Please use this identifier to cite or link to this item: doi:10.22028/D291-34136
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Title: Manufacturing execution systems driven process analytics: A case study from individual manufacturing
Author(s): Mayer, Lea
Mehdiyev, Nijat
Fettke, Peter
Editor(s): Makris, Sotiris
Language: English
Title: Procedia CIRP
Startpage: 284
Endpage: 289
Publisher/Platform: Elsevier
Year of Publication: 2021
Place of publication: Amsterdam
Title of the Conference: CIRP CATS 2020
Place of the conference: Athens, Greece
Publikation type: Conference Paper
Abstract: The ability to trace and analyze the processes over various stages of the production value chain has become vital to generate the added value. In this context a thorough integration of the business and manufacturing processes is an essential prerequisite to facilitate the data-driven decision making which is crucial for product personalization. Implementing process analytics techniques on the critical production process data delivered by Manufacturing Execution Systems (MES) provides excessive opportunities to enhance the traceability and planning processes. To illuminate such an analytical approach, we examine in this article a use case from the individual manufacturing domain and discuss the details of the integration scenarios for MES data-driven analytics. The necessity of embedding the construction design data generated in Computer-Aided Design (CAD) systems to the proposed analytics process is particularly highlighted. The scientific and practical implications of the process analytics for different manufacturing analytics scenarios are discussed as well.
DOI of the first publication: 10.1016/j.procir.2020.05.239
URL of the first publication: https://www.sciencedirect.com/science/article/pii/S2212827120314608
Link to this record: hdl:20.500.11880/31473
http://dx.doi.org/10.22028/D291-34136
ISSN: 2212-8271
Date of registration: 5-Jul-2021
Notes: Procedia CIRP, Volume 97, 2021, Pages 284-289
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Wirtschaftswissenschaft
Professorship: HW - Prof. Dr. Peter Loos
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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