TY  - JOUR
AB  - 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, fuzzyand S control chart has been developed and second, the fuzzy formulation of the PCIs such as Cpm,Cpmu,Cpml, Cpmk, Pp, Ppl, Ppu, Ppkare 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 Cp, Cpk, Cpmand Cpmkindices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, Ppand Ppkindices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.
VL  - 33
IS  - 1
SN  - 0265-671X
DO  - 10.1108/IJQRM-08-2013-0130
UR  - https://doi.org/10.1108/IJQRM-08-2013-0130
AU  - Avakh Darestani Soroush
AU  - Nasiri Mina
PY  - 2015
Y1  - 2015/01/01
TI  - Statistical process control: Fuzzy -S control chart and process capability indices in normal data environment
T2  - International Journal of Quality & Reliability Management
PB  - Emerald Group Publishing Limited
SP  - 2
EP  - 24
Y2  - 2024/04/25
ER  -