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The 7 Basic Tools of Quality

(Ishikawa Tools of Quality)

FREE Professional Development Seminar Series

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Agenda

• About 3FOLD

• What is the seven basic tools of quality?

• Tools

• Q&A

• Certificate Collection


About 3FOLD

• Established in 2008

• Branches in Abu Dhabi, Dubai and Doha

• Approved by KHDA, PMI, AACE, ASQ, IMA and AACE

• The most economical institute for the official ASQ trainings

in UAE


Improve Quality Decrease Costs Improve Productivity Decrease Price Increase Market Stay in Business Provide More Jobs Return on Investment

Why Do This?


• Identify – recognize the symptoms

• Define – Agree on the problem and set boundaries

• Investigate – Collect data

• Analyze – Use quality tools to aid

• Solve – Develop the solution and implement

• Confirm – Follow up to ensure that the solution is effective

PROBLEM SOLVING STEPS


What is it?

• The Seven Basic Tools of Quality is a designation given to a

fixed set of graphical techniques identified as being most

helpful in troubleshooting issues related to quality.

• They are called basic because they are suitable for people

with little formal training in statistics and because they can

be used to solve the vast majority of quality-related issues.


1. Cause and Effect Diagram

• Use a cause-and-effect (fishbone or Ishikawa) diagram to organize brainstorming information about potential causes of a problem.

• Diagramming helps you to see relationships among potential causes.

• You can draw a blank diagram, or a diagram filled in as much as you like, including sub-branches.

• Although there is no "correct" way to construct a fishbone diagram, some types lend themselves well to many different situations.


Example

• Using a Pareto chart, you discovered that your parts were

rejected most often due to surface flaws. This afternoon,

you are meeting with members of various departments

to brainstorm potential causes for these flaws.

Beforehand, you decide to print a cause-and-effect

(fishbone) diagram to help organize your notes during the

meeting. The example below illustrates how to generate

a complete cause-and-effect diagram with sub-branches.


Example


2. Check Sheet

• Description

– A check sheet is a structured, prepared form for collecting and analyzing data. This is a generic tool that can be adapted for a wide variety of purposes.

• When to Use

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

– When collecting data on the frequency or patterns of events, problems, defects, defect location, defect causes, and so forth, or . . .

– When collecting data from a production process


2. Check Sheet (Procedures)

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 the form. Set it up so that data can be recorded simply by making check marks or Xs or similar symbols and so that data does 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.


Example

• The below figure shows a check sheet used to collect data on telephone interruptions. The tick

marks ere added as data as collected o er se eral eeks’ time. What days are worst for

interruptions? Which interruptions are most frequent? This check sheet was designed in the

same format as a contingency table, so that the data can be analyzed with chi-square

hypothesis tests without recopying into a different format.


3. Histogram

• Use to examine the shape and spread of sample data.

Histograms divide sample values into many intervals called

bins . Bars represent the number of observations falling

within each bin (its frequency).


Histogram (Cont…)

• In the histogram below, for example, there are two

observations with values between 2.5 and 7.5, three

observations with values between 7.5 and 12.5, and so on.


Histogram Example (Minitab)

• You work for a shampoo manufacturer and need to ensure that the caps on your bottles are being fastened properly. If fastened too loosely, they may fall off during shipping. If fastened too tightly, they may be hard for you customers to open (especially in the shower).

• You collect a random sample of bottles and test the amount of torque required to remove the caps. Create a histogram with a fitted normal distribution to evaluate how close your samples were to the target value of 18 and whether the data are distributed normally.


Output

Interpreting the results

Mean torque for the sample was

21.26, slightly more than the

target value of 18. Only one cap

was very loose, with a torque of

less than 11. However, the

distribution is positively skewed

and several caps were much

tighter than they should be.

Many caps required a torque of

greater than 24 to remove and

five caps required a torque of

greater than 33, nearly two

times the target value.

Because the sample data are so

skewed, the normal distribution

does not fit very well.


4. Pareto Chart

• A Pareto chart is a bar graph. 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. In this way the chart visually depicts which

situations are more significant.


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.


Minitab Example

• Suppose you work for a company that manufactures motorcycles. You hope to reduce quality costs arising from defective speedometers.

• During inspection, a certain number of speedometers are rejected, and the types of defects recorded. You enter the name of each defect into a worksheet column called Defects, and the corresponding counts into a column called Counts.

• You know that you can save the most money by focusing on the defects responsible for most of the rejections. A Pareto chart will help you identify which defects are causing most of your problems.



5. Scatter 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.


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 and effect 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.


Minitab Example

• You are interested in how well your company's camera batteries are meeting customers' needs. Market research shows that customers become annoyed if they have to wait longer than 5.25 seconds between flashes.

• You collect a sample of batteries that have been in use for varying amounts of time and measure the voltage remaining in each battery immediately after a flash (VoltsAfter), as well as the length of time required for the battery to be able to flash again (flash recovery time, FlashRecov). Create a scatterplot to examine the results. Include a reference line at the critical flash recovery time of 5.25 seconds.


Interpreting the results

As expected, the lower the voltage

in a battery after a flash, the

longer the flash recovery time

tends to be.

The reference line helps to

illustrate that there were many

flash recovery times greater than

5.25 seconds


6. Flowchart

• Flowcharts are used in designing and documenting simple

processes or programs. Like other types of diagrams, they

help visualize what is going on and thereby help

understand a process, and perhaps also find flaws,

bottlenecks, and other less-obvious features within it.


Flowchart

Send memo

Load files

Audit files

Distribute

Credit

Helps understand the steps and sequence of steps to any process


7. Control Charts

• 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).


Choosing a Control Chart


Control Charts – Variable Data

• Variables control charts for subgroups plot statistics from

continuous measurement data , such as length or pressure,

for subgroup data.

• Variables control charts for individuals, time-weighted

charts , and multivariate charts also plot measurement

data.

• Attributes control charts plot count data, such as the

number of defects or defective units .


X-bar and S Chart - Example

• You work at an automobile engine assembly plant. One of the parts, a camshaft, must be 600 mm +2 mm long to meet engineering specifications.

• There has been a chronic problem with camshaft length being out of specification, which causes poor-fitting assemblies, resulting in high scrap and rework rates.

• Your supervisor wants to run X and R charts to monitor this characteristic, so for a month, you collect a total of 100 observations (20 samples of 5 camshafts each) from all the camshafts used at the plant, and 100 observations from each of your suppliers. First you will look at camshafts produced by Supplier 2.


191715131197531

602

600

598

Sample

Sa

mp

le M

ea

n

__

X=600.23

UCL=602.376

LCL=598.084

191715131197531

8

6

4

2

0

Sample

Sa

mp

le R

an

ge

_

R=3.72

UCL=7.866

LCL=0

11

Xbar-R Chart of Supp2 Interpreting the results

The center line on the X

chart is at 600.23, implying

that your process is falling

within the specification

limits, but two of the points

fall outside the control

limits, implying an unstable

process. The center line on

the R chart, 3.72, is also

quite large considering the

maximum allowable

variation is +2 mm. There

may be excess variability in

your process.


Control Charts – Attribute Data

• Attributes control charts are similar in structure to

variables control charts, except that they plot statistics

from count data rather than measurement data.

• For instance, products may be compared against a standard

and classified as either being defective or not. Products

may also be classified by their number of defects .


Minitab Example

• You work in a toy manufacturing company and your job is

to inspect the number of defective bicycle tires. You

inspect 200 samples in each lot and then decide to create

an NP chart to monitor the number of defectives.


28252219161310741

30

25

20

15

10

5

0

Sample

Sam

ple

Co

un

t

__

NP=10.6

UCL=20.10

LCL=1.10

1

1

NP Chart of RejectsInterpreting the results

Inspection lots 9 and 20 fall above

the upper control limit, indicating

that special causes may have

affected the number of defectives

for these lots. You should

investigate what special causes

may have influenced the out-of-

control number of bicycle tire

defectives for inspection lots 9

and 20.


Q & A

ANY QUESTIONS ?


END OF SEMINAR

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RECEPTION.

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