供应链韧性

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.

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In today’s fast-paced business settings, the metaverse as a shared marketplace has gained popularity and is helping businesses to develop crucial business strategies in their pursuit of sustainable performance. However, a lack of understanding and knowledge about the effectiveness of the metaverse and its related technologies creates a barrier. Therefore, the current study fills this gap and uses organizational information-processing theory to develop the theoretical framework to examine metaverse-related technologies (artificial intelligence and blockchain technology—BCT) and their direct and indirect effects on sustainable business performance, which no other study has examined. Using purposive sampling, the sample data from 326 SMEs were gathered and analyzed using a partial least square structural equation modeling (PLS-SEM). This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context. BCT facilitates ensuring a transparent, visible, traceable, and immutable supply chain, which helps make it more resilient and improves the closed-loop supply chain (CLSC) system with positive technological advancements and significant effects on increasing sustainable business performance (SBP). This study’s findings help organizations understand the potential benefits of AI-enabled SMEs’ presence in the metaverse. The current investigation provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance.

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Should supply chain resilience and viability be based on building redundancies for risk avoidance or adaptability accepting risk existence? Biological systems clearly favour the latter. Organising our analysis around management, technology, organisation, and network pillars, we theorise a bioinspired framework of supply chain adaptability in Industry 5.0. We illustrate framework elements through four case studies deducing major adaptation principles shared by both biological and supply chain systems. Industry 5.0 is unique in its combination of resilience with sustainability and human-centricity, which biological systems widely use in their evolution. Based on the adaptation principles identified, we propose an implementation plan for improving resilience and viability. This plan is based on four major elements observed in biological systems and their adaptation mechanisms, i.e. complexity and variety, information sharing, learning, and recovery. The implementation blueprint combines prediction-based risk mitigation and adaptation-based risk acceptance strategies. We stress that while resilience assessment of individual supply chains is important for firms, viability analysis of whole ecosystems from a human-centric perspective is crucial for both companies and society representing a novel and impactful research direction.

As global manufacturing competition increasingly emphasizes supply chain resilience, enhancing the risk resistance of manufacturing supply chains through digital empowerment has become a critical priority. This study leverages the opportunities of the digital economy to deeply investigate the mechanisms that enhance supply chain resilience. Using data from China’ s A-share listed manufacturing companies from 2012 to 2020, a research framework is constructed based on information asymmetry and transaction cost theories. Employing text analysis and factor analysis, the study develops indicators for digital empowerment and supply chain resilience and examines their relationship through both theoretical analysis and empirical testing. The findings reveal that: (a) Digital empowerment significantly enhances supply chain resilience in the manufacturing sector, and this conclusion is robust across various robustness checks. (b) Mechanism analysis demonstrates that digital empowerment drives supply chain resilience primarily by enhancing innovation vitality within enterprises. (c) The moderating analysis shows that environmental uncertainty positively influences the resilience of digitally empowered manufacturing supply chains. (d) Further analysis indicates that the effects of digital empowerment on supply chain resilience vary depending on factor intensity, supply chain position, and industry competition levels. These results validate the positive role of digital empowerment in promoting supply chain resilience and explore the ’black box’ mechanism from the perspective of innovation vitality. The study also highlights the moderating influence of environmental uncertainty. By advancing the understanding of how the digital economy fosters high-quality development in manufacturing, this research provides actionable insights for strengthening supply chain resilience, achieving greater control over supply chain dynamics, and promoting deeper integration between digital technologies and the real economy.

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The national economy’s steady flow can be ensured by strengthening supply chain resilience, and one of the main factors influencing supply chain resilience is business digital transformation. This study examines whether business digital transformation affects supply chain resilience using the panel data of Chinese listed manufacturing companies from 2012 to 2022. The findings indicate that supply chain resilience could be greatly enhanced by the digital transformation of manufacturing firms. The mediation mechanism test results show that supply chain integration plays a partial mediating role in the positive impact of enterprise digital transformation on supply chain resilience, and enterprises can promote digital transformation and enhance supply chain resilience through this mechanism of supply chain integration. The moderating effect test results show that both environmental uncertainty and enterprise risk-taking level play a positive moderating role in the relationship between digital transformation and supply chain resilience. This study can provide ideas and lessons for industrial supply chain management within the age of digitization.

Supply chain resilience has been extensively investigated at the network and firm levels. More granular studies at the level of product supply chain resilience are scarce. In this paper, we examine relationships between product supply chain resilience, firm resilience, and network resilience. We simulate supply chains with two products in different settings of structural and process diversity, connectivity, and flexibility. The methodology is based on discrete‐event simulation. The focus of the analysis is on managerial insights. Our main insights show that the resilience of product supply chains depends on the firm and network resilience, and higher firm and network resilience do not always automatically translate into higher resilience at the product level. Managerial implications are discussed and generalized. The outcomes of our study can be used by supply chain and operations managers to improve the resilience of supply chain with consideration of both product and network levels. We contribute to the literature by offering novel insights on the interrelations between firm and network resilience practices and product supply chain resilience.

Purpose Building digital supply chains to strengthen supply chain viability (SCV) has become essential for manufacturing firms seeking competitiveness. However, the roles of digital supply chain capabilities (DSCC), digital leadership (DL), supply chain resilience (SCRe) and their impact on SCV remain underexplored. Based on the contextual practice-based view, this study aims to investigate whether DSCC and DL, when integrated with SCRe, could realize SCV. Design/methodology/approach Using a survey data set of 349 Ghanaian manufacturing small and medium enterprises (SMEs) from different industries, the study empirically tests a mediated and moderated model to validate its hypotheses. It also conducts hierarchical linear modeling and bootstrapping to test the study’s hypotheses. Findings The results show that DSCC and DL positively enhance SCRe. SCRe partially mediates the effect of DSCC on SCV. DSCC positively moderates the relationship between SCRe and SCV, but there is statistically insignificant evidence that DL moderates this relationship. Originality/value To the best of the authors’ knowledge, this study is one of the very first attempts to develop an integrated model of SCV by exploring the interplay among digital capabilities, leadership and resilience. The findings of the study contribute to the digital transformation and supply chain management literature by systematically investigating the explanatory variables (digital SC capabilities and digital leadership) – practices (DSCP) – intermediate outcomes (SCR) – and performance (SCV). The paper offers fresh practical insights for owners/managers of SMEs, governments and policymakers from which they can understand how to navigate digital literacy to drive resilience and viability. Practically, managers should prioritize investments in digital supply chain capabilities as they both strengthen resilience and enhance its impact on viability. The focus should be on implementing practical digital tools like inventory tracking systems and supplier communication platforms, rather than extensive leadership development programs.

This study looks at how supply chain resilience can be strengthened across U.S. regions by linking logistics performance analytics with machine learning. We started by framing resilience in measurable terms, focusing on key performance indicators such as on-time delivery, cost efficiency, and variability across carriers and regions. With these metrics in place, the next step was to clean and structure shipment data so that patterns could be revealed. Using that foundation, we built models to predict delays, optimize carrier selection, and detect anomalies that might signal underlying fragility. Forecasting methods were applied to anticipate future shipping costs and route performance, while clustering was used to distinguish between resilient and fragile connections within the network. From there, we moved beyond standard predictive tasks. We experimented with resilience-aware objectives that penalize misclassifying delayed shipments more heavily, tested whether models trained in one region could adapt to another, and subjected the models to stress scenarios that mimicked shocks like surges in demand, noisy data, or carrier disruptions. What stood out is that while traditional metrics and models capture average performance well, resilience-aware methods provide a sharper view of vulnerabilities and recovery capacity. The insights are not just academic. They show that resilience can be operationalized through a combined framework of analytics and machine learning, producing tools that managers and policymakers can use to spot risks earlier, choose more reliable carriers, and plan for disruption. In practice, this makes it possible to see resilience as more than a buzzword: it becomes a measurable, actionable quality of the supply chain that can be managed and improved across U.S. regions.

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Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time visibility across the supply chain, reducing the risks of stockouts and surplus inventory. Furthermore, I-driven automation and robotics enhance operational efficiency by minimizing human error and streamlining processes. Methodology/Approach: This paper proposes a conceptual framework for strengthening supply chain resilience through AI integration. The framework leverages AI technologies to improve key aspects of supply chain resilience, including risk management, operational efficiency, and real-time visibility. Result/Conclusions: Additionally, it underscores the importance of collaborative relationships with supply chain partners, enabled by AI-powered data-sharing and communication tools that foster trust and coordination within the network. Originality/Value: This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively.

Supply chain resilience is on the agenda of academia and industry like never before. One strong instigator for this phenomenon has been the COVID-19 pandemic, which opened the era of global uncertainties and vulnerabilities. In this paper, we analyse the transformation of supply chain resilience research through the COVID-19 pandemic. Methodologically, we use a hybrid approach based on a combination of elements of a bibliometric and expert analysis to compare the main topics of resilience research before, during, and after the pandemic. Along with an expected observation about an exponential growth of literature on supply chain resilience in and after 2020, we observe a major shift from preparedness and disruption predictions in the pre-pandemic literature towards recovery and proactive adaptation in the pandemic and post-pandemic research. Our analysis systematically reveals some new topics, management practices, and future research areas in supply chain resilience. In particular, digital technology, supply chain viability, the cross-industry ripple effect, and intertwined networks have become new and impactful research areas during the COVID-19 pandemic. Further developments of these topics are expected to be continued in future. Managerial and theoretical implications of the said developments conclude this paper.

PurposeThe agricultural supply chain is susceptible to disruptive geopolitical events. Therefore, agri-food firms must devise robust resilience strategies to hasten recovery and mitigate global food security effects. Hence, the central aim of this paper is to investigate how supply chains could leverage digital technologies to design resilience strategies to manage uncertainty stemming from the external environment disrupted by a geopolitical event. The context of the study is the African agri-food supply chain during the Russian invasion of Ukraine.Design/methodology/approachThe authors employ strategic contingency and dynamic capabilities theory arguments to explore the scenario and conditions under which African agri-food firms could leverage digital technologies to formulate contingency strategies and devise mitigation countermeasures. Then, the authors used a multi-case-study analysis of 14 African firms of different sizes and tiers within three main agri-food sectors (i.e. livestock farming, food-crop and fisheries-aquaculture) to explore, interpret and present data and their findings.FindingsDownstream firms (wholesalers and retailers) of the African agri-food supply chain are found to extensively use digital seizing and transforming capabilities to formulate worst-case assumptions amid geopolitical disruption, followed by proactive mitigation actions. These capabilities are mainly supported by advanced technologies such as blockchain and additive manufacturing. On the other hand, smaller upstream partners (SMEs, cooperatives and smallholders) are found to leverage less advanced technologies, such as mobile apps and cloud-based data analytics, to develop sensing capabilities necessary to formulate a “wait-and-see” strategy, allowing them to reduce perceptions of heightened supply chain uncertainty and take mainly reactive mitigation strategies. Finally, the authors integrate their findings into a conceptual framework that advances the research agenda on managing supply chain uncertainty in vulnerable areas.Originality/valueThis study is the first that sought to understand the contextual conditions (supply chain characteristics and firm characteristics) under which companies in the African agri-food supply chain could leverage digital technologies to manage uncertainty. The study advances contingency and dynamic capability theories by providing a new way of interacting in one specific context. In practice, this study assists managers in developing suitable strategies to manage uncertainty during geopolitical disruptions.

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era.

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

Highlights • Insights into the impact of COVID-19 outbreak on automobile and airline supply chain is provided.• Integrated time-to-recovery and financial impact analysis, empirical survey and semi-structured interviews were used.• Localized supply sources and industry 4.0 technologies identified as significant strategies by automobile industry.• Business continuity by defining operations at the airport and flights perceived significant strategy by airline industry.• Real-time information sharing and cooperation among supply chain stakeholders is critical.

PurposeWith the increasing adoption of big data analytics (BDA), academics and practitioners are examining how and under what conditions BDA capabilities enhance supply chain resilience (SCR). Drawing on dynamic capability theory (DCT) and inertia theory, this study investigates the differentiated impacts of BDA capabilities on proactive and reactive SCR. It further explores the mediating roles of supply chain visibility and flexibility as well as the moderating role of firm size.Design/methodology/approachSurvey data were collected from 277 Chinese manufacturing enterprises. Data were used to test the conceptual model, using the structural equation modeling-partial least square approach.FindingsThe results demonstrate that BDA capabilities positively influence reactive SCR, but their effect on proactive SCR is insignificant. Furthermore, visibility and flexibility mediate the relationship between BDA and SCR, exhibiting distinct mediating effects on proactive and reactive SCR. Additionally, the influence of BDA on visibility is more pronounced in large firms than in small firms, whereas its effect on flexibility is less significant in large firms compared to small firms.Originality/valueFirst, this study extends understanding of BDA’s distinct roles in the pre- and post-disruption contexts. Second, drawing on DCT, it uncovers the mediating effects of visibility and flexibility on the relationship between BDA and SCR, elucidating the differentiated mechanisms through which BDA delivers value to SCR. Finally, it highlights the moderating role of firm size in explaining the effects of BDA on visibility and flexibility, providing a richer and more detailed picture of how BDA impacts supply chain management.

Against the backdrop of the global industrial chain characterized by "fragmentation" and "networking" and frequent emergencies, the issue of supply chain vulnerability among small and medium-sized enterprises (SMEs) has become prominent. Focusing on the context of the digital economy, this paper aims to explore the paths to enhance the supply chain resilience of SMEs. By adopting literature research, case analysis, and questionnaire survey methods, it sorts out relevant theories and analyzes the current status and challenges of SME supply chain resilience. The study finds that the digital economy can enhance SME supply chain resilience through three paths: building lightweight digital collaboration platforms, developing low-cost intelligent risk management tools, and leveraging the industrial internet ecosystem, with "lightweight" and "ecologicalization" as key features. Meanwhile, transformation must match the enterprises' own resource endowments. Finally, corresponding policy recommendations are put forward for the government, platform enterprises, and industry associations, and prospects for future research directions are provided.

The Fast Moving Consumer Goods (FMCG) sector is a critical component of the global economy, providing consumers with a wide range of products that are consumed daily. However, this sector faces vulnerability during disruptions. Therefore, this research amid to examine the effect of supplier trust and integrated technology on supply chain resilience (SCR) for sustainable supply chains in the FMCG sector. Data was collected from 409 respondents from the FMCG sector in the United States. The hypotheses were tested using Structural Equation Modeling through SmartPLS. The study findings found that all the direct and mediating hypotheses were supported. The findings suggest that better supplier trust and the use of integrated technology enhance the capability of an organization to better respond to disruptions. It makes the supply chain more resilient. Further, SCR brings sustainable supply chains to the FMCG sector. Similarly, SCR is a significant mediator in the relationships between independent and dependent variables, highlighting the importance of resilient supply chains. This research contributes to the literature on study variables and the Dynamic Capability View theory, as supplier trust integrated technologies are crucial factors in building resilient supply chains. This research has several managerial implications, including managers’ need to prioritize building trust with suppliers to facilitate increased information sharing. They should invest in integrated technologies to sense, forecast and be proactive in building SCR and, eventually, a sustainable supply chain.

Purpose Global events revealed the vulnerability of global supply chain (SCs) and triggered the debate about how to improve supply chain resilience (SCRES). Industry 4.0 (I4.0) provides promising opportunities. However, there is still great uncertainty about its future implementation. Hence, this study aims to identify the potential of integrating I4.0 technologies to improve SCRES. Design/methodology/approach Based on current literature and grounded in the organizational information processing theory as a theoretical lens, 12 future-oriented projections on the implication of I4.0 on supply chain risk management (SCRM) were developed. A two-round Delphi study among 49 SC management experts from industry, academia and professional service companies was conducted to assess and discuss the projections regarding their expected probability of occurrence in 2035, their impact on SCRES and their desirability. A fuzzy c-means algorithm was applied to cluster the projections based on expert assessments. Findings Based on the experts’ assessments, three clusters of I4.0 influence on SCRES were identified. First, the study suggests that in 2035, companies will have integrated an SCRM perspective into their strategic decision-making and their daily operations. Second, companies strive for collaborative SCRM, based on I4.0-enhanced risk information sharing, but struggle with full implementation until 2035. Third, I4.0 technologies will support, but not replace, SC risk managers in making risk-related decisions in 2035. Originality/value Thus, this study addresses the necessity for future-oriented empirical research on I4.0-enhanced SCRM and analyzes how SCRES can be improved through the combination of cultural, personnel and strategic factors supported by I4.0 technologies in the long term.

Purpose Supply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management. Design/methodology/approach This study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software. Findings This study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC. Research limitations/implications This study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC. Originality/value The conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

Supply chain resilience and data analytics capability have generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organisational information processing theory (OIPT). Four research hypotheses are tested using responses from 213 Indian manufacturing organisations collected via a pre-tested survey-based instrument. We further test our model using variance-based structural equation modelling, popularly known as PLS-SEM. All of the hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions.

We combine insights from information processing theory (IPT), supply chain resilience literature, and collaboration with a leading supply chain planning technology provider to study the effects of excess inventory (a buffering tactic) and usage of supply chain planning systems (SCPSs) (a bridging technology) on supply chain resilience. Utilizing the exogenous disruptions caused by the COVID-19 pandemic as the setting for quasi-natural experiments, we compare profit impact and time-to-recover across manufacturing firms that operated with varied levels of inventory and SCPS use in the period leading up to the onset of the pandemic. The results of multiple tests provide no evidence that inventory buffering aided firms in being more resilient, even for firms in industries that experienced positive demand shocks during the pandemic. In contrast, we find that firms that used SCPS evidenced fewer negative financial impacts throughout the disruptive period, and they recovered faster than their peers. The results are robust to sample characteristics, time frame, and control-group matching procedures. Our study extends a growing literature on supply chain resilience by offering a more refined explanation of IPT in a disruptive context, highlighting the limitations of inventory as a buffering tactic, and describing how SCPSs help planners cope with uncertainty and disruptions. In addition, interviews with managers from a leading SCPS provider and from user firms highlight specific ways in which SCPSs provide faster and more effective responses to disruptions. We discuss the implications of these findings for future research.

In light of the frequent occurrence of uncertain events, supply chain resilience has emerged as a critical issue for the survival and development of enterprises. This study empirically examines the impact of corporate environmental, social, and governance (ESG) performance on supply chain resilience, utilizing data from A-share listed companies in China from 2015 to 2023. The findings reveal that strong ESG performance positively influences supply chain resilience. The concept of “new quality productive forces” provides a novel perspective for understanding corporate sustainable development. Mechanism tests indicate that new quality productive forces play a significant mediating role between ESG performance and supply chain resilience. Specifically, by enhancing ESG performance, enterprises indirectly promote the growth of new quality productive forces, thereby further strengthening supply chain resilience. The robustness of these results is confirmed through tests involving the replacement of core explanatory variables, expansion of sample size, inclusion of additional control variables, and Hausman Tests. Furthermore, heterogeneity analysis demonstrates that state-owned enterprises exhibit a more pronounced effect of ESG performance on supply chain resilience compared to private enterprises.

Purpose This study aims to empirically investigate the impacts of a supplier’s position within the extended supply network on supply chain resilience and the moderating effects of the speed of the supplier’s operational processes in e-commerce supply chains. Design/methodology/approach Monthly operational data, including data from 441 suppliers, were collected from a Chinese e-commerce platform, with their extended supply networks constructed using binary relationship data obtained from TianYanCha. Fixed effects models were used to analyze the data. Findings The results indicate that suppliers’ structural holes in the extended supply network improve supply chain resilience, whereas network centrality and spatial complexity are negatively associated with it. Furthermore, the speed of suppliers’ operational processes not only enhances supply chain resilience but also mitigates the negative impact of network centrality and weakens the positive impact of structural holes while strengthening the negative impact of spatial complexity. Practical implications E-commerce platforms can increase supply chain resilience by strategically leveraging suppliers’ network positions and operational capabilities. Platforms should leverage structural holes for diverse resources, mitigate risks from centralized suppliers through diversification and invest in technologies for visibility. Balancing the speed of processes with visibility and coordination helps mitigate disruptions. Originality/value These findings provide empirical evidence on the joint effects of a supplier’s position and its operational capabilities on supply chain resilience, enhancing the current understanding of the antecedents of supply chain resilience from a supply network perspective. It offers insights into how platforms leverage suppliers’ positions in extended supply networks and operational capabilities to bolster supply chain resilience during disruptions.

PurposeManufacturing firms must strengthen their supply chain resilience to survive in turbulent business environments. This study explores how artificial intelligence (AI) can be leveraged to enhance supply chain resilience.Design/methodology/approachDrawing on organizational information processing theory, the research investigates the impact of AI usage on proactive and reactive supply chain resilience by fostering referent power in the context of demand dynamism. The study analyzes survey data from 285 Chinese manufacturing firms using structural equation modeling and regression analysis.FindingsThe results indicate that AI usage can enhance both proactive and reactive supply chain resilience. Referent power only mediates the relationship between AI usage and reactive supply chain resilience. Furthermore, this mediating effect is stronger under high-level demand dynamism.Originality/valueThis study highlights the value of AI usage in strengthening supply chain resilience and uncovers its underlying mechanisms. Theoretical and practical implications are discussed.

While the importance of improving supply chain resilience (SCR) is gaining increasing recognition, measuring it remains challenging. Existing literature focuses on modelling operational SCR, emphasising post-disruption impact and recovery, but lacks practical tools and metrics for assessing resilience from a strategic perspective. Strategic decisions, such as supply network design and strategy for selecting suppliers, are critical to mitigate disruption risks and impacts. To address this gap, we propose a supply chain strategic resiliency index (SRI) to comprehensively measure strategic SCR. The index integrates the consideration of supply network structure, supplier reliability (especially geopolitical risks), and each supplier’s contribution of material flow into a unified metric. Such an index can serve three primary purposes: (1) benchmarking the resilience of the existing supply chain, (2) evaluating the impact of strategic supply chain management decisions, and (3) assessing the resilience impact of new technology developments. As a use case, we analyzed the lithium supply chain of U.S. electric vehicle manufacturers, utilising real-world lithium imports and vehicle production data. Our results show that automakers are increasing their SCR, as exemplified by General Motors’ SRI increase from 27 in 2022 to 59 in 2023, and further to 65 in 2024.

The COVID-19 pandemic has significantly affected the way supply chains function and operate. Supply chain resiliency (SCR) has become increasingly more relevant to the pandemic, with corporations and governments realising that their supply chains were not as resilient leading to shortages/delays of many consumer products. Delays in the delivery of essential items, including medicines, food supplies and healthcare equipment, have exposed the challenges that a supply chain might face during a major disruption such as the global pandemic, regional conflicts and natural disasters. The purpose of this study is to identify and evaluate some of the critical inhibitors associated with SCR during COVID-19. The study employs multi-criteria decision-making utilising the fuzzy analytical hierarchy process. This research was conducted in the context of the Indian pharmaceutical supply chains. The research showed that there are seven major inhibitors to SCR. The findings of the current study are expected to aid the pharmaceutical supply chain managers in identifying and evaluating the critical inhibitors to achieving SCR and designing strategies to mitigate any future catastrophe like a global pandemic.

PURPOSE Drug shortages pose a serious threat to the quality of healthcare provided to patients, particularly in a pediatric care setting. The Delphi method was used to develop acuity scores for medication supply chain redundancy planning. We prioritized redundancy measures for medications for which a shortage would result in the greatest disruption to patient care. SUMMARY The acuity scoring system developed at St. Jude Children's Research Hospital included 4 primary criteria: historical supply disruption, medication unit cost, operational impact, and clinical impact. A list of medications was developed based on disruptive supply distributor patterns and carrying cost classification for inventory valuation. To rank the medications of highest priority, the Delphi method facilitated consensus over 2 rounds of scoring to assess operational and clinical impact. Medications with less than 75% agreement were addressed in the first round, and 80% consensus was achieved in the second round. After weighting operational and clinical impact scores based on cutoff standard deviations, the scores for all 4 primary criteria were combined, with the final aggregated shortage list containing 95 medications and formulation combinations. The top 10 medications of highest priority (acuity) had similar operational and clinical impact scores. CONCLUSION This approach and the proposed scoring algorithm can be used by other institutions to implement shortage mitigation and resilience measures. Future research could assess the reliability of this scoring algorithm for use at other institutions.

Supply chain disruption can occur for a variety of reasons, including natural disasters or market dynamics for which resilient strategies should be designed. If the disruption is profound and with dire consequences for the economy, it calls for the regulator's intervention to minimize the impact for the betterment of the society. This paper considers a shipping company with limited capacity which will ship a group of products with heterogeneous transportation and production costs and prices, and investigates the minimum quota regulation on transportation amounts stipulated by the government. An interesting example can happen in North American rail transportation market, where the rail capacity is used for a variety of products and commodities such as oil and grains. Similarly, in Europe supply chain of grains produced in Ukraine is disrupted by the Ukraine war and the blockade of sea transportation routes, which puts pressure on rail transportation capacity of Ukraine and its neighboring countries to the west that needs to be shared for shipping a variety of products including grains, military, and humanitarian supplies. Such situations require a proper execution of government intervention for effective management of the limited transportation capacity to avoid the rippling effects throughout the economy. We propose mathematical models and solutions for the market players and the government in a Canadian case study. Subsequently, the conditions that justify government intervention are identified, and an algorithm to obtain the optimum minimum quotas is presented.

Resilience is a topic that has recently emerged concerning the basics of the construction project supply chain and we can consider it as a response to disruption in the supply chain of the project. Disruption also is an unavoidable reality in today’s complex and dynamic construction supply chain, the occurrence of which can cause irretrievable damages to the system, such as financial losses. Successful companies seek to minimize disruption and maintain adequate supply chain performance before disruption occurs, rather than looking for costly and challenging post-disruption solutions. This paper covers this gap by proposing a scenario-based mixed integer-programming model aiming to minimize logistics costs and delays, while scheduling projects to address selecting the appropriate supplier at risk of disruption. So far, this quantitative view was not presented in discussions about disruptions in the project supply chain, therefore different scenarios are applied in the process to validate the model. To improve its resilience level, this model benefits from back-up suppliers’ strategy. This study focuses on providing the required materials for the project site in an emergency without incurring additional costs using a back-up supplier. Results reveal the model’s suitability in confronting the unavailability of a supplier due to disruption.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

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Disruption is one of the causes of supply chain instability that may affect supply chain performance. This paper proposes a Mixed Integer Nonlinear Programming (MINLP) model to analyze a location-allocation problem with supply disruption in a three-echelon network. This study uses (s, S) and (s, Q) inventory policies to manage the inventory. Sourcing strategy is evaluated to create supply chain resiliency. The sourcing policy is conducted by considering single-sourcing and multiple-sourcing. Furthermore, the model also considers environmental effects by calculating carbon emissions resulting from transportation and manufacturing processes. Numerical analysis is evaluated using a Lingo solver. According to the result, implementing multi-sourcing under supply disruption can reduce the total cost of the supply chain by 4 %.

Due to the high risk in the business environment, supply chains must adopt a tailored mechanism to deal with disruptions. This research proposes a multi-objective formulation to design a robust and resilient forward supply chain under multiple disruptions and uncertainty. The mentioned objective functions include minimizing the total cost, environmental impacts, and the network nonresiliency associated with the supply chain simultaneously countered using an augmented ε-constraint method. A Mulvey robust optimization approach is also utilized to deal with uncertainty. Ultimately, the developed model is validated based on three datasets associated with a case study of the steel industry. The results indicate that preventive and mitigation resilience strategies have significantly promoted the supply chain’s capabilities to deal with disruptions. Controlling network resiliency via non-resiliency measures has also created a risk-aware and robust structure in the incidence of disturbances. Numerical results reveal that multiple sourcing, lateral transshipment, and fortification of facilities will lead to the greatest cost-efficiency in the case study. Observations also indicate that the fortified supply chain will be highly economically viable in the long run due to the reduction of costs resulting from lost sales, unnecessary inventory holding, and the company’s credit risk. Mathematics Subject Classification. 90B06. Received April 6, 2021. Accepted August 11, 2021.

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Supply Chain Global Systems are currently in constant states of uncertainty; Disruptions will occur and can rapidly move across all global connected Supply Chains due to their Interconnected Nature. Traditionally, Supply Chain Planning is based upon static forecasting with periodic optimization which does not allow for sufficient responsiveness and scalability. This paper proposes an integrated Architecture for Predictive and Adaptive Supply Chain Resiliency utilizing AI technology in conjunction with multilevel components which incorporate Large Scale Data Engineering, Hybrid Forecasting and Autonomous Optimization. The Framework utilizes Advanced Time Series Models including LSTMs, Temporal Convolutional Network, ARIMA-AI Hybrid Ensembles and Transformer Based Predictor Models for Disruption Sensitive Forecasting. The models utilized for Forecasting are inputted into Adaptive Decision Mechanisms using Reinforcement Learning (i.e. DQN, PPO), Multi-Objective Optimization Engines that balance Service Levels, Operational Costs and Carbon Efficiency. Additionally, Resilience Analytics including Agent-Based Simulation, Monte Carlo Stress Testing and Network Robustness Metrics measure how Disruptions Propagate and how Quickly Systems Can Recover. Finally, The Implementation Layer of the Framework utilizes Cloud Edge Orchestration, Micro-Services Architecture and Real-Time Decision Intelligence Dashboards to Support Human-AI Collaboration. Furthermore, this Paper discusses Governance, Model Transparency and Ethical Deployment Considerations to Ensure Responsible Use of Autonomous Decision Systems. Finally, the Paper discusses potential future Research Directions including Quantum Enhanced Optimization, Digital Twin Ecosystems, Generative Scenario Modeling and Federated Intelligence for Multi-Enterprise Collaboration.

Abstract Nowadays, one of the main objectives of supply chain design is to lessen the supply chain-threatening risks in order to reduce costs, preserve market share, and satisfy stakeholders. This paper presents an integrated hybrid approach based on data envelopment analysis (DEA) and mathematical programming method to design a resilient supply chain. First, the efficiency of potential suppliers is evaluated by a fuzzy DEA model. Afterwards, using the obtained efficiency, a two-stage possibilistic-stochastic programming model is developed for integrated supplier selection and supply chain design under disruption and operational risks. The model incorporates partial and complete disruptions of suppliers as well as quantity discount for procurement of various raw materials. Furthermore, we utilize several proactive strategies such as fortification and pre-positioning emergency inventory at fortified suppliers, and using multiple sourcing to enhance the resiliency of supply chain. Atra pharmaceutical company (APC) is used as a case study to investigate the applicability of the proposed model and analyze the solution results. The results indicate the validation of proposed model and the impact of various resiliency strategies.

Robust supply chain network design that considers supply resiliency, plays vital role in supply chain risk management in dealing with various operational and disruption risks. This study developed a novel three-stage decision approach to consider two echelons robust and resilient supply chain networks. We present a mixed-integer non-linear programming model with two objective functions. The objectives are maximization of SCN profit and maximization of resiliency, where robustness, agility, leanness, flexibility, and integrity can be defined as the five resiliency criteria. Fuzzy Simultaneous Evaluation of Criteria and Alternatives (FSECA) and Simple Multi-Attribute Rating technique (SMART) have been used to obtain the supplier resiliency and weighted importance of resilience criteria. Then, a robust optimization model is built based on uncertainty parameters considering supplier resiliency. A Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Particle Swarm optimization (MOPSO) were used to solve the robust model on a large scale. parameters calibrated by the Taguchi method and five metrics of performance evaluation were considered to compare the meta-heuristic algorithms. We demonstrate the proposed NSGAII algorithm over a competing method based on five performance metrics. The research findings reveal the optimal level of robust supply chain networks based on algorithm performance and Taguchi analyses. Moreover, the results indicate that when profit increases, resilience can increase simultaneously.

As a new sustainable building production mode, prefabricated building supply chains can realize energy saving, environmental protection and full cycle value maximization of building products. Prefabricated building supply chains often experience disruptions due to supply instability, transportation delay and force majeure, resulting in project delays and cost escalations and posing challenges to the sustainable development objectives of enterprises. Therefore, it is important and essential to study the strategy of enhancing the resiliency of prefabricated building supply chains, which has not been comprehensively explored in previous papers. This paper constructs decision-making models for supply chain cost resilience strategies under varying scenarios of supply disruptions, incorporating both redundant inventory and back-up supplier strategy. It considers the total cost and resilience of the supply chain as dual objective functions. Parameter-tuned non-dominated sorting genetic algorithm-Π (NSGA-Π) algorithms were used innovatively to solve the project case, and the impacts of the redundant inventory coefficient and back-up supplier supply price coefficient on the model result were analyzed. The results indicate that the supply chain with resilience construction has a superior capability to cope with disruption. The results show that when there is a mild supply disruption, the general contractor uses the capacity within the supply chain and chooses a redundant inventory strategy to restore resilience. In the event of moderate disruption, both the easy inventory strategy and back-up supplier strategy are selected to maintain supply chain stability. In the event of a severe disruption, only the back-up supplier strategy is selected to cover the losses and maintain the project schedule. In addition, the choice of resilience strategy is impacted by the inventory levels and component prices of back-up suppliers. It further verifies the effectiveness of the model and the impacts of uncertain parameters in the model on the results. This study contributes to enhancing the resilience management of the prefabricated building supply chain by the general contractor, thereby elevating the overall efficiency and competitiveness of the supply chain and furthering the sustainable development of prefabricated buildings.

Supply chains are facing disruptions in succession, and recovery remains a challenge. Disruptions challenge supply chain managers to find solutions for a faster recovery. However, building supply chain resiliency may lead to foregoing some globalization cost-benefits. While professionals and academicians research this conundrum, it's evident that reactive approaches do not support sustenance and present a unique challenge with each disruption. Therefore, it becomes significant to predict risk probabilities and severity and act to mitigate the risks strategically. It also calls for timely decision-making. This paper identifies the need for a proactive approach to predicting risks and detecting trigger points for well-timed decision-making. The paper recommends the existing frameworks of Failure Mode and Effects Analysis (FMEA) Risk Priority Number (RPN), Uppsala model, and Multicriteria Decision Making (MDM) for quantifying and reducing the risk and improving resiliency. The FMEA model helps assess and prioritize risks, while the Uppsala model guides commitment based on changes in risk. MDM acknowledges that other criteria may also be important in strategic decision-making beyond just risk.

The viable closed-loop supply chain network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility into a circular economy. We suggest a hybrid robust stochastic optimization by minimizing the weighted expected, maximum, and entropic value at risk (EVaR) of the cost function for this problem. This form considers robustness against demand disruption. Finally, CLSC components are located, and quantity flows are determined in the automotive industry. The results show that the VCLSCND cost is less than not considering viability and has a − 0.44% gap. We analyze essential parameters. By increasing the conservative coefficient, confidence level, and the scale of the main model, decreasing the allowed maximum energy, the cost function, time solution, and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing an upper bound for large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy, and time solution is approximately 6.10%, − 8.28%, and 75.01%.

Abstract Supply chain disruptions have caused hundreds of shortages of medically-necessary drugs since 2011. Once a disruption occurs, the industry is limited in its ability to adapt, and improving strategic resiliency decisions is important to preventing future shortages. Yet, many shortages have been of low-margin, generic injectable drugs, and it is an open question whether resiliency is optimal. It is also unknown what policies would be effective at inducing companies to be resilient. To study these questions, we develop new supply chain design models that consider disruptions and recovery over time. The first model is a two-stage stochastic program which selects the configuration of suppliers, plants, and lines. The second is a multi-stage stochastic program which selects the configuration and target safety stock level. We then overlay incentives and regulations to change the market conditions and evaluate their effects on two generic oncology drug supply chains. We find that profit-maximizing firms may maintain vulnerable supply chains without intervention. Shortages may be reduced with: moderate failure-to-supply penalties; mandatory supply chain redundancy; substantial amounts of inventory; and/or large price increases. We compare policies by evaluating the societal costs to reduce the expected shortages to 2% and 5% of demand.

Global production networks that took shape to optimise costs and efficiency often contain hidden vulnerabilities — and external shocks exploit those weaknesses. The COVID-19 pandemic exposed existing challenges across business operations and thrust an organisation’s ability to adapt to dramatic shifts in supply and demand into the spotlight. However, even before COVID-19, a multitude of events in recent years temporarily disrupted production at many companies. All of this is occurring against a backdrop of changing cost structures across countries and growing adoption of revolutionary digital technologies in global manufacturing. Organisations need a new approach to manage risk and build resiliency. This research highlights the many options for strengthening value chain resilience in response to risk and evaluates strategies to minimise the growing cost of disruption, including opportunities arising from new technologies, strengthening risk management capabilities and improving transparency, building redundanc, reducing product complexity, and improving the financial and operational capacity to respond to shocks and recover quickly from them.

Supply chains are at the heart of the way in which organisations operate and compete today; they also play a critical role in overall organisation performance. In the context of increasingly complex and global supply chains, the actions taken to drive down costs are likely to drive risk into the supply chain. The frequency of supply chain disruptions is high and this paper offers practical advice to help reduce the frequency and cost associated with these. There is advice to help with the understanding of how to identify critical suppliers. The reader is guided through comprehensive risk assessment and mitigation approaches and a selection of practical risk solutions and tools that you can use is described. There is a section on the 'dos and don'ts' relating to supplier due diligence. For those organisations facing the challenge of drawing up a business case relating to investment in improving supply chain resiliency, there is also a section outlining some of the business benefits of improving supply chain resiliency.

Agribusiness supply chains are sensitive to various sources of uncertainty. Therefore, it is essential to consider resiliency for such supply chains to decrease the impacts of various types of risks. This article presents a two-stage stochastic model considering disruption scenarios for both suppliers and distribution centers to design resilient agro-food supply chain. To react quickly despite of any disruptive incidents, three following strategies are intended to develop a resilient model; 1)devoting backup facility to suppliers and distribution centers, 2)multiple-sourcing in suppliers and distribution centers, 3)mitigation strategies for suppliers to reduce the disruption probability. The model is solved using simulated data set and the results prove that implementing resilient strategies for mentioned supply chains will gain more profit and economize its costs. An analytical comparison between both classic and resilient models as well as analysis on wrong strategy selecting effect have been considered.

Supply chains are increasingly becoming integrated to address new opportunities and growth while continuously being threatened by complex challenges. Companies can leverage resiliency, leagility and sustainability (RLS) capabilities to address these consequential issues. In this context, supply chain resiliency is the ability to continue operations in the face of a supply chain disruption. Leagility is the combination of lean and agility concepts and refers to the speed of changing directions and the practices of improving operational efficiency and effectiveness by eliminating waste. Sustainability is the management of a company’s supply chain effect on environmental, social and economic issues. Opportunities exist for companies to view these three capabilities as related and complementary and to evaluate how they can improve their business performance. In the context of supply chain management, this paper will define RLS, present an organisational construct for providing supply chain context, and give examples of how companies can leverage RLS to mitigate risks, capture opportunities and create a competitive advantage. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

ABSTRACT Traditionally, by centralization, economic aspects of decisions in supply chain were only intended, while today, by increasing attention to the environmental and social issues, the addressed aspects should also be considered in supply chain planning, as well. In this paper, two mathematical programming problems called basic and extended were studied. In the basic problem, the allocation of several dairy factories to some existing livestock farming centers was carried out via three separate models with economic, environmental and social objectives. Regarding the social aspect, due to the subsidy paid to the livestock farming companies, the amount of raw milk wasted is minimized. In the extended problem, the addressed allocation is done considering the occurrence of disruption due to internal or external risk factors. Some new candidate locations for new livestock farming centers are selected in order to compensate the capacity losses in the existing livestock farming centers. The raw milk shipping costs are reduced by up to 29%, carbon dioxide emission is reduced by up to 8.05%, and the raw milk wasted is reduced by up to 7.87%. Furthermore, by modeling and solving the extended problem, the total cost of the stochastic model is higher than that of a base-case as much as 0.67% which is insignificant.

Today's coffee shop and coffee roastery businesses are more mushrooming from the past. The coffee supply chain in Thailand, especially in the northern region, produces the most Arabica coffee in the country. In the last few years, the demand and coffee consumption volume has been higher than the output, and any disruption at any stage can directly impact the firm. This paper aims to prioritize the significant factors by using the fuzzy analytic hierarchy process (Fuzzy AHP) method based on the supply chain resilience concept under two dimensions, vulnerable and respond capacity, characteristic of supply chain resilience. This article will help researchers to understand the resilience concepts by prioritizing factors that influenced the coffee roastery and any related field under the supply chain resilience concept to prepare and resist possible disruption and unpredicted events.

There has been great concern about building resilient supply chains to expedite the supply chain’s recovery after a crisis or disruption. Few attempts, however, were made to study the resiliency of a supply chain after disruptions caused by counterfeit parts, especially in critical domains like information and communication that are embedded in almost every aspect of our daily lives and critical life-supporting systems. Counterfeits will penetrate a supply chain at one of the suppliers’ or manufacturers’ points. Hence, rigorous countermeasures should be taken at these stages. Using a hybrid simulation model, this paper studies the performance of an Information and Communication Technology (ICT) manufacturing supply chain subject to counterfeit parts risks and specific countermeasures. The system’s service levels, delivery time, and proportion of good products are the performance measures adopted to determine the effectiveness of the countermeasures and thus the supply chain resiliency. The model can be extended to other types of supply chain networks and help manufacturers adopt the optimum countermeasures.

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Today’s business environment is complex, dynamic, and uncertain which makes a supply chain facility increasingly vulnerable to disruption from various risk accidents as one of the main threats to the whole supply chain’s operation. However, in general, most of the studies on facility location problems assume that the facilities, once built, will always run availably and reliably. In fact, although the probability is very low, supply chain disruptions often incur disastrous consequences. Therefore, it is critical to account for disruptions during designing supply chain networks. To accomplish planned outcomes and greater supply chain resiliency, this article proposes a continuous approximation approach based on regular hexagon partition to address the reliable facility location problems with consideration of facility disruptions risk. The optimization goal is to determine the best facility location that minimizes the expected total system cost on the premise that the supply chain network is not disrupted as a whole when one or some facilities are subject to probabilistic failure. Our numerical experiment discusses the performance of the proposed solution approaches which demonstrates that the benefits of considering disruptions in the supply chain design can be significant. In addition, considering the impact of disruption probability estimation error on the optimal decision, the misestimating of the disruption probability is also investigated in this paper.

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The past few years have highlighted that lean supply chains are both unreliable and lack resiliency; there is no longer any cushion or room for error, and they perform best with predictability. The increased instability caused by major global disruptive events has placed immense pressure on supply chains. Therefore, companies must adjust how their supply chains function. This paper seeks to answer the question: what can be done to increase supply chain resiliency and reliability?

Supply chains are frequently exposed to disruptions, which can be either positive, driven by technological advancements, or negative, caused by natural and man-made disasters. This study aims to explore the possibilities and implications of building supply chain resilience through AI-driven AR/VR simulations. In light of the disruptions experienced during the COVID-19 pandemic, there has been a growing interest among both researchers and practitioners in the role of digital technologies in enhancing end-to-end visibility within supply chains and their potential for boosting resilience.The study provides insights into how leveraging the dynamic capabilities of supply chains through AI technology can strengthen resilience. It offers a forward-looking perspective on how emerging technologies will shape modern supply chains and play a crucial role in improving their resilience. The article underscores the transformative potential of AI, highlighting its ability to equip supply chains to better withstand disruptions and mitigate associated risks.

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The authors document how the COVID-19 pandemic disrupted medical product supply chain and procurement in Zimbabwe, impacting medicine access and prices. Policies and interventions are needed to ensure ongoing supply chain resilience. Key Findings Movement restrictions and lockdowns in response to the COVID-19 pandemic affected stock management procedures, procurement processes, and competitive supplier bidding, resulting in price increases and health system disruptions. Manufacturing constraints and restrictions of exports from other countries resulted in shortages of medicines and some commodities, supply chain delays, and increased freight costs, which, in turn, significantly increased the cost of certain medical products and impacted access to medicines not related to COVID-19. Programmatic and operational shifts within the health system to focus on the COVID-19 pandemic response led to the diversion of human and financial resources from other essential health areas and a decrease in procurement of medicines and commodities that were not related to COVID-19. Key Implications Policymakers should craft policies and interventions to ensure that the supply chain of medical products is resilient for future pandemics and other shocks. Policymakers and other health system stakeholders should apply lessons from the COVID-19 supply chain disruptions toward broader health system strengthening efforts. Abstract Background: The COVID-19 pandemic has disrupted global health supply chains including manufacturing, storage, and delivery of essential medicines, testing kits, personal protective equipment, and laboratory reagents. We sought to document how pandemic impacted the procurement, prices, and supply chain of medical products in Zimbabwe. Methods: We conducted semistructured in-depth key informant interviews with 36 health system stakeholders in Zimbabwe involved in medicine procurement. Respondents included pharmacists, regulatory officers, and procurement and supply chain management professionals from public and private sectors. Results: Before the COVID-19 pandemic, respondents described experiencing long-standing resource constraints, medicine shortages, foreign currency shortages, and supply chain inefficiencies. The pandemic exacerbated this situation due to supply constraints, export restrictions, medicine shortages, and movement restrictions that disrupted logistical and stock management systems. Competitive bidding and tendering processes experienced reduced participation by international suppliers. Significant price increases were initially observed among internationally shipped medicines and for personal protective equipment to cover additional freight costs. COVID-19 pandemic impacts were moderated by reduced patient demand and lower health services utilization, resulting in fewer supply shocks and less price volatility. Further, health system adaptations such as switching treatment regimens, modifying dispensing schedules based on stock availability, redistributing stock of medicines among facilities, and new service delivery models such as integrated outreach services helped ensure continued patient access to medicines. Conclusions: Our findings highlight the need for policies that ensure continuity in access to health services and medical products, even during a pandemic, by avoiding blanket restrictions on medical product exports and imports. Pooled procurement, especially at regional and global levels, with long-term service agreements may help achieve greater resiliency to supply and price shocks from supply chain disruptions. Interventions across manufacturing, trade, and regulatory policy and service delivery models are also needed for supply chain resiliency.

Considerable research has focused on how supply chains can better handle disruptions. Consequently, concepts such as supply chain robustness and engineering resilience have emerged, with the dominant emphasis being that disruptions are a wholly bad thing to be avoided or resisted. However, recent discourse in the supply chain disruption management literature, such as the social–ecological interpretation of supply chain resilience, suggests that disruptions can be positioned more positively as potential catalysts for growth. Yet little is known about the capabilities required for a supply chain to grow following disruption. The emerging concept of supply chain antifragility focuses specifically on growth, providing an arrowhead for investigating what enables firms to grow following disruption. Utilizing a metaphorical transfer method, this research translates the capabilities of individuals—those who grow psychologically and emotionally after experiencing trauma—into supply chain capabilities that enhance antifragility. Five key capabilities for building antifragility in supply chains are identified: supply chain mindfulness, supply chain transformative learning, supply chain plasticity, supply chain bricolage, and supply chain collaboration. Furthermore, a hierarchy of capabilities is revealed that points to a sequential approach to capitalizing on the potential growth opportunities presented by supply chain disruptions. The findings are sense‐checked through focus groups with practitioners, informing the development of five propositions. This research contributes to theory development on handling supply chain disruptions by providing a capability blueprint for post‐disruption growth that complements the literature on social–ecological supply chain resilience. Finally, this research highlights the value of metaphorical transfer as an innovative approach for understanding contemporary supply chain phenomena and advancing novel theoretical frameworks.

Against the backdrop of the deep interweaving of the digital economy and supply chain restructuring, archive digitalization, as the core carrier of data factorization, has become a key path to address information asymmetry in the supply chain and enhance its resilience. From an interdisciplinary perspective of management science, this study aligns with the policy orientations of the 14th Five-Year Plan for the Development of National Archives Undertakings and the Guidelines for Improving the Supply Chain Management Level of Manufacturing Enterprises, focusing on the collaborative dilemmas of archive digitalization between core enterprises and small-to-medium-sized suppliers. A two-stage evolutionary game model incorporating variables of outsourcing costs and policy subsidies is constructed to analyze the equilibrium conditions of strategy choices for both parties. The research is validated using panel data of the digital archive processing industry from 2021 to 2025 (market size: 4.8 billion to 16 billion yuan) and the Changhong supply chain case. Results indicate that three key variables drive the collaborative digitalization of supply chain archives: the digital spillover effect of core enterprises (elasticity coefficient: 0.72), the Shapley value-based cost-sharing ratio (45%-65%), and policy subsidy intensity (accounting for more than 15% of collaborative costs). When the collaborative benefit coefficient is ≥ 0.65, both parties will form a stable equilibrium of "co-construction and sharing", reducing the supply chain disruption recovery cycle by 22% and the operational loss rate by 30%. This study constructs an analytical framework of "policy guidance - interest coordination - resilience enhancement", enriches the application scenarios of game theory in supply chain digital transformation, and provides dual references for enterprise practice and policy formulation, meeting the theoretical depth and empirical requirements for publication in CSSCI-indexed journals.

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This research aims to investigate the impact of supply chain resilience and integration on the performance of FMCG firms in Bangladesh. The primary objective is to synthesize existing literature to comprehend how these two interdependent capabilities enhance operational efficiency, delivery reliability, flexibility, innovation, and customer satisfaction in a developing economy. This research used a narrative and integrative review methodology, examining peer-reviewed journal articles, conference proceedings, and industry reports mostly published in the last decade. The technique included the thematic classification of literature into resilience, integration, and performance dimensions, followed by an iterative process of synthesis and interpretation. The study highlights findings that supply chain resilience, which includes adaptation, flexibility, visibility, redundancy, and recovery capacity, allows organizations to successfully absorb and recover from disturbances. The outcomes of the study emphasize the significant role of Supply chain integration to facilitate coordination, information exchange, and cooperation, hence enhancing resilience. Therefore, the research offers actionable insights for managers in cultivating robust and cohesive supply chains, governments in fostering enabling infrastructure and technology integration, and industry stakeholders in advancing collaborative networks.

As a vital driver of supply chain management, data has evolved into both a foundational resource and a critical production factor for optimizing supply chains and mitigating risk. This study adopts a four-dimensional framework (i.e., visibility, coordination, flexibility, and redundancy) to investigate how data asset information disclosure (DAID) shapes supply chain risk (SCR). Relative to the existing literature, this paper contributes by examining the determinants of supply chain risk from the perspective of data asset information disclosure and by conducting empirical analyses using double debiased machine learning and causal mediation analysis. The results show that DAID significantly lowers SCR, with results robust to multiple sensitivity checks. Economically, a one-standard-deviation increase in DAID leads to an average decline in SCR of 0.63%. Causal mediation analysis, aligned with the theoretical dimensions, reveals that DAID mitigates SCR through four channels: enhancing information transparency, improving visibility, strengthening agile responsiveness, and increasing supply chain concentration. Heterogeneity tests reveal stronger effects among firms facing fewer financing constraints, operating in more marketized environments, and designated as chain master firms. Further evidence suggests that reduced SCR promotes a greater capacity for coordinated innovation within the supply chain.

Agri-food supply chains (AFSCs) are vulnerable to disruptive shocks, making supply chain resilience critical for continuous food supplies. Developing countries are under-prepared and over-exposed to shocks, and their strategies to build supply chain resilience remain limited in scope. The purpose of this study is to explore whether outward foreign direct investment (OFDI) could serve as an innovative strategy for developing countries to improve home-country AFSC resilience, using the case of the 2008 Chinese melamine crisis. This study uses a novel theoretical intersection between supply chain resilience and OFDI theory. Applying conditional logistic regressions to Chinese dairy investment data between 2008 and 2020, collected from Thomson Reuters, the Financial Times and the Chinese Global Investment Tracker, the paper analyses key resilience elements including redundancy, flexibility and visibility to explore potential OFDI strategies. Results show that strategies aimed at reducing AFSC vulnerabilities appear favoured in international investment decisions over those promoting closer partner linkages. This emphasises firms may stabilise domestic AFSCs following shocks by investing internationally in host countries with excess capacities. The study introduces OFDI as a potential strategy for enhancing home-country AFSC resilience, an angle largely absent from current research. It contributes novel empirical insights in a field where quantitative studies remain scarce and suggests policy makers in developing countries could learn from the Chinese experience when dealing with shocks. Novel policy instruments - such as capital controls and food safety regulation - could be considered to proactively enhance AFSC resilience by incentivising OFDI.

Supply chain resilience has become a key focus of business, policymakers, and governments in an increasingly interconnected global economy. The occurrence and severity of interruptions including the COVID-19 pandemic, geopolitical disputes, climate-related incidents, and cybersecurity attacks have highlighted the weaknesses of the conventional supply chain systems. This paper starts with a review of the literature on resilience, its major frameworks, which focus on flexibility, redundancy, visibility, collaboration, and digital transformation. It subsequently adopts a mixed-method approach by incorporating secondary data in the form of industry reports in addition to the analysis of case studies of multinational corporations. The results show that resilient supply chains are not defined as risk-free but resilient in terms of their capacity to predict, absorb, adapt, and recover shocks. There are graphical illustrations and tabular summaries that show significant disruption drivers and recovery trends. The analysis shows that digitalization, predictive analytics, multisourcing, nearshoring and collaborative risk-sharing arrangements are key drivers in establishing adaptive capacity. Nonetheless, the strategies of resilience need to be applied in a setting-specific manner as companies across various fields and locations have specific weaknesses. The paper ends with business and policy recommendations that focus on technology investment, enhanced partnerships, and sustainability. Organizations can protect continuity, minimize vulnerabilities, and remain competitive in a turbulent international environment by promoting resilience thinking

The COVID-19 pandemic has fundamentally reshaped how supply chain resilience (SCR) is understood and operationalized. This study synthesizes key resilience strategies and re-examines the conceptual foundations of SCR in light of prolonged global disruption. Drawing from a comprehensive analysis of recent literature, the findings identify ten core dimensions of resilience—robustness, visibility, flexibility, agility, collaboration, situation awareness, security, knowledge management, redundancy, and contingency planning. These dimensions are explored through technological, organizational, and relational lenses, highlighting the role of digitalization, strategic redundancy, and cross-functional coordination. The study finds that resilience is no longer limited to recovery capabilities but must be viewed as a proactive, dynamic, and integrative capability embedded across supply chain design, operations, and governance. Furthermore, the pandemic has catalyzed a shift from cost-efficiency to resilienceefficiency, urging firms to reconsider lean paradigms and embrace strategic slack, digital tools, and learning cultures. The findings advance the theoretical discourse on SCR and offer actionable insights for managers seeking to future-proof their supply chains.

Growing concerns regarding climate change and extreme weather events have spurred heightened interest among supply chain professionals, researchers, and policymakers, leading to increased focus on supply chain resilience. This study aims to develop a model specifically geared toward enhancing supply chain endurance and contribute to the ongoing debate on supply chain resilience. Employing a mixed‐method approach, the research initially utilizes the qualitative methodology to delineate the facets of supply chain endurance. Subsequently, through a multiple cross‐sectional survey design, the study empirically examines the relationship between supply chain endurance, a firm's supply chain resilience, and community resilience. The results engender discussions on fortifying firms' supply chain endurance by cultivating adaptive leadership, adaptability, visibility, flexibility, collaboration, redundancy, and conditioning. This research underscores the significance of nurturing supply chain endurance capabilities in bolstering a firm's sustainable supply chain resilience and the resilience of the broader community.

PurposeThe present study investigates the impact of supply chain disruption orientation (SCDO) on four supply chain disruption (SCD) mitigation strategies: supply chain integration (SCI), supply chain agility (SCA), supply chain visibility (SCV) and supply chain redundancy (SCR). It also examines the impact of the four mitigation strategies on SCD. The impact of the latter on business performance (BP) is also explored.Design/methodology/approachThis study employs an empirical approach through survey research methodology. It analyzes data collected from 304 managers from pharmaceutical distribution companies in Jordan. Appropriate validity and reliability tests were employed for the study constructs. Path analysis using AMOS software was performed to test the study hypotheses.FindingsSCDO was found to positively affect all SCD mitigation strategies. Furthermore, among the four mitigation strategies examined, SCV exhibited the highest significant impact in reducing SCD, followed by SCA and then SCR. However, the results revealed that SCI did not significantly impact SCD. Additionally, SCD proved to be negatively and significantly related to BP.Originality/valueThe present study fills a gap in the literature regarding the management of SCDs in pharmaceutical supply chains (SCs) generally and SCs of pharmaceutical distribution companies specifically. It also addresses an under-investigated area in the literature concerning the role of SCDO in promoting the adoption of SCD mitigation strategies.

The COVID-19 pandemic exposed the weaknesses in healthcare supply chains, leading to shortages, increased costs, and untrustworthy vendors. This has put patients at risk for treatment delays, worse care, and higher death rates. The healthcare supply chain's dependence on single sourcing has made these issues worse. Geopolitical conflicts, climatic catastrophes, data privacy issues, cybersecurity threats, and counterfeit goods are emerging risks. Resilient solutions are needed due to the complex ecosystem of manufacturers, wholesalers, regulatory organizations, and healthcare providers. Supply Chain Resilience (SCRES) is crucial for uninterrupted healthcare services, with redundancy, visibility, and adaptability being key components. Future research directions include blockchain integration, data analytics, and regulatory frameworks to improve resilience. Successful mitigation solutions require understanding interdependence, resilience measurements, and behavioral elements. Addressing these vulnerabilities is crucial for patient safety and healthcare effectiveness.

This study investigates how data-driven capabilities and sustainable resource-allocation policies jointly influence supply chain resilience (SCRes) under disruption and uncertainty. Adopting a quantitative, cross-sectional, multiple case-study design, the research integrates survey-based measurement, archival operational data, and stochastic simulation–optimization to link organizational capabilities—visibility, collaboration, flexibility, supplier diversification, redundancy, risk orientation, and allocation efficiency—to key resilience outcomes, including service recovery, time-to-recovery (TTR), backorder intensity, cost variance, and emissions. Data were collected from 190 professionals across four international firms in discrete manufacturing, FMCG, healthcare logistics, and electronics sectors, each providing both perceptual and objective data spanning 12–24 months of operations. Hierarchical multiple regression models, supported by mediation and moderation analyses, revealed that collaboration, digital visibility, and allocation efficiency were the strongest predictors of resilience performance, while flexibility and diversification contributed moderate incremental effects. Allocation efficiency partially mediated the impact of visibility and collaboration on outcomes, demonstrating that information and coordination enhance resilience primarily through improved resource allocation. Moreover, capability effects intensified under uncertainty—visibility’s and collaboration’s benefits were significantly amplified by demand volatility and lead-time variability. A composite Resilience Performance Index (RPI) was developed to align statistical and operational metrics, linking survey constructs to observed KPIs. Monte Carlo simulation experiments using empirically calibrated disruption parameters validated these statistical insights: sustainability-aware optimized policies improved mean service levels by 3–6 percentage points, reduced TTR by 15–27%, lowered backorder intensity by up to 24%, and cut emissions intensity by 6–11% compared to status quo. These findings confirm that resilience and sustainability can be jointly enhanced through data-driven allocation strategies. The study contributes an integrated, replicable framework that bridges measurement, inference, and decision experimentation—offering both theoretical clarity on capability mechanisms and practical guidance for managers seeking to design resilient, carbon-conscious supply chains under stochastic conditions.

Since the Covid-19 pandemic entered Indonesia in April 2020, the automotive industry experienced the most significant decline compared to the other sectors. The downfall was due to weak demand for cars and motorcycles from the domestic and foreign market, leading to production cuts. This incident forced the entire automotive industry to adjust to returning to normal conditions after the disruption occurred as quickly as possible. There in need for Key Performance Indicators (KPI) for Supply Chain Resilience (SCR) to control and manage the company's target plans when a disruption occurs at any time. This study aims to design KPI to help firms in assessing the indicators related to disruption as long-term measures. This study utilized expert assessments, gathered from questionnaires, while quantitative data was processed using the Content Validity Index (CVI). From the literature review, 11 indicators such as security, knowledge management, visibility, risk management, collaboration, agility, flexibility, efficiency, redundancy, financial strength, market position and 46 sub-indicators of SCR were collected. Using the CVI approach for validity test results, 27 SCR sub-indicators were validated by six experts in the sector, with an average I-CVI value of 0.81.

Global supply networks, once designed for maximum efficiency and Just-in-Time (JIT) delivery, are now shown to be highly vulnerable due to the escalating polycrisis of geopolitical instability, climatic disruptions, and pandemics. This vulnerability results in significant costs: a recent study estimates total losses exceeding $2.3 trillion in global production during major disruptions, such as ElectroLean Inc.’s disastrous $1.2 billion failure during Southeast Asian floods. This study addresses systemic vulnerability by proposing and experimentally validating a transformational framework: Antifragile Supply Chain Management (A-SCM). A-SCM is a six-pillar system designed to actively gain strength from instability, going beyond simple resilience (recovery). We demonstrate how combining Strategic Redundancy and Optionality, Enhanced Visibility and Sensing, Decentralization and Modularity, Adaptive Capacity, Ecosystem Collaboration and Trust, and Continuous Learning and Stress Testing enables organizations to not only withstand shocks but also turn them into engines for innovation and competitive advantage. Case evidence highlights its effectiveness: while vulnerable JIT systems collapse, A-SCM practitioners like MediTech Global turned the Suez Canal blockage into a €85 million EBITDA gain through strategic near-shoring and ecosystem flexibility. Implementing this approach requires reevaluating metrics—such as adopting Mean Time To Improve (MTTI) and Optionality Value—and creating environments that reward smart risk-taking. This study offers a comprehensive framework for this vital transformation, exploring pathways, challenges, and sectoral adjustments. The evidence is clear: in the tumultuous early 21st century, survival depends on moving beyond fragile efficiency. Embracing antifragility is a critical strategic shift—turning disruptions into lasting competitive advantages, structural improvements, and ongoing innovation.

This study questions the need for more visibility to improve supply chain network resilience (SCNR). It investigates how disruptions propagate through real-world supply chain networks and evaluates the effectiveness of different strategies for fortifying key nodes against such disruptions. The aim is to identify practical, data-driven methods that enhance SCNR by prioritising critical nodes for protection using social network analysis (SNA) metrics. Agent-based modelling combined with the susceptible–infected–recovered (SIR) model from epidemiology literature is applied to simulate disruption propagation in ten real-world supply chain networks. Fortification strategies are based on five SNA metrics and evaluated against random node selection. Fortification is implemented by increasing a node's resistance to disruption and accelerating its recovery, an abstract representation of real-world resilience measures such as redundancy, information sharing or collaborative strategies. Each scenario is tested under single-node and multi-node disruption conditions, with 100 repetitions per configuration to ensure robustness. Targeted node fortification based on SNA metrics significantly outperforms random fortification in reducing performance loss. While page rank yields best resilience benefits on average, simpler metrics like node degree deliver nearly equivalent improvements, demonstrating that effective resilience strategies can be implemented without requiring full network visibility. This research closes a relevant gap in SCNR literature by validating fortification strategies on realistic, large-scale supply chain networks, moving beyond idealised or synthetic structures. Findings provide actionable, scalable guidance for supply chain practitioners, demonstrating that even basic network metrics enable meaningful resilience improvements in complex supply chains.

Smart supply chain visibility and predictive logistics represent a transformative framework for modern enterprise management, addressing the escalating complexities of global trade networks and heightened customer expectations. By integrating Internet of Things (IoT) technologies, real-time tracking systems, and artificial intelligence-based demand forecasting with SAP S/4HANA and external logistics providers, organizations can transition from reactive to proactive operations. The comprehensive framework leverages SAP Business Technology Platform services, including SAP IoT Services, SAP Event Mesh, SAP AI Core, and SAP Integration Suite to create a cohesive ecosystem enabling end-to-end visibility. The implementation follows a systematic workflow transforming raw data into actionable intelligence through acquisition, event processing, predictive analysis, cross-system synchronization, and continuous learning mechanisms. This transformation delivers substantial benefits across operational efficiency, service levels, inventory optimization, risk mitigation, and sustainability dimensions. While implementation challenges related to data quality, integration complexity, change management, security concerns, and ROI justification exist, organizations can maximize success through thoughtful planning and strategic approaches, ultimately achieving competitive advantages through enhanced resilience, agility, and customer responsiveness.

Purpose: In the era of digital transformation, enhancing supply chain visibility through digital technologies is critical, yet there is limited consensus on their application across strategic, tactical, and operational levels within focal organization. This study investigates how digital technologies enhance supply chain visibility by enabling focal firms to acquire and exploit information from key stakeholders: suppliers, customers, and internal teams. Design/Methodology/Approach: Grounded in the resource-based view theory and employing structural equation modeling, the study analyzes the relationships between different levels of digital technology application and three dimensions of supply chain visibility: supplier, customer, and internal visibility. Findings: The findings reveal that strategic and tactical applications of digital technologies significantly enhance all three dimensions of supply chain visibility, while operational applications show no significant effect. Implications/Originality/Value: Findings advance the supply chain management literature by empirically validating the differential impacts of tactical and strategic digital technology applications on three distinct dimensions of supply chain visibility, conceptualized herein for the first time. Addressing recent calls for deeper inquiry into digital technology and supply chain management integration, it offers theoretical grounding via the Resource-Based View and practical guidance for manufacturing firms in emerging economies to enhance visibility and performance through targeted digital investments.

Global supply chains today are characterized by increasing complexity, volatility, and uncertainty. Traditional supply chain systems often struggle to provide timely insights, leading to inefficiencies and vulnerability to disruptions. The COVID-19 pandemic exposed significant weaknesses in supply chain visibility, accelerating the adoption of digital technologies to enhance resilience and agility (Ivanov & Dolgui, 2020). Among these technologies, Digital Twin solutions have emerged as a transformative approach, enabling virtual replicas of supply chain assets, processes, and networks that integrate real-time data for continuous monitoring, simulation, and predictive analytics. This paper explores the role of digital twins in supply chain management, examining their architecture, applications, benefits, and challenges. The study also presents examples of cases and discusses future trends that position digital twins as critical enablers of next-generation supply chain visibility and decision-making.

The convergence of smart vendor analytics with offline-compatible Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in supply chain management, particularly addressing the critical challenges faced in low infrastructure environments. This comprehensive research review examines the integration mechanisms, technological frameworks, and operational strategies that enable real-time supply chain visibility despite connectivity constraints and resource limitations. By analyzing the intersection of advanced analytics, edge computing capabilities, and resilient ERP architectures, this study reveals how organizations can achieve supply chain transparency and operational efficiency in environments characterized by intermittent connectivity, limited technological infrastructure, and resource constraints. The investigation explores the multifaceted implications of smart analytics integration, demonstrating the capacity to transform supply chain operations through intelligent data processing, predictive insights, and adaptive system architectures that maintain functionality regardless of connectivity status. Through systematic analysis of empirical evidence and theoretical frameworks, this review illuminates the transformative potential of integrated smart analytics and offline-compatible ERP systems to create resilient supply chain ecosystems that transcend traditional infrastructure limitations and establish new paradigms of operational excellence in challenging environments.

This study investigates key drivers influencing real-time supply chain visibility and their impact on supply chain performance in the modern retail business. The objectives are: (1) to identify the causal factors that influence real-time supply chain visibility and supply chain performance in modern retail business, 2) to analyze the impact of the causal factors of supply chain performance through the mediating role of real-time supply chain visibility within the modern retail business, and 3) to develop marketing strategies based on the causal factors of supply chain performance through the mediating role of real-time supply chain visibility within the modern retail business. Data were gathered through structured interviews and online questionnaires, and analyzed using descriptive, inferential, and content analysis methods. The findings reveal that process integration, predictive data management, and digital logistics significantly enhance real-time supply chain visibility. These factors also indirectly improve supply chain performance through the mediating role of visibility. Additionally, real-time visibility itself has a direct positive impact on performance. The study offers strategic insights for modern retail business, highlighting the importance of operational integration, predictive analytics, and digital logistics technologies in enhancing visibility and overall supply chain effectiveness.

Purpose: This research examines the role of supply chain visibility in enhancing supply chain risk management amidst increasing global uncertainties. Drawing on Information Processing Theory, it investigates how visibility across suppliers, customers, and internal operations enables firms to effectively identify, assess, and mitigate risks within their supply chain networks. Design/Methodology/Approach: The study integrates a comprehensive literature review on supply chain visibility, risk management, and information processing, complemented by empirical evidence derived from a survey of 268 manufacturing firms. Findings: The results reveal that supplier visibility and internal operational visibility significantly influence both reactive and proactive dimensions of supply chain risk management. Conversely, customer visibility did not demonstrate a significant relationship with these risk management dimensions. Implications/Originality/Value: This research contributes to the academic discourse on supply chain risk management by empirically demonstrating the differential impact of various visibility dimensions. It also offers actionable guidance for managers, highlighting the strategic importance of fostering supplier and internal visibility as key assets for improved information processing and effective risk mitigation in complex global supply chains.

Global supply chains exhibit increasing susceptibility to disruptions arising from geopolitical instability, demand volatility, and systemic logistical inefficiencies. A lack of end-to-end visibility remains a critical barrier to resilience, resulting in delayed decision-making and inefficiencies. This paper proposes a unified supply chain visibility framework that integrates SAP S/4HANA, Machine Learning (ML), SAP Integrated Business Planning (IBP), Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The novelty lies in demonstrating a closed-loop, prescriptive decision-support model that improves forecast accuracy, reduces inventory costs, and accelerates disruption recovery. Using a simulation-based, case-driven methodology, the framework enables closed-loop forecasting, real-time tracking, and prescriptive decision support. Results indicate a 50% improvement in forecast accuracy, a 20% reduction in inventory costs, and a significantly faster recovery from disruptions. The findings highlight that visibility, when treated as a strategic capability, delivers measurable operational and financial benefits, positioning integrated digital supply chains as a competitive advantage.

Despite the importance of supply chain visibility, there is a limited understanding of the mechanisms through which supply chain visibility is developed and how it influences supply chain risk management. This study conceptualizes and empirically investigates the role of emotional intelligence in enhancing supply chain visibility and risk management. While drawing on Goleman’s emotional intelligence model, this study utilizes structural equation modeling to test the proposed model by collecting data from manufacturing firms in Pakistan. The findings indicate that relationship management significantly improves all three dimensions of supply chain visibility (i.e.-supplier, customer, and internal visibility) whereas self-awareness, self-management, and social-awareness exhibit no significant relationship with various dimensions of supply chain visibility. Additionally, we also find that supplier visibility and internal visibility exhibit positive relationships with both the dimensions of risk management (reactive and proactive risk management) whereas customer visibility does not exhibit any significant relationship with any of its dimension. The findings contribute to the existing body of knowledge on supply chain management and offer actionable insights for supply chain professionals and human resource managers seeking to enhance visibility and risk management through emotional intelligence development.

Supply chain visibility platforms have emerged as critical tools for managing increasingly complex global networks, providing organizations with the transparency needed to navigate modern challenges. These platforms integrate real-time tracking technologies, data integration architectures, analytics capabilities, collaboration tools, inventory management systems, and performance visualization functionalities to create comprehensive awareness across extended supply networks. The conceptual foundation of visibility has evolved from basic tracking to predictive intelligence, reflecting technological advances and organizational needs. Implementation requires careful assessment of organizational readiness, stakeholder engagement, technology selection, and change management. Organizations implementing these platforms experience substantial benefits including operational efficiencies, enhanced risk management, improved customer experiences, sustainability advantages, and competitive differentiation through information leverage. The framework presented integrates technological components with organizational factors to provide both theoretical understanding and practical guidance for visibility enhancement initiatives in today's volatile business environment.

In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy.

The paper reviews how Generative Artificial Intelligence (Generative AI) and intelligent control tower systems may help eliminate the burgeoning requirement of real-time openness in supply chains, on a progressively intricate and international logistics system. Legacy supply chain management systems tend to experience issues with a divided amount of data, limited visibility, and constrained forecasting abilities, limiting the effectiveness and actionability of the operations. The combination of Generative AI and intelligent control towers results in a framework that allows dynamic data to be generated, risks identified and forecasted, and scenarios planned autonomously. It is case-based research that provides an insight into the positive effect of these technologies regarding ramping up the speed of decision-making, improving prediction, and cross-functional synchronization within the procurement, inventory, and transportation chains. According to significant results, using AI-enabled control towers in organizations positively impacts latency reduction, demand sensing, and disruption management. Moreover, the paper indicates how Generative AI can serve adaptive learning due to its ability to create value by creating actionable insights through unstructured data sources, including supplier communication and market signals. Such developments render intelligent control towers no longer tools of monitoring but strategic tools of building resiliency, agility, and innovativeness in a digital supply ecosystem. The paper's conclusion points out the strategic implications to businesses, and some recommendations can be adopted for implementation and future research.

No abstract available

The COVID-19 crisis has notably impacted global supply chains as it has disrupted manufacturing operations. To recover from the aforementioned disruptions, supply chain digitalization [SCD] is increasingly being acknowledged to help the recovery process. Based on this, scholars have called for additional research on how SCD can enhance supply chain visibility [SCV] and boost supply chain performance [SCP] in turbulent environments. Based on 399 valid responses collected through cross-sectional method from Turkish manufacturing firms and using a non-probabilistic sampling method [i.e., purposive sampling], this research explores the effect of SCD on SCP. The mediating role of SCV and the moderating role of supply chain survivability [SCS] on the SCD-SCP relationship were also explored. The findings showed that SCD has a positive effect on SCP. SCD has a positive effect on SCV. SCV has a positive effect on SCP. The link between SCD and SCP is mediated by SCP. The results also revealed that SCS moderated the SCD-SCV link such that SCD has a stronger, positive relationship with SCV when SCS is high than when it's low. SCS moderates the SCD-SCP link, such that at low levels of SCS, the positive effect of SCD on SCP is weakened. The indirect positive effect of SCD on SCP via SCV is strongest when supply chain survivability is high. The findings suggest that SCD can improve cost-effectiveness, promote communication and information efficiency, and enhance supply chain resilience to improve performance after disruptions. This study provides insightful new implications for both supply chain literature and practitioners.

ABSTRACT This study investigates the connection between supply chain resilience (SCRES) capabilities and dynamic capabilities theory (DCT) in the context of the Irish food industry, specifically in the face of supply chain disruption. Based on a theoretical synthesis of the SCRES and dynamic capabilities (DC) literature and empirical evidence from a multi-case study, this study elaborates a theory of dynamic resilience capabilities (DRC) comprising five SCRES capabilities: anticipation, adaptation, response, recovery, and learning. These five capabilities align with the DC dimensions of sensing, adapting, coordinating, reconfiguring, and learning, and enable firms to adjust their strategies before, during, and after disruptive events. This study contributes to the SCRES literature by distinguishing between dynamic and operational capabilities and providing insights into building DRC through strategic processes, such as sensing threats and opportunities, adapting to changes, coordinating tasks, reconfiguring resources, and developing learning routines to mitigate supply chain disruptions.

The fragility of agriculture makes the food supply chain vulnerable to external risks such as epidemic, conflict, disaster, climate change, economic and energy crisis. The COVID-19 pandemic has spread and continued globally in recent years, resulting in food supply chain disruption and insecurity, which triggers profound reflection on the impacts of public health events (PHEs). Studying the impacts of PHEs on the resilience of food supply chain has great significance to effectively reduce the risks of disruption and insecurity in the future.Based on the composition of PHEs and the division of food supply chain, this paper adopted the nonlinear Granger causality test to verify the nonlinear causal relationship between PHEs and proxy variables in the food supply chain; then the TVP-VAR-SV model was constructed and its three-dimensional pulse response results were matched with the sensitivity, recovery, and adaptation of the food supply chain resilience to deeply explore the dynamic impacts of PHEs.PHEs has significant nonlinear conduction effects on the resilience of food supply chain, the impacts of PHEs on the partial sector resilience have significant dynamic characteristics in the whole sample period, and the impacts of PHEs on the recovery and adaptation aspects of food supply chain resilience have structural break characteristics.The differences, dynamic characteristics and structural breaks of the impacts of PHEs on the resilience of food supply chain are caused by the infectivity and mortality of PHEs, attributes of food products, regulation of supply and demand in the market, behavioral decisions of all participants, changes in the policy environment, and coordination and upgrading of all sectors in the supply chain.

This article investigates the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies. The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages.

For the first time, the ripple effect is examined in the setting of an intertwined supply network. Through simulations, we model the disruption propagation in supply chains having common suppliers. We explore conditions under which a collaborative coordination of re-purposed capacities and shared stocks can help mitigate the ripple effect and improve recovery performance. As a result, we conceptualize the notion of collaborative emergency adaptation contributing to development of “network-of networks” and viability perspective in supply chain resilience management. We illustrate our approach with anyLogistix simulations and deduce some generalized theoretical and managerial insights on how and when a collaborative emergency adaptation can be implemented and help improve supply chain resilience and viability.

Entering the COVID-19 pandemic wreaked havoc on supply chains. Reacting to the pandemic and adaptation in the “new normal” have been challenging tasks. Exiting the pandemic can lead to some after-shock effects such as “disruption tails.” While the research community has undertaken considerable efforts to predict the pandemic’s impacts and examine supply chain adaptive behaviors during the pandemic, little is known about supply chain management in the course of pandemic elimination and post-disruption recovery. If capacity and inventory management are unaware of the after-shock risks, this can result in highly destabilized production–inventory dynamics and decreased performance in the post-disruption period causing product deficits in the markets and high inventory costs in the supply chains. In this paper, we use a discrete-event simulation model to investigate some exit strategies for a supply chain in the context of the COVID-19 pandemic. Our model can inform managers about the existence and risk of disruption tails in their supply chains and guide the selection of post-pandemic recovery strategies. Our results show that supply chains with postponed demand and shutdown capacity during the COVID-19 pandemic are particularly prone to disruption tails. We then developed and examined two strategies to avoid these disruption tails. First, we observed a conjunction of recovery and supply chain coordination which mitigates the impact of disruption tails by demand smoothing over time in the post-disruption period. Second, we found a gradual capacity ramp-up prior to expected peaks of postponed demand to be an effective strategy for disruption tail control.

No abstract available

The speed of recovery from supply chain disruption has been identified as the predominant factor in building a resilient supply chain. However, COVID-19 as an example of an evolving crisis may challenge this assumption. Infection risk concerns may influence production resumption decision-making because any incidents of infection may lead to further shutdowns of production lines and undermine firms’ long-term cash flows. Sampling 244 production resumption announcements by Chinese manufacturers in the early COVID-19 crisis (February–March 2020), our analysis shows that, generally, investors react positively to production resumptions. However, investors perceived the earlier production resumptions were higher risk (indicated by declined stock price). Such concerns were exacerbated by more locally confirmed cases of COVID-19 but were less salient for manufacturers with high debts (liquidity pressure). This study calls for a reassessment of the current disruption management mindset in response to new evolving crises (e.g., COVID-19) and provides theoretical, practical, and policy implications for building resilient supply chains.

A recent global outbreak of Corona Virus Disease 2019 (COVID-19) has led to massive supply chain disruption, resulting in difficulties for manufacturers on recovering their supply chains in a short term. This paper presents a supply chain disruption recovery strategy with the motivation of changing the original product type to cope with that. In order to maximize the total profit from product changes, a mixed integer linear programming (MILP) model is developed with combining emergency procurement on the supply side and product changes by the manufacturer as well as backorder price compensation on the demand side. The model uses a heuristic algorithm based on ILOG CPLEX toolbox. Experimental results show that the proposed disruption recovery strategy can effectively reduce the profit loss of manufacturer due to late delivery and order cancellation. It is observed that the impact of supply chain disruptions is reduced. The proposed model can offer a potentially useful tool to help the manufacturers decide on the optimal recovery strategy whenever the supply chain system experiences a sudden massive disruption.

Background: Omni-channel retailing is blurring the lines between online and physical stores for consumers as it provides consumers with more choices, convenience and a seamless shopping experience. An integral aspect of implementing an omni-channel retail strategy is having an efficient reverse logistics process. However, retailers seem reluctant to implement omni-channel reverse logistics because of the various risk types that affect the economic wellbeing of a firm, especially during supply chain disruption recovery (SCDR) when the firm is in distress. Prior research primarily examines the risks associated with reverse logistics in a single channel. While the existing findings are promising, there is a lack of understanding regarding the specific risks involved in omni-channel reverse logistics and how to mitigate these risks in the context of SCDR.Objectives: The purpose of this study was to explore omni-channel reverse logistics risks and mitigation strategies during SCDR in the South African fashion retail industry.Method: The study employed a generic qualitative design using purposive sampling methods. Fourteen semi-structured interviews were conducted to collect data. The data were analysed using a thematic analysis approach.Results: The study identified specific types of omni-channel reverse logistics risks. The findings indicate that omni-channel reverse logistics risk during SCDR is managed through proactive and reactive strategies such as technology implementation, collaborative relationships, quality insurance inspections, customised policy changes and disruption-specific reverse logistics teams. The findings show that mitigating omni-channel reverse logistics risk can help create a competitive advantage because of increasing customer loyalty, value recovery and profits.Conclusion: The findings provide valuable insight on how to manage omni-channel reverse logistics risk during SCDR and, if mitigated correctly, can contribute to a competitive advantage.Contribution: This study expands on the current literature by identifying multiple types of omni-channel reverse logistics risks and strategies used to manage omni-channel reverse logistics risk in a SCDR context.

This paper examines the recovery of a three-level manufacturing supply chain under supply and demand disruptions. The paper proposes new combined recovery strategies, which aim to cope with interruption by adjusting the supply chain structure and material flows. This study integrates both supply chain performance and supply chain capability dimensions. We develop a bi-criteria mixed integer linear programming model with profit and resilience maximization as the objective. The model combines supply-side supply expansion, manufacturer capacity impairment, and demand regulation on the demand side. In a numerical example, we find that a "reciprocal disruption overlay" occurs when supply and demand disruptions, but the supply chain still loses some profit. The results suggest that the combined recovery strategies reduce profit loss and increase supply chain resilience. Furthermore, the strategies are also the optimal recovery strategies under unilateral disruptions. This model facilitates the coordination of a disrupted supply chain and can help managers decide on the best recovery plan.

The increasing frequency of global supply chain disruptions, driven by geopolitical tensions, pandemics, and climate-related shocks, has heightened the need for resilient and adaptive logistics systems. Artificial Intelligence (AI) is emerging as a transformative tool for enhancing supply chain resilience and recovery capabilities. This study evaluates the impact of AI on supply chain resilience and disruption recovery within the Ghana Ports and Harbours Authority (GPHA), Takoradi. Specifically, it examines how AI-driven technologies—such as predictive analytics, real-time monitoring, automation, and decision-support systems—contribute to mitigating risks, improving operational efficiency, and accelerating recovery from disruptions. Using a mixed-methods approach, data will be collected through structured questionnaires, interviews with key port officials, and analysis of operational records. The study seeks to establish the extent to which AI adoption enhances the port’s ability to anticipate disruptions, optimize resource allocation, and maintain continuity of port services. Findings are expected to provide empirical insights into the role of AI in strengthening supply chain resilience in emerging economies, offering policy and managerial implications for GPHA and similar port authorities in Sub-Saharan Africa. Ultimately, the research contributes to the growing body of knowledge on digital transformation in supply chain management and its strategic role in ensuring sustainable port operations

The Coronavirus (COVID-19) pandemic has generated a notable increase in the demand for online shopping, driving the global adoption of an omni-channel (OC) strategy by retailers. While it is well established that supply chain mitigation capabilities and recovery strategies can minimize the impact of supply chain disruptions (SCDs), it has not been explored in the increasingly relevant OC context. The purpose of this study was to explore the SCD mitigation capabilities and recovery strategies present in the South African OC fashion retail industry. The study was conducted among senior supply chain managers employed by OC retailers in South Africa. A generic qualitative design was employed to collect data through semi-structured interviews with fifteen participants. A thematic analysis approach was used to analyze the data. This study identified the types and causes of OC-related SCDs that produce the negative effects associated with an OC retailing strategy. The findings showed that South African OC fashion retailers do not engage in the most effective SCD mitigation and learning practices evident in the literature. Furthermore, the findings also revealed that OC retailers’ multiple touchpoints can aid SCD recovery efforts by transferring order fulfilment between its online and offline channels. This study provides managers with an understanding of the nature of OC-related SCDs that can be used to reduce their negative effects or prevent their occurrence altogether. Managers should revisit their SCD mitigation capabilities and learning techniques to improve supply chain resiliency.

Purpose. This study investigates how supply chain disruptions affect firm productivity and examines the differential mediating roles of proactive and reactive recovery strategies in manufacturing firms operating in emerging economies. Methodology. Drawing on the Resource-Based View and Dynamic Capabilities Theory, the research employs a cross-sectional survey design with data collected from 250 pharmaceutical and automotive manufacturing firms in Ghana. Structural Equation Modelling using SmartPLS 4.0 with bootstrapping procedures (5,000 subsamples) was applied to test direct effects and mediation hypotheses. Results. Supply chain disruptions negatively impact firm productivity (β = -0.247, p = 0.001). Proactive recovery strategies significantly mediate this relationship with a large positive indirect effect (β = 0.396, p < 0.001), indicating that anticipatory capabilities substantially buffer disruption-induced productivity losses. Reactive recovery strategies show no significant mediating effect (β = -0.039, p = 0.452), suggesting that post-disruption responses alone are insufficient for maintaining productivity. Theoretical contribution. The study advances supply chain resilience theory by reconceptualizing recovery strategies as dynamic capabilities with differential effectiveness. It provides empirical evidence distinguishing proactive from reactive mechanisms and demonstrates that the indirect effect of proactive recovery substantially exceeds the direct negative effect of disruptions, indicating that well-developed anticipatory capabilities can more than offset disruption impacts. Practical implications. Supply chain managers should prioritize investments in proactive recovery capabilities, including supply base diversification, contingency planning, scenario analysis, and real-time monitoring systems. For transport and logistics firms, proactive strategies such as alternative routing plans, carrier diversification, and fleet redundancy represent critical resilience investments with measurable productivity returns. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production

The global pandemic of COVID-19 has caused severe damage to the supply chain, and manufacturers may face long-term supply disruptions. A new product design change program taking into account product life cycle and lead time is introduced and incorporated into a disruption recovery model for a serial supply chain that minimizes manufacturer losses after supply chain disruption. A mixed-integer linear programming (MILP) model is presented addressing this multi-period, multi-supplier, and multi-stage problem with long-term supply disruptions. A heuristic algorithm is designed to solve the model. In a numerical example, five disruption scenarios of the recovery model are solved. The results show that the proposed disruption recovery strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the role of the product life cycle in the selection of product design change options. The proposed model can offer a potentially useful tool to help the manufacturers decide on the optimal recovery strategy whenever the supply chain system experiences a massive disruption.

No abstract available

to ensure business stability, continuity and sustainability (Azadegan et al. 2020:748). Orientation: Coronavirus disease 2019 (COVID-19) alcohol sale prohibitions have significantly impacted the South African liquor industry. This act of government halted the supply chain flow of both locally produced and imported products during this period. Both internal and external supply chain integration (SCI) became essential to ensure a rapid response to disruption recovery strategies. Purpose: The purpose of this study in the South African liquor industry was to explore the impact of and the role that internal SCI antecedents, mechanisms and measurement play during supply chain disruption recovery using information processing theory as a lens. Motivation for the study: Internal SCI has shown to improve performance and dependability during non-disrupted periods, but the influence of internal integration during a disruption period has yet to be determined. Research design, approach and method: A single case study design was employed. Data were collected through 15 semi-structured interviews with executive and senior managers across the case organisation’s supply chain. The collected data were analysed using a thematic analysis approach. Main findings: Findings show that the antecedents of goal alignment, cross-functional awareness and a holistic management approach improved both the identification of and reaction to supply chain disruptions. During a disruption period, the recognised internal integration mechanisms have a threefold purpose: they collect information, eliminate information ambiguity and build recovery action plans. Although the most important internal integration measurement during disruption recovery was identified as on-time in full, all of the indicated measurements serve as both an output measure and a disruption indicator. Practical/managerial implications: The study bridges the gap between the importance of information flow both during internal integration and disruption recovery and how internal integration implementation assists with disruption recovery. Contribution/value add: The study introduces a framework to explain the interconnectivity between internal integration and disruption recovery.

In very recent years, large-scale disruptions brought by major global and local emergencies have posed many challenges with respect to the recovery control of supply chain systems. This work investigates a problem regarding the optimal control of a supply chain by considering product design change in order to enable manufacturers to recover their disrupted supply chain quickly. A two-layer optimization model is developed, in which the lower model is used to optimize the product design change path, and the upper model is used to select the appropriate alternative suppliers and schedule the delivery of customer orders. To solve the developed model, a hybrid ant colony optimization (HACO) algorithm is designed, which is combined with a Gurobi solver and uses some special strategies. The validity of the proposed algorithm is illustrated experimentally through computational tests and systematic comparison with the existing methods. It is reported that the losses caused by supply chain disruptions are reduced significantly. The proposed model and algorithm can provide a potentially useful tool that can help manufacturers decide upon the optimal form of recovery control when a supply chain system experiences a massive supply disruption.

No abstract available

Against the backdrop of escalating uncertainty in the global supply chain, steel enterprises are confronted with multiple risks such as dependence on iron ore imports, disruptions in continuous blast furnace production, blockages in bulk logistics, and demand fluctuations. Empirical data indicates that deep digital transformation can reduce the probability of disruptions by 40%-65%, shorten recovery time by 50%-75%, and reduce economic losses by 35%-60%. However, the effectiveness of transformation is moderated by three key factors: data governance level (breaking down silos in ERP/MES/SCM systems), organizational adaptability (establishing a flat emergency decision-making chain), and ecological collaboration breadth (co-building industrial cluster-level platforms). The study shows that digital transformation is the core path for steel enterprises to build resilient supply chains, but it requires simultaneous breakthroughs in technology integration, organizational change, and ecological cooperation bottlenecks to achieve a paradigm shift from passive risk resistance to active risk control.

Supply chain disruptions pose escalating threats to operational continuity and competitive advantage, necessitating effective resilience mechanisms. This study investigated the effectiveness of collaborative strategies in enhancing supply chain resilience through agent-based simulation comparing four recovery approaches: no coordination, backup supplier, rapid response, and combined collaborative mechanisms. A 120-day simulation incorporating multipoint disruption scenarios across a multitier supply chain network revealed that the combined collaborative strategy achieved 34.6% faster recovery, maintained 6.9 percentage points higher order fulfillment rates, reduced cost volatility by 36.3%, and demonstrated 139% higher system stability compared to baseline no-coordination approaches. Statistical validation confirmed highly significant differences across all performance metrics. Critically, findings demonstrate that response speed rather than capacity redundancy emerges as the binding constraint for moderate-duration disruptions, and synergistic effects generate 6-15% performance premiums exceeding additive predictions from individual mechanisms. Results provided simulation-based validation for integrated collaborative resilience investments and offered actionable guidance for prioritizing response agility over resource redundancy.

PurposeThis study aims to examine the robustness and resilience of supply chain networks under various disruption scenarios, focusing on how these disruptions propagate through the network, a phenomenon known as the ripple effect.Design/methodology/approachA theoretical model is developed to assess product flow through a multi-echelon supply chain under demand uncertainties. Within this setting, numerical analysis is conducted to measure the customer fill rate and at the same time to assess the impact of disruptions at the final echelon, capturing the ripple effect from distant nodes.FindingsThe study provides insights into the types and intensities of risks faced by multi-echelon supply chain networks. It highlights the repercussions of disruptions and identifies recovery measures to minimize and manage their impact, enabling the system to regain stability.Originality/valueThis research contributes to a deeper understanding of supply chain risks and their management by exploring the ripple effect in multi-echelon supply chains and offering strategies to enhance network resilience and robustness.

Global supply chains are increasingly exposed to complex disruptions arising from natural disasters, pandemics, geopolitical tensions, and technological failures. Traditional risk assessment techniques often rely on deterministic assumptions or static network representations, limiting their ability to capture stochastic propagation and cascading effects. This paper introduces a graph-based Monte Carlo simulation framework designed to model multi-tier supply chains as dynamic networks in which nodes represent suppliers, production facilities, or distribution centers, and edges represent transport or contractual relationships with probabilistic attributes such as lead time, capacity, and reliability. The framework integrates stochastic disruption sampling with graph-theoretic propagation rules, allowing the generation of thousands of disruption scenarios. Resilience is evaluated using composite key performance indicators, including recovery time, service level maintenance, and stockout probability. A prototype implementation demonstrates practical utility in an electronics supply chain case study, illustrating how mitigation strategies such as alternate sourcing and buffer stock can reduce expected recovery time and improve service performance. The results suggest that combining Monte Carlo methods with network analysis provides actionable insights for decision-makers seeking to enhance resilience in volatile supply environments. This study offers both a methodological contribution and a practical tool for operational risk management.

The resilience of supply chain networks (SCNs) is critical for economic stability. This study examines SCN resilience by analysing their response to disruptions in complex, multi-tier structures. Using a network generator algorithm, we simulated disruption impacts and recovery across SCNs ranging from two to seven tiers. Resilience was assessed through average functionality and recovery duration, leading to eight significant observations. The study addresses literature gaps by exploring tier-specific effects, realistic disruption dynamics, facility and connection disruptions, and the role of intra-/inter-tier connections. It evaluates mitigation strategies such as redundancy, supplier diversification, and government support. Case studies on a biofuel SCN and a municipal solid waste system are included in the supplementary material. Results indicate that disruptions persist longer in higher tiers, with Tier 7 experiencing up to 26% disruption duration, compared to 3% and 7% in the first and second tiers. Increasing redundancy reduces recovery time by up to 57% in seven-tier SCNs while expanding the supplier base from one to two regions cuts recovery time by up to 50%. These insights offer strategies for enhancing SCN resilience and guiding future research.

Abstract Supply chain disruption has become an interesting topic for many researchers recently. Although disruption has been previously modelled, unexpected disruptions are unavoidable. Thus, a reactive strategy is still needed. This article proposes an efficient production recovery strategy for the three-stage supply chain network consisting of manufacturers, distribution centres, and retailers when facing production disruptions. The manufacturers produce multiple products with different production priorities. All manufacturing plants are fully coordinated and able to produce all types of products. Multi-production periods are considered. The model describes the optimal production and distribution allocation before and after disruption (recovery period). The recovery model revises the production and distribution allocation based on the remaining available capacity in the respective production period. The recovery model determines what unfulfilled demand will be assigned either as backorders in the next production period or considered as lost sales. Our results show that the proposed recovery model produces a lower total cost than the non-recovery strategy. Further, the simulation confirms that the longer the disruption duration is, the higher the cost-saving efficiency is. In addition, the demand to capacity tightness ratio is the most impactful parameter to decide on backorder or lost sales strategy.

The growing interdependency between physical and cyber-supply networks makes it possible for disruptions to trigger cascading failures with a mix of structure failures and function failures. There are studies that proposed recovery strategies to improve the resilience of interdependent supply chain networks (ISCNs). However, they hardly ever consider the impacts of real-world failure delay time and recovery resource allocation on ISCN resilience. In this article, a delay-time mixed cascading failure (MCF) model is first proposed to describe the disruption propagation process in ISCNs. Then, three common boundary node-based recovery strategies are implemented in ISCNs subject to MCFs, and the recovery sequence of network nodes is optimized based on efficient resource allocation. Finally, through case studies on a real-life supply chain network and three artificial networks, the effectiveness of recovery strategies is evaluated by using two resilience-based metrics from the perspectives of network function and network structure. Moreover, the impacts of important tunable parameters on ISCN resilience are examined. The experimental results demonstrate that the proposed recovery strategies are superior to traditional recovery strategies. This study provides insights for future investment decision-making toward the enhancement of ISCN resilience with limited recovery resources.

The coronavirus (COVID-19) pandemic has had wide-ranging industry-level impacts. As COVID-19 creates further economic uncertainty and loss, maximizing returns, and managing risk. The study starts by looking at underlying factors that lead to supply chain interruptions from COVID-19 to unforeseen uncertainty. The study also highlights the significance of continuous improvement and adaptation. In essence, the COVID-19 pandemic-related supply chain disruptions are being addressed by the global steel and rolling industries. We learn from this research which strategies essential for sustainability have the greatest positive effects. Aside from the strategies, the various impacts will be quantified. For studying the impacts based on various strategies and criteria, analytical Fuzzy TOPSIS and Best Worst Method (BWM) systems are utilized. Implementing a continuous learning culture and conducting post-disruption analysis facilitates the identification of lessons learned and the implementation of preventive measures. Agile and adaptable supply chain structures enable organizations to quickly respond to unforeseen disruptions and adjust their operations accordingly. Bangladeshi steel & rolling industries may successfully handle disruptions, improve supply chain resilience, and guarantee ongoing company operations in an increasingly unstable global environment by implementing these techniques and forming solid relationships.

In the context of the economic globalization, there is an increased disruption risk in the supply chain network due to the outsourcing, complexity and uncertainty. At the same time, the disruption may propagate across the entire supply chain network because of the interdependence. With the resource constraints, appropriate recovery strategies which can minimize the impact of disruption propagation and effectively improve the supply chain network resilience have attracted a great deal of attention. In this paper, we first construct the disruption propagation model considering the recovery strategy based on the characteristics of the competitiveness, time delay and underload cascading failure in the supply chain network. This model uses the memetic algorithm to determine the set of recovery nodes among all disruption nodes, which can minimize the impact of disruption propagation. And then, the simulation analysis is conducted on the synthetic network and the real-world supply chain network. We compare the proposed recovery strategy with other strategies (according to the genetic algorithm, according to the descending order of the load of failure node, according to the ascending order of the load of failure node, according to the descending order of the node degree, according to the ascending order of the node degree) and provide decision-making reference against supply chain disruptions.

No abstract available

Cross-border supply chains have become increasingly complex and critical in today's interconnected global economy. However, effective risk management in this context remains a significant challenge. The study analyzes and quantifies the impact of different factors on cross-border supply chains by establishing a Bayesian network model and identifies and evaluates interruption risks based on causal reasoning and diagnostic reasoning techniques. The results show that the combination of Bayesian networks can comprehensively reveal the causal relationships and influences of interruption risks in cross-border supply chains, providing theoretical support for risk control implementation in cross-border supply chain business for banks and other enterprises. This study also proposes control strategies for interruption risks in cross-border supply chains, including strengthening information construction, formulating emergency plans and recovery mechanisms, etc., to reduce the losses caused by interruption risks and ensure the stable operation of the supply chain.

Modern supply chains are increasingly exposed to disruptions from geopolitical events, demand shocks, trade restrictions, to natural disasters. While many of these disruptions originate deep in the supply network, most companies still lack visibility beyond Tier-1 suppliers, leaving upstream vulnerabilities undetected until the impact cascades downstream. To overcome this blind-spot and move from reactive recovery to proactive resilience, we introduce a minimally supervised agentic AI framework that autonomously monitors, analyses, and responds to disruptions across extended supply networks. The architecture comprises seven specialised agents powered by large language models and deterministic tools that jointly detect disruption signals from unstructured news, map them to multi-tier supplier networks, evaluate exposure based on network structure, and recommend mitigations such as alternative sourcing options. \rev{We evaluate the framework across 30 synthesised scenarios covering three automotive manufacturers and five disruption classes. The system achieves high accuracy across core tasks, with F1 scores between 0.962 and 0.991, and performs full end-to-end analyses in a mean of 3.83 minutes at a cost of \$0.0836 per disruption. Relative to industry benchmarks of multi-day, analyst-driven assessments, this represents a reduction of more than three orders of magnitude in response time. A real-world case study of the 2022 Russia-Ukraine conflict further demonstrates operational applicability. This work establishes a foundational step toward building resilient, proactive, and autonomous supply chains capable of managing disruptions across deep-tier networks.

ABSTRACT This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance.

Abstract Recent studies have conceptualized the potential for a dark and bright perspective of network complexity in relation to supply chain disruption and resilience respectively. Few empirical studies have been conducted on the relationship among supply chain network complexity, supply chain disruption and supply chain resilience. However, prior studies have not yet investigated how different measures of network complexity relate to both resilience strategies and disruption. The current study, therefore, examines the dark and bright side of supply chain network complexity dimensions using supply chain disruption (SCD) and three supply chain resilience (SCR) strategies (collaboration, flexibility and redundancy) as endogenous variables. The dimensions of the supply chain network complexity utilised in this study are—supply complexity (SNC), customer complexity (CNC), and logistics complexity (LNC) whereas the three SCR strategies considered included; collaboration, flexibility and redundancy. The study uses PLS-SEM and a sample of 690 manufacturing firms in Accra Metropolis. Results show that supply complexity has a positive relationship with both disruption and resilience strategies, while customer complexity is only related to disruption, and logistics complexity is related to all resilience strategies. The study provides theoretical, practical, and political implications.

Abstract Understanding human behaviour in supply chain disruption management (SCDM) requires moving beyond purely rational models. While traditional decision‑making frameworks focus on empirical factors, they often overlook the role of behavioural economics and organizational culture in shaping responses to crises. This study examines how supply chain managers navigated risks and cultural shifts during the COVID‑19 pandemic, offering insights into the interplay between personal risk values, cultural cohesion, and SCDM risk levels. Using a retrospective approach, the study gathered data from 21 supply chain managers in the fast‑moving consumer goods (FMCG) and food supply chains. Questionnaires captured their attitudes towards risk, decision‑making patterns, and organizational cultural shifts before, during, and after the pandemic. Descriptive statistical analyses revealed that SCDM risk levels peaked at the height of the crisis, while cultural cohesion and personal risk values declined. Interestingly, the relationship between cultural cohesion and personal risk value intensified during the pandemic and continued to strengthen post‑pandemic. A similar trend was observed between personal risk value and SCDM risk levels, which became more pronounced over time. However, the link between cultural cohesion and SCDM risk level was strongest during the crisis but faded in pre‑ and post‑pandemic periods. These findings contribute to the growing field of behavioural operations by demonstrating the significance of psychological and cultural factors in crisis decision‑making. They underscore the need for supply chain strategies that integrate behavioural insights, recognizing that human responses to disruption are shaped by more than just rational calculations. By acknowledging the evolving dynamics of risk perception and cultural adaptation, organizations can develop more resilient and human‑centric approaches to supply chain management in times of crisis.

The performance of military supply chain networks (MSCNs) against disruptions is an important consideration for defense logistics decision making, and it is crucial to evaluate it scientifically and accurately. This paper highlights the problem from the perspective of targeted defense strategies before being attacked and analyzes the acceptable recovery time against attacks. A topological structure model, with three exclusive features, in contrast with traditional networks, is used to describe the structure of military supply chain networks. In order to provide a platform for evaluating performance, a simulation method based on exploratory analysis is presented. Considering supply capability against disruptions and the acceptable recovery time for an MSCN after disruptions, evaluation metrics including supply capability and disruption recovery are proposed. By applying the model and algorithms to a POL supply network in a theater, we obtain the values of supply capability and disruption recovery against different disruptions. We also identify the key entities which can easily cause catastrophic failure to this network and which need to be protected against carefully. The results show that new evaluation metrics can capture important performance requirements for military supply chain networks. We also find that the proposed method in this paper can solve the problem of evaluating performance and analyzing disruption recovery in a feasible and effective manner.

The COVID-19 pandemic has significantly disrupted global supply chains, with the retail food sector being one of the most affected. In Malaysia, challenges such as labor shortages, transportation restrictions, and fluctuating consumer demand have exposed vulnerabilities in the food supply chain. This study proposes the Integrated Supply Chain Resilience and Adaptation (ISCRA) Framework to enhance supply chain sustainability and resilience. By integrating Supply Chain Resilience Theory, Disruption Management Theory, Resource-Based View (RBV) Theory, and Systems Theory, the ISCRA Framework provides a holistic approach to mitigating disruptions. The study utilizes secondary data and predictive analytics, employing linear regression and CART decision tree models to assess disruption factors and recommend strategic interventions. Findings highlight the need for digital transformation, diversified sourcing strategies, and enhanced inventory management to ensure supply chain sustainability. This research contributes to supply chain management literature and offers practical insights for policymakers and industry stakeholders in strengthening food security and resilience against future disruptions.

No abstract available