Although there are many Statistical Process Control (SPC) software tools available, many engineers still often create control charts in Excel. The Control Chart Template is design as an educational tool to help you see what equations are involve in setting control limits for a basic Shewhart control chart.
Control chart template
This template contains a pre-made control chart for sample Mean and Range, or sample Mean and Standard Deviation (2 worksheets in one). Just add your own data. However, Control limits are on the data you enter.
How to create a control chart in excel?
Certainly, The Control Chart Template works for the most common types of control charts: the X-Bar chart (plotting the mean of a sample over time), the R chart (plotting the range or Max-Min of a sample over time), and the s chart (plotting the sample standard deviation over time).
Control charts are used to routinely monitor quality. Depending on the number of process characteristics , there are two basic types of control charts. The first, referred to as a univariate control chart, is a graphical display (chart) of one quality characteristic.
Benefits of Process Control Charts
Most importantly organizations that practice continuous quality improvement use control charts to:
- Provide a simple, common language for talking about process performance and behavior
- Make informed decisions about which processes to leave alone and which to subject to an improvement cycle
- Limit the need for inspection
- Determine process capability based on past performance and trends
- Predict future performance if the system is stable and in control
- Assess the impact of process changes
- Visualize the performance of the process over time
- Create a baseline for future improvements
- Communicate the performance of a process
Types of Control Charts
After the basic chart is done, one can use various menus and options to make necessary changes that may be in a format, type or statistics of the chart.
To create a chart, it is not necessary to know the name or structure of any chart. As a result you need to select the columns or variables that are to be chart and drag them in respective zones. When the data column is place to the workplace, the user starts working on it to create an accurate chart that is based on the data type and given sample size.
Control Charts for Variables
The control charts of variables can be classified based on the statistics of subgroup summary plotted on the chart.
X¯ chart describes the subset of averages or means, R chart displays the subgroup ranges, and S chart shows the subgroup standard deviations. Regarding the quality that is measure on a continuous scale, a particular analysis makes both the process mean and its variability apparent along with a mean chart that is aligned over its corresponding S- or R- chart.
Levey Jennings Charts
This chart displays a mean process base on a long-term sigma with control limits. The control limits are such that the distance between them and the centerline is ‘3s’. The standard deviation value ‘s’ for these charts is determine by the same method as the standard deviation for the distribution platform.
Control Charts for Attributes
Similarly, This type of data is usually continuous and base on the theoretical concept of continuous data. Count data is a different kind of data available which is also known as level counts of character data. The interesting variable is a unique count here for the number of blemishes or defects per subgroup. These attribute charts are appropriately applied for such discrete count data.
Features of Control Charts
Likewise a control chart consists of various attributes as listed below:
- Points represent a statistic of measurements of a quality characteristic in samples taken from the process of the data at different times. Here, the statistic can be a mean, range, proportion, etc.
- For all samples, the mean of this statistic is calculate. For example, the mean of the means, the mean of the ranges, the mean of the proportions.
- At the mean of the statistic, a centerline must draw.
- For all the samples, the standard deviation of the statistic is to be calculate.
- The natural process limits, i.e. upper and lower control limits (these are separate lines), indicate the origin at which the process output is consider statistically ‘unlikely’ and typically drawn at three standard deviations from the centerline. That means two standard deviations above and below the centerline.