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Data-driven algorithm for throughput bottleneck analysis of production systems

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Název: Data-driven algorithm for throughput bottleneck analysis of production systems
Autoři: Subramaniyan, Mukund, 1989, Skoogh, Anders, 1980, Salomonsson, Hans, 1985, Bangalore, Pramod, 1983, Gopalakrishnan, Maheshwaran, 1987, Sheikh, Muhammad Azam, 1979
Zdroj: DAIMP - Dataanalys inom underhållsplanering Production and Manufacturing Research. 6(1):225-246
Témata: productivity, statistical approach, Bottleneck, Smart Maintenance, Data-driven, Smart manufacturing, Maintenance, data science, Manufacturing Execution System, production, bottlenecks, big data, Production system, Analytics, machine learning, active period, manufacturing, maintenance, Throughput, MES, industry 4.0, Throughput bottleneck detection
Popis: The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/504561
https://research.chalmers.se/publication/504761
https://research.chalmers.se/publication/504761/file/504761_Fulltext.pdf
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  Data: Data-driven algorithm for throughput bottleneck analysis of production systems
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Subramaniyan%2C+Mukund%22">Subramaniyan, Mukund</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Skoogh%2C+Anders%22">Skoogh, Anders</searchLink>, 1980<br /><searchLink fieldCode="AR" term="%22Salomonsson%2C+Hans%22">Salomonsson, Hans</searchLink>, 1985<br /><searchLink fieldCode="AR" term="%22Bangalore%2C+Pramod%22">Bangalore, Pramod</searchLink>, 1983<br /><searchLink fieldCode="AR" term="%22Gopalakrishnan%2C+Maheshwaran%22">Gopalakrishnan, Maheshwaran</searchLink>, 1987<br /><searchLink fieldCode="AR" term="%22Sheikh%2C+Muhammad+Azam%22">Sheikh, Muhammad Azam</searchLink>, 1979
– Name: TitleSource
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  Data: <i>DAIMP - Dataanalys inom underhållsplanering Production and Manufacturing Research</i>. 6(1):225-246
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22productivity%22">productivity</searchLink><br /><searchLink fieldCode="DE" term="%22statistical+approach%22">statistical approach</searchLink><br /><searchLink fieldCode="DE" term="%22Bottleneck%22">Bottleneck</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+Maintenance%22">Smart Maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Data-driven%22">Data-driven</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+manufacturing%22">Smart manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Maintenance%22">Maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22data+science%22">data science</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+Execution+System%22">Manufacturing Execution System</searchLink><br /><searchLink fieldCode="DE" term="%22production%22">production</searchLink><br /><searchLink fieldCode="DE" term="%22bottlenecks%22">bottlenecks</searchLink><br /><searchLink fieldCode="DE" term="%22big+data%22">big data</searchLink><br /><searchLink fieldCode="DE" term="%22Production+system%22">Production system</searchLink><br /><searchLink fieldCode="DE" term="%22Analytics%22">Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22active+period%22">active period</searchLink><br /><searchLink fieldCode="DE" term="%22manufacturing%22">manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22maintenance%22">maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Throughput%22">Throughput</searchLink><br /><searchLink fieldCode="DE" term="%22MES%22">MES</searchLink><br /><searchLink fieldCode="DE" term="%22industry+4%2E0%22">industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Throughput+bottleneck+detection%22">Throughput bottleneck detection</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.
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  Data: electronic
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/21693277.2018.1496491
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 225
    Subjects:
      – SubjectFull: productivity
        Type: general
      – SubjectFull: statistical approach
        Type: general
      – SubjectFull: Bottleneck
        Type: general
      – SubjectFull: Smart Maintenance
        Type: general
      – SubjectFull: Data-driven
        Type: general
      – SubjectFull: Smart manufacturing
        Type: general
      – SubjectFull: Maintenance
        Type: general
      – SubjectFull: data science
        Type: general
      – SubjectFull: Manufacturing Execution System
        Type: general
      – SubjectFull: production
        Type: general
      – SubjectFull: bottlenecks
        Type: general
      – SubjectFull: big data
        Type: general
      – SubjectFull: Production system
        Type: general
      – SubjectFull: Analytics
        Type: general
      – SubjectFull: machine learning
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      – SubjectFull: active period
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      – SubjectFull: manufacturing
        Type: general
      – SubjectFull: maintenance
        Type: general
      – SubjectFull: Throughput
        Type: general
      – SubjectFull: MES
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      – SubjectFull: industry 4.0
        Type: general
      – SubjectFull: Throughput bottleneck detection
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      – TitleFull: Data-driven algorithm for throughput bottleneck analysis of production systems
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