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A production quality monitoring approach based on a condition index: an application on the glass container industry.

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Title: A production quality monitoring approach based on a condition index: an application on the glass container industry.
Authors: Oliveira, Maria Alexandra1 (AUTHOR) alexandra.oliveira@fe.up.pt, Guimarães, Luís1 (AUTHOR), Borges, José L.1 (AUTHOR), Almada-Lobo, Bernardo1 (AUTHOR)
Source: International Journal of Production Research. Sep2025, Vol. 63 Issue 18, p6704-6731. 28p.
Subjects: Glass industry, Quality control charts, Measuring instruments, Flexible manufacturing systems, Quality assurance, Manufacturing processes
Abstract: Ensuring process quality in modern manufacturing is increasingly challenging due to the complexity of production processes and reliance on skilled operators, which can lead to suboptimal solutions and poor quality. To address these challenges, we introduce a novel, unsupervised, robust, nonparametric control chart for Phase II monitoring. This chart tracks the degradation of a quality characteristic using a condition index that captures mean and scale shifts without relying on assumptions, offering high flexibility and adaptability. Comparative studies with state-of-the-art nonparametric schemes demonstrate faster detection capabilities and competitive accuracy across various scenarios. We validate our approach through its application in the glass container production process, showcasing its effectiveness in monitoring multiple defective rates. Although tested on defective rates, the methodology is adaptable to any quantifiable quality characteristic. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A production quality monitoring approach based on a condition index: an application on the glass container industry.
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  Data: <searchLink fieldCode="AR" term="%22Oliveira%2C+Maria+Alexandra%22">Oliveira, Maria Alexandra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alexandra.oliveira@fe.up.pt</i><br /><searchLink fieldCode="AR" term="%22Guimarães%2C+Luís%22">Guimarães, Luís</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Borges%2C+José+L%2E%22">Borges, José L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Almada-Lobo%2C+Bernardo%22">Almada-Lobo, Bernardo</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Sep2025, Vol. 63 Issue 18, p6704-6731. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Glass+industry%22">Glass industry</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink><br /><searchLink fieldCode="DE" term="%22Measuring+instruments%22">Measuring instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Flexible+manufacturing+systems%22">Flexible manufacturing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink>
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  Data: Ensuring process quality in modern manufacturing is increasingly challenging due to the complexity of production processes and reliance on skilled operators, which can lead to suboptimal solutions and poor quality. To address these challenges, we introduce a novel, unsupervised, robust, nonparametric control chart for Phase II monitoring. This chart tracks the degradation of a quality characteristic using a condition index that captures mean and scale shifts without relying on assumptions, offering high flexibility and adaptability. Comparative studies with state-of-the-art nonparametric schemes demonstrate faster detection capabilities and competitive accuracy across various scenarios. We validate our approach through its application in the glass container production process, showcasing its effectiveness in monitoring multiple defective rates. Although tested on defective rates, the methodology is adaptable to any quantifiable quality characteristic. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/00207543.2025.2485316
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      – Code: eng
        Text: English
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        PageCount: 28
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        Type: general
      – SubjectFull: Quality control charts
        Type: general
      – SubjectFull: Measuring instruments
        Type: general
      – SubjectFull: Flexible manufacturing systems
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      – SubjectFull: Quality assurance
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      – SubjectFull: Manufacturing processes
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            NameFull: Oliveira, Maria Alexandra
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            NameFull: Guimarães, Luís
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            NameFull: Borges, José L.
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            NameFull: Almada-Lobo, Bernardo
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          Dates:
            – D: 15
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
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