This is an addon to RS Production ACT.
Avoid quality issues by helping operators and leaders react on early warnings on quality measures.
Digitize manual reports on paper and in spreadsheets. Let operators report manually or read automatically from machine PLCs or other data sources.
Statistical process control, abbreviated SPC, is a method package in quality technology that is used to monitor processes of various kinds.
The methods aim to monitor whether the process is in control or not and what ability it has to produce approved units that are within set tolerances.
Variation occurs in all types of processes. In a manufacturing process, this can be, for example, play in bearings and controls, varying temperature and humidity or poorly calibrated measuring instruments. This variation is a source of poor quality, and must therefore be mapped, monitored and, if possible, reduced.
Samples are collected in data series. One data serie per combination of measure (width, temperature, humidity etc) and part number (article in RS Production).
Input to a data serie can come from either manual reporting or from a Machine Data Interface (ie OPC).
Operator Tools - Form for reporting measurement and process values
Operators uses forms for reporting measurements and other quality follow-ups.
Reported values then build up data series that are used for static process controls.
With the OPC Client plug-in or with the Integration Server, data series can be created automatically from, for example, PLCs or databases.
Operator Tools - Real-time viewing of data series, trends and capabilities
Operators follow critical process values and processes in real time. The values are plotted in a control card that clearly shows +/- 3 standard deviations (6 Sigma).
Trends and disturbances are identified in real time with Western Electric's accepted model.
The capability figures (Cp and Cpk) for the data series are updated in real time.
Screens – Real time views of data series
This addon comes with additional Screens component showing critical process values and processes in real time. The values are plotted in a control card that clearly shows +/- 3 standard deviations (6 Sigma).
In Office tools
Define data series
Table reports to follow up on all reports
Export data to Excel
Glossary of Static Process Controls (SPC)
Also called control diagrams. Visual diagram with time on the X-axis and value on the Y-axis. The Y-axis clearly shows the expected value and upper / lower control limits.
A process or machine's capability describes the ability and ability to produce quality products.
Cp and Cpk are two capability figures that complement each other and together describe how efficient the process is.
After a long period of collection of measured values, you can use Cp and Cpk to assess the ability in the process. It shows how well a process produces in relation to the tolerance limits.
Describes how tightly collected the data series' hit image is in comparison with the upper and lower tolerance limits. However, Cp says nothing about how close the expected value of the data series is.
The "narrower" the normal distribution of the data series, the higher the CP number. If the normal distribution of the data series is "narrower" than the "distance" between the upper and lower tolerance limits, it gives a Cp number that is above 1.0. This is regardless of whether the normal distribution of the data series is within or outside the tolerance limits.
In short, it can be said that a densely collected hit image gives a CP number that is above 1.0. You can compare with arrows on an arrow board that all end up close but far out at one edge. It is usually said that you want a CP above 1.33 to have a margin.
The Cpk number describes the position of the process in relation to the tolerance range. It does not help to have a high CP number and an overall "hit image" if it is incorrectly centered in relation to the center of the tolerance area.
A high Cpk number thus means that you have a "centered" process in relation to the tolerance range. If Cpk is as large as Cp, it means that the process is set to produce exactly in the middle of the tolerance.
A hit image that has its "center of gravity" within the tolerance limits gives a Cpk number that is above 1.0. To have a good margin, it is usually said that you want a Cpk number that is above 1.33.
Upper and lower specification levels (USL, LSL) are used to control the decision whether the produced detail is ok to deliver on to the customer or not.
The tolerance limits are calculated as three standard deviations from the mean value in a stable process. This means that 99.7% of all samples in a stable process reach the two limits.
A standard deviation is called Sigma. It is these 3 + 3 standard deviations that gave the name to the quality tools called 6 Sigma.
When the tolerance limits exist for approving individual produced units, there are control limits for reacting when the process begins to "slide away".
The upper and lower control levels (UCL, LCL) are used to show where the limits of a stable process go.
The mean in a stable process.
Basic concepts in statistics used to calculate the normal distribution. You measure the distance from the mean (at the top where the curve turns) to the point on the curve where it starts outwards instead of downwards.
This means that you do not have to measure several hundred details to get how a process varies. You can instead calculate the spread using the standard deviation.