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Sustainability
Special Issues
Industry 4.0 and Artificial Intelligence for Resilient Supply Chains
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Industry 4.0 and Artificial Intelligence for Resilient Supply Chains

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 3957

Special Issue Editors

Dr. Matevz Obrecht

E-Mail Website
Guest Editor
Department for Managing Logistics and Supply Chain, Faculty of Logistics, University of Maribor, 2000 Maribor, Slovenia
Interests: sustainable supply chains; green logistics; environmental sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Augustyn Lorenc

E-Mail Website
Guest Editor
Department of Rail Vehicles and Transport, Cracow University of Technology, 31-878 Cracow, Poland
Interests: IoT; big data; warehouses/distribution centers; logistics; risk analysis; simulation; cargo forwarding; artificial neural networks

Special Issue Information

Dear Colleagues,

Industry 4.0 is the term related to the rapid growth of technology, connected devices to the global network, automatization of processes, automatic analysis, artificial intelligence, and big data analysis. These technologies have a major impact on the effectiveness of almost all logistics processes in companies as well as on their sustainability performance.

The digitalisation of data entails the implementation of many systems such as ERP, MRP, WMS, TMS, YMS and more. Due to the use of at least a few systems in each company, the problem of big datasets and with connecting the data to find useful information is crucial. Many managers complain about reports, which are automatically generated once a week or month. These kinds of reports are useless; the company needs quick access to data, alerting systems to predict the disruption and probability of risk. Moreover, companies need new technologies such as sensors for monitoring the position of tools or transport modes, sensors for measuring the state of devices and also conditions in transport to be able to act in real time. It is very important to predict potential disruptions in the transport chain, so artificial intelligence, simulation & digital twins, and big data have a great impact to develop such kinds of systems. Thanks to this, the effectiveness of logistics processes can be higher. All these new digital technologies also enable significant improvements in environmental sustainability of logistics processes and operations as well as managing supply chains.

This Special Issue focuses on the modern technologies and solutions related to Industry 4.0 and makes the effectiveness of logistics higher as well as it contributes to the sustainability of logistics and supply chains.

This Special Issue welcomes contributions from the following themes (this list is not exhaustive):

  • Industry 4.0;
  • Internet of Things (IoT);
  • Sensors;
  • Automatization of production;
  • Automatization of logistics processes;
  • Artificial intelligence (AI);
  • Artificial neural network (ANN);
  • Deep learning (DL);
  • Big data analysis;
  • Modern technologies;
  • Improving sustainability with digital technologies;
  • Impact of ML/AI, IoT and real-time operations on sustainability;
  • Smart solutions for logistics, air, maritime and road transport.

Dr. Matevz Obrecht
Dr. Augustyn Lorenc
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • industry 4.0
  • Internet of Things (IoT)
  • sensors
  • automatization of production
  • automatization of logistics processes
  • artificial intelligence (AI)
  • artificial neural network (ANN)
  • deep learning (DL)
  • big data analysis
  • modern technologies
  • digital technologies and sustainability
  • green logistics
  • smart solutions for logistics and transport.

Published Papers (2 papers)

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Research

22 pages, 6060 KiB  
Article
Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics
by Augustyn Lorenc, Jakub Szarata and Michał Czuba
Sustainability 2023, 15(6), 4976; https://0-doi-org.brum.beds.ac.uk/10.3390/su15064976 - 10 Mar 2023
Cited by 3 | Viewed by 2115
Abstract
The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can [...] Read more.
The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m. Full article
(This article belongs to the Special Issue Industry 4.0 and Artificial Intelligence for Resilient Supply Chains)
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22 pages, 758 KiB  
Article
Performance-Oriented UWB RTLS Decision-Making Approach
by Simona Šinko, Enej Marinič, Blaž Poljanec, Matevž Obrecht and Brigita Gajšek
Sustainability 2022, 14(18), 11456; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811456 - 13 Sep 2022
Cited by 1 | Viewed by 1467
Abstract
When introducing new technologies, companies are repeatedly faced with choosing between solutions from different providers. Regardless of all the good technical characteristics of the technology, if it is chosen inappropriately, it can prove to be a cost driver instead of something that brings [...] Read more.
When introducing new technologies, companies are repeatedly faced with choosing between solutions from different providers. Regardless of all the good technical characteristics of the technology, if it is chosen inappropriately, it can prove to be a cost driver instead of something that brings added value to the system. Aware of this, we considered selecting a real-time location system (RTLS) based on Ultra-wideband technology in the indoor work environment. In practice and theory, it has been proven that the introduction of the RTLS can have highly positive effects on performance and business sustainability indicators. When reviewing the literature, it was noticed that authors solely focus on the technical properties of the systems and prices when giving guidelines on selecting the optimal RTLS. This article aims to provide advanced guidelines for UWB RTLS selection, proposing a phased selection process which is the main novelty proposed and investigated in this research. The guidelines are based on fragmented recommendations in the scientific literature that have been identified, gathered, considered, and reasonably allocated to the advanced performance-oriented phased selection process. In practice, this approach enables decision-makers to choose the most efficient and most appropriate UWB RTLS for specific logistics systems. Full article
(This article belongs to the Special Issue Industry 4.0 and Artificial Intelligence for Resilient Supply Chains)
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