A platform architecture for industry 4.0 driven intelligence amplification in logistics

Jean Paul Sebastian Piest*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The aim of this doctoral consortium paper is to introduce my doctoral research proposal in the field of enterprise computing. The scientific problem that I address in my research is the limited usage of real-time data, originating from Industry 4.0 (I4.0) technologies (e.g. smart IoT devices and sensors), by Small-and Medium sized Enterprises (SMEs) in the logistics industry. I argue that the development of an industry platform for real-time data streaming and analytics would allow SMEs to benefit from such data and help them streamline their operational processes and overall performance. The main contribution of my research is a reference architecture for such a platform, geared for the needs of SMEs, and incorporating: 1) a logistics canonical data model to collect and harmonize I4.0 data, 2) an automatic schema matcher to map SME data to the logistics canonical data model, 3) autonomous data mining agents, 4) an adoption strategy based on the concept of intelligence amplification and 5) key performance indicators to measure adoption effects on operational and decisional performance.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop, EDOCW 2019
PublisherIEEE
Pages174-178
Number of pages5
ISBN (Electronic)9781728145983
DOIs
Publication statusPublished - 21 Nov 2019
Event23rd IEEE International Enterprise Distributed Object Computing Conference, EDOCW 2019: the Enterprise Computing conference - Paris, France
Duration: 28 Oct 201931 Oct 2019
Conference number: 23

Publication series

NameProceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW
Volume2019-October
ISSN (Print)1541-7719

Conference

Conference23rd IEEE International Enterprise Distributed Object Computing Conference, EDOCW 2019
Abbreviated titleIEEE EDOC 2019
CountryFrance
CityParis
Period28/10/1931/10/19

Keywords

  • Data mining
  • Enterprise architecture
  • Industry 4.0
  • Intelligence amplification
  • Logistics
  • Small and medium sized enterprises

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  • Cite this

    Piest, J. P. S. (2019). A platform architecture for industry 4.0 driven intelligence amplification in logistics. In Proceedings - 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop, EDOCW 2019 (pp. 174-178). [8907312] (Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW; Vol. 2019-October). IEEE. https://doi.org/10.1109/EDOCW.2019.00038