Manufacturing Supply Chain and Logistics Technology
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 21
Special Issue Editors
Interests: product R&D management; logistics and supply chain management; knowledge management; manufacturing information technology
Interests: logistics smulation and optimization; data-driven decision making; knowledge service
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid advancement of digital technologies, intelligent manufacturing, and global trade networks, manufacturing supply chains and logistics technologies are undergoing profound transformation. This Special Issue aims to advance knowledge on the digital and intelligent transformation of manufacturing supply chains and logistics systems. Emerging technologies—such as artificial intelligence, knowledge graphs, digital twins, blockchain, and the Internet of Things (IoT)—enable new possibilities for real-time monitoring, predictive analytics, and intelligent decision-making in complex supply chain and logistics systems. Meanwhile, global market volatility, geopolitical risks, and sustainability pressures have made supply chain resilience and adaptability critical concerns for both academia and industry.
The Special Issue ‘Manufacturing Supply Chain and Logistics Technology’ will focus on key theoretical issues and technological challenges in the field, seeking original research contributions with both innovation and practical value. Topics of interest include the digital and intelligent transformation of manufacturing supply chains, logistics system optimization and intelligent scheduling, predictive decision-making methods based on big data and artificial intelligence, knowledge-driven supply chain management, green and sustainable supply chain design, supply chain risk management and resilience enhancement, as well as related case studies and interdisciplinary explorations. The Special Issue aims to build a bridge between academic research and industrial practice, fostering both theoretical development and engineering applications of supply chain and logistics technologies.
We look forward to receiving your contributions.
Dr. Lina He
Dr. Zhenyong Wu
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
- manufacturing supply chain
- logistics technology
- smart manufacturing
- digital twin
- artificial intelligence (AI)
- AI agent
- Internet of Things (IoT)
- blockchain
- knowledge service
- sustainable supply chain
- supply chain resilience
- risk management
- predictive decision-making
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