SPC Charts
SPC Charts are used to analyze process performance by plotting data points, control limits, and a centerline. A process should be in control to assess the process capability. Objective: Monitor process performance and maintain control with adjustments only when necessary and with caution not to over adjust. These are used as predictive tools. Regular monitoring of a process can save unnecessary inspection, adjustments, and prevent trouble by being proactive.
Common Cause and Special Cause VariationCommon cause variation is natural and inherent variation with the process and occurs with every data point (or part being measured). Before assessing the process capability the variation must be common cause variation. It is important to have a meaningful process capability that won't be subject to outliers and unique variation from an unstable process.Special cause variation is usually identified by points lying outside the upper control limit and lower control limit. However, there are many cases that points may lie within the control limits and still represent special cause variation. Such as trends and other typical influenced variation. In either case it is assignable and not necessarily affecting every part. Sometimes found to be a results of a machine change, operator change, or major underlying condition change. If you're tracking the miles per gallon of vehicles and you switch surfaces from asphalt to dirt to concrete there will be special cause variation.
The special causes must be eliminated to have an statistically controlled process. When a process is in statistical control (only common cause variation is present) the next and only possible steps to improve it to a lower level of variation is to minimize the common cause variation. Recall that SPECIFICATION LIMITS are provided by the customer (LSL, USL) so these may be adjustable. These are the components of the Voice of the Customer. CONTROL LIMITS are set by the process and formulas, they are not the voice of the customer, rather they are the voice of the process. Most statistical software will run a series of tests (if selected) to check for special cause condition(s) and provide the type of violation it is. A couple of common misconceptions for using SPC charts are that the data used on a control chart must be normally distributed and that the data must be in control in order to use a control chart. Selecting the proper SPC chart is essential to provide correct process information and prevent wrong and costly decisions. Understanding them and doing them long hand is tedious and time consuming but you will learn to better interpret them and comprehend statistical concepts within. Select a link below to learn more about most common control charts used in a Six Sigma project. Recall the data type, discrete or continuous.
CONTINUOUS DATA: Determine whether the data is in INDIVIDUALS or SUBGROUPS. INDIVIDUALS Each measurement is free from a rational subgrouping. Each measurement is taken as time progresses and can have its own set of circumstances. SUBGROUPS Each subgroup contains data of a similar short term setting (one lot, one shift, one operator). If the control chart exhibits a pattern similar to below then it was probably collected in subgroups. Easier analysis of subgroup data is done when the amounts of measurements per subgroup are equal. For example, if you are studying the MPG of a car at various speeds, collect the same amount of data points for each interval of speed. This is often a source of error when using statistical software programs. Align the data set by subgroups and input the correct sample size of the subgroup as the software needs.
ATTRIBUTE or DISCRETE DATA: Attribute charts are usually easier and more economical to create; however, the detail and amount of information is less than continuous data charts. Larger sample sizes are needed and indicate on a change in the rate of defects or defective units. Use when Poisson (# of defects) or Binomial (defective or non-defective) assumptions. Recall the type of attribute data being analyzed, determine whether it is Defects or Defectives and choose the proper chart based on the diagram below: Since you are plotting based on Binomial or Poisson assumptions, the determination of conforming versus non-conforming must be clearly defined and consistent. In other words, you should have passed an MSA and Gage R&R prior to obtaining the data used in these charts (same rationale applies for using any type of variables or discrete SPC chart).
C-Chart Option: If the sample size, n, is larger than 1,000 (either constant or variable) and you are plotting DEFECTIVES, the Individual and Moving Range (I-MR) charts may be used.
Control Limits for Attribute SPC ChartsControl limits are located 3 standard deviations above and below the center line. Data points outside the limits are indicative of and out of control process. Recall, just because points are within the limits does not always indicate the process is in control.These charts can be done by hand by often statistical software can be used once the creation of them are understood. Any data point(s) that statistical software recognizes as failing the common cause variation test means there is likely a nonrandom pattern in the process and should be investigated as special cause variation before proceeding with a capability analysis. There a numerous tests that are used to detect non-random ("out of control") variation such as Nelson tests and Western Electric tests. Attribute and Variable DataSeveral other non-Shewart based control charts exists and most statistical software has these options. The EWMA is one that is commonly used for detecting smaller shifts quickly, less than or equal to 1.5 standard deviations. The data is still based on a normal distribution (same a I-MR and X-bar & R) but the process mean is not necessary a constant.EWMA - Exponentially Weighted Moving Average
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