Abstract
Identification of the assignable causes of process variability and the restriction and elimination of their influence are the main goals of statistical process control (SPC). Identification of these causes is associated with so called tests for special causes or runs tests. From the time of the formulation of the first set of such rules (Western Electric rules) several different sets have been created (Nelson rules, Boeing AQS rules, Trietsch rules). This paper deals with the comparison analysis of these sets of rules, their basic statistical properties and the mistakes accompanying their application using SW support. At the end of this paper some recommendations for the correct application of the runs tests are formulated.
Highlights
statistical process control (SPC) is an approach to process control that has been widely used in both industrial and non‐industrial fields
SPC is based on Shewhart’s conception of process variability. This conception distinguishes the variability caused by obviously effected common causes from the variability caused by abnormal, special causes using control charts
The control chart displays a value of the quality characteristic that has been measured or some sample statistics that have been computed from the measured values in a sample versus the sample number or time
Summary
SPC is an approach to process control that has been widely used in both industrial and non‐industrial fields. The main goal of SPC is the identification of abnormal variability caused by special (assignable) causes, with the aim to make the process stable, to minimize process variability and to improve the process performance. To meet these goals SPC must be built as a problem‐solving instrument and the sequence of the subprocesses “Out‐of control signal revelation – Root cause identification – Action acceptance – Verification of action” must be the axis of the SPC application. A control chart is the main SPC instrument for the analysis of process variability over time. It is a graphical depiction of process variability and its natural and unnatural patterns. Darja NoskiIenvt.ičjo. veán:gC. obmusp.lmexaCnaogn.t,ro2l0C1h3a, rVtoInl.t5er,p1r3et:2at0io1n3 1 random (unnatural) pattern, it is assumed that an assignable cause of the abnormal process variability is present and it must be removed from the process via searching for it and a corrective action or some improvement must be realized
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Engineering Business Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
