Statistics and Statistical Process Control SPC

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

Statistical Process Control (SPC) The use of statistical techniques (such as control charts) to analyze a process and take appropriate action to maintain statistical control and improve process capability. 

The alternative is hexane, which because of the explosion hazard requires a more expensive type of extractor construction. After the extraction the product is dull gray. The continuos sheet is slit to the final width according to customer requirements, searched by fully automatic detectors for any pinholes, wound into rolls of about 1 m diameter (corresponding to a length of 900-1000 m), and packed for shipping. Such a continuous production process is excellently suited for supervision by modern quality assurance systems, such as statistical process control (SPC). Figures 7-9 give a schematic picture of the production process for microporous polyethylene separators. 

Statistical process control (SPC) is an important on-line method in real time by which a production process can be monitored and control plans can be initiated to keep quality standards within acceptable limits. Statistical quality control (SQC) provides off-line analysis of the big picture such as what was the impact of previous improvements. It is important to understand how SPC and SQC operate. 

Detailed illustrations and examples are used throughout to develop basic statistical methodology for dealing with a broad area of applications. However, in addition to this material, there are many specialized topics as well as some very subtle areas which have not been discussed. The references should be used for more detailed information. Section 8 discusses the use of statistics in statistical process control (SPC). 

In pharmaceutical technology, quality assurance of the pharmaceutical formulation is important. When a pharmaceutical formulation is produced, on-line quality monitoring and control has to be performed in order to check the quality of the outgoing products. Methodology to perform this task is Statistical Process Control (SPC) and is not included in this book. Good text books in the area of SPC exists [6-9]. In this book the focus is on off-line quality control, e.g. how to make products that are intrinsic robust against process variations. 

The traditional approach to quality control is to generate charts of various kinds to monitor the performance of a production unit. At a superficial level, statistical process control (SPC) and statistical quality control (SQC) [9] are terms used interchangeably to describe traditional... 

Many of the quality improvement goals for implementation of PAT in the pharmaceutical industry have been achieved by companies in other industries, such as automobile production and consumer electronics, as a direct result of adopting principles of quality management. The lineage of modern quality management can be traced to the work of Walter Shewhart, a statistician for Bell Laboratories in the mid-1920s [17]. His observation that statistical analysis of the dimensions of industrial products over time could be used to control the quality of production laid the foundation for modern control charts. Shewhart is considered to be the father of statistical process control (SPC) his work provides the first evidence of the transition from product quality (by inspection) to the concept of quality processes [18,19]. 

Is there a computerized, analytical results tracking and graphing system [process management software or statistical process control (SPC)] in place, as well as manual checks ... 

Use a statistical process control (SPC) computer software monitoring and control program to improve the record keeping and results trend analysis. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

If the pharmaceutical industry adopted the lessons learned in other branches (e.g., aircraft industry, automotive industry) it could realize an increased efficiency in the qualification and validation processes. To this end effort should be made to investigate statistical process control (SPC), house of quality, Deming circuits, and so on. 

Some current publications address process validation from an almost exclusively statistical approach. The effect of such articles on nonstatisticians usually ranges from dismay to panic and, unfortunately drives them away, instead of toward use of statistics. Statistical process control (SPC) can be especially valuable when applied to process validation, both before and after the validated process enters commercial use. By statistically analyzing critical process parameter data throughout a batch or continuous process, SPC provides the opportunity to predict problems (trend analysis) and even take corrective action (trend control), before the problems occur, yet relatively few firms appear to be actually implementing SPC universally across all processing today, probably because SPC... 

A bioprocess system has been monitored using a multi-analyzer system with the multivariate data used to model the process.27 The fed-batch E. coli bioprocess was monitored using an electronic nose, NIR, HPLC and quadrupole mass spectrometer in addition to the standard univariate probes such as a pH, temperature and dissolved oxygen electrode. The output of the various analyzers was used to develop a multivariate statistical process control (SPC) model for use on-line. The robustness and suitability of multivariate SPC were demonstrated with a tryptophan fermentation. 

In industrial plants, large numbers of process variables must be maintained within specified limits in order for the plant to operate properly. Excursions of key variables beyond these limits can have significant consequences for plant safety, the environment, product quality and plant profitability. Statistical process control (SPC), also called statistical quality control (SQC), involves the application of statistical techniques to determine whether a process is operating normally or abnormally. Thus, SPC is a process monitoring technique that relies on quality control charts to monitor measured variables, especially product quality. 

A common parameter used by quality professionals to gauge whether or not the manufacturing process and analytical methodology is within the desired state of statistical process control (SPC) is a process capability index [11], such as the Cpk value. The Cpk value is the value of the tolerance specified for the given performance characteristic divided by the actual process capability. 

The Shotscope system also maintains and displays statistical process control (SPC) data in a variety of formats, including trend charts, X-bar and R charts, histograms, and scatter diagrams. This information provides molders with the knowledge that their processes are in control, and, should they go out of control, Shotscope can alert to an out-of-control condition and divert suspect-quality parts. Furthermore, because the Shotscope system can measure and archive up to 50 process parameters (such as pressures, temperatures, times, etc.) for every shot monitored and the information archived, the processing fingerprint for any part can be stored and retrieved at any time in the future. This functionality is extremely important to any manufacturer concerned with the potential failure of a molded part in its end-use application (for example, medical devices). 

Statistics refers to the scientific methods applied to the collection, organization, interpretation, and presentation of information—numerical data. For statistical process control (SPC), data types are divided into attributes or variables. 

Finally, this year, a standard catalyst supplier accreditation procedure is being implemented. Primary emphasis is on the implementation of control charts and statistical process control (SPC) procedures in the manufacture of commercial catalysts in order to improve lot to lot consistencies (3) for purchased catalysts. 

Statistical process control (SPC) is used in accordance with the principle of prevention instead of detection by measuring variables and attributes on a real-time basis. 

Statistical analysis such as Statistical Process Control (SPC) may be used to derive performance parameters as well as track and trend for alert/alarm conditions. Automated moifitoring tools may be available to assist in the collection of relevant data. A record of any such tools used should be maintained and any validation requirements considered. 

Discrete data point, extracted irom the log file, can be viewed. The data can also be viewed in tabular form and as a size distribution curve. Data can also be integrated over any selected range. A Statistical Process Control (SPC) option enables the file data to be viewed in standard control chart format either as an X or R chart. 

Malvern (Insitec) ECPS2 is designed to monitor and control particle size distributions from 0.5 to 1,500 pm, at concentrations up to 10,000 ppm, directly in pneumatic powder flow streams. Up to one thousand size distribution measurements per second are carried out at flow velocities from static to ultrasonic. Discrete data point, extracted from the log file, can be viewed. The data can also be viewed in tabular form and as a size distribution curve. Data can also be integrated over any selected range. A Statistical Process Control (SPC) option enables the file data to be viewed in standard control chart format either as an X or R chart. Various interface arrangements have been described, [203] ... 

Testing is not just executing a program using a test data file or randomly selected test cases just prior to implementation. It is an on going process using techniques based on Statistical Process Control (SPC) principles and product quality concepts implemented as components of a Statistical Quality Control (SQC) program.f ... 

Statistical process control (SPC) provides a statistical approach for evaluating processes and for improving the quality of these processes through elimination of special causes. When SPC is effectively implemented within a company, benefits can be derived through a reduced cost of manufacture, improved quality, fewer troubleshooting crises, and improved relationships with customers. Process capability is a companion tool—one that can be used once a state of statistical control is achieved—to assess the performance of a process relative to its product specifications. Process capability can be used to determine whether processes are capable of continually operating within their stated specification limits. 

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. 

To accomplish this, we must collect data. But how we collect that data is as important as the data themselves. Some data are worthless, some are priceless. The conditions and procedures used to find data ultimately determine their value. Statistical quality control (SQC), statistical process control (SPC), total quality management (TQM), and six sigma are all passive approaches to data collection. These procedures only observe and report what is happening. They cannot find the analytical cause-and-effect relationships needed for true process understanding and for controlling the sources of variability. 

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