What are Seven QC Tools & How to Remember them?

Understanding and using hard-core statistics for continuous improvement is an issue with the shop-floor people. In order to overcome this issue it was felt necessary to present statistics in graphical forms so that everyone can understand it.

The 7QC tools made the quality control more simpler so that it could be comprehended easily by all. Now statistics is not a prerogative of some experts in the company. It could easily be percolated down the ranks, irrespective whether someone has a statistical background or not.

7QC tools is a collection of statistical tools which need not to be applied in a particular sequence. However, to understand and remember it we need to connect them with each other.

  1. Flow chart
  2. Cause & Effect diagram
  3. Control charts
  4. Check list
  5. Histogram
  6. Pareto Chart
  7. Scatter Plot

One can easily remember the list by using following relationship between the above tools (you can develop some other relationship).


If you want to remember 7QC tools then remember these sequence of events used in continuous improvement.

For starting any continuous improvement program, the first step is about defining the problem (quality characteristic ‘Y’ to be addressed). Once we define the problem, we need to understand the process in-depth using Process Flow Diagram to find the problem areas and non-value adding steps.

From the process flow diagram, find the probable sources of variations (X)  affecting the desired output (Y) using Cause & Effect Diagram.

Once we have identified the probable cause (X), then start monitoring ‘X’ and ‘Y’ using proper Control Charts. This will drop some of the ‘X’s’ came from the cause and effect diagram. Make note of ‘X’ that really affects the ‘Y’.

Once you have real ‘X’ that can affect ‘Y’ then prepare a plan for data collection using Check List to support the cause and effect relationship.

Data thus collected using check list is then arranged in graphical form using Histogram to have a quantitative pictorial view of the effect of ‘X’.

The bars of the histogram constructed above is then re-arranged in descending order to give Pareto Chart. This arranges the causes (X) in descending order of their effect on ‘Y’. Take the list of ‘X’ (usually top 3) having prominent effect on ‘Y’ for continuous improvement.

Finally show a quantitative relationship between top three ‘X’ and ‘Y’ using Scatter Plot in laboratory or by collecting more data from the plant and propose the improvement strategy by providing best conditions for ‘X’ so that ‘Y’ remains within the desired limits.

Related Blogs

7QC Tools: Flow Chart, Know Your Process Thoroughly

7QC Tools: Fish Bone or Ishikawa Diagram

7QC Tools: How to Extract More Information from the Scatter Plot?

7QC Tools: How to Draw a Scatter Plot?

7QC Tools: Scatter Plot — Caution! Misuse of Statistics!

7QC Tools: Scatter Plot

7QC Tools — How to Prioritize Your Work Using Pareto Chart?

7QC Tools — How to Interpret a Histogram?

7QC Tools — How to Draw a Histogram?

7QC Tools — Histogram of Continuous Data

7QC Tools — Histogram of Discrete Data

7QC tools — Check List

Excellent Templates for 7QC tools from ASQ

Kindly do provide feedback for continuous improvement

Why it is so Important to Know the Monster “Variance”? — part-2


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Variance occupies the central role in the six-sigma methodology. Any process whether from manufacturing or service industry has many inputs and the variance from each input gets add up in the final product.

Hence variance has an additive property as shown below


 Note: you can add two variances but not the standard deviations

Consequence of the variance addition and six sigma

Say if a product/services which is the output of some process, which in turn have many inputs. Then the variance from the input (Picture41) and from the process (Picture42) adds up to give the final variance (Picture43) in the product/services.

DMAIC methodology of 6Sigma try to identify the inputs that contributes maximum towards the variance in the final product and once identified, its effect is studied in detail to minimize the variance from the input. This is done by reducing the variance in the input itself.

Example: if the quality of a input material used to manufacture a product is found to be critical, then steps would be taken to reduce the fluctuation of the quality of that input material from batch to batch either by requesting/threatening the vendor or by performing the rework of the input material at your end.

Related articles:

Understanding the Monster “Variance” part-1

You just can’t knock down this Monster “Variance” —- Part-3

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ANOVA by Prof. Hunter

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We are excited about the quality of videos available on youtube, on almost every topic. Look at this video on ANOVA by none other than Prof. Hunter himself.  These video was shot in 1966 in black & white but experience the contents.



Six Sigma Video lectures from IIT Karagpur by Prof. Bagachi

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This is a collection of 40 excellent video lectures from Prof. Bagachi which would be helpful in understanding the concepts.

6sigma-Video Lectures by Prof. Bagachi

The Red Bead Experiment — by Deming

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Dr. W. Edward taught the principles of Quality Improvement using this famous Red Bead Experiment developed by him. He used to perform this experiments live on workers. This experiments demonstrate that the main hurdle to a quality improvement is the flawed system not the willing workers.

See this video from youtube