Data-Driven Digital Transformation in Industry 4.0

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1528

Special Issue Editors


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Guest Editor
Department of Computer Science, DEIS, Aalborg University, 9220 Aalborg, Denmark
Interests: industrial informatics; smart grid; blockchain; IoT platforms

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Guest Editor
School of Engineering, Faculty of Technology, University of Sunderland, Sunderland SR1 3SD, UK
Interests: asset management; industry 4 and 5 technologies; predictive maintenance

Special Issue Information

Dear Colleagues,

Digital technologies are transforming industrial frameworks. Recent technology breakthroughs in fields such as AI, robotics, and the Internet of Things, often under the umbrella term of Industry 4, have shown to be significant in allowing organizations to adopt new manufacturing processes using real-time data analytics. Digital manufacturing technologies, including the real-time management of assets, have provided new insights for manufacturers to better understand the root cause of issues and therefore embrace and adopt Industry 4 technologies. At the core of Industry 4 is the ability to link networks together as one system to allow a real-time flow of information via a secure and reliable digital infrastructure. The MDPI Electronics journal seeks submissions for a Special Issue on data-driven digital transformation in Industry 4.0. We welcome submissions from researchers working in the field of data-driven digital transformation and Industry 4.0 applications. Contributions should include discussions on real-life applications and new research perspectives.

The topics of interest include, but are not limited to, the following:

  • Big Data applied to Industry 4.0;
  • Machine learning applied to asset management;
  • Predictive maintenance and condition-based maintenance;
  • Industrial communication protocols;
  • Communication middleware for Industry 4.0;
  • Ontologies for data management;
  • Cybersecurity issues of industry 4.0;
  • Industry 4 and 5 emerging

Dr. Michele Albano
Prof. Dr. David Baglee
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CBM
  • digitalization
  • industrial cyber security
  • emerging technologies
  • Industry 4.0
  • Industry 5.0
  • MIMOSA
  • Big Data
  • asset management
  • predictive mainte-nance
  • industrial protocols
  • ontologies

Published Papers (1 paper)

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Research

23 pages, 3221 KiB  
Article
Digital Transformation Management of Supply Chain Firms Based on Big Data from DeFi Social Media Profiles
by Damianos P. Sakas, Nikolaos T. Giannakopoulos, Marina C. Terzi, Nikos Kanellos and Angelos Liontakis
Electronics 2023, 12(20), 4219; https://doi.org/10.3390/electronics12204219 - 12 Oct 2023
Cited by 1 | Viewed by 1138
Abstract
Emerging technologies in the digital context can favor industrial sector firms in their aim to improve their performance. Digitalization is mainly expressed through the utilization of big data that originate from various sources. Blockchain technology has led to the extended adoption of capitalization [...] Read more.
Emerging technologies in the digital context can favor industrial sector firms in their aim to improve their performance. Digitalization is mainly expressed through the utilization of big data that originate from various sources. Blockchain technology has led to the extended adoption of capitalization of Decentralized Finance (DeFi) services, such as cryptocurrency trade platforms. Supply chain firms, in their quest to exploit any means and collaborations available to promote their services, could place advertisements on DeFi’s social media profiles to boost their financial performance. Social media analytics, as a part of the big data family, are an emerging tool for promoting a firm’s digital transformation, based on the plethora of customer behavioral data they provide. This study aims to examine whether the social media analytics of DeFi platforms are capable of affecting their website visibility, as well as the financial performance of supply chain firms. To do so, the authors collected data from the social media profiles of the most-known DeFi platforms and web analytics from the most significant supply chain firms’ websites. For this purpose, proper statistical analysis, Fuzzy Cognitive Mapping, Hybrid Modeling, and Cognitive Neuromarketing models were adopted. Throughout the present research, it has been discerned that from an increase in the social media analytics of DeFi platforms, their website visibility increases, while the organic and paid traffic costs of supply chain firms decrease. Supply chain firms’ website customers tend to increase at the same time. Full article
(This article belongs to the Special Issue Data-Driven Digital Transformation in Industry 4.0)
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