research-article

Digital Shop Floor Management: A Practical Framework For Implementation

Authors:

Raphael Hartner,

Vitaliy Mezhuyev,

Martin Tschandl,

Christian BischofAuthors Info & Claims

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

Pages 41 - 45

https://doi.org/10.1145/3384544.3384611

Published: 17 April 2020 Publication History

Abstract

In the context of manufacturing, shop floor management (SFM) is employed to ensure efficient production operations and workflows. Advanced technologies and methods can be used to improve the SFM and achieve close to real-time responsiveness. Even though there is a number of research available for the digitalized SFM (DSFM), a supportive framework for implementation purposes was not considered yet. Consequently, this paper utilizes concepts from related disciplines and research areas to derive an architectural framework for a DSFM. This particular architecture is then implemented to ensure its practicability and foster the understanding of challenges and opportunities. The proposed multi-layer framework and supportive methods can be employed by manufacturing companies to implement a DSFM focused on interoperability, security and low-latency.

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Cited By

Saraswat PAgrawal RRane S(2024)Technological integration of lean manufacturing with industry 4.0 toward lean automation: insights from the systematic review and further research directionsBenchmarking: An International Journal10.1108/BIJ-05-2023-0316Online publication date: 14-Jun-2024
https://doi.org/10.1108/BIJ-05-2023-0316
Eichenseer PWinkler H(2024)A data-oriented shopfloor management in the production context: a systematic literature reviewThe International Journal of Advanced Manufacturing Technology10.1007/s00170-024-14238-8134:9-10(4071-4097)Online publication date: 17-Sep-2024
https://doi.org/10.1007/s00170-024-14238-8
Teubl MMezhuyev VTschandl M(2023)Development of an ML model for the classification of surface quality in a milling processProceedings of the 2023 9th International Conference on Computer Technology Applications10.1145/3605423.3605449(214-219)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3605423.3605449
Show More Cited By

Index Terms

Digital Shop Floor Management: A Practical Framework For Implementation
1. Applied computing
  1. Enterprise computing
    1. IT architectures
2. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. n-tier architectures

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cover image ACM Other conferences

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

February 2020

382 pages

ISBN:9781450376655

DOI:10.1145/3384544

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 April 2020

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ICSCA 2020

ICSCA 2020: 2020 9th International Conference on Software and Computer Applications

February 18 - 21, 2020

Langkawi, Malaysia

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Cited By

Saraswat PAgrawal RRane S(2024)Technological integration of lean manufacturing with industry 4.0 toward lean automation: insights from the systematic review and further research directionsBenchmarking: An International Journal10.1108/BIJ-05-2023-0316Online publication date: 14-Jun-2024
https://doi.org/10.1108/BIJ-05-2023-0316
Eichenseer PWinkler H(2024)A data-oriented shopfloor management in the production context: a systematic literature reviewThe International Journal of Advanced Manufacturing Technology10.1007/s00170-024-14238-8134:9-10(4071-4097)Online publication date: 17-Sep-2024
https://doi.org/10.1007/s00170-024-14238-8
Teubl MMezhuyev VTschandl M(2023)Development of an ML model for the classification of surface quality in a milling processProceedings of the 2023 9th International Conference on Computer Technology Applications10.1145/3605423.3605449(214-219)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3605423.3605449
Stadlhofer AMezhuyev V(2023)Approach to provide interpretability in machine learning models for image classificationIndustrial Artificial Intelligence10.1007/s44244-023-00009-z1:1Online publication date: 2-Aug-2023
https://doi.org/10.1007/s44244-023-00009-z
Ojo-Gonzalez KBonilla-Morales BVargas-Lombardo M(2023)Software Quality in the IOT in Health Sector and Commerce SectorAdvanced Research in Technologies, Information, Innovation and Sustainability10.1007/978-3-031-48858-0_2(14-25)Online publication date: 20-Dec-2023
https://doi.org/10.1007/978-3-031-48858-0_2
Tran TRuppert TEigner GAbonyi J(2022)Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 SolutionsIEEE Access10.1109/ACCESS.2022.318249110(64348-64374)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3182491
Malaga AVinodh S(2021)Benchmarking smart manufacturing drivers using Grey TOPSIS and COPRAS-G approachesBenchmarking: An International Journal10.1108/BIJ-12-2020-062028:10(2916-2951)Online publication date: 26-Mar-2021
https://doi.org/10.1108/BIJ-12-2020-0620

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