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Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics
The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932793/ https://www.ncbi.nlm.nih.gov/pubmed/36820130 http://dx.doi.org/10.1016/j.jii.2023.100443 |
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author | Wu, Wei Shen, Leidi Zhao, Zhiheng Harish, Arjun Rachana Zhong, Ray Y. Huang, George Q. |
author_facet | Wu, Wei Shen, Leidi Zhao, Zhiheng Harish, Arjun Rachana Zhong, Ray Y. Huang, George Q. |
author_sort | Wu, Wei |
collection | PubMed |
description | The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative solutions to tackle the challenges remaining in CCL. Accordingly, this study proposes a cyber-physical platform framework applying the Internet of Everything (IoE) and Digital Twin (DT) technologies to promote information integration and provide smart services for different stakeholders in the CCL. In the platform, reams of data are generated, gathered, and leveraged to interconnect and digitalize physical things, people, and processes in cyberspace, paving the way for digital servitization. Deep learning techniques are used for accident identification and indoor localization based on Bluetooth Low Energy (BLE) to actualize real-time staff safety supervision in the cold warehouse. Both algorithms are designed to take advantage of the IoE infrastructure to achieve online self-adapting in response to surrounding evolutions. Besides, with the help of mobile and desktop applications, paperless operation for shipment, remote temperature and humidity (T&H) monitoring, anomaly detection and warning, and customer interaction are enabled. Thus, information traceability and visibility are highly fortified in this way. Finally, a real-life case study is conducted in a pharmaceutical distribution center to demonstrate the feasibility and practicality of the proposed platform and methods. The dedicated hardware and software are developed and deployed on site. As a result, the effectiveness of staff safety management, operational informatization, product quality assurance, and stakeholder loyalty maintenance shows a noticeable improvement. The insights and lessons harvested in this study may spark new ideas for researchers and inspire practitioners to meet similar needs in the industry. |
format | Online Article Text |
id | pubmed-9932793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99327932023-02-16 Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics Wu, Wei Shen, Leidi Zhao, Zhiheng Harish, Arjun Rachana Zhong, Ray Y. Huang, George Q. J Ind Inf Integr Article The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative solutions to tackle the challenges remaining in CCL. Accordingly, this study proposes a cyber-physical platform framework applying the Internet of Everything (IoE) and Digital Twin (DT) technologies to promote information integration and provide smart services for different stakeholders in the CCL. In the platform, reams of data are generated, gathered, and leveraged to interconnect and digitalize physical things, people, and processes in cyberspace, paving the way for digital servitization. Deep learning techniques are used for accident identification and indoor localization based on Bluetooth Low Energy (BLE) to actualize real-time staff safety supervision in the cold warehouse. Both algorithms are designed to take advantage of the IoE infrastructure to achieve online self-adapting in response to surrounding evolutions. Besides, with the help of mobile and desktop applications, paperless operation for shipment, remote temperature and humidity (T&H) monitoring, anomaly detection and warning, and customer interaction are enabled. Thus, information traceability and visibility are highly fortified in this way. Finally, a real-life case study is conducted in a pharmaceutical distribution center to demonstrate the feasibility and practicality of the proposed platform and methods. The dedicated hardware and software are developed and deployed on site. As a result, the effectiveness of staff safety management, operational informatization, product quality assurance, and stakeholder loyalty maintenance shows a noticeable improvement. The insights and lessons harvested in this study may spark new ideas for researchers and inspire practitioners to meet similar needs in the industry. Elsevier Inc. 2023-06 2023-02-16 /pmc/articles/PMC9932793/ /pubmed/36820130 http://dx.doi.org/10.1016/j.jii.2023.100443 Text en © 2023 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Wu, Wei Shen, Leidi Zhao, Zhiheng Harish, Arjun Rachana Zhong, Ray Y. Huang, George Q. Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title | Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title_full | Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title_fullStr | Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title_full_unstemmed | Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title_short | Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics |
title_sort | internet of everything and digital twin enabled service platform for cold chain logistics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932793/ https://www.ncbi.nlm.nih.gov/pubmed/36820130 http://dx.doi.org/10.1016/j.jii.2023.100443 |
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