PI-SQC automates the task of charting process history and performing statistical calculations to achieve a consistent long-term SQC history of process behavior.
OSI Software, Inc.SQC Calculations and Charting for Process History
PI-SQC automates the task of charting process history and performing statistical calculations to achieve a consistent long-term SQC history of process behavior. It is a client application add-in to PI-ProcessBook. It retrieves data from the PI Data Archive and/or from an ODBC database and then performs statistical calculations to determine data behavior. The results are shown on a three-part PI-SQC Chart. This Chart is placed within a standard PI-ProcessBook display or an independent display file.
PI-SQC Chart Components
When the user designs a PI-SQC Chart, there are three major components, the Control Chart, the Histogram, and a Legend of basic information. Each of these components may be configured and formatted in a variety of ways, and each of them may be omitted.
What is SQC?
SQC (Statistical Quality Control) is used to identify process variations. SQC is typically illustrated in a format known as a Control Chart. Control Charts (with control limits or specifications) on actual processes differentiate between random fluctuations of data and true process shifts. Control Charts graphically evaluate whether or not a process is in a "state of statistical control". Different types of Control Charts are commonly used including average, moving average, range, standard deviation and CUSUM.
SQC in the Process Industry
While the customer often determines product specifications, the production process is theoretically capable of making all of its output meet or exceed the most stringent product specifications. Plant profitability can often be improved by increasing the amount of highest quality product produced. SQC is a technique for accomplishing these quality goals and for establishing benchmarks to monitor improvements in performance. SQC has been applied in many companies with large economic returns. Implementation has often been limited however, because of lack of available data and because of the extensive calculations and graphing required with large data sets.
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