Control charts are one of the most useful tools in Lean Six Sigma for monitoring and improving process performance. They help you identify and eliminate sources of variation that affect quality, efficiency, and customer satisfaction. In this article, you will learn how to create and interpret control charts effectively using some simple steps and examples.
Control charts are graphical displays that show how a process variable behaves over time. They consist of a central line that represents the average or target value, and upper and lower control limits that define the range of normal variation. The control limits are usually calculated based on the standard deviation of the process data. Control charts can be used for different types of data, such as continuous, discrete, or attribute data.
Control charts show process behaviour and give true sense of what's right or wrong with it.With the trends they show ,one can identify reasons behind variation. There are multiple types of them ,used as per data type. These are one of the best and widely used tools to keep process in check and under control.
Control charts are important because they help you understand the behavior and stability of your process. By plotting the process data on a control chart, you can see if the process is in control or out of control. A process is in control if all the data points fall within the control limits and show no patterns or trends. A process is out of control if any of the data points fall outside the control limits or show signs of non-random variation, such as runs, cycles, or shifts. Out-of-control signals indicate that there are special causes of variation that need to be investigated and eliminated.
Control charts suggest the reactive action plan .Even one can rightly find out whether the problem is with man, machine, material,specs, measurement I.e. the cause of variation.
Creating a control chart requires several steps. Firstly, you need to collect and organize your process data, ensuring there is enough data to represent the variation in your process, and deciding how to group it into subgroups or samples. Secondly, you must select the appropriate type of control chart for your data - for instance, an X-bar and R chart for continuous data with subgroups, or a p-chart for attribute data with proportions. Then, you need to calculate the central line and the control limits, usually based on the standard deviation or range of your data. After that, plot your data on the control chart, labeling the axes, central line, and control limits, as well as marking the data points and connecting them with a line. Finally, analyze your control chart by looking for any out-of-control signals or patterns that indicate special causes of variation and comparing your process performance with customer requirements.
Select the Right Control Chart: Choose the appropriate type of control chart based on the type of data you're analyzing. Common control charts include: - X-bar and R (or X-bar and S) charts: Used for monitoring the mean and variability of continuous data. -Individuals (I) chart: Used for monitoring individual data points, typically when sample sizes are one. -P-chart (Proportion chart): Used for monitoring the proportion of defective items in a process. -NP-chart (Number of Defects chart): Used for monitoring the number of defects in a process when sample sizes are constant. -C-chart (Count of Defects chart): Used for monitoring the count of defects in a process when sample sizes can vary.
As per data a suitable control chart is used. For continuous data there are different control charts like,X bar & R,IMR,Xbar S, etc and for discrete data those are different P,nP,C,U charts.
Interpreting a control chart requires using rules and guidelines to identify non-random variation. Different sets of rules, such as the Western Electric, Nelson, or Shewhart rules, can be used. If any of these rules are violated, it's important to investigate potential causes and take corrective actions. For example, a data point falling outside the control limits is the most obvious sign of an out-of-control process. Additionally, two out of three consecutive data points beyond two standard deviations from the central line indicates a shift in the process mean. Four out of five consecutive data points beyond one standard deviation from the central line indicates a trend in the process, while eight or more consecutive data points on the same side of the central line indicates a run in the process. Lastly, six or more consecutive data points steadily increasing or decreasing indicates a cycle in the process. After taking corrective action, it is important to update your control chart with new data and monitor the process performance.
Control charts are not only useful for detecting problems, but also for improving your process. By creating and interpreting control charts effectively, you can establish a baseline for your process, identify and eliminate special causes of variation, reduce common causes of variation, and monitor and sustain your process improvement. This will ultimately enhance your quality, efficiency, and customer satisfaction. Control charts are powerful tools that can help you achieve and maintain process excellence.
With control plan not only process variation can be studied but also the righteousness of spec limits. So it's a very powerful tool to make process with stronger control.