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PurposeDrawing on dynamic capability theory, this study investigates how online–offline channel integration (OOCI) affects a firm's supply chain resilience and how such an effect is moderated by market turbulence and regulatory uncertainty.Design/methodology/approachA sample of 273 Chinese firms that conduct online and offline business and hierarchical regression analysis were used to examine the research model.FindingsThe results suggest that the effect of OOCI on supply chain resilience differs in terms of its dimensions (i.e. information integration, transaction integration and service integration). While information integration and service integration were positively associated with supply chain resilience, transaction integration had a non-significant relationship with supply chain resilience. Moreover, market turbulence negatively moderated the effect of transaction integration and positively moderated the effect of service integration. Regulatory uncertainty positively moderated the effect of transaction integration and negatively moderated the effect of service integration. Implications and suggestions for future research are discussed.Originality/valueThis study examines the effect of OOCI on supply chain resilience. It further explores the influence of market turbulence and regulatory uncertainty on the relationship between OOCI and supply chain resilience.
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When the COVID‐19 pandemic began in 2020, the medical product industry faced an unusual demand shock for personal protective equipment (PPE), including face masks, face shields, disinfectants, and gowns. Companies from various industries responded to the urgent need for these potentially life‐saving products by adopting ad hoc supply chains in an exceptionally short time: They found new suppliers, developed the products, ramped‐up production, and distributed to new customers within weeks or even days. We define these supply chains as ad hoc supply chains that are built for a specific need, an immediate need, and a time‐limited need. By leveraging a unique sampling, we examined how companies realize supply chain agility when building ad hoc supply chains. We develop an emergent theoretical model that proposes dynamic capabilities to enable companies building ad hoc supply chains in response to a specific need, moderated by an entrepreneurial orientation allowing firms to leverage dynamic capabilities at short notice and a temporary orientation that increases a company's focus on exploiting the short‐term opportunity of ad hoc supply chains.
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PurposeThis paper aims to investigate the conditional indirect effect of innovativeness on performance via supply chain agility (SCA) in the service industry at higher and lower collaborative relationships.Design/methodology/approachThe hypothesised model is operationalised with survey data from 245 Australian service firms collected via LinkedIn and analysed using structural equation modelling and fuzzy set qualitative comparative analysis (fsQCA).FindingsThe analysis found that SCA significantly mediates the relationship between innovativeness and performance. Further, the conditional indirect effect of innovativeness on performance via SCA was significant when the collaborative relationship was high. Results also revealed that a configuration of both innovativeness and agility better predicts performance.Originality/valueThis study is an early attempt to investigate SCA in service industries by scrutinizing SCA from an innovative point of view. While previous studies have demonstrated the role of innovativeness in enhancing a firm's performance, this study explores this link further by investigating the conditional indirect effect of innovativeness on performance via SCA at different levels of collaborative relationships.
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An increasing number of disruptions in ports, plants and warehouses have generated ripple effects over supply networks impacting economic activity. We demonstrate how the spread of the pandemic geographically expands the ripple effect by reducing the workers' participation in production, so undermining the ability of firms and, as a result, the entire cross-border sup- ply chain network to satisfy customers' demands. Our model of the spatio-temporal dynamics of the propagation of Covid-19 infection for supply networks contributes toward ripple effect visualisation and quantification by combining the flow of goods and materials through a typical global supply chain with an epidemiological model. The model enables prospective analyses to be performed in what-if scenarios to simulate the impact on the workforce in each node. The outcome should be helpful tools for managers and scholars. Results from this research will help mitigate the impact and spread of a pandemic in a particular region and the ability of a supply network to overcome the ripple effect. A stylised case study of a cross-border supply chain illustrates the ripple effect by showing how waves with crests at varying dates impact the ability to serve demand showing how a supply chain manager can obtain a forward-looking picture.
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A firm's ability to manage risk and resilience in supply chains has turned out to be an invaluable capability during the COVID-19 pandemic. Fast responsiveness, quick decision-making, and the ability to reconfigure the resource base have helped firms during the pandemic, which caused rapid disruptive effects for which they were unprepared. This research applies the dynamic capability view as a theoretical framework to study the impact of the COVID-19 pandemic on firms' dynamic capabilities and, the influence of dynamic capabilities on supply chain resilience. These capabilities are highly needed to survive during the pandemic. Using the survey data, we found that the impacts that COVID-19 had on a firm's upstream supply chain influence firms' capabilities to seize opportunities or neutralise threats. Furthermore, we found that reconfiguring ability has a strong influence on supply chain resilience. Thus, the impacts of COVID-19 on the downstream supply chain pushed firms to realign resources to respond better to demand. Upstream disruptions pushed companies to react to threats and opportunities in the supply market, while downstream disruptions leveraged reconfiguring capabilities.
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PurposeThe purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).Design/methodology/approachConsidering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.FindingsThe results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.Originality/valueThis paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.
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PurposeGiven the increasingly turbulent business landscape and unprecedented incidents (e.g. Covid-19), firms must achieve supply chain resilience (SCRes) as a dynamic capability to bounce back from adversities and ensure continuity of operations. The purpose of this study is to integrate the three interrelated [knowledge management, risk management culture (RMC) and resilience] but often separately discussed concepts to advance the understanding of their intertwined influence on SCRes in the agri-food supply chains.Design/methodology/approachThis study uses a cross-sectional survey approach where quantitative data is collected from 349 participants from the Australian agri-food supply chains to test the proposed hypotheses.FindingsExposure to supply chain risks triggers the deployment of specific knowledge management practices in the agri-food supply chains. Further, the analysis on serial mediation suggests that firms' knowledge management practices work sequentially (knowledge acquisition, assimilation and application) and develop a RMC to achieve SCRes amid supply chain risks.Practical implicationsThe findings of this study inform practitioners and policymakers who seek to understand the key mechanisms that facilitate the development of SCRes when facing supply chain risks, particularly in the Australian agri-food supply chains.Social implicationsThe growth of the food industry through more resilient food supply chains could ensure sustained food supply and more employment opportunities.Originality/valueUsing dynamic capability theory, the authors devise a novel empirical model that explicates how knowledge management practices and RMC instigate the dynamic capability of SCRes amid supply chain risks facing agri-food supply chains.
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This paper develops an integrated methodology aimed at diagnosing supply chain resilience in terms of (1) internal dynamic capabilities of an enterprise, and (2) resilience of its suppliers. In addition, unlike other research, it integrates the suppliers' resilience evaluation into the order size allocation plan. Multi-attribute decision making (MADM) algorithms were employed to quantify the relative importance to evaluate the internal and external resilience of an enterprise. Furthermore, the MADM output was combined with a multi-objective programming model formulated to solve the order size problem considering economic and resilience objectives. The applicability of the developed methodology is demonstrated via a dairy manufacturing enterprise that suffered from disruptions attributed to COVID-19. The results translate the enterprise's non-viable manufacturing due to its poor external and internal resilience profiles. It is emphasized that if an enterprise fails to develop internal capabilities such as readiness and sensing, the enterprise could also fail in managing external resilience. A resilient supply chain requires a blend of internal and external resilience. This work represents the first quantitative attempt to provide a unified methodology for identifying and measuring internal and external resilience.
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Improving the supply chain resilience of the mineral resources industry is crucial for ensuring national economic security in China. Based on the supply and demand data of China's mineral resources industry from 2002 to 2018, this study adopts system dynamics model to simulate the supply chain resilience of the mineral resources industry, the mining industry, and the smelting and processing industry under the scenario of steady economic development and the scenario of supply chain crisis. From the simulation results, the reserves of the mineral resources industry and the smelting and processing industry under the two scenarios are nearly the same, indicating that they are weakly affected by the foreign market, and both have strong resilience. The mining industry has a high dependence on imports and a lack of supply chain resilience. Under the condition of steady economic development, the output of the mining industry needs to develop at a low speed to reduce production capacity. More attention should be paid to the high level of import dependence and insufficient supply chain resilience of the mining industry. In the stable international trade situation, reserves of important minerals should be increased to alleviate the resource shortage during the supply chain crisis.
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The recent COVID-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience. © 2022 Elsevier Ltd
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In 2004, we asked David Murray1,2 and Allan Donner,3 two pioneers in the development and application of group or cluster randomized trials, to provide extensive primers on their design, application, and analysis, and invited them to help keep these tools in our analytic toolbox by providing methodological updates in 2017.4,5 In 2018, we embraced the notion of returning "cause" to our public health vocabulary, and employing causal inference methods to our data when appropriate. METHODOLOGICAL ADVANCES First, Wang and Chakraborty (p. 49) present an outstanding review of a method that overcomes some of the downsides oftraditional randomized controlled trials, which often tend to compare one novel treatment to standard or usual care, where participants remain in their assigned treatment groups over the life of the trial. [...]it can help, in real time, predict the number of tests that will be needed to better inform the supply chain needs and can incorporate the often-dynamic parameters of a pandemic rather than relying on fixed and perhaps outdated inputs.
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The recent Covid-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience.
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In this paper, we reflect on the supply chain issues and operational challenges that we experienced through the various stages of the Covid-19 pandemic. We identify some phenomena that were attributable in some way to the pandemic, and apply core principles of operations management, and a simple numerical model, to explain and understand their occurrence. We highlight some key lessons and discuss implications for supply chain design and planning, to prepare for the next global disruption.
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The risk of frequent disasters is becoming a huge challenge for enterprises and their supply chains. In particular, sudden global public health events have brought a great test to the supply chain. How to make sustainable planning and preparedness and smoothly carry out supply chain operations and obtain sustainable firm performance in the complex market environment requires urgent attention from industries and academia. The different effects of supply chain operational capability and dynamic capability on the long-term performance and short-term performance of enterprises are still unclear;therefore, a model was established to discuss this. Based on the theory of dynamic capability, a relational model between supply chain dynamic capability, supply chain operational capability, and firm performance was constructed, a hypothesis testing method and Amos software were used to verify the set model, and the mechanisms of supply chain dynamic capability and supply chain operational capability on firm performance were discussed. The empirical results show that supply chain operational capability has a mediating effect on supply chain dynamic capability and firm performance, and supply chain dynamic capability has a moderating impact on supply chain operational capability and firm performance. The supply chain and its enterprises should cultivate and continuously improve the supply chain dynamic capability as soon as possible, so that in the face of emergencies, the supply chain operation capability can be reasonably configured to avoid damage, improve firm performance, and gain competitive advantages.
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Due to the dynamic nature of the food supply chain system, food supply management could suffer because of, and be interrupted by, unforeseen events. Considering the perishable nature of fresh food products and their short life cycle, fresh food companies feel immense pressure to adopt an efficient and proactive risk management system. The risk management aspects within the food supply chains have been addressed in several studies. However, only a few studies focus on the complex interactions between the various types of risks impacting food supply chain functionality and dynamic feedback effects, which can generate a reliable risk management system. This paper strives to contribute to this evident research gap by adopting a system dynamics modelling approach to generate a systemic risk management model. The system dynamics model serves as the basis for the simulation of risk index values and can be explored in future work to further analyse the dynamic risk’s effect on the food supply chain system’s behaviour. According to a literature review of published research from 2017 to 2021, nine different risks across the food supply chain were identified as a subsection of the major risk categories: macro-level and operational risks. Following this stage, two of the risk groups identified first were integrated with a developed system dynamics model to conduct this research and to evaluate the interaction between the risks and the functionality of the three main dairy supply chain processes: production, logistics, and retailing. The key findings drawn from this paper can be beneficial for enhancing managerial discernment regarding the critical role of system dynamics models for analysing various types of risks across the food supply chain process and improving its efficiency.
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Risk propagation is occurring as an exceptional challenge to supply chain management. Identifying which supplier has the greater possibility of interruptions is pivotal for managing the occurrence of these risks, which have a significant impact on the supply chain. Identifying and predicting how these risks propagate and understanding how these risks dynamically diffuse if control strategies are installed can help to better manage supply chain risks. Drawing on the complex systems and epidemiological literature, we research the impact of the global supply network structure on risk propagation and supply network health. The SIR model is used to dynamically identify and predict the risk status of the supply chain risk at different times. The results show that there is a significant relationship between network structure and risk propagation and supply network health. We demonstrate the importance of supply network visibility and of the extraction of the information of node firms. We build up an R package for geometric graphs and epidemics. This paper applies the R package to model the supply chain risk for an automotive manufacturing company. The R package provides a firm to construct the complicated interactions among suppliers and display how these interactions impact on risks. Theoretically, our study adapts a computational approach to contribute to the understanding of risk management and supply networks. Managerially, our study demonstrates how the supply chain network analysis approach can benefit the managers by developing a more holistic framework of system-wide risk propagation. This provides guidance for network governance policies, which will lead to healthier supply chains.
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In order to cope with the sudden disasters such as floods, COVID-19,etc.,a discrete time Markov chain and multi-objective programming model（DTMC-MOP） with the maximum supply satisfaction rate, the shortest supply time and the lowest supply cost was proposed to dynamically identify, analyze and respond to the emergency supply chain risk.The improved self-adaptive Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） was used to solve the optimization model, and the feasibility and effectiveness of the model were verified by testing and evaluation with standard test functions.Through the example analysis, the Pareto optimal front with higher precision and more uniform distribution was obtained.The results showed that the decision-maker could choose the appropriate emergency scheme based on the core objective of emergency management or different preferences.It provide a scientific method for the decision-making optimization of emergency supply chain, which has positive significance for ensuring the life safety of victims and maintaining the social harmony and stability.
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Purpose>In the ever-increasing dynamics of global business markets, firms must use all possible sources to innovate continually. This study aims to explore how supply chain innovation can be fostered through joint efforts between firms and their supply chain partners.Design/methodology/approach>At least two areas advocate innovation through external relations, namely, supply chain collaboration and open innovation. This study aims to provide a holistic insight into how vertical and horizontal partnerships can be implemented to help supply chains become more innovative, building upon commonalities and differences between the two areas.Findings>This study proposes a conceptual framework for supply chain innovation based on the following three ambidextrous capabilities: purpose (i.e. knowledge exploration and exploitation), span (horizontal and vertical collaboration) and orientation (i.e. incremental and radical innovation). With five propositions, the link between the three ambidextrous capabilities and supply chain innovation is explained. The implementation of the framework is articulated through an illustrative real-life case.Originality/value>The concept of open innovation in supply chain settings is progressively essential yet under-researched. This study is an early attempt to draw on the available theories and literature on open innovation and supply chain collaboration and elaborates how supply chains can facilitate and adopt a more open approach toward innovation.
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The increased level of complexity in the case of Closed Loop Supply Chains (CLSCs) turns them into vulnerable systems under a disaster event. The latter calls for a methodological approach that allows a dynamic study under alternative policies in mitigating the disaster effects with a focus on creating sustainable CLSCs. For this reason, we provide a System Dynamics (SD)-based analysis for disaster events on the operation of CLSCs. By “disaster event”, we mean three different categories taking shape on the basis of duration. Furthermore, three different demand patterns emerging due to the disaster event are examined. We assume that the disaster event affects the manufacturer, and we examine the system response under different mitigation policies. For each demand pattern two different mitigation policies at the manufacturer level are examined by considering the total CLSC profit and demand backlog as measures of policy performance. For each combination, extensive simulation experimentation reveals sustainable policy recommendations under alternative settings regarding the reduction in the manufacturer’s production.
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This paper aims to provide a case study related to two small and medium-sized enterprises (SMEs) of the Italian footwear supply chain, comparing sales and production data from pre-pandemic years with those affected by the COVID-19 (SARS-CoV-2) pandemic. Specifically, two Tuscany companies in the world of fashion footwear sector have been assisted in the analysis of their supply chains. In particular, the case research method has been employed for theory building to evaluate how companies reacted to the disruption caused by the COVID-19 pandemic to focus on potential resilience strategies that could be adopted to deal with a disruption, such as that caused by the COVID-19 pandemic. Specifically, in order to understand the dynamics of the supply chains, the standard production processes were initially studied and mapped, analyzing in detail the planning, programming, and control phase. After conducting a descriptive analysis of the data, possible resilience factors of SMEs’ fashion supply chains have been extracted, and strategies that SMEs could adopt to better cope with the disruption caused by the pandemic have been suggested. The outcomes of this study can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the supply chains with some suggestions of potential resilience improvement strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda.