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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.

Statistical processs control

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).

Statistical processs control