Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data

Autor(es)

Fonseca, Anderson ^[1] ; Ferreira, Paulo Henrique ^[1] ; do Nascimento, Diego Carvalho ^[2] ; Fiaccone, Rosemeire ^[1] ; Ulloa-Correa, Christopher ^[3] ; Garcia-Pina, Ayon ^[3] ; Louzada, Francisco ^[4]

Afiliação do(s) autor(es)

^[1] Univ Fed Bahia, Dept Stat, BR-40170110 Salvador, BA - Brazil

^[2] Univ Atacama, Fac Ingn, Dept Matemat, Copiapo 1530000 - Chile

^[3] Univ Atacama, Lab Invest Criosfera & Aguas, IDICTEC, Copiapo 1530000 - Chile

^[4] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: AXIOMS; v. 10, n. 3 SEP 2021.
• Citações Web of Science: 0

Resumo

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapo watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool's potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 20/09174-5 - Recomendação de itens de interesse da BeeNet
• Beneficiário: Diego Carvalho do Nascimento
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico