The document discusses the Seven Basic Tools of Quality, which are graphical techniques used to troubleshoot quality issues. The seven tools are: cause and effect diagram, check sheet, control chart, histogram, Pareto chart, scattered diagram, and stratification. Each tool is briefly described. For example, a cause and effect diagram displays potential causes for a quality issue, a check sheet collects quantitative or qualitative data, and a control chart determines if a process is in statistical control. The tools can help identify factors affecting quality and determine appropriate corrective actions.

1. Quality management
tools

2. Seven Basic Tools of Quality


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 .

3. The seven tools are

Cause and effect diagram (also known as the "fishbone" or Ishikawa diagram)

Check sheet

Control chart

Histogram

Pareto chart

Scattered diagram

Stratification (alternately, flow chart or run chart)

4. The Seven Basic Tools stand in contrast to more
advanced statistical methods such as survey
sampling, acceptance sampling, statistical hypothesis
testing, design of experiments, multivariate
analysis, and various methods developed in the field of
operations research.

5. Ishikawa diagrams
(also called fishbone diagrams, herringbone diagrams, cause-and-effect
diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa
(1968) that show the causes of a specific event. Common uses of the Ishikawa
diagram are product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or reason for
imperfection is a source of variation . Causes are usually grouped into major
categories to identify these sources of variation.

6. The categories typically include:

People: Anyone involved with the process

Methods: How the process is performed and the specific requirements for doing it, such as
policies, procedures, rules, regulations and laws

Machines: Any equipment, computers, tools, etc. required to accomplish the job

Materials: Raw materials, parts, pens, paper, etc. used to produce the final product

Measurements: Data generated from the process that are used to evaluate its quality

Environment: The conditions, such as location, time, temperature, and culture in which the
process operates.

7. Cause and effect diagram for defect
XXX

8. Check sheet
The check sheet is a form (document) used to collect data in
real time at the location where the data is generated. The data
it captures can be quantitative or qualitative. When the
information is quantitative, the check sheet is sometimes called
a tally sheet.
The check sheet is one of the so-called Seven Basic Tools of
Quality Control.

9. Format
The defining characteristic of a check sheet is that
data are recorded by making marks ("checks") on it.
A typical check sheet is divided into regions, and
marks made in different regions have different
significance. Data are read by observing the location
and number of marks on the sheet.

10. Check sheets typically employ a
heading that answers the Five Ws:
Who filled out the check sheet
What was collected (what each check represents, an identifying batch or lot
number)
Where the collection took place (facility, room, apparatus)
When the collection took place (hour, shift, day of the week)
Why the data were collected

11. Function
To check the shape of the probability distribution of a process
To quantify defects by type
To quantify defects by location
To quantify defects by cause (machine, worker)
To keep track of the completion of steps in a multistep procedure (in other
words, as a checklist)

13. Frequency distribution for
film coater

14. Quality Control Checksheet
Example

15. Control chart
Control charts, also known as Shewhart charts (after Walter
A. Shewhart) or process-behavior charts, in statistical process
control are tools used to determine if a manufacturing or
business process is in a state of statistical control

16. Xbar chart for a paired xbar and R chart

17. Chart details
A control chart consists of:
Points representing a statistic (e.g., a mean, range, proportion) of measurements of a quality
characteristic in samples taken from the process at different times [the data]
The mean of this statistic using all the samples is calculated (e.g., the mean of the means, mean
of the ranges, mean of the proportions)
A centre line is drawn at the value of the mean of the statistic
The standard error (e.g., standard deviation/sqrt(n) for the mean) of the statistic is also
calculated using all the samples
Upper and lower control limits (sometimes called "natural process limits") that indicate the
threshold at which the process output is considered statistically 'unlikely' and are drawn typically
at 3 standard errors from the centre line

18. The chart may have other optional
features, including:
Upper and lower
warning or control
limits, drawn as
separate
lines, typically two
standard errors
above and below
the centre line
Division into
zones, with the
addition of rules
governing
frequencies of
observations in
each zone
Annotation with
events of
interest, as
determined by the
Quality Engineer in
charge of the
process's quality

20. Types of charts
Chart
Process observation
Process observations
relationships
Process
observations type
Size of
shift to
detect
Xbar and R chart
Quality characteristic measurement within one
subgroup
Independent
Variables
Large (≥ 1.5σ)
Xbar and S chart
Quality characteristic measurement within one
subgroup
Independent
Variables
Large (≥ 1.5σ)
Shewhart
individual control
chart (ImR chart or
XmR chart)
Quality characteristic measurement for one
observation
Independent
Variables†
Large (≥ 1.5σ)
Three-way chart
Quality characteristic measurement within one
subgroup
Independent
Variables
Large (≥ 1.5σ)
p-chart
Fraction nonconforming within one subgroup
Independent
Attributes†
Large (≥ 1.5σ)
np-chart
Number nonconforming within one subgroup
Independent
Attributes†
Large (≥ 1.5σ)
c-chart
Number of nonconformances within one subgroup
Independent
Attributes†
Large (≥ 1.5σ)
u-chart
Nonconformances per unit within one subgroup
Independent
Attributes†
Large (≥ 1.5σ)

21. Chart
EWMA chart
Process observation
Exponentially weighted moving average
of quality characteristic measurement
within one subgroup
Process observations
relationships
Process
observations type
Size of
shift to
detect
Attributes or variables
Small (<
1.5σ)
Independent
CUSUM chart
Cumulative sum of quality characteristic
measurement within one subgroup
Independent
Attributes or variables
Small (<
1.5σ)
Time series
mode
Quality characteristic measurement
within one subgroup
Autocorrelated
Attributes or variables
N/A
Regression
control chart
Quality characteristic measurement
within one subgroup
Dependent of process
control variables
Variables
Large (≥
1.5σ)

22. Histogram
In statistics, a histogram
is a graphical
representation of the
distribution of data. It is
an estimate of the
probability distribution
of a continuous variable
and was first introduced
by Karl Pearson

23. Uses
Histograms are used
to plot the density of
data, and often for
density estimation:
estimating the
probability density
function of the
underlying variable.

24. Histogram of arrivals per minute

25. Pareto chart
A Pareto chart is a type of chart that contains both
bars and a line graph, where individual values are
represented in descending order by bars, and the
cumulative total is represented by the line.

28. Scattered Diagram
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to display
values for two variables for a set of data.
The data is displayed as a collection of points, each having the
value of one variable determining the position on the horizontal
axis and the value of the other variable determining the position
on the vertical axis . This kind of plot is also called a scatter
chart, scattergram, scatter diagram, or scatter graph.

30. Stratified sampling
In statistics, stratified sampling is a
method of sampling from a population

31. Advantages
If the population is large and enough resources are
available, usually one will use multi-stage
sampling. In such situations, usually stratified
sampling will be done at some stages. However the
main advantage remains stratified sampling being
the most representative of a population.

32. Disadvantages
Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint
subgroups.
It would be a misapplication of the technique to make subgroups' sample sizes proportional to the
amount of data available from the subgroups, rather than scaling sample sizes to subgroup sizes (or to
their variances, if known to vary significantly.
by means of an F Test). Data representing each subgroup are taken to be of equal importance if
suspected variation among them warrants stratified sampling. If subgroups' variances differ significantly
and the data need to be stratified by variance, then there is no way to make the subgroup sample sizes
proportional (at the same time) to the subgroups' sizes within the total population.
For an efficient way to partition sampling resources among groups that vary in their means, their
variances, and their costs, see "optimum allocation"