Control Charts is a statistical tool to determine if a process is in control.
Types of Control Charts
- Variable Control Charts
- Attribute Control Charts
Variable Control Charts
Deal with items that can be measured. For examples:
1) Weight
2) Height
3) Speed
4) Volume
Types of Variable Control Charts
- X-Bar chart: deals with a average value in a process.
- R chart: takes into count the range of the values.
- MA chart: take into count the moving average of a process.
Attribute Control Charts
Control charts that factor in the quality attributes of a process to determine if the process is performing in or out of control.
Types of Attribute Control Charts
- P Chart: a chart of the percent defective in each sample set.
- C chart: a chart of the number of defects per unit in each sample set.
- U chart: a chart of the average number of defects in each sample set.
Reasons for Using Control Charts in Pharma Industry
- Improve productivity
- Make defects visible
- Determine what process adjustments need to be made
- Determine if process is “in” or “out of control
Control Chart Key Terms
- Out of Control: the process may not performing correctly
- In Control: the process may be performing correctly
- UCL: upper control limit
- LCL: lower control limit
- Average value
Process is OUT of control if:
- One or multiple points outside the control limits
- Eight points in a row above the average value
- Multiple points in a row near the control limits
Process is IN control if:
- The sample points fall between the control limits
- There are no major trends forming, i.e.. The points vary, both above and below the average value.
Calculating Major Lines in a Control Chart
- Average Value: take the average of the sample data
- UCL: Multiply the Standard deviation by three. Then add that value to the Average Value.
- LCL: Multiply the Standard deviation by three. Then subtract that value from the Average Value.
Procedure
- First Step: Determine what type of data you are working with.
- Second Step: Determine what type of control chart to use with your data set.
- Third Step: Calculate the average and the control limits.
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