The key characteristics of test and measurement (T&M) manufacturing are short-run, multi-product families and testing at multi-stations. These characteristics render statistical process control (SPC) inefficacious because inherently meagre data do not warrant meaningful control limits. Measurement errors increase the risks of false acceptance and rejection, thereby leading to such consequences as unnecessary process adjustments and loss of confidence in SPC. This study presents a modified SPC model that incorporates measurement uncertainty from guard bands into the \( \overline\mathrmZ \) and W charts, thereby addressing the implications of short runs, multi-stations and measurement errors on SPC. The implementation of this model involves two phases. Phase I retrospective analysis computes the input parameters, such as the standard deviation of the measurement uncertainty, measurement target and estimate of the population standard deviation. Thereafter, five-band setting and sensitivity factor are proposed to estimate process standard deviation to maximise the opportunity to detect the assignable causes with low false-reject rate. Lastly, the \( \overline\mathrmZ \) and W charts are generated in Phase II using standardised observation technique that considers the measurement target and estimated process standard deviations. Run tests based on Nelson rules interpret the charts. Validation was performed in three case studies in an actual industry.
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Real-Time SPC delivers efficiency and productivity gains by eliminating manual data collection efforts, but its real power lies in the ability promote continuous improvement and identify out-of-control process parameters.
A contract manufacturer specializing in electromechanical and PCB assembly for high end laptop manufacturers and the Department of Defense implements WinSPC statistical process control software at two US based facilities.
A common problem in business is managers who treat all variation asif it were due to assignable causes. This leads to the overadjustmentof processes (which increases variation), criticism of employees for variation that is out of their control, and reward of employees for being the "best" when that distinction is the result of just common cause variation. Deming's Red Bead Experiment provides graphic proof of the folly of treating all variation as if it had an assignable cause. The Funnel Experiment shows the result of overadjustment of a process.
IPC-9191 reflects the principals of statistical process control (SPC) represented by ISO/DIS 11462-1, Guidelines for Implementation of Statistical Process Control (SPC) -- Part 1: Elements of SPC. This document outlines the SPC philosophy, implementation strategies, tools, and techniques used for relating process control and capability to final product requirements. Supersedes IPC-PC-90.
Process Capability (CP) is the measurement to determine if the process is capable of holding the tolerance allowed. To find the CP first you find the Standard Deviation. The Mean + ( Standard Deviation * 3) gives you the Upper Control Limit (UCL). Next find the Lower Control Limit (LCL), Mean - ( Standard Deviation * 3). The capability is the ratio of the specification limits over the control limits, (CP = (USL - LSL) / (UCL - LCL). If the CP equals one the control limits fit exactly within the specification limits.
If a process is in statistical control, most of the points will be near the average, some will be closer to the control limits and no points will be beyond the control limits. The 8 control chart rules listed in Table 1 give you indications that there are special causes of variation present. Again, these represent patterns.
It is difficult to list possible causes for each pattern because special causes (just like common causes) are very dependent on the type of process. Manufacturing processes have different issues that service processes. Different types of control chart look at different sources of variation. Still, it is helpful to show some possible causes by pattern description. Table 3 attempts to do this based on the type of pattern.
This publication took a look at the 8 control chart rules for identifying the presence of a special cause of variation. The rules describe certain patterns of variation that will give you insights on where to look for the special cause of variation. No one table can give you the reasons for out of control points in your process. You have to use your own knowledge (and that of those closest to the process) to discover the reason.
It is back in control, in my opinion, if the next point is back within the control limits - if it is a fleeting special cause of variation that comes and goes. But suppose that out of control point stays around. You have a point above the upper control limit. The next point is back within the limits but it is above the upper control limit. If it stays about the average for a run and you can't find out why, then you have re-calculate the control limits or adjust the process to bring it back into control. This link has more details:
Thank you. The data does not have to be normally distributed to use a control chart. Most Xbar data is symmetrical assuming the subgroup size is large enough. The zones tests require some symmetry about the average, but basically, you should not worry about normality. You know your process and will know if a control chart is signalling a special case most likely.
You can theortically put a statistical probability to each rule assuming a normal distribution - they are all about the same probability. In practical terms, start with the points beyond the control limits, then add the test for zone C later and then zone A and B after that. This approach seems to work well.
Hi Bill, I learned that we need to interpret control charts based on the 68-95-99 rule; and I would like to know, in your opinion, if there are no points outside the 3 Sigma limit (all points with 3Sigma each side), is a process still considered in control, if for example: only 1 of 3 consecutive points fall within 1 Sigma either side of the average.. meaning two of the three are either in the 2 or 3 sigma zones. If we have 100 points of data, we would expect 68 of them to be within 1Sigma from the average, if this is not true, but the process has no data point outside the 3Sigma, is the process considered "not in control"?Thank you.
I would not worry too much about probabilities - like 68 points out of 100 should be within one sigma of the average. That is true for a perfect normal distribution but there are not no perfect normal distributions in real life processes. If there are no points beyond the limits and none of teh zones tests have been violated, then the process is in statistical control.
Hi there,Thank you for this really great article, I have returned to it so many times since I became aware of run charts. Given that Covid had such an impact on data all over the world would you consider this to be a "fleeting" change and control for it with process shifts or "the new normal" and leave the data as is? I work in the world of crime data so shops closing nd people staying at home impacted Theft from shop and Burglary. TIA
If the LCL is below zero, then there really is not a lower control limit. I don't set it to 0. Yes 4 points in a row at zero is in statistical control. You need 7 to 9 below the average to be an out of control situation.
Statistical Process Control (SPC) is a method of quality control. It utilizes statistical methodology in order to monitor, control, and ultimately improve a process or its output. Utilizing SPC should ultimately lead to better overall process quality, lower costs due to reduction of waste/scrap, and better understanding of process details and capabilities. Organizations expect Six Sigma or better conformance of set specifications for their products. For best-in-class quality, companies require continuous analysis and real-time quality control. ICONICS provides an extensive set of SPC calculations, control charts, portal dashboard views, and standard quality reports to help pinpoint quality outliers and drive corrective actions based on process trends.
The Food and Beverage industry provides the perfect environment for Statistical Process Control (SPC). Powerful workflow technology can initiate control actions based on quality conditions or alarm violations, providing changes to process equipment.
I know I'm eventually going to get asked about how the values for d2 and d3 are calculated for the X-bar and R charts. These factors are the mean and standard deviation of the statistic W = R/s, respectively and can be found tabulated in most text books or references about control charts. W is commonly referred to as the relative range or studentized range and is used to estimate the process standard deviation when only the sample mean and range are known. After trying to read through reference [3], I decided not to try the numerical integration of the range distribution within Excel, so I just hard-coded the values for the factors into an array. This is why the X-bar chart is limited to sample sizes of 2 to 25. The hardest part of creating the s-chart is calculating the c4 factor. This requires the use of the Gamma function for calculating factorials of half-integer numbers (see this blog post).
The American Society for Quality defines statistical quality control (SQC) as the application of statistical and analytical tools to monitor process outputs. Statistical process control (SPC), on the other hand, is the application of the same tools to control process inputs.
SPC leverages statistical methods and sampling programs to help plant managers understand and control variability in manufacturing. With the help of Plant Management Software, process variations are displayed in real-time charts. When a process deviates from standard limits, it sets off an alert. This helps manufacturers better manage their lines and reduce rework and waste. The notifications allow them to step in and address an issue promptly. 2ff7e9595c
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