Attribute control chart for monitoring the mean and the variance of univariate processes when the parameters are estimated.

• Grant number: 23/04923-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2023
• End date: June 30, 2024
• Field of knowledge: Engineering - Production Engineering - Production Management
• Principal Investigator: Marcela Aparecida Guerreiro Machado de Freitas
• Grantee: Gabriel Henrique Fradique
• Host Institution: Faculdade de Engenharia (FEG). Universidade Estadual Paulista (UNESP). Campus de Guaratinguetá. Guaratinguetá , SP, Brazil

Abstract

Control charts were developed by Shewhart to monitor processes quality using statistical concepts. Since the desing of the first control charts, many researchers have been developing new strategies to increase the efficiency of this tool. A new trend in research in this area seeks to reduce costs with inspection steps during process monitoring, without losing the efficiency of the control chart in detecting a disturbance. Recent studies propose the use of attribute inspection to monitor continuo continuous variables. Commonly the process parameters, such as mean and variance, are assumed to be known. In practice, however, the parameters are rarely known, and it is necessary to estimate them. The results found in the literature show that parameter estimation significantly changes the performance of control charts when compared under the same conditions with known parameters. In this work, the npx attribute control chart will be studied for monitoring the mean and variance of univariate processes when the parameters are estimated. The chart´s properties will be obtained through simulations, using the R language. The simulation results will be used in the validation of the theoretical results to be obtained by the research group of which the advisor is part. In addition, a Web application using the R package called Shiny will be developed so that the general public, free of charge, can enter data from a process to monitor it. The availability of low-cost monitoring tools, through Web applications, is a trend in the Industry 4.0 era.