How do you design and implement control charts to monitor quality performance?

2 Select the type of control chart

The second step is to select the type of control chart that suits your data and your quality characteristic. There are different types of control charts for different types of data, such as attribute data (categorical or discrete) or variable data (continuous or numerical). For example, you can use a p-chart to monitor the proportion of defective items, or an x-bar chart to monitor the average value of a variable.

3 Determine the sampling method

The third step is to determine the sampling method that you will use to collect and analyze your data. You should decide how often, how many, and how to select your samples from your process or product. You should also consider the cost, feasibility, and reliability of your sampling method. For example, you can use a random, stratified, or systematic sampling method, depending on your situation.

4 Calculate the control limits

The fourth step is to calculate the control limits that define the acceptable range of variation for your quality characteristic. The control limits are usually based on the mean and the standard deviation of your data, and they are calculated using statistical formulas or software. The control limits are usually set at three standard deviations above and below the mean, which means that 99.7% of the data points should fall within the control limits, assuming a normal distribution.

5 Plot the data and draw the control chart

The fifth step is to plot the data and draw the control chart using a graphing tool or software. You should plot the data points over time, and draw the mean line and the upper and lower control limits on the same graph. You should also label the axes, the title, and the units of your chart. The control chart should look like a horizontal band with the data points fluctuating within the control limits.

6 Interpret and analyze the control chart

The sixth and final step is to interpret and analyze the control chart and look for any patterns, trends, or signals that indicate a change or a problem in your quality performance. You should compare the data points with the control limits and the mean line, and check for any points that fall outside the control limits, or any runs or cycles that show a consistent deviation from the mean. These are called out-of-control signals, and they suggest that there is a special cause of variation that needs to be investigated and eliminated.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?