Supply Chain Risk Management Research Articles

EXPRESS: Balancing Resilience and Efficiency: A Literature Review on Overcoming Supply Chain Disruptions

The supply chain risk management literature differentiates between disruption risk that arises from supply disruptions to normal activities and recurrent risk that arises from problems in coordinating supply and demand in the absence of disruptions (Kleindorfer and Saad 2005). Over the past decades, significant research has been carried out to better understand supply chain resilience, i.e., the ability of a supply chain to mitigate disruptions. Supply chain efficiency, i.e., the ability to mitigate recurrent risks in the absence of disruptions has been studied even longer. But only recently have the topics of efficiency and resilience been coupled in the supply chain literature. In this literature review, we focus on the intersection of supply chain resilience and supply chain efficiency. We provide a thematic overview of literature streams according to the structure of the underlying supply chains. We identify various gaps in the current literature including areas in multi-echelon and multi-product supply chain research. Furthermore, we consider dual-purpose and dedicated levers for building resilience. Dual-purpose levers are resources that are able to promote efficiency in a supply chain while enhancing resilience in expectation. By contrast, dedicated resilience levers are resources that are able to guarantee the resilience of a supply chain in the face of particular disruptions without benefiting the supply chain in the absence of disruptions. We call for more research to better understand the value of dual-purpose and dedicated resilience levers to overall supply chain performance.

The Impact of AI on the Development of Manufacturing, Logistics, and Financial Company from Capital Market Perspectives

Abstract: This paper explores the impact of AI technology on enterprises, especially in finance and capital markets. With the rapid development of AI technology, enterprises hope to improve productivity, financial management, and market competitiveness through AI, and ultimately achieve sustainable development. The article aims to analyze the application cases of AI in head enterprises and their effects and put forward corresponding suggestions and insights. By analyzing case studies of enterprises such as Tesla, Amazon and JP Morgan, it elaborates on the application of AI in various areas such as enterprise manufacturing, supply chain management, automated trading, risk management and customer service. It reveals the great potential of AI in improving enterprise operational efficiency, reducing costs and optimizing decision-making. By analyzing the above cases, it is suggested that enterprises should strengthen data privacy and security management and improve technology transparency and interpretability when introducing AI technology. The study reveals that AI technology has potential but also faces challenges, and enterprises need to keep overcoming obstacles to achieve the wide application and deep integration of AI technology.

RETRACTION: Supply Chain Risk Management of Badminton Supplies Company Using Decision Tree Model Assisted by Fuzzy Comprehensive Evaluation

<scp>RETRACTION</scp>: Supply Chain Risk Management of Badminton Supplies Company Using Decision Tree Model Assisted by Fuzzy Comprehensive Evaluation

Post-Covid Impact on Supply Chain Risk Management: A Case Study of a Semiconductor Company

Post-Covid Impact on Supply Chain Risk Management: A Case Study of a Semiconductor Company

Exploring the critical factors in pharmaceutical supply chains revealed during COVID-19 and addressing missing links

The COVID-19 pandemic revealed profound weaknesses in global pharmaceutical supply chains, resulting in widespread shortages of critical medical supplies, delayed drug manufacturing, and disruptions in vaccine distribution. These supply chain vulnerabilities were exacerbated by several key factors, including an overreliance on a limited number of countries for active pharmaceutical ingredients (APIs), insufficient local manufacturing capacity, and logistical constraints in transportation and distribution networks. The pandemic also exposed inefficiencies in regulatory frameworks, leading to delays in approvals and limited agility in responding to rapidly changing demands. Furthermore, the lack of transparency and real-time monitoring across global supply chains hindered effective decision-making and coordination, aggravating the crisis. This paper provides an in-depth analysis of the critical factors that contributed to pharmaceutical supply chain disruptions during COVID-19, identifying missing links such as the need for diversification of supply sources, enhanced flexibility in manufacturing processes, and the integration of advanced technologies like artificial intelligence (AI), blockchain, and automation for supply chain optimization. The analysis highlights how supply chain visibility, digital innovation, and risk management strategies could improve resilience in the face of future global health crises. In addition to identifying these challenges, the paper proposes solutions to address the missing links in pharmaceutical supply chains, including the decentralization of production, the development of regional manufacturing hubs, and the creation of public-private partnerships to bolster local production. The role of government policies in facilitating faster regulatory approvals, fostering international cooperation, and incentivizing innovation is also explored. Case studies on successful post-COVID-19 adaptations, such as the rapid scale-up of vaccine production and the reconfiguration of personal protective equipment (PPE) supply chains, offer insights into effective strategies for building more resilient systems. Ultimately, this paper underscores the importance of a collaborative, multi-stakeholder approach to ensuring the stability and security of pharmaceutical supply chains. It calls for proactive measures to strengthen global supply chain infrastructures, improve regulatory agility, and embrace emerging technologies that can predict and respond to disruptions. These steps are crucial to safeguarding the continuous delivery of essential pharmaceuticals during future pandemics or other large-scale disruptions, ensuring global health security in an increasingly interconnected world.

تأثير إدارة مخاطر سلسلة التوريد على أداء المشروع في قطاع البناء والتشييد في المملكة العربية السعودية: دراسة حالة لشركة سابك

This research aims to analyze the impact of supply chain risk management on project performance in the construction sector in Saudi Arabia, by studying SABIC. The researcher used an analytical descriptive and questionnaire as a tool for collecting data from a sample of 30 respondents from SABIC staff and engineers. The questionnaire has been divided into three main axes: sources and types of supply chain risk, and the effect of supply chain risk management on project performance, practices, challenges and success factors for supply chain risk management. The data has been analyzed using descriptive, graded, broken and unit statistics. The study concluded that there was a positive and moral impact on the management of the chain of supply chain to perform the project in the construction and construction sector in Saudi Arabia. The study recommended that the concept and methodology for the risk management of supply chain at all stages of projects, and to benefit from technology and innovation in improving and developing the supply chain, communication and cooperation with all parties concerned with the supply chain, and commitment to standards Environmental, health and safety laws and safety related to the supply chain.

Cyber resilience in organisations and supply chains: from perceptions to actions

PurposeThis empirical study investigates the direct and indirect effects on managers’ perceptions of cyber risks, the implementation of cyber resilience strategies and the perceived effectiveness of these strategies for supply chains. Cyber risks pose significant threats to organisations and supply chains. Yet they remain insufficiently addressed and managed.Design/methodology/approachPrimary data were collected from a sample of Italian organisations using a survey. The structural equation modelling methodology was employed to empirically investigate cyber resilience strategies in supply chains.FindingsResults indicate that effective cyber resilience is linked to awareness of the negative impacts of cyber risks, particularly supply chain disruptions. This awareness leads to the adoption of various cyber resilience strategies. According to managers' perceptions, several strategies are identified in the study as the most effective in enhancing the cyber resilience supply chains. The findings offer insights for managers regarding the relationship between cyber risk perceptions, supply chain cyber resilience strategies and their effectiveness. These relationships are studied using the theory of perceived risk and the dynamic capabilities theory.Originality/valueThis study advances knowledge for academics and practitioners in the fields of supply chain resilience and supply chain risk management. It contributes to the development of a risk-based thinking model in organisations and supply chains by drawing upon a dual theoretical perspective.

Nexus supplier transparency and supply network accessibility: effects on buyer ESG risk exposure

PurposeThis paper investigates the role of nexus supplier transparency, which involves the collective information disclosure to the public by second-tier nexus suppliers, as an alternative mechanism for mitigating buyer environmental, social and governance (ESG) risk exposure. We also examine buyer supply network accessibility as a moderating factor that facilitates collecting detailed information and undertaking corrective actions accordingly.Design/methodology/approachWe collected a sample of 428 focal buyer firms and their supply networks up to third-tier suppliers. Data were obtained from Bloomberg and RepRisk databases. We identified critical nexus suppliers using data envelopment analysis (DEA) and tested hypotheses using regression analysis.FindingsThe results show that the benefits of nexus supplier transparency, such as reducing buyer ESG risk exposure, differ depending on the type of nexus supplier disclosing information and buyer supply network accessibility. Informational nexus supplier transparency was found to be beneficial. However, the results revealed the double-edged sword of monopolistic nexus supplier transparency, which benefits buyers with higher levels of accessibility but increases risk exposure for buyers with lower accessibility.Originality/valueThis study demonstrates that the transparency of critical second-tier suppliers mitigates buyer ESG risk exposure by providing information about lower tiers in the supply network. Challenging the notion of the focal buyer as the main orchestrator of supply chain initiatives, our alternative perspective opens a new avenue for risk management in multi-tier supply chains.

Supply chain risk management strategies and the performance of food processing companies

Supply chain risk management strategies and the performance of food processing companies

Sustainability and risk management in supply chains: a comparative analysis of methods and practices

Sustainability and risk management in supply chains: a comparative analysis of methods and practices

The Role of Information Processing and Digital Supply Chain Technology in Supply Chain Resilience through Supply Chain Risk (Management in Manufacturing Companies in DKI Jakarta)

The 2019 COVID-19 pandemic highlighted the importance of supply chain resilience in the face of global disruptions. Supply chain disruptions caused by isolation measures during the pandemic triggered market turmoil, logistical uncertainty, and reduced production capacity, which sometimes led to shutdowns. In this context, research on supply chain resilience is crucial to prepare companies for future disruptions. This research aims to examine the impact of supply chain resilience on supply chain continuity in manufacturing companies in DKI Jakarta, focusing on the role of digital supply chains, information processing capabilities, and supply chain risk management. This research method uses a quantitative approach. Data were collected from 300 respondents in the basic and chemical industry, miscellaneous industries, and consumer goods sectors through purposive sampling techniques. The analysis was conducted using Structural Equation Modeling (SEM) to identify the relationship between variables. The results showed that supply chain resilience has a significant positive influence on supply chain continuity. In addition, digital supply chains, information processing capabilities, and effective supply chain risk management were shown to support supply chain resilience in the studied sectors. The implication of this research is that it is important for companies to invest in digital technology, develop better information processing capabilities, and implement effective risk management to strengthen their supply chain resilience and continuity in the face of future global uncertainty.

Risk Management of Supply Chain in Prefabricated Buildings: A Case Study of Leishenshan Hospital

Based on the research of other scholars, this article divides the prefabricated building supply chain into six stages: design and development, production procurement, warehousing and transportation, lifting construction, and facility recycling. Taking the well-known prefabricated building of Leishenshan Hospital as an example, this study analyzes the possible risks of these six stages in the prefabricated building supply chain, such as insufficient design capabilities, production efficiency, the low standardization level of molds, transportation policy constraints, insufficient protection of components during the warehousing and transportation stage, insufficient organizational capacity, inadequate supervision, and problems such as missing recycling processes. Due to the special nature of Leishenshan Hospital, which has received great attention, some risks that have been avoided due to special reasons have been proposed, and the power of management personnel has been strengthened for these risks, digitization and standardization of molds, and strengthen on-site management level. From this study, construction, component production, and design enterprises can learn from measures such as increasing awareness of component recycling.

Supply chain risk management for humanitarian aid delivery: risk identification and contingency analysis

PurposeHumanitarian organizations are responsible for delivering aid under diverse circumstances in various contexts. We argue that humanitarian organizations face specific challenges in their logistics and operations. Thus, adequate supply chain risk management is required to facilitate effective and efficient humanitarian aid delivery.Design/methodology/approachWe first identify potential supply chain risks in humanitarian aid delivery from the literature. We then utilize an expert survey to determine the probability and impact ratings of the identified risks, assessing the importance of six aggregated risk categories. To examine the influence of contingencies on a country, organization, and crisis level, we investigate the effect of ten contextual factors on the importance of different supply chain risks in humanitarian aid delivery.FindingsWe derive six relevant risk categories for humanitarian aid delivery and develop five propositions concerning criticality, context-sensitivity, relevant contingencies, and risk management approaches. Findings and propositions encourage and guide future research on the topic.Practical implicationsRisk identification and assessment support mission-specific decision-making. Our findings encourage and foster organizational learning between missions. A differentiated view of risk types allows for stronger inter-organizational collaboration within the sector and beyond.Originality/valueThis study is among the first to derive supply chain risks and categories specific to humanitarian aid delivery. Contingency analysis on a country, organization, and crisis level offers unique insights into risk management in humanitarian aid delivery.

EXAMINING THE INTEGRATION OF INDUSTRY 4.0 TECHNOLOGIES IN MANUFACTURING

The integration of Industry 4.0 technologies is transforming manufacturing, enhancing efficiency, flexibility, and sustainability. This research paper explores the application of key technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics in the sector. By analyzing a comprehensive dataset that includes supply chain metrics from major corporations like Apple, Amazon, and Google, the study examines the impact of Industry 4.0 practices on critical supply chain performance indicators, including inventory turnover ratio, lead time, and customer satisfaction. Industry 4.0 technologies are reshaping supply chain management (SCM) strategies such as lean manufacturing, agile SCM, and cross-docking. AI and block chain are shown to significantly enhance supply chain agility and resilience. Companies that integrate these technologies tend to have more streamlined operations, shorter lead times, and higher customer satisfaction rates. Environmental sustainability practices are becoming increasingly important as businesses strive for more eco-friendly manufacturing processes. The study finds that organizations employing agile supply chain practices and leveraging advanced technologies often demonstrate superior operational efficiency and financial performance. The integration of these technologies also introduces challenges, such as increased supply chain complexity and risk management concerns. This paper provides a comprehensive overview of how Industry 4.0 technologies are revolutionizing manufacturing and supply chain dynamics. It highlights the future trajectory of smart manufacturing and emphasizes the need for continued innovation to address emerging challenges. The research offers insights into how companies can adapt their supply chain strategies to thrive in the evolving landscape of digital transformation and automation.

Supply chain vertical common ownership and cost of loans

This study explores how vertical common ownership reduces supply chain risk and influences creditors' decisions, focusing on the cost of loans. Findings reveal that creditors view such firms as less likely to default, evidenced by lower loan spreads. This effect is stronger for suppliers with more relationship-specific investments, weaker bargaining power, higher information asymmetry, and long-term common institutional ownership. Results remain robust through quasi-natural experiments from financial institutions' M&As, supplier-customer pair analyses, etc. We also compare vertical common ownership with the horizontal one and demonstrate its incremental contributions. Moreover, the fraction of customer sales and the institutional investors' share ownership likely influence the likelihood of vertical common ownership, with our results consistently holding under Heckman analysis. Overall, the results suggest that vertical common ownership enhances coordination and monitoring, reducing risks and creditor risk premiums. The findings may offer valuable insights into managing supply chain risks and understanding their financial implications.

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using piecewise cost-based consensus mechanism in social trust network. Firstly, the trust relationship dependent piecewise cost is formally defined and formulated by considering the impact of trust relationship on the preference adjustments willingness to achieve sorting consensus. Then, based on the trust relationship dependent piecewise cost, a preference strategic manipulation model is proposed to achieve strategic behavior in sorting consensus, which is implemented by minimizing the cost of strategically guiding decision makers in adjusting their preferences. Considering that social trust network affects strategic manipulation, a preference-network collaborative strategic manipulation model is further proposed, which is designed to strategically guide decision makers in adjusting both their preferences and social trust networks. Moreover, the practicality of proposed strategic manipulation models is exemplified through a case study on supply chain risk management and their performance is validated via simulation analysis.

Risk management in international supply chains: geopolitical and geoeconomic dimensions

In the current climate of global instability driven by dramatic geopolitical and geoeconomic transformations, international supply chains face numerous risks that significantly impact the continuity and efficiency of logistics processes. Threats such as military conflicts, port blockades, border instability, climate changes, and cyber threats introduce new challenges for companies, particularly Ukrainian ones, which require adaptive strategies to ensure the resilience of their supply chains. The purpose of this study is to systematize key geopolitical and geoeconomic risks affecting international supply chains and to identify optimal approaches to managing these risks under conditions of instability, especially for Ukrainian companies facing heightened threats. The study includes an analysis of global risks based on international scientific literature, specific risks in Ukraine and their impact on import, export, and transit chains, and the development of a generalized risk management model tailored to Ukrainian enterprises. The research identified effective risk management methods such as supply chain diversification, the creation of alternative routes, and the use of insurance mechanisms. A practical case study on risk management in the import of industrial fasteners illustrates the stages of collaboration with forwarders and carriers, partner selection, and response to disruptions, demonstrating approaches that can be used to minimize the impact of risks. The findings provide practical recommendations for Ukrainian companies, highlighting an adaptive approach to risk management that will help them maintain competitiveness and ensure supply chain resilience in a rapidly changing global environment. Future research should focus on integrating digital technologies, such as blockchain, artificial intelligence, and big data analytics, to strengthen the resilience and flexibility of international supply chains.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Integrating machine learning and deep learning for enhanced supplier risk prediction

The importance of anticipating and preventing disruptions is underscored by the increased operational complexity and vulnerability caused by advancements in supply chain management (SCM). This has spurred interest in integrating machine learning (ML) and deep learning (DL) into supply chain risk management (SCRM). In this paper, we introduce a tailored method using ML and DL to improve SCRM by predicting supplier failures, thus boosting efficiency and resilience in SC operations. Our method involves five phases focused on classifying and predicting supplier failures in non-conforming deliveries. This involves forecasting failure quantities and estimating total disruption costs. Initially, data from an automotive company is selected, and appropriate potential features and algorithms are selected, performance metric aligns with case study objectives, facilitating method evaluation are used such as: Precision, recall, F1-score, and accuracy metrics assess classification models, while Mean Squared Error (MSE) is used for regression tasks. Finally, an experimental design optimizes models, assessing success rates of various algorithms and their parameters within the chosen feature space. Experimental results underscore the success of our methodology in model development. In the classification task, the Random Forest (RF) classifier achieved 86% accuracy. When combined with the Gradient Boosting classifier, the ensemble exhibited enhanced accuracy, highlighting the complementary strengths of both algorithms and their synergistic impact, surpassing the performance of RF, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). Noteworthy is the performance in regression tasks, where Linear Regression, ANN, and RF Regressor displayed exceptionally low MSE compared to other models.

Research on the Impact of Supply Chain Integration on Supply Chain Resilience in NEV Manufacturing Enterprises

This paper explores the direct impact of different types of supply chain integration on supply chain resilience in new energy vehicle manufacturing enterprises. It also elucidates the mediating role of supply chain risk management and the moderating role of regulatory uncertainty, proposing nine research hypotheses. Finally, it employs SPSS 26.0 software to analyze the research hypotheses using collected 309 sample data. The research results indicate the following: (1) Internal integration, supplier integration, and customer integration all positively influence supply chain resilience, with supplier integration having the most significant impact. (2) Supply chain risk management mediates the relationship between internal integration, supplier integration, customer integration, and supply chain resilience. (3) Regulatory uncertainty significantly negatively moderates the impact of internal integration and customer integration on supply chain resilience, but it does not significantly negatively moderate the impact of supplier integration on supply chain resilience.