Control charts for quality Assurance

Title -Control charts for Quality Assurance  

Description-:

Control charts are graphs used to study how a process changes over time.

Data is plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These three lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).

Variable data uses two control charts. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range, or the width of the distribution.

If your data were shots in target practice, the average shows the shots clustering. The range shows how tight they are clustered.

When To Use:

• When controlling ongoing processes by finding and correcting problems as they occur.
• When predicting the expected range of outcomes from a process.When determining whether a process is stable (in statistical control).
• When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).
• When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

Basic Procedure:

1. Choose the appropriate control chart for your data.

2. Determine the appropriate time period for collecting and plotting data.

3. Collect data, construct your chart and analyze the data.

Different Types Of Control Charts

The the type of chart depends on your measurement data. The two broadest groupings are for variable data and attribute data.

• Variable data are measured on a continuous scale. For example: time, weight, distance or temperature can be measured in fractions or decimals. The possibility of measuring to greater precision defines variable data.
• Attribute data are counted and cannot have fractions or decimals. Attribute data arise when you count the presence or absence of something: success or failure, accept or reject, correct or not correct. For example, a report can have four errors or five errors, but it cannot have four and a half errors.

Types Of Variables Charts:

• X and R chart (also called average and range chart)
• X and S chart 
• Chart of individuals (also called X chart, X-R chart, IX-MR chart, Xm R chart, moving range chart)
• Moving average moving range chart (also called MAMR chart)
• Target charts (also called difference charts, deviation charts and nominal charts)
• CUSUM (also called cumulative sum chart)
• EWMA (also called exponentially weighted moving average chart)
• Multivariate chart (also called Hotelling T2)

Types Of Attributes Charts

• p chart (also called proportion chart)
• np chart
• c chart (also called count chart)
• u chart

How To Choose Control Chart-:

Example

An accounts department started an improvement project to try to reduce the number of internal purchase forms that its users completed incorrectly. As an overall measure of their success, they used a p-type Control Chart to measure the proportion of purchase forms that were not completed correctly. This was chosen, rather than measuring the actual number of defects, because any number of defects on a form required about the same effort to revise.

Each point on the chart represented all purchase forms for one day. This was chosen as it allowed a 25-point chart to be drawn reasonably quickly. This subgroup size was permissible as, even though the number of forms in each group was less than 50, the number of defective forms in each subgroup was more than 4.

A Pareto Chart indicated that the development department made most mistakes, and a survey indicated that they did not understand the form. On the 15th of the month, a half-hour training class was held for the development people. The table and illustration below shows the calculation and Control Chart for the month. It can be seen that after the training, there were nine points in a row below the center line indicating a statistically significant improvement.

In the next month, the proportion defective was further reduced by extending the training to other departments. Before long, there were so few wrongly completed purchase forms that the subgroup period had to be extended to one week.