| John Burson ![]() edited Friday, August 11, 2017 |
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Quality Tools for Process Improvement:
Process Maps
They visually represent the relationship between activities and tasks that make up the process, and they are used at the beginning of the process improvement event. You begin by describing the process event, frequencies, and timing at the highest level downwards. At the high levels, this quality tool will help you to analyze and also improve the process.
Fishbone
It visually represents causes or effects of a problem and assists you to determine the cause of the problem. It is referred to as a fishbone diagram due to its appearance. The cause-and-effect diagram is often used in the cause analysis, to determine and organize the cause of a problem and prioritize them.
Check Sheets
These are matrices that are intended to assist in recording, tallying, and analyzing event occurrences or test results. This is one of the quality tools that are used in production to collect process data and count defects to help you identify any opportunities for improvement.
Histogram Charts
They consist of side-by-side and vertical bars that help you understand the relationships between data over time and in representing frequency distributions in tables of numbers. Most often this quality tool is used during the process improvement analysis.
Pareto Charts
This quality tool views that 20% of some of the factors often account for 80% potential complications. It ranks causes, defects and other data from the most important to the least in descending order. From the seven tools, only this tool can distinguish between the vital few from the many trivial ones.
Scatter Charts
This quality tool displays the relationship between dependent and independent variables that are used during the testing of hypothesis to determine the correlation between two variables and the strength of their correlation. When there is less scattering, it’s an indication that the correlation is stronger.
Control Charts
This statistical process tool helps in the plotting of performance over time against lower and upper control limits. Therefore, you can easily identify process variations and be able to determine common causes of variations.
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