Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience--the capability to maintain core functions during disruptions--is crucial for proactive risk mitigation and robust network design. However, existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity dependencies inherent in supply chain networks. These limitations motivate the definition of a novel problem and the development of targeted modeling solutions. To address these challenges, we formalize a novel problem: Supply Chain Resilience Inference (SCRI), defined as predicting supply chain resilience using hypergraph topology and observed inventory trajectories without explicit dynamic equations. To solve this problem, we propose the Supply Chain Resilience Inference Hypergraph Network (SC-RIHN), a novel hypergraph-based model leveraging set-based encoding and hypergraph message passing to capture multi-party firm-product interactions. Comprehensive experiments demonstrate that SC-RIHN significantly outperforms traditional MLP, representative graph neural network variants, and ResInf baselines across synthetic benchmarks, underscoring its potential for practical, early-warning risk assessment in complex supply chain systems.

Abstract: Based on economic theories and integrated with machine learning technology, this study explores the collaborative model of Supply Chain Management - Financial Supply Chain Management (SCM - FSCM), aiming to solve supply chain issues (efficiency l oss, financing constraints, risk transmission) caused by the disconnection of the "three flows" (capital flow, logistics flow, information flow) and further improve overall economic benefits. Firstly, the study combines Transaction Cost Theory and Information Asymmetry Theory, adopts algorithms such as random forests to process multi - dimensional supply chain data, identifies obstacles to the collaboration of the "three flows", and constructs a data - driven three - dimensional (cost - efficiency - risk) a nalysis framework. Secondly, it designs a Financial Supply Chain Management model of "core enterprise credit empowerment + dynamic pledge financing". Based on inventory/order data in Supply Chain Management, it applies Long Short - Term Memory (LSTM) netwo rks to realize demand forecasting, and at the same time uses clustering/regression algorithms to quantify benefit distribution, so as to achieve reasonable allocation of financing costs. In addition, the study also combines Game Theory and reinforcement learning to optimize the supply chain inventory - procurement mechanism (adjusts strategies through scenario simulation to solve problems caused by the "bullwhip effect"); and integrates accounts receivable financing in Financial Supply Chain Management with credit assessment based on eXtreme Gradient Boosting (XGBoost) to realize rapid monetization of inventory.

Global supply lines have been severely disrupted by the COVID-19 epidemic and the conflict between Russia and Ukraine, which has sharply increased the price of commodities and generated inflation. These incidents highlight how critical it is to improve supply chain resilience (SCRES) in order to fend off unforeseen setbacks. Controlling both internal and external interruptions, such as transportation problems brought on by natural catastrophes and wars, is the responsibility of SCRES. Enhancing resilience in supply chains requires accurate and timely information transfer. Promising answers to these problems can be found in the Metaverse and ChatGPT, two new digital technologies. The Metaverse may imitate real-world situations and offer dynamic, real-time 3D representations of supply chain data by integrating blockchain, IoT, network connection, and computer power.Large-scale natural language processing model ChatGPT improves communication and data translation accuracy and speed. To manage risk and facilitate decision making in Supply Chain management, firms should increase information transmission, Speed and quality. This study aim to show the importance of ChatGPT and Metaverse technologies to improve SCRES, with an emphasis on the most important criteria for SCRES, and maturity factor that can influence directly the SC development.

ABSTRACT In light of the Industry 4.0 era, the global pandemic, and wars, interest in deploying digital technologies to increase supply chain resilience (SCRes) is rising. The utilization of recommender systems as a supply chain (SC) resilience measure is neglected, although these systems can enhance SC resilience. To address this problem, this research proposed a data-driven supply chain disruption response framework based on intelligent recommender system techniques. A prototype implementation was conducted to validate the developed framework through a practical use case. Results show that the proposed framework can be implemented as an effective SC disruption mitigation measure in the SCRes response phase and help SC participants better react after the SC disruption. Keywords: Supply chain resilience, Disruption risk, Recommender System, Supply chain risk management, Decision Support System 1 INTRODUCTION Supply chains (SC) are becoming more sophisticated and complex with globalization, as well as more risks and uncertainty (Manners-Bell 2017).

Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is often intransparent and incomplete, making it difficult to obtain precise predictions about prospective risks. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers. To predict missing information in the graph, we apply state-of-the-art knowledge graph completion methods and attain a mean reciprocal rank of 0.4377 with the best model. Further, we apply graph analysis algorithms to identify critical entities in the supply network, supporting supply chain managers in automated risk identification.

Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and others endangers sustainable production and life on land (SDG goal 15), which translates into food insecurity and poorer populations. Fortunately, modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather in our societies. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear. This makes it challenging to emploring digital technologies within the spheres of extreme weather. In this paper, we employed the Delphi Best Worst method and Machine learning approaches to identify and assess the push factors of technology. The BWM evaluation revealed that predictive nature was AI's most important criterion and role, while the mass-market potential was the less important criterion. Based on this outcome, we tested the predictive ability of machine elarning on a publilcly available dataset to affrm the predictive rols of AI. We presented the managerial and methodological implications of the study, which are crucial for research and practice. The methodology utilized in this study could aid decision-makers in devising strategies and interventions to safeguard sustainable production. This will also facilitate allocating scarce resources and investment in improving AI techniques to reduce the adverse impacts of extreme events. Correspondingly, we put forward the limitations of this, which necessitate future research.

In recent years, the high complexity of the business environment, dynamism and environmental change, uncertainty and concepts such as globalization and increasing competition of organizations in the national and international arena have caused many changes in the equations governing the supply chain. In this case, supply chain organizations must always be prepared for a variety of challenges and dynamic environmental changes. One of the effective solutions to face these challenges is to create a resilient supply chain. Resilient supply chain is able to overcome uncertainties and disruptions in the business environment. The competitive advantage of this supply chain does not depend only on low costs, high quality, reduced latency and high level of service. Rather, it has the ability of the chain to avoid catastrophes and overcome critical situations, and this is the resilience of the supply chain. AI and IoT technologies and their combination, called AIoT, have played a key role in improving supply chain performance in recent years and can therefore increase supply chain resilience. For this reason, in this study, an attempt was made to better understand the impact of these technologies on equity by examining the dimensions and components of the Artificial Intelligence of Things (AIoT)-based supply chain. Finally, using nonlinear fuzzy decision making method, the most important components of the impact on the resilient smart supply chain are determined. Understanding this assessment can help empower the smart supply chain.

Turbulent times can expose weaknesses in distribution chains, putting stress on chokepoints and reducing access to critical components, suppliers, and capital. The ability to respond to changes rapidly and effectively depends on a variety of assets and business capabilities: replacing or augmenting supply sources in response to partner inventory issues or trade war-induced tariffs or restrictions, and having agile manufacturing processes that reduce redundancies and streamline product inputs. Each thread of this complex web of factors that affects supply chain resilience must be examined and assessed separately to identify potential vulnerabilities and mitigate them. At the same time, most of this web simplifies down to two primary strands, common capabilities that run through every resilient business: increasing visibility and maintaining sufficient diversity in the supply chain. Capability 1--Insight Developing data capabilities and analysis tools that reach from suppliers and partners all across the value chain through to end customers, allowing companies to anticipate and prevent supply disruptions.

Supply chain resilience is more critical than ever amid unprecedented disruptions due to the COVID-19 pandemic. As a result, supply chains are viewed more as lifelines connecting business and society to goods and services -- from personal protective equipment to personal digital assistants -- with robotic process automation (RPA), artificial intelligence (AI) and machine learning pressed into service at an accelerated pace. Working virtually alongside humans or replacing them, RPA software bots are as essential as the front-line workers who keep the supply chain flowing from product conception to delivery. But the rush to take full advantage of RPA for highly repetitive, time-consuming tasks and AI for more complex supply chain processes is not without roadblocks as companies shift priorities, digitally transform and contend with the aftershocks of the pandemic. According to KPMG's 2020 Outlook, CEOs surveyed during July and August identified supply chain risk as one of the "greatest threats to their organizations' growth."