Abstract
Algorithmic Statistical Process Control (ASPC) is an approach to quality improvement that reduces predictable quality variations using feedback and feedforward techniques, and then monitors the entire system to detect changes. As such, it is a marriage of control theory and statistical process control (SPC). Control theoretical concepts are used to minimize deviations from target by process adjustments; sec is used to gain fundamental improvements. Where applicable, ASPC is a logical next step in the drive for continuous quality improvement. This paper presents the ASPC concept and its applications to practitioners. Technical and non-technical requirements and factors conducive to the use of ASPC are emphasized, and pre-planning for the use of ASPC in new processes is discussed.
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