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Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Production and Automation Engineering)ORCID iD: 0000-0003-4647-9363
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering & Management, Uppsala University, Sweden. (Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 737-748Conference paper, Published paper (Refereed)
Abstract [en]

Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these technologies can be used to meet the long-term enterprise goals of profitability, sustainability, and stability by increasing the throughput and reducing production costs. Owing to digitization, manufacturing companies can now explore operational level data to track the performance of systems making processes more transparent and efficient. This untapped granular data can be leveraged to better understand the system and identify constraining activities or resources that determine the system’s throughput. In this paper, we propose a data-driven methodology that exploits the technique of data integration, process mining, and analytics based on factory physics to identify constrained resources, also known as bottlenecks. To test the proposed methodology, a case study was performed on an industrial scenario were a discrete event simulation model is built and validated to run future what-if analyses and optimization scenarios. The proposed methodology is easy to implement and can be generalized to any other organization that captures event data.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022. p. 737-748
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords [en]
Process Mining, Factory Physics, Data Analytics, Manufacturing, Bottleneck Detection
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-21114DOI: 10.3233/ATDE220192ISI: 001191233200062Scopus ID: 2-s2.0-85132820944ISBN: 978-1-64368-268-6 (print)ISBN: 978-1-64368-269-3 (electronic)OAI: oai:DiVA.org:his-21114DiVA, id: diva2:1656119
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Note

CC BY-NC 4.0

Corresponding Author: Mahesh Kumbhar, School of Engineering Science, University of Skövde, Skövde, Sweden; E-mail: mahesh.kumbhar@his.se

Available from: 2022-05-04 Created: 2022-05-04 Last updated: 2024-05-17Bibliographically approved

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Kumbhar, MaheshNg, Amos H. C.Bandaru, Sunith

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