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Digital Technologies in Supply Chain Risk Management
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Prof. Dr. Zongsheng Huang
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
Prof. Dr. Decui Liang
School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China
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Digital Technologies in Supply Chain Risk Management

Abstract submission deadline
30 October 2025
Manuscript submission deadline
31 December 2025
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1032

Topic Information

Dear Colleagues,

The global supply chain landscape has been profoundly reshaped by disruptive events such as the COVID-19 pandemic, geopolitical conflicts, regional wars, and escalating trade disputes. These challenges have exposed critical vulnerabilities and highlighted the urgent need for advanced tools to assess and mitigate risks. Emerging digital technologies, including large language models (LLMs), blockchain, artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), digital twins, and big data analytics, are transforming supply chain risk management. These innovations enhance visibility, traceability, and predictive capabilities, enabling stakeholders to address disruptions, geopolitical tensions, environmental challenges, and global economic volatility with greater precision. By integrating real-time data from sensors, automated systems, and distributed networks, digital tools empower stakeholders to make informed decisions, enhance collaboration, and foster resilience. For example, AI-driven predictive analytics anticipate risks, blockchain ensures transaction transparency, and IoT-enabled devices provide continuous monitoring for rapid response. Digital twins simulate supply chain networks to evaluate vulnerabilities, while Industry 5.0 merges human expertise with automation to create adaptive, human-centric systems. The maritime supply chain, in particular, has leveraged IoT, AI, blockchain, and digital twins to boost operational efficiency and mitigate disruptions effectively. This Topic focuses on the cutting-edge applications of digital technologies in supply chain risk management, with a particular emphasis on enhancing resilience amid global uncertainties. We welcome submissions that present innovative methodologies, case studies, and theoretical frameworks demonstrating the transformative impact of digital tools.

Topics of interest include the following:

  • Applications of AI, LLMs, and ML in risk prediction and mitigation;
  • Digital twins for simulating and addressing supply chain vulnerabilities;
  • Blockchain for enhancing transparency and security;
  • IoT and sensor networks for real-time risk monitoring;
  • Industry 5.0 integration of human expertise and automation;
  • Big data analytics for informed decision-making;
  • Cybersecurity solutions for digitalized supply chains;
  • Digital technologies in maritime logistics;
  • Digital tools for optimizing global shipping networks.

We invite researchers and practitioners to contribute to this Topic by sharing insights and advancements that deepen our understanding of digital technologies in supply chain risk management. By exploring these developments, we aim to foster the creation of more resilient, adaptive, and sustainable supply chains in an increasingly uncertain world.

Prof. Dr. Zongsheng Huang
Prof. Dr. Decui Liang
Topic Editors

Keywords

  • supply chain risk management
  • supply chain robustness
  • supply chain resilience
  • supply chain networks
  • Artificial Intelligence (AI)
  • Large Language Models (LLMs)
  • blockchain
  • Internet of Things (IoT)
  • maritime supply chain

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Logistics
logistics 		3.6 6.6 2017 28.5 Days CHF 1400 Submit
Sustainability
sustainability 		3.3 6.8 2009 19.7 Days CHF 2400 Submit
Systems
systems 		2.3 2.8 2013 19.6 Days CHF 2400 Submit
Journal of Marine Science and Engineering
jmse 		2.7 4.4 2013 16.4 Days CHF 2600 Submit

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Published Papers (2 papers)

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28 pages, 1220 KiB  
Article
Livestream Scheme Selection in the E-Commerce Supply Chain: Under Agency and Resale Sales Modes
by Tao Li, Shanping Xu, Qi Tan and Wenbo Teng
Systems 2025, 13(5), 397; https://doi.org/10.3390/systems13050397 - 21 May 2025
Abstract
As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate [...] Read more.
As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article
(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)
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27 pages, 6390 KiB  
Article
Resilience Analysis of Seaport–Dry-Port Network in Container Transport: Multi-Stage Load Redistribution Dynamics Following Cascade Failure
by Zhigang Lu and Wenhao Qiu
Systems 2025, 13(4), 299; https://doi.org/10.3390/systems13040299 - 19 Apr 2025
Viewed by 277
Abstract
Container shipping networks are vulnerable to cascading failures due to seaport disruptions, underscoring the need for resilient multimodal transport systems. This study proposes a cascading failure model for the seaport–dry-port network in container transport, incorporating a multi-stage load redistribution strategy (CM-SDNCT-MLRS) to enhance [...] Read more.
Container shipping networks are vulnerable to cascading failures due to seaport disruptions, underscoring the need for resilient multimodal transport systems. This study proposes a cascading failure model for the seaport–dry-port network in container transport, incorporating a multi-stage load redistribution strategy (CM-SDNCT-MLRS) to enhance network resilience. Extending the Motter–Lai framework, the model introduces multiple port state transitions and accounts for uncertainties in load redistribution, tailoring it to the cascading failure dynamics of SDNCT. Using empirical data from China’s coastal port system, the proposed MLRS dynamically reallocates loads through dry-port buffering, neighboring seaport sharing, and port skipping. This strategy effectively contains cascading failures, mitigates network efficiency losses, and protects major seaports while reducing mutual disruptions. Resilience analysis demonstrates that the network exhibits scale-free properties, with its resilience being highly sensitive to random port failures and critical port vulnerabilities. The experimental results highlight the pivotal role of dry ports, where operational numbers influence resilience more significantly than capacity. In addition, the study identifies the optimal port-skipping probability that mitigates cascading disruptions. These findings provide valuable insights for port management and logistics planning, contributing to the development of more resilient container transport networks. Full article
(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)
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