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How to use a control chart to increase productivity

The control chart or Shewhart control chart, as it is sometimes called, is named after W. A. Shewhart who is generally credited as being the first to introduce the methods of using a chart to express or illustrate the extent to which our products meet specifications.Dr. Shewhart created what is now commonly used for statistical control in the everyday production of goods. Shewhart's carefully designed experiments lead modern production to its ability to ensure quality even with massive amounts of goods being produced.

When a process is in complete control and deviations are caught before too much damage is done, future production predictions can more accurately be made and the entire output process can be managed at a capacity that is economically sound. The use of a control chart can in effect assess the nature or behavior of variation in any given process and use that knowledge of variation to forecast future deviations and manage them.This way, rejecting products due to faults are reduced, time is not spent restarting processes and ultimately productivity is increased.

Product quality and productivity can be effect largely by deviations and variations from target specifications.So what the quality control team can do to eliminate these problems is extract samples of a certain size from the ongoing production process, produce control charts of the variability in those samples, and consider their closeness to target specifications.If the specifications are not met within the pre-specified limits that the process is immediately re-evaluated and corrected.Using control charts provide a way to detect small problems and fix them before they get worse.

A control chart is a run chart of a sequence of quantitative data with five horizontal lines drawn on the chart to represent the samples.The lines on the control chart are defined as follows:
1. A center line is drawn at the process mean, the center of the acceptable range.
2. An upper warning limit is drawn two standard deviations above the center line.
3. An upper control-limit (also called an upper natural process-limit) is drawn three standard deviations above the center line.
4. A lower warning limit is drawn two standard deviations below the center line;
5. A lower control-limit (also called a lower natural process-limit) is drawn three standard deviations below the centre line.

Depending on the variable attributes to be measured the control chart may be referred to by another name.Below are some examples of different variables for attribute measuring control charts and their definitions:
- X-bar chart = The sample means are plotted in order to control the mean value of a variable.
- R chart = The sample ranges are plotted in order to control the variability of a variable.
- S chart = The sample standard deviations are plotted in order to control the variability of a variable.
- S**2 chart = The sample variances are plotted in order to control the variability of a variable.
Dr. Shewhart established the essential principles with his control charts that are used in today's process of quality control.Any floor manager knows that having a system in place, such as the use of control charts, to keep production in check from deviating too far from the acceptable range of production not only saves the company money that would have been lost on faulty products but also increases the productivity of the company by not having to take time to fix otherwise preventable errors.

Simply put, control charts are used to increase productivity by eliminating mistakes.Increased productivity brings with it other favorable attributes such as guaranteed quality, fewer production shut downs and happier customers.

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