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The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

In anticipation of price hikes and shortages caused by supplier disruptions and manufacturer production stops, customers might stockpile extra products. In the case of a supplier disruption, a manufacturer may decide to continue producing using a contingent source. Capturing the price dynamics in four disruption-related periods (i.e., responding, rising, recovering, and recovered), we derive optimal hoarding policies for customers. The results indicate that customer hoarding decisions fall into multiple patterns depending on the interactions between disruption events, market responses (quick and slow), and market recovery (instant, quick, slow, and never). We next present contingent sourcing tactics for manufacturers to mitigate disruptions with and without customer hoarding. We find that future price increases could induce contingent sourcing even if it is unprofitable to resume production during the price-responding phase. Our results offer recommendations regarding when and how to use hoarding and contingent sourcing accounting for uncertain disruption duration and asymmetric information along with disruption- and recovery-driven price dynamics. These recommendations can be of particular value for supply chain decision-making at times of growing inflation. We also demonstrate the impacts of customer hoarding and disruption information on the value of contingent sourcing. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered new research areas in supply chain resilience. One of these new areas is viability. Viability extends the resilience understanding from performance-based assessment of firm’s responses to disruptions towards survivability of both supply chains and associated ecosystems not only during some short-term disruptions but also under conditions of long-term crises. To explore the state-of-the-art knowledge on methods, models, capabilities, and technologies of supply chain viability, we edited this important IJPR special issue. To introduce the special issue, we review the existing literature on supply chain viability, conceptualise seven major pillars of supply chain viability theory (i.e. viable supply chain design, viability in process planning and control, ripple effect, intertwined and reconfigurable supply networks, ecosystems, digital supply chain, and Industry 5.0), and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by researchers and practitioners alike to consolidate recent advances and practices of viability in supply chain networks and lay the solid foundation for further developments in this area. [ABSTRACT FROM AUTHOR]

Industry 5.0 is a combination of organisational principles and technologies to design and manage operations and supply chains as resilient, sustainable, and human-centric systems. While the general notion of Industry 5.0 has been elaborated, its implications for future operations and supply chains remain underexplored. This paper contributes to the conceptualisation of Industry 5.0 from the perspective of viability. We contextualise a framework of Industry 5.0 through the lens of the viable supply chain model, the reconfigurable supply chain, and human-centric ecosystems. Our study uncovers the major dimensions that characterise Industry 5.0 as a technological-organisational framework. First, the major technological principles of Industry 5.0 are collaboration, coordination, communication, automation, data analytics processing, and identification. Second, Industry 5.0 covers four areas: organisation, management, technology, and performance assessment. Third, Industry 5.0 spans three levels: society level, network level, and plant level. Last but not least, Industry 5.0 frames a new triple bottom line: resilient value creation, human well-being, and sustainable society. We provide a definition of Industry 5.0 and discuss its implications by elaborating on the understanding of value in Industry 5.0, which spans the dimensions of profit, people, and society. We also discuss open research areas. [ABSTRACT FROM AUTHOR]

Supply chain (SC) and operations management has been developed for decades under the fundamental assumption of continuous availability (with some temporary fluctuations and disruptions) of resources to satisfy demand. Under extreme shocks and long-lasting disruptions, SCs and operations face long-term shortages of components, energy, capital, and labour, as well as rapidly rising prices. These long-term resource shortages and risks of hyper-inflation pose novel and unexpected challenges potentially leading to global ripple effects. In this conceptual paper, we first analyse and categorise the available literature on SC and operations management with resource shortages. We then systemise some managerial implications of resource shortages and rising inflation, conceptualising some potential research directions which appeared or may appear in the settings of shortage economy. We conclude that the shortage economy provides a distinct and specific context which goes beyond the conventional problems under constrained or disrupted resources – i.e. the long-term resource shortages with simultaneous disruptions in labour, material, energy, and capital availability. This paper could be useful for researchers and practitioners alike to systemise potential impacts of a shortage economy on SCs and operations, and to navigate adaptation and recovery processes in a structured way to help society cope with deep uncertainties. [ABSTRACT FROM AUTHOR]

While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has challenged supply chains (SC) on an unprecedented scale testing viability and adaptation under severe uncertainty. However, the literature on the adaptation strategies and quantification of their impacts is still scarce. Mixing literature analysis, case study approach, and quantitative techniques for performance assessment under disruptions, our study generalises four adaptations strategies – intertwining, scalability, substitution, and repurposing – to maintain SC viability when facing a pandemic, and offers a model to analyse and quantify deployment and impact of adaptation. First, we analyse the recent literature and identify some of the general characteristics of adaptation strategies during the COVID-19 pandemic. We then describe case studies to illustrate the practical context and supplement the literature analysis in order to derive relevant determinants for building of a conceptual framework and construction of a formal model. In the conceptual framework, we show how the adaptation strategies can be aligned with the SC viability, encompassing the levels of the ecosystem, network, and resources. In the generalised model, we formalise the impacts and efforts in deploying and assessing the adaptation strategies as both a process and an outcome. We close by proposing some open research questions and outline several future research directions. [ABSTRACT FROM AUTHOR]

Ripple effect is a specific area of SC disruptions and a strong stressor to SC resilience. Research on the ripple effect analyses how one or more disruptive events propagate through the SC and impact its resilience and performance. The phenomenon of the ripple effect, immensely existing in practice, has received great research interest in recent years. Ripple effect management, modelling and assessment became visible research avenues with a growing number and scope of contributions. This Special Issue presents recent developments on the ripple effect in SCs. The Special Issue focuses on studies that address the ripple effect and provide a comprehensive picture of the state of the art and future perspectives. The methodologies comprise of mathematical optimisation, simulation, game theory, control theoretic, data-driven analytics, network complexity, reliability theory research, and empirical research. Even though a variety of valuable insights have been developed in this area in recent years, new research avenues and ripple effect taxonomies are identified for further exploring the ripple effect in the settings of the COVID-19 pandemic, SC viability, viable SC model, and reconfigurable SCs. [ABSTRACT FROM AUTHOR]

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis. [ABSTRACT FROM AUTHOR]

Research on supply chain (SC) digitalization, resilience, sustainability and leagility has remarkably progressed, most of it focused on the individual contributions of these four major frameworks. However, a lack of integration spanning these individual frameworks can be observed. In this conceptual paper, we hypothesize that reconfigurability can be considered such an integral perspective that consolidates the research in SC adaptation to ever changing environments. We theorize a new notion – a Reconfigurable SC or the X-network – that exhibits some crucial design and control characteristics for complex value-adding systems in highly vulnerable environments. We support our argumentation and conceptual viewpoints by a literature analysis along with tertiary studies to review and structure contextual factors of designing the X-networks. We propose respective frameworks and discuss the implementation principles and technologies at the macro and micro levels. Two novel concepts – dynamic SC meta-structures and dynamic autonomous services – are introduced. Distinctively, we go beyond the existing knowledge to predict proactively the future directions in the reconfigurable SCs. Our results can be of value for decision-makers to decipher chances and barriers in contemporary SC transformations. [ABSTRACT FROM AUTHOR]

The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies. [ABSTRACT FROM AUTHOR]

We investigate the interrelations of structural and operational vulnerabilities in the supply chain (SC) using discrete-event simulation for a real life case study. We theorise a notion of SC overlays and explore conditions surrounding their appearance. Such overlays occur if the negative consequences of changes in a SC structure as a result of a disruption are either amplified or mitigated by changes in the operational environment. We hypothesise that these overlays can be both reciprocal (i.e. complementary or mitigating) and aggravate (i.e. concurrent or enhancing). Our approach can be used for an efficient management of SC resilience capabilities by varying their levels over time. We show different ripple and bullwhip effect profiles, which lead to either reciprocal or aggravate overlays, and then we develop recommendations on the overlay-driven dynamic variation of resilience capability levels in order to enhance both SC resilience and efficiency through dynamic redundancy allocation. The results can be of value in selecting and deploying operational policies at the right time and scale during and after the recovery periods. Restricting analysis to the disruption period only and ignoring operational dynamics after capacity recovery can result in misleading or inefficient SC resilience and recovery policies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods. [ABSTRACT FROM AUTHOR]

This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation. [ABSTRACT FROM AUTHOR]

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics. [ABSTRACT FROM AUTHOR]

Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect. [ABSTRACT FROM AUTHOR]

In this study, the ripple effect in the supply chain is analysed. Ripple effect describes the impact of a disruption propagation on supply chain performance and disruption-based scope of changes in supply chain structural design and planning parameters. We delineate major features of the ripple effect as compared to the bullwhip effect. Subsequently, we review recent quantitative literature that tackled the ripple effect explicitly or implicitly and give our vision of the state of the art and perspectives. The literature is classified into mathematical optimisation, simulation, control theoretic and complexity and reliability research. We observe the reasons and mitigation strategies for the ripple effect in the supply chain and present the ripple effect control framework that includes redundancy, flexibility and resilience analysis. Even though a variety of valuable insights has been developed in the said area in recent years, some crucial research avenues have been identified for the near future. [ABSTRACT FROM AUTHOR]

Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on the other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified. [ABSTRACT FROM AUTHOR]

In light of low-frequency/high-impact disruptions, the ripple effect has recently been introduced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, production and distribution networks. While optimisation modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling. First, recent literature on both optimisation and simulation modelling is analysed. Second, a simulation model for multi-stage supply chain design with consideration of capacity disruptions and experimental results is presented in order to depict major areas of simulation application to the ripple effect modelling. Based on both literature analysis and the modelling example, managerial insights and future research areas are identified in regard to simulation modelling application to the ripple effect analysis in the supply chain. The paper concludes by summarising the most important insights and outlining a future research agenda. [ABSTRACT FROM AUTHOR]

• We examine the impact of adapting payment terms during and after disruptions on supply chain resilience. • We perform a discrete event simulation analysis in anyLogistix for a complex supply chain network. • Collaboratively adjusting payment terms is an effective strategy for coping with disruptions. • Shorter cash conversion cycles are recommendable when coping with disruptions. Supply chain resilience and the ripple effect have been widely studied, mostly focusing on material flow-related practices. The financial flow adjustments to cope with supply chain disruptions have received much less attention. We contribute to the literature by examining the impact of adapting payment terms during and after disruptions. In particular, we perform a discrete event simulation analysis in anyLogistix for a complex supply chain network to investigate the impact of adjusting payment terms on supply chain cash flows. Our results suggest that collaboratively adjusting payment terms is an effective strategy for coping with disruptions. In contrast, ad hoc adjustments and immediate returns to pre-disruption payment schemes do not yield visible improvements. Positive effects on cash and loans are observed if an adjustment of payment terms occurs proactively and in a coordinated manner, especially when expediting payments downstream and payment slowing down upstream. The results from our sensitivity analysis on the impact of accelerating/decelerating cash conversion cycles favour shorter cycles when coping with disruptions. We deduce useful managerial insights and reveal some new theoretical tensions related to the impact of payment adjustments on cash flows in supply chains. [ABSTRACT FROM AUTHOR]

• We review and classify the major features of models proposed in supply chain resilience. • The analysis is rooted in original concept of resilience capacity. • It introduces a structured analysis based on different levels of capacity resilience. • The gaps and limitations of existing supply chain resilience literature are identified and future research opportunities are suggested. Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested. [ABSTRACT FROM AUTHOR]

In this paper, the existing models of supply chain resilience assessment are extended by incorporating ripple effect and structure reconfiguration. Ripple effect mitigation control is vital for supply chain risk management from positions of structural resilience and recoverability. The research approach is based on a hybrid fuzzy-probabilistic approach. The genome method is applied with the objective of including the structural properties of supply chain design into resilience assessment. A supply chain design resilience index is developed, and its computation and application are demonstrated. The results suggest a method of comparing different supply chain designs regarding the resilience both to disruption propagation and with recovery consideration. It also allows the identification of groups of critical suppliers whose failure interrupts supply chain operation. [ABSTRACT FROM PUBLISHER]

• We examine the impact of the COVID-19 pandemic on food retail supply chains. • We use a digital SC twin for modeling and performance assessment. • We identify most severe scenarios of pandemic disruptions for food supply chains. • We propose a framework for resilience improvement. In this study, we examine the impact of the COVID-19 pandemic on food retail supply chains (SCs) and their resilience. Based on real-life pandemic scenarios encountered in Germany, we develop and use a discrete-event simulation model to examine SC operations and performance dynamics with the help of anyLogistix digital SC twin. The computational results show that food retail SC resilience at the upheaval times is triangulated by the pandemic intensity and associated lockdown/shutdown governmental measures, inventory-ordering dynamics in the SC, and customer behaviours. We observe that surges in demand and supplier shutdowns have had the highest impact on SC operations and performance, whereas the impact of transportation disruptions was rather low. Transportation costs have spiked because of chaotic inventory-ordering dynamics leading to more frequent and irregular shipments. On bright side, we observe the demand growth and utilization of online sales channels yielding higher revenues. We propose several directions and practical implementation guidelines to improve the food retail SC resilience. We stress the importance of SC digital twins and end-to-end visibility along with resilient demand, inventory, and capacity management. The outcomes of our study can be instructive for enhancing the resilience of food retail SCs in preparation for future pandemics and pandemic-like crises. [ABSTRACT FROM AUTHOR]

• We presented the first review of application of BN in SC risk and resilience. • We analyzed reviewed journal papers using network analysis and clustering analysis. • The gaps and future research gaps are identified and discussed. In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing literature to ascertain recent developments and uncover future areas of research. Despite the increasing number of publications on BNs in the domain of SC uncertainty, an extensive review on their application to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peer-reviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions. The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed. [ABSTRACT FROM AUTHOR]

• We examine the relationships among business model design (BMD), supply chain resilience (SCR) and firm performance considering Blockchain technology. • Efficiency-cnetered BMD can increase firm performance and SCR through Blockchain. • Novelty-centered BMD significantly enhances agility part of SCR. • Efficiency-oriented BMD can improve firm performance by influencing SCR, which acts as a mediator. Supply chain resilience (SCR) is imperative for operation and performance continuity in the presence of disruptions. One of the important SCR capabilities is end-to-end visibility which can be supported by Blockchain technology in facilitating transparency, traceability, and trust in using data and information. Meanwhile, Blockchain can change and enhance the existing efficiency- and novelty-oriented business model design (BMD). However, the impact of Blockchain-supported BMD on SCR and firm performance has not been investigated so far. This study fills this research gap and empirically examines the relationships among BMD, SCR, and firm performance with consideration of Blockchain technology and using data collected from Chinese firms. Our findings contribute to the literature by revealing the antecedents of SCR and its mediating role between Blockchain-enabled BMD and firm performance. We also provide insights into enhancing SCR in light of the Blockchain-supported BMD. Our research offers several important findings. We demonstrate that companies with high strategic emphasis on BM efficiency can achieve higher firm performance and SCR through Blockchain usage than those without such a focus. This emphasizes the importance of process transparency and information symmetry which can be implemented by Blockchain. Blockchain-enabled novelty-centered BMD significantly enhances the agility part of SCR. SCR can improve firm performance acting as an intermediary between efficiency-centered BMD and firm performance. [ABSTRACT FROM AUTHOR]