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A Case Study Initiating Discrete Event Simulation as a Tool for Decision Making in I4.0 Manufacturing
University West, Department of Engineering Science, Division of Production Systems. (PTW iAIL)ORCID iD: 0000-0001-8962-0924
Siemens Energy AB, Trollhättan, (Swe).
2021 (English)In: Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356, Vol. 414 LNBIP, p. 84-96Article in journal (Refereed) Published
Abstract [en]

Smart manufacturing needs to handle increased uncertainty by becoming more responsive and more flexible to reconfigure. Advances in technology within industry 4.0 can provide acquisition of large amounts of data, to support decision making in manufacturing. Those possibilities have brought anew attention to the applicability of discrete event simulation for production flow modelling when moving towards design of logistics systems 4.0. This paper reports a study investigating challenges and opportunities for initiation of discrete event simulation, as a tool for decision making in the era of industry 4.0 manufacturing. The research has been approached through action research in combination with a real case study at a manufacturing company in the energy sector. The Covid-19 pandemic fated that adjusted and new ways of communication, collaboration, and data collection, in relation to the methods, had to be explored and tried. Throughout the study, production data, such as processing times, have been collected and analyzed for discrete event simulation modelling. The complexity of introducing discrete event simulation as a new tool for decision making is highlighted, where we emphasize the human knowledge and involvement yet necessary to understand and to draw conclusions from the data. The results also demonstrate that the data analysis has given valuable insights into production characteristics, that need addressing. Thus, revealing opportunities for how the initiative of introducing discrete event simulation as an anew tool in the wake of industry 4.0, can act as a catalyst for improved decision making in future manufacturing. © 2021, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2021. Vol. 414 LNBIP, p. 84-96
Keywords [en]
Decision making; Decision support systems; Industrial research; Industry 4.0, Action research; Large amounts of data; Logistics system; Manufacturing companies; Processing time; Production characteristics; Production flows; Smart manufacturing, Discrete event simulation
National Category
Reliability and Maintenance
Research subject
Production Technology; Work Integrated Learning
Identifiers
URN: urn:nbn:se:hv:diva-17354DOI: 10.1007/978-3-030-73976-8_7Scopus ID: 2-s2.0-85111005436OAI: oai:DiVA.org:hv-17354DiVA, id: diva2:1587350
Conference
7th International Conference on Decision Support System Technology, ICDSST 2021, Loughborough, 26 May 2021 - 28 May 2021
Note

The study was carried out within the AHIL-project, Artificial and Human Intelligence through Learning, funded by the Swedish Knowledge Foundation and University West

Available from: 2021-08-24 Created: 2021-08-24 Last updated: 2023-02-27Bibliographically approved

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Eriksson, Kristina M.

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