Integrating Smart Production Logistics with Network Diagrams: A Framework for Data Visualization
2024 (English)In: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning: Proceedings of the 11th Swedish Production Symposium (SPS2024) / [ed] Joel Andersson; Shrikant Joshi; Lennart Malmsköld; Fabian Hanning, IOS Press, 2024, p. 601-612Conference paper, Published paper (Refereed)
Abstract [en]
This paper introduces a framework that integrates smart production logistics (SPL) with network diagrams. This integration enhances visibility in the material and information flow within the manufacturing sector, thereby adding value through data visualization. Drawing from a detailed case study in the automotive industry, we outline the essential components of network diagrams that are tailored to depict spatial-temporal data linked with material handling processes in an SPL context. This integrated approach presents managers with a new tool for optimizing planning and executing tasks related to the transport of materials and information. Furthermore, while the framework brings about significant technological progress, it also emphasizes the managerial implications of SPL data visualization. In particular, it highlights its potential to foster informed decision-making, resource optimization, and strategic forecasting. The paper also discusses prospective research avenues, stressing the importance of dynamic diagrams that decode complex patterns from digital data and the incorporation of sustainability metrics in SPL assessments.
Place, publisher, year, edition, pages
IOS Press, 2024. p. 601-612
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 52
Keywords [en]
Automotive industry, Case study, Data visualization, Network diagram, Smart production logistics, Data integration, Decision making, Materials handling, Visualization, Case-studies, Handling process, Information flows, Manufacturing sector, Material Flow, Material handling, Network diagrams, Production logistics, Smart production logistic, Spatial-temporal data
National Category
Production Engineering, Human Work Science and Ergonomics Environmental Management Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-23829DOI: 10.3233/ATDE240202ISI: 001229990300048Scopus ID: 2-s2.0-85191318100ISBN: 978-1-64368-510-6 (print)ISBN: 978-1-64368-511-3 (electronic)OAI: oai:DiVA.org:his-23829DiVA, id: diva2:1857269
Conference
11th Swedish Production Symposium, SPS 2024 Trollhättan 23 April 2024 through 26 April 2024
Note
CC BY-NC 4.0 DEED
© 2024 The Authors
Correspondence Address: Y. Jeong; KTH Royal Institute of Technology, Sweden; email: yongkuk@kth.se
The authors would like to acknowledge the support of the National Research Infrastructure for Data Visualization (InfraVis).
2024-05-132024-05-132025-02-10