Product quality improvement based on statistical capture-removal modeling under imperfect inspections

Authors
DOI:
https://doi.org/10.47456/bjpe.v12i2.50546
Keywords:
Quality Control, Imperfect Inspections, Capture–Removal Model
Abstract

This article proposes an approach for evaluating and improving product quality in production processes subject to imperfect inspections. The objective is to estimate the number of nonconforming items after inspections and to support decision-making in quality control planning. The methodology is based on a statistical capture–removal model, which estimates the proportion of defective items and the efficiency of inspections in detecting nonconformities. In addition, it allows estimating the expected number of nonconforming items before inspections and determining the minimum number of stages required to achieve acceptable defect levels. The model was applied in a factory of elevator components, using data from 2,447 inspected boxes over a 13-week period. The results indicate that approximately 3.7% of the approved boxes still present nonconformities, highlighting limitations of the current control system. Furthermore, predictions of the number of nonconforming items before inspections were close to the estimates obtained after inspections, indicating the consistency and applicability of the proposed model. Overall, the proposed methodology proved to be an effective tool for quality planning and control, contributing to decision-making in production environments.

Author Biographies
  1. Nágilla Aléxia Ferreira Rocha Vaz, State University of Maringá

    É bacharel em Engenharia Elétrica pela Universidade Norte do Paraná (UNOPAR). Atualmente, cursa o Mestrado Acadêmico em Engenharia de Produção na Universidade Estadual de Maringá (UEM). Possui formação técnica em Administração de Empresas e em Eletricista de Manutenção Industrial, além de certificações complementares em AutoCAD, Melhoria Contínua, NR10 e Matemática Aplicada. Trajetória acadêmica e profissional concentra-se nas áreas de engenharia elétrica, produção e qualidade industrial, com foco em melhoria contínua, gestão de processos e análise da qualidade. https://orcid.org/0009-0008-4461-1125

  2. Vanessa Rufino da Silva, State University of Maringá

    Possui mestrado em Estatística pelo Programa Interinstitucional de Pós-Graduação em Estatística (USP/UFSCar) e graduação em Estatística pela Universidade Estadual de Maringá (UEM). Atualmente é docente do Departamento de Estatística da UEM e doutoranda em Bioestatística pelo Programa de Pós-Graduação em Bioestatística da mesma instituição. Desenvolve atividades de pesquisa nas áreas de Teoria de Resposta ao Item (TRI), análise de dados e modelos probabilísticos para mensuração de habilidades e atitudes, com foco na aplicação de métodos estatísticos à avaliação educacional. https://orcid.org/0009-0008-5735-1334

  3. Edwin Vladimir Cardoza Galdamez, State University of Maringá

    É Professor Associado da Universidade Estadual de Maringá. Atua como Docente Permanente do Programa de Pós-graduação em Engenharia de Produção. É Doutor em Engenharia de Produção e graduado em Engenharia Mecânica. Desenvolve pesquisas nas áreas de Engenharia da Qualidade e Engenharia do Trabalho (Segurança e Saúde do Trabalho). https://orcid.org/0000-0002-1763-9332

  4. George Lucas Moraes Pezzott, State University of Maringá

    Possui doutorado em Estatística pelo Programa Interinstitucional de Pós-Graduação em Estatística (USP/UFSCar), mestrado em Estatística pela Universidade Federal de São Carlos (UFSCar) e bacharelado em Estatística pela Universidade Estadual de Maringá (UEM). Atualmente é Professor Adjunto da Universidade Estadual de Maringá (UEM). Tem experiência nas áreas de Probabilidade e Inferência Estatística com ênfase em Modelos Probabilísticos Discretos e Inferência Bayesiana, atuando principalmente em inferências sobre tamanho populacional em modelos de captura-recaptura. https://orcid.org/0000-0002-2483-7388

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Cover Image
The image depicts a brushed stainless-steel elevator control panel being operated by a human hand, with emphasis on an illuminated button, symbolizing a decision-making process based on selection and inspection. The visual composition evokes the concept of capturing and removing specific events or items within a system, establishing an analogy with statistical models applied to quality control in production environments. The scene represents the use of quantitative techniques to improve product quality through statistical capture-removal modeling under imperfect inspection conditions, contributing to failure reduction, enhanced reliability, and optimized operational performance. At the top, the article title “Improving Product Quality through Statistical Capture-Removal Modeling under Imperfect Inspections” is displayed, followed by the authors Vaz, Silva, Galdamez & Pezzott (2026). At the bottom, the identification of the Brazilian Journal of Production Engineering and the journal’s ISSN are shown.
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Published
2026-06-15
Issue
Vol. 12 No. 2 (2026): Regular Issue (April - June)
Section
QUALITY ENGINEERING
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Copyright (c) 2026 Vaz, N. A. F. R., Silva, V. R. da, Galdamez, E. V. C., & Pezzott, G. L. M.

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How to Cite

Vaz, N. A. F. R., Silva, V. R. da, Galdamez, E. V. C., & Pezzott, G. L. M. (2026). Product quality improvement based on statistical capture-removal modeling under imperfect inspections. Brazilian Journal of Production Engineering, 12(2), 193-204. https://doi.org/10.47456/bjpe.v12i2.50546