1. Quality Circle
-A.M. Joshi

2. What is a Quality Circle?
A small group of employees (6-8) in the same work area voluntary meet regularly for an hour every week to
identify,
analyze
resolve
work related problems leading to improvement in their total performance and enrichment of their work life.
Employee participation in decision-making and problem-solving improves the quality of work

5. Brainstorming
Brainstorming is the name given to a situation when a group of people meet to generate new ideas around a specific area of interest.
This helps to :
reduce social inhibitions among group members.
stimulate idea generation.
increase overall creativity of the group.
go for quantity: This rule is a means of enhancing divergent production, aiming to facilitate problem solving through the maxim quantity breeds quality. The assumption is that the greater the number of ideas generated, the bigger the chance of producing a radical and effective solution.
Withhold criticism: In brainstorming, criticism of ideas generated should be put 'on hold'. Instead, participants should focus on extending or adding to ideas, reserving criticism for a later 'critical stage' of the process. By suspending judgment, participants will feel free to generate unusual ideas.
Welcome wild ideas: To get a good long list of suggestions, wild ideas are encouraged. They can be generated by looking from new perspectives and suspending assumptions. These new ways of thinking might give you better solutions.
Combine and improve ideas: As suggested by the slogan "1+1=3". It is believed to stimulate the building of ideas by a process of association.

6. Pareto Analysis
Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job.
Or in terms of quality improvement, large majority of problesm (80%) are produced by few key cause (20%).

8. Ishikawa diagrams
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.[1][2] 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. 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

12. Scatter Diagram
A scatter diagram is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically 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.[4]

15. 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, thecheck sheet is sometimes called a tally sheet.

17. Stratification
Stratification is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be impossible to see. This technique separates the data so that patterns can be seen.

18. Stratification Example
The ZZ–400 manufacturing team drew a scatter diagram to test whether product purity and iron contamination were related, but the plot did not demonstrate a relationship. Then a team member realized that the data came from three different reactors. The team member redrew the diagram, using a different symbol for each reactor’s data: