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Applications of Digital Technologies in Manufacturing and Sustainable Supply Chains

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 195

Special Issue Editor


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Guest Editor
Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 228A, 2800 Kongens Lyngby, Denmark
Interests: bioprocess engineering; digital twins; modelling and optimization; digitalization and sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Growing concerns about climate change and environmental burdens are shifting the paradigm and consumers' mindsets. Over the years, the development and implementation of sustainable manufacturing supply chains has increasingly gained more attention from academics and industries. However, it still represents a significant challenge. Furthermore, Industry 4.0 and related enabling technologies have brought about several technologies that have great potential to improve and overcome environmental limitations as well as streamline processes. This Special Issue calls for papers that aim at understanding the opportunities, enablers, and barriers to incorporating digital technologies into the design, planning, and optimization of supply chains towards sustainability. It also targets the collection of valuable technical, empirical, and theoretical insights regarding the adoption of these new technologies into supply chain management and overall operations. Therefore, we encourage contributions that address the following research topics:

  • Digital technologies and supply chain management towards sustainable operations;
  • Machine learning and artificial intelligence in modelling and optimization of sustainable manufacturing supply chains;
  • Surrogate modelling in sustainable manufacturing supply chain modelling and optimization;
  • Smart sensors and big data analytics in manufacturing supply chain operations;
  • Enablers, drivers, and barriers to integrating digital technologies in various stages of manufacturing supply chains;
  • Application of digital technologies in closed-loop manufacturing supply chains.

Dr. Carina L. Gargalo
Guest Editor

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

  • sustainability
  • modelling and optimization of manufacturing supply chains
  • environmental assessment of supply chains
  • sustainable manufacturing supply chains
  • digital twins
  • process systems engineering
  • smart sensors
  • machine learning
  • information technologies

Published Papers

There is no accepted submissions to this special issue at this moment.
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