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The Seven Basic Tools of Quality (7 QC Tools) originated in Japan when the country was undergoing major revolution in quality standards. It had become a mandatory topic as part of Japanese’s industrial training program. These tools have simple graphical and statistical techniques which are helpful in solving critical quality related issues.


They are also known as Seven Basics Tools of Quality because these tools could be implemented by any person with very basic training in statistics. They are simple to apply and solve quality-related complex issues.

The seven QC tools are:


Stratification (Divide and Conquer)

It is a method of dividing data into sub–categories. After that, one has to classify it based on various parameters into groups and sub-groups. It helps in deriving meaningful information and analyse the existing problem.

It helps in dividing the data and deriving meaning out of it to solve a problem.



Karl Pearson introduced Histogram. It is a bar graph representing the frequency distribution on each bars.

It helps us to study the density of data in any given distribution and understand the factors or data that repeat more often.

Histogram helps in prioritizing factors and identify which are the areas that needs utmost attention immediately.

Check list

Check Sheet (Tally Sheet)

A check sheet can be metrics, structured table or form for collecting data. After this, it helps us analyse the data.  When the information is quantitative in nature, the check sheet can also be called as tally sheet.

It lists down all the important data points and events. Then it presents them in a tabular format. Thus, it keeps on updating or marking the status on their occurrence which helps in understanding the progress, defect patterns and even causes for defects.
They are non-statistical and easy to understand. They help us capture data in a standardised manner. This helps us to make decisions based on facts and not assumptions.  Data is graphically represented .  Thus, one can analyse the areas for improvement , either directly from the check sheet, or by feeding the data into one of the other seven basic tools.

Fish Tail

Cause-and-effect diagram (“fishbone” or Ishikawa diagram)

Cause–and–effect diagram introduced by Kaoru Ishikawa helps in identifying the various causes (or factors) leading to an effect (or problem) and also helps in deriving meaningful relationship between them. First used by Ishikawa in the 1940s, they are employed to identify the underlying symptoms of a problem or “effect” as a means of finding the root cause.

The structured nature of the method forces the user to consider all the likely causes of a problem, not just the obvious ones, by combining brainstorming techniques with graphical analysis. It is also useful in unraveling the convoluted relationships that may, in combination, drive the problem..

The very purpose of this diagram is to identify all root causes behind a problem.

Once a quality related problem is defined, the factors leading to the causal of the problem are identified. We further keep identifying the sub factors leading to the causal of identified factors till we are able to identify the root cause of the problem. As a result we get a diagram with branches and sub branches of causal factors resembling to a fish bone diagram.

In manufacturing industry, to identify the source of variation the causes are usually grouped into below major categories:


Pareto Chart

Pareto chart (80/20 Rule)

This is named after Vilfredo Pareto. It revolves around the concept of 80-20 rule which underlines that in any process, 80% of problem or failure is just caused by 20% of few major factors which are often referred as Vital Few, whereas remaining 20% of problem or failure is caused by 80% of many minor factors which are also referred as Trivial Many.

The very purpose of Pareto Chart is to highlight the most important factors that is the reason for major cause of problem or failure.

Pareto chart is having bars graphs and line graphs where individual factors are represented by a bar graph in descending order of their impact and the cumulative total is shown by a line graph.

Pareto charts help experts in following ways:

Distinguish between vital few and trivial many.
Displays relative importance of causes of a problem.
Helps to focus on causes that will have the greatest impact when solved.


Scatter diagram (Shewhart Chart)

Scatter diagram or scatter plot is basically a statistical tool that depicts dependent variables on Y – Axis and Independent Variable on X – axis plotted as dots on their common intersection points. Joining these dots can highlight any existing relationship among these variables or an equation in format Y = F(X) + C, where is C is an arbitrary constant.

Very purpose of scatter Diagram is to establish a relationship between problem (overall effect) and causes that are affecting.

The relationship can be linear, curvilinear, exponential, logarithmic, quadratic, polynomial etc. Stronger the correlation, stronger the relationship will hold true. The variables can be positively or negatively related defined by the slope of equation derived from the scatter diagram.


Control Chart

Control chart

Control chart is also called as Shewhart Chart named after Walter A. Shewhart is basically a statistical chart which helps in determining if an industrial process is within control and capable to meet the customer defined specification limits.

The very purpose of control chart is to determine if the process is stable and capable within current conditions.

In Control Chart, data are plotted against time in X-axis. Control chart will always have a central line (average or mean), 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, experts can draw conclusions about whether the process variation is consistent (in control, affected by common causes of variation). We can understand if it is unpredictable (out of control, affected by special causes of variation) or not. It helps in differentiating common causes from special cause of variation.

Control charts are very popular and one can use it in Quality Control Techniques, Six Sigma (Control Phase). It also plays an important role in defining process capability and variations in productions. This tool also helps in identifying how well any manufacturing process is in line with respect to customer’s expectation.

Control chart helps in predicting process performance, understand the various production patterns. Also, we can study how a process changes or shifts from normally specified control limits over a period of time.

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