SPC chart

The auditor should establish that the supplier has made provision to link all the processes and should follow trails through departments and processes to verify correct use of outputs from interfacing processes e.g. use of SPC charts, FMEA, MSA, control plans and changes to these when the products or processes change. 

Validation of extraction procedures is frequently lacking. A good assessment of quality assurance implies that the extraction recoveries are verified, e.g. by spiking of standard addition. A major drawback is that the spike is not always bound the same way as the compounds of interest. For the development of good extraction methods, materials with an incurred analyte (i.e. bound to the matrix in the same way as the unknown), which is preferably labelled (radioactive labelling would allow verification of the recovery), would be necessary. Such materials not being available, the extraction method used should be validated by other independent methods, e.g. by verification against known samples and by use of a recovery SPC chart. A mere comparison of extraction methods is no validation. 

In this example, data interpretations are based on g-statistic limits. These are computed by assuming the data are normally distributed in the multivariate sense. The diagnostic limits are used to establish when a statistically significant shift has occurred. Charts based on these statistics and used in this manner are analogous to conventional SPC charts. 

Control charts display the data (and variance) in the order they occur with statistically determined upper and lower control limits. The SPC charts are used to monitor the process for maintenance of the target to zero and then to determine whether process changes have accomplished their desired quality effect. ... 

Statistical process control (SPC) chart (Fig. 23) of the averages is another must have real-time display. Each point on the chart represents a revolution average of the compression forces or corresponding tablet weights. The limit lines are calculated at one standard deviation of the mean, and there are certain rules that are used to determine when and if the process gets out of control. These rules are available in any textbook on the SPC. 

The value for a error can be computed for simple SPC charts such as Shewhart charts using theoretical derivations. For more complex SPC... 

P Nomikos and JF MacGregor. Multivariate SPC charts for monitoring batch processes. Technometrics, 37 41-59, 1995. 

FIGURE 15.34 SPC chart based on a seven-day period of data for two different controllers on the same process. 

Most companies keep statistical process control (SPC) charts that track the laboratory analysis of final prodncts, which are typically sampled one to three times daily. Fignre 15.34 is an example of an indnstrial SPC chart for two different controllers, for two different seven-day periods. It is easy to see which controller performed better. 

SPC charts have proved their value over many years and no longer need justification or a detailed explanation. They allow for the normal expected process variability, e.g. action can follow a failure at 99.8% probability (1/1,000), with a warning at 95% probability (1/40). They allow easy operator understanding of results, without which data can be misconstrued leading to excessive operator-induced variability (under the impression that the process is out of control). 

The methodology for MSSPC is obtained from the general methodology by setting up univariate or multivariate SPC charts for the coefficients at each... 

The V-mask can now be placed over the graph with P-0 parallel to the x-axis and the point P over the current data point. If any part of the cumulative function protrudes the boundaries prescribed by the V-mask, we would conclude that the current mean of the process has deviated from the target. The CUSUM chart, in general, is more effective than the Shewhart Control Chart when used to monitor continuous processes that tend to drift over time. The CUSUM Chart, however, is quite vulnerable to the impact of process interruptions. Another drawback of the CUSUM Chart is that its direct relationship to the actual time variation of the process is not always clear, making it rather difficult for us to analyze and to improve the process. Refer to Stepwise SPC Chart. 

Chart employs exactly the same weighting function as the CUSUM Chart. To detect outside interruptions the Stepwise SPC Chart places 100% of the weight onto the last data point in the same way as the Shewhart Control Chart. The Stepwise SPC Chart is, therefore, a combination of the two common SPC charts the Shewhart Control Chart and the CUSUM Chart. Figure 5 illustrates the weighting function of the EWMA Chart. Most emphasis is placed on the last data point and regressively less emphases are placed on the previous data points. In this example, about 20.6% of the total weight is put on observation 16, (20.6 x 0.8)% of the total weight is put on observation 15 and (20.6 x 0.8 x 0.8)% is put on observation 14. 

This pattern, then, is a blend of the weighting functions employed by the Shewhart Control Chart and the CUSUM Chart. We make use of all data points yet more emphasis is placed on the recent ones. Although the Stepwise SPC Chart is a combination of the Shewhart Control Chart and the CUSUM Chart, the Exponentially Weighted Moving Average Chart is a compromise of the two. Figure 6 illustrates the EWMA. 

The Stepwise SPC Chart is a method that combines the functions of the CUSUM Chart and the Shewhart Control Chart. A sequential analysis is used to estimate the current mean of the process. Shewhart Control Charts are then constructed about the step functions representing the current mean. Figure 1 illustrates the Stepwise Control Chart. The step functions, based on sequential analysis, respond to process drift the same way as the CUSUM Chart. Each segment of the control chart then helps to identify the interruptions, in the same way as the Shewhart Control Chart does. With the proper choice of parameters, the Stepwise SPC Chart can effectively separate the three types of variations drift, fluctuations and interruptions, and thus help us to monitor and analyze the quality of the product. Refer to EWMA Chart. 

Figure 7(a) SPC charts shov/ing yttria and surface area variations in the milled powders... 

Figure 7(b) SPC charts showing oxygen variation in as-milled and YjOj precipitated powder... 

Control charts (SPC charts)—statistical tools used to determine and control process variations. Flowchart—a picture of the activities that take place in a process. 

Statistical process control charts (SPC charts) are used to plot quality parameter points from samples taken at different times during a run. Even if all of the points are within specifications, when they are plotted on a graph you may see quite clearly that there is a trend that in time will result in off-specification material unless an adjustment is made. An upset or out-of-control situation is both vividly revealed and documented by such a chart (see Figure 16-3). 

Control charts (SPC charts)—statistical tools used to determine and control process variations. 

Prior to the widespread implementation of supervisory control and data acquisition (SCADA) and human-machine interface (HMI) systems, most SPC and SQC was performed by quality-control departments as an off-line process. Data was collected from test stations, laboratories, etc. and statistical analysis was performed later. SCADA/HMI systems, however, have made it feasible to provide plant-floor SPC charts using data collected in real time directly from the process. Fabricators that want to standardize SPC and SQC to increase their use find they need the two following functions (1) provide the plant floor with SPC charts and (2) make data collected by SCADA systems available for off-line analysis. Available is SPC and SQC software to support these efforts. Recognize that the bulk of SPC s value is derived from process improvements developed from offline SQC analysis. 

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