A Control Chart shows how a process varies over time while identifying special causes of variation and changes in performance. Similar to a run chart, it includes statistically generated upper and lower control limits. This type of chart prevents changing a process that is varying randomly within the control limits (no special cause present). Variables data in a control chart measure units in length, temperature, etc. Show
Purpose of Control ChartsThe purpose of a control chart is to show Program Managers and project personnel if a process is varying over time which will allow them to correct those processes if needed. Best Time to Use a Control ChartDetermining the best time to use a control chart is important. The following is a list of when it’s a good time to use a control chart.
When Not to Use a Control ChartUnless the process question is clearly identified and the data supports an investigation of the process to control, control charts should not be the first tool used to analyze data. Steps in Developing a Control ChartDeveloping a control chart involves the following list of steps and activities:
Control Charts use two types of data:
Control Chart Negative OutcomesDefective: A unit that fails to meet acceptance criteria due to one or more defects. Defective data is used when a quality characteristic of an item cannot be easily measured but can be classified as conforming or non-conforming. It involves the fraction, or percent of defectives in a sample, and are represented in either an np chart or an n chart. Defect: A failure to meet one part of the acceptance criteria. Defect data is used when the quality of the item can be determined by the number of defects in the item or by counting the number of occurrences of some event per unit of time. The data can be shown in either the c chart or the u chart. AcqLinks and References:Updated: 7/11/2021 Rank: G27.2
Choose subgroups so that differences between measurements within the same subgroup are small and so that you can detect differences between subgroups. For initial process studies, subgroups of 4 or 5 units that are collected every hour or so are common. As the process demonstrates stability (or as improvements are made), you can decrease the subgroup size and frequency. Collect subgroups for a duration that is long enough to ensure that major sources of variation have the chance to occur. Usually, 100 observations or more (for example 25 subgroups with 4 observations each) is enough. Usually, industry prefers small, frequent samples to signal a process shift before too much defective product is made. X Bar S charts often used control chart to examine the process mean and standard deviation over the time. These charts are used when the subgroups have large sample size and S chart provides better understanding of the spread of subgroup data than range. X bar S charts are also similar to X Bar R Control chart, the basic difference is that X bar S charts plots the subgroup standard deviation whereas R charts plots the subgroup range Selection of appropriate control chart is very important in control charts mapping, otherwise ended up with inaccurate control limits for the data. Manually it is very easy to compute X Bar R Control chart, where as sigma chart may be difficult due to tedious calculations and large sample size. With large sample size in the subgroup, the standard deviation is better measure of variation than the range because it considers all the data not just minimum and maximum values. It is actually a two plots to monitor the process mean and the process range (as described by standard deviation) over time and is an example of statistical process control. These combination charts helps to understand the stability of processes and also detects the presence of special cause variation. The cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) charts are also monitors the mean of the process, but the basic difference is unlike X bar chart they consider the previous value means at each point. Moreover these charts are considered as a reliable estimate when correct standard deviation exists. X Bar S Control Chart DefinitionsX-bar chart: The mean or average change in process over time from subgroup values. The control limits on the X-Bar brings the sample’s mean and center into consideration. S-chart: The standard deviation of the process over the time from subgroups values. This monitors the process standard deviation (as approximated by the sample moving range) Use X Bar S Control Charts When:
How to Interpret the X Bar S Control Charts
Steps to follow for X bar S chartObjective of the chart and subgroup size
Note: To demonstrate an example, we just took subgroup size 4 in the below example, but it is always recommended to take 10 and above for X bar S chart. Example: A packing organization monitoring the performance of a packing machine, each container should weigh 35 lb, during Measure phase, project team performed the process capability study and identified that the process is not capable(less than one sigma). In Analyze phase collected 12 sets of container weights with a subgroup size of 4. Compute X bar and S values
Determine the Control LimitsThe first set of subgroups are to determine the process mean and standard deviation, these values are to be consider for creation of control limits for both standard deviation and mean of each subgroup The process to be in control in the early phase of the production. Special causes to be identified if any of the points are out of control during initial phase and also the subgroup has to be removed for calculation. Sometimes in the initial phase it would be also good to have few points out of control on the x-bar portion. Otherwise, if all the values are within the control limits may be because of slop in the measurement system, team won’t focus on it. Identify appropriate Measurement System Evaluation (MSE).
The below control chart constants are approximate values to measure the control limits for X bar S chart and other control charts based on subgroup size
Example cont: In the above example n=4 Interpret X bar and S chart
Example Cont: Use the above values and plot the X bar and Sigma chart From the both X bar and S charts it is clearly evident that most of the values are out of control, hence the process is not stable Monitor the process after improvementOnce the process stabilizes and control limits are in place, monitor the process performance over the time. Example cont: Control Phase- Once the process is improved and matured, team identified the X bar S chart is one the control method in Control plan to monitor the process performance over the time period Following are the measurement values in Control phase of the project Compute X bar and Sigma Find the control limits From the both X bar and S charts it is clearly evident that the process is almost stable. During initial setup at 2nd data set both S chart and X bar chart value are out of control, team has to perform the root cause analysis for the special cause and also the process is smoothing out from the data set number 4. If that continued, the chart would need new control limits from that point.
Important notes on X Bar S Control Charts
X Bar S Control Chart Videos |