Point estimation of descriptive univariate process capability indices with allowable limits and uncertain quality measurement method

ABSTRACT

The main purpose of this paper is to use fuzzy logic to provide a systematic approach for designing fuzzy control charts and compare the average run length type 2 of these charts in three different modes: Gaussian fuzzy, triangular fuzzy, and crisp cases. The process of solving and presenting the research results is by calculating the fuzzy control chart conditions and fuzzy descriptive capability indices through theoretical calculations and a project-based organizational case study and comparing the average run length type 2, which represents the expected interval between out-of-control events. Average run length type 2 illustrates that Gaussian and triangular quality control charts are able to detect an out-of-control signal more quickly. The main contributions of this paper are to use Gaussian fuzzy numbers in order to provide a systematic approach for designing fuzzy control charts, estimating descriptive capability indices, and analyzing the results of their estimation under fuzzy logic.

Graphical abstract

Keywords:

• Fuzzy quality control charts
• descriptive fuzzy process capability indices
• fuzzy logic
• fuzzy characteristic limits
• fuzzy average run length type 2

Disclosure statement

The author declares that he has no relevant or material financial interests that relate to the research described in this paper.

Additional information

Notes on contributors

Seyed Sina Jalali

Seyed Sina Jalali graduated with a master’s degree in industrial engineering, who is working in the field of systems and methods in an oil and gas company. He has also published two papers in the field of statistical quality control and fuzzy logic in international conferences. He is very interested in publishing more papers in this field in international journals. His research interests are quality management, productivity and performance management, production planning, and Fuzzy Quality Control Charts.

Milad Mohammadi

Milad Mohammadi graduated in 2012 and 2015 with BS and MS degrees in Operations Research from Vali-e-Asr University, Rafsanjan, Iran, and University of Tehran, Tehran, Iran. He achieved his Ph.D. in Operations Research in 2020 from University of Isfahan, Isfahan, Iran. His research interests are mathematical programming, productivity and performance management, sustainable supply chain management, industrial planning, transportation and hub location problem. He has published 12 papers in international journals.