Statistical process control

Soroush Avakh Darestani (Department of Industrial Engineering, Faculty of Mechanical & Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Mina Nasiri (Department of Industrial Engineering, Faculty of Mechanical & Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Publication date: 31 December 2015

Abstract

Purpose

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues.

Design/methodology/approach

In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages.

Findings

The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings.

Research limitations/implications

For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN.

Practical implications

The application case is related to a piston company in Konya’s industry area.

Originality/value

In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

Keywords

Citation

Avakh Darestani, S. and Nasiri, M. (2015), "Statistical process control", International Journal of Quality & Reliability Management, Vol. 33 No. 1, pp. 2-24. https://doi.org/10.1108/IJQRM-08-2013-0130

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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