P Chart is a statistical process control chart that is used to monitor the proportion of nonconforming items or defects in a sample. It is commonly used in quality control to monitor the quality of a process over time, and to identify any trends or patterns that may indicate a problem.
The P Chart plots the proportion of nonconforming items in each sample against time or sample number, and uses statistical limits to indicate when the process is out of control. This allows process engineers to identify any issues early, and to take corrective action to prevent further defects from occurring.
When to use P Chart?
P charts are commonly used in manufacturing and quality control to ensure consistent production and detect patterns of variation that may impact quality. They can also be used in healthcare and other industries where process consistency is critical to ensure high levels of performance and safety.
Overall, the P chart is a valuable tool for identifying process variability over time, making it an essential component of quality assurance and continuous improvement initiatives.
Guidelines for correct usage of P Chart
Classify items into one of two categories: pass or fail
Use appropriate charts based on whether you can determine only whether an item is defective or non defective or count the number of defects on each item
Be aware of over dispersion or under dispersion in your data, as it can affect the accuracy of traditional attributes charts
Ensure the data is in time order, with the oldest data at the top of the worksheet
Collect data at equally spaced time intervals
Collect data in subgroups that are similar and subject to the same process conditions
Ensure the subgroups are large enough by using the formula to determine the required subgroup size based on the average proportion of defective items
Include enough subgroups to obtain precise control limits
Re-estimate control limits if you don't have enough subgroups to ensure precision
Alternatives: When not to use P chart
When counting the number of defects on each item, you can use the U Chart or C Chart to plot the number of defects per unit.
When your data exhibit over dispersion or under dispersion, relying on the traditional P chart might not be accurate. Over dispersion can lead to an increased number of points outside the control limits, while under dispersion can result in too few points outside the control limits.
Example of P Chart?
The call center supervisor aims to assess the effectiveness of the customer call answering process. For 21 days, the supervisor tracks the total number of incoming calls and the count of unanswered calls. A P chart is established by the supervisor to monitor the proportion of unanswered calls. The following data was collected:
After gathering the data, she uses mathematical formula for finding the p bar, q bar, n bar, Upper Control Limit (UCL) and Lower Control Limit(LCL).
Next, update the data manually or can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.
Next, you need to select the desired Check Rules.
Finally, click on calculate at the bottom of the page and you will get desired results.
On the dashboard of P Chart, the window is separated into two parts.
On the left part, Data Pane is present. Data can be fed manually or the one can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.
On the right part, there are many options present as follows:
Process proportion: If process proportion is provided, this value is considered to be the centerline. If not, Zometric Q-Tools calculates the centerline from the data provided.
Check Rule 1: 1 point > K Stdev from center line: If a data point is K standard deviations from the center line, it means that it is K times the standard deviation away from the mean. This is important in statistical process control because it indicates whether a data point is within acceptable limits or whether there may be a problem with the process that needs to be addressed. Typically, data points that are more than three standard deviations from the center line are considered outliers and may require further investigation.
Check Rule 2: K points in a row on same side of center line: If there are K points in a row on the same side of the center line in a dataset, it suggests that there may be a bias or trend in the data that is causing the values to cluster together. This could be due to a variety of factors, such as measurement error, sampling bias, or a true underlying pattern in the data.
Check Rule 3: K points in a row, all increasing or all decreasing: If there are K points in a row, it is certain that at least one of two things must be true:
The points are all increasing (i.e. each point has a greater y-coordinate than the one before it)
The points are all decreasing (i.e. each point has a smaller y-coordinate than the one before it)
Check Rule 4: K points in a row, alternating up and down:
If the trend is upwards, it indicates that the process is becoming less consistent and more variable over time. This can be caused by factors such as equipment deterioration, operator error or changes in raw material quality.
If the trend is downwards, it indicates that the process is becoming more consistent and less variable over time. This could be due to process improvements or tighter control measures being implemented.