Statistical Process Control
von 
Mettler-Toledo International Inc.
17.09.2010 - 11:04 - Medicine & Health
(prcenter.de) Statistical process control
Statistical process control (SPC) has been around for a long time. But it is only in the last several years that many modern companies have begun working with it more actively - not least because of the propagation of comprehensive quality systems, such as ISO, QS9000, Six Sigma and MSA (Measurement System Analysis). SPC is far more than a control chart or a mere capability index. It is a system that uses process data to describe a prototypical manufacturing process in connection with its environment.
The goal of the method is to intervene in the process before tolerance violators occur, and thereby optimize the entire process. The method uses a variety of elements, which in their totality form the SPC module of FreeWeigh.Net. Control limits, CuSum, specification limits, cp and cpk are the elements available in FreeWeigh.Net that allow you to have even better control over the processes being monitored, to document them, and, if needed, to intervene even faster. The following sections describe the individual elements and their benefits in greater detail.
The heart of SPC: normal distribution
Normal distribution as described by C.F. Gauss (1777-1855) with its typical Gaussian distribution curve (also called the bell curve), lies at the heart of the mathematical model used to illustrate statistical process control. Normal distribution is based on the principle of a limitless totality. Since more than 200 samples already allow a good approximation, this relatively simple model is sufficient for describing processes in a suitable way. The distribution generated by the model is described in terms of the mean and the standard deviation. In addition, the difference, minimum value and maximum value, sample size and - in the case of FreeWeigh.Net - tolerance limits and specification limits also play a role. A basic knowledge of these individual terms is needed in order to understand how they work together.
Control limits
In contrast to tolerance limits, which are used for the individual values of a sample series, control limits are used for the mean and the mean variation of a series. These control limits are like guardrails that are narrower than the tolerances for the individual values.
The control limits for the mean value are defined in FreeWeigh.Net with three parameters: The upper and lower control limits, and the target value for the mean. As soon as the mean (and not an individual value) goes beyond this guardrail, an appropriate distribution can be shown. As a rule, the target value for the mean is slightly higher than the nominal, since in the food industry the mean value for packaged products must equal or exceed the nominal over a defined period (e.g. batch).
No general recommendation can be given for the initial value of any of these three parameters. Although the sample series comprises individual products taken one after the other from the production process, other factors, such as the mean variation of the process and environmental influences, play a key role. This is why we suggest setting the initial values for the upper and lower control limits to approximately 60-70% of T1.
With the SPC module from FreeWeigh.Net, however, the control limits can also be continuously calculated. This is particularly advisable when a relevant process mean variation (through decisive influential factors that can change over time) should be considered. The time of this recalculation is only practical, however, if a representative set of data was recorded between the calculation times. Moreover, the calculation does not depend on the size of the sample, and is based on the conventional trade factors of A2 and A3 for the mean value, B3 and B4 for the standard deviation, and D3 and D4 for the spread. The sample size used for the calculation can also be defined. The smaller the number selected, the larger the influence of the current measurement value and the faster an alarm can theoretically occur. If a large value is selected, this effect is lessened.
In addition to mean value monitoring, FreeWeigh.Net also allows control of the spread or the mean variation. In most cases, the initial value for monitoring the mean variation is the same or slightly higher than the mean variation of the process. On the other hand, the mean variation of a sampling series can never be zero, because this would contradict the theory of normal distribution. With a lower and upper limit for the mean variation, it can be maintained within a defined tolerance. We view the monitoring of the mean variation as a suitable tool for the food industry, because a separate report can be configured in the case of a tolerance violation (and its associated large spread).
The monitoring of the spread limits the difference between the maximum and minimum value within a sample. With two limits, this tolerance is also defined in such a way that the lower limits are, more appropriately, not zero. In theory, a series of products will never exactly show the same measuring results - the use of measuring instruments with suitable resolution is taken for granted, however. The upper limits define the maximum difference between the extreme values of a sampling series. This monitoring is especially appropriate for checking for the kind of uniformity required, for example, by the various pharmacopeias.
No matter the basis selected, this function can be used to monitor processes within a narrower tolerance. This gives the user the security of knowing that he will receive sufficient warning well before a real tolerance violation, and that he can then take appropriate measures.
Contact Informations
Mettler-Toledo International Inc.
Im Langacher, Zip Code 8606
Greifensee, Switzerland
E-Mail : ePublicRelations@mt.com
Phone : +41-44-944 33 85
Website : www.mt.com
METTLER TOLEDO ist auf Präzisionsinstrumente für den professionellen Einsatz spezialisiert. Neben vielen anderen Einsatzbereichen in der pharmazeutischen und chemischen Industrie, Nahrungsmittelherstellung und Kosmetik kommen Laborinstrumente von METTLER TOLEDO in der wissenschaftlichen Forschung, der Suche nach neuen Pharmaka und in Labors der Qualitätskontrolle zur Anwendung. Bei vielen dieser Kunden decken die umfangreichen industriellen Lösungen von METTLER TOLEDO neben der Laborausstattung auch die verschiedenen Stufen der Produktionsverfahren ab. Die Bandbreite der angebotenen Lösungen reicht von der Annahme der Rohmaterialien über verschiedene Herstellungsverfahren, In-Line-Prozesssteuerung und Verpackungsüberwachung am Linienende bis hin zu Logistik und Versand. Mehr und mehr integrieren die Kunden diese Lösungen voll in ihre IT-Produktionsumgebung, wo sie zur Automatisierung der Arbeitsabläufe beitragen. Im Lebensmitteleinzelhandel umfasst unser Angebot zur Verwaltung der Frischware die Warenannahme, Einlagerungsmethoden für Abteilungen mit Selbstbedienung, Feinkostsektionen und den Kassenbereich.
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