Digital Technologies in Supply Chain Risk Management

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Logistics logistics	3.6	8.0	2017	25.6 Days	CHF 1400	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit
Systems systems	3.1	4.1	2013	18.8 Days	CHF 2400	Submit
Journal of Marine Science and Engineering jmse	2.8	5.0	2013	15.6 Days	CHF 2600	Submit
Platforms platforms	-	-	2023	15.0 days *	CHF 1000	Submit

* Median value for all MDPI journals in the first half of 2025.

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Published Papers (6 papers)

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25 pages, 1872 KiB  

Open AccessArticle

Food Safety Risk Prediction and Regulatory Policy Enlightenment Based on Machine Learning

by Daqing Wu, Hangqi Cai and Tianhao Li

Systems 2025, 13(8), 715; https://doi.org/10.3390/systems13080715 - 19 Aug 2025

Viewed by 182

Abstract

This paper focuses on the challenges in food safety governance in megacities, taking Shanghai as the research object. Aiming at the pain points in food sampling inspections, it proposes a risk prediction and regulatory optimization scheme combining text mining and machine learning. First, [...] Read more.

This paper focuses on the challenges in food safety governance in megacities, taking Shanghai as the research object. Aiming at the pain points in food sampling inspections, it proposes a risk prediction and regulatory optimization scheme combining text mining and machine learning. First, the paper uses the LDA method to conduct in-depth mining on over 78,000 pieces of food sampling data across 34 categories in Shanghai, so as to identify core risk themes. Second, it applies SMOTE oversampling to the sampling data with an extremely low unqualified rate (0.5%). Finally, a machine learning prediction model for food safety risks is constructed, and predictions are made based on this model. The research findings are as follows: ① Food risks in Shanghai show significant characteristics in terms of time, category, and pollution causes. ② Supply chain links, regulatory intensity, and consumption scenarios are among the core influencing factors. ③ The traditional “full coverage” model is inefficient, and resources need to be tilted toward high-risk categories. ④ Public attention (e.g., the “You Order, We Inspect” initiative) can drive regulatory responses to improve the qualified rate. Based on these findings, this paper suggests that relevant authorities should ① classify three levels of risks for categories, increase inspection frequency for high-risk products in summer, adjust sampling intensity for different business entities, and establish a dynamic hierarchical regulatory mechanism; ② tackle source governance, reduce environmental pollution, upgrade process supervision, and strengthen whole-chain risk prevention and control; and ③ promote public participation, strengthen the enterprise responsibility system, and deepen the social co-governance pattern. This study effectively addresses the risk early warning problems in food safety supervision of megacities, providing a scientific basis and practical path for optimizing the allocation of regulatory resources and improving governance efficiency. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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33 pages, 6586 KiB  

Open AccessArticle

Pricing Strategy for Sustainable Recycling of Power Batteries Considering Recycling Competition Under the Reward–Penalty Mechanism

by Hairui Wei and Ziming Qi

Sustainability 2025, 17(16), 7224; https://doi.org/10.3390/su17167224 - 10 Aug 2025

Viewed by 392

Abstract

With the large-scale power batteries approaching their retirement phase, efforts are being made to advance the recycling and cascade utilization of power batteries for electric vehicles (EVs). This paper constructs a closed-loop supply chain (CLSC) of power batteries led by the battery manufacturer [...] Read more.

With the large-scale power batteries approaching their retirement phase, efforts are being made to advance the recycling and cascade utilization of power batteries for electric vehicles (EVs). This paper constructs a closed-loop supply chain (CLSC) of power batteries led by the battery manufacturer (BM) and composed of the electric vehicle manufacturer (EVM) and third-party recycler (TPR). The study investigates the optimal pricing strategies of this CLSC with the consideration of recycling competition under the government’s reward–penalty mechanism. This paper establishes five recycling modes, namely independent recycling and cooperative recycling, under dual-channel recycling, and further discusses the effects of the government reward–penalty mechanism and recycling competition on the recycling rate, profits, and recycling pricing of the CLSC in each recycling mode. The following conclusions are found: (1) An increase in the reward–penalty intensity will increase the recycling rate, sales price of EVs, wholesale price, transfer price, recycling price, and the profit of each recycler in the CLSC. (2) An increase in the recycling competition will result in the reduction of the profit of each enterprise, and will also lead to the reduction of the recycling rate. (3) Cooperation between enterprises can inhibit the recycling volume of other enterprises to a certain extent. The cooperation between the EVM and BM can increase the recycling volume and the sales volume of EVs. (4) The leadership of the BM in the supply chain is embodied in the recycling and profit. For other members of the supply chain, it is very important to strive for cooperation with the leaders in the supply chain. These research conclusions can provide theoretical support for optimizing the power battery recycling system, formulating relevant policies, and improving the efficiency of resource recycling, thereby promoting the sustainable development of the new energy industry. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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18 pages, 1065 KiB  

Open AccessArticle

A Machine Learning-Based Model for Predicting High Deficiency Risk Ships in Port State Control: A Case Study of the Port of Singapore

by Ming-Cheng Tsou

J. Mar. Sci. Eng. 2025, 13(8), 1485; https://doi.org/10.3390/jmse13081485 - 31 Jul 2025

Viewed by 281

Abstract

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and [...] Read more.

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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33 pages, 1578 KiB  

Open AccessArticle

Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction

by Daqing Wu, Tianhao Li, Hangqi Cai and Shousong Cai

Systems 2025, 13(7), 615; https://doi.org/10.3390/systems13070615 - 21 Jul 2025

Viewed by 421

Abstract

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory [...] Read more.

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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28 pages, 1220 KiB  

Open AccessArticle

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

Viewed by 898

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  

Open AccessArticle

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 719

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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