• Networking

• Certification

• ASQ Learning Institute™

• World Conference and Events

• World Quality Month

• Knowledge Center

• Standards

• Quality Press

• Research

• Social Networking

• Social Responsibility

• Quality for Life™Advocacy

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Central California Section 626

Has served members and professionals in and surrounding Fresno for over 20 years. Our territory extends from Modesto to Bakersfield. The section holds quarterly meetings in its efforts to promote community, networking, and professional development opportunities.

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Mission

To provide resources and an educational forum for ASQ members and interested community members to share and promote quality technology, concepts, and tools, with the purpose of improving ourselves and our organizations for the betterment of society.

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Leadership

Chair: Mr. Rangel Melendez – JAIN Irrigationrangelmelendez@hotmail.com

Secretary: Mr. Jeff Lahodny – Pacific Southwest Containerjlahodny@teampsc.com

Treasurer: Michael Murphy - California Tomato Machinerymsmurphy62@hotmail.com

Auditing Chair: Mr. Jeffrey Berry - Baltimore Aircoil Company jeff.cb2011@gmail.com

Membership Chair: Mr. Sam Sit – Grundfos Pumpsssit@grundfos.com

Nominating Committee Chair: Mr. Jeffrey Berry - Baltimore Aircoil Company jeff.cb2011@gmail.com

Certification & recertification Chair: Mr. Rangel Melendez – JAIN Irrigation rangelmelendez@hotmail.com