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This study aims to empirically investigate the impact of operational risk (i.e. supply, manufacturing and demand risks) on supply chain performance and the moderating role of integration (i.e. supplier, internal and customer integrations) in mitigating the impact of these risks, respectively.

A research framework of hypotheses is tested by structural equation modeling with data collected from the fourth round of the high-performance manufacturing project.

It is revealed that manufacturing and demand risks negatively impact operational performance, and more importantly, internal and customer integrations help to reduce the impact of these two risks. Additionally, the effects of both supply risk and supplier integration are only significant for large firms.

Supply chain managers need to appropriately develop the levels of integration to mitigate the adverse impact of operational risk.

Operational performance is always threatened by different types of risk that adversely affect the supply, production and demand sides of manufacturing firms. Despite this fact, large-scale data-based empirical research on the impact of operational risk on the performance of supply chains has been scarce. This study aims to fill this literature gap.

The goal of the paper is twofold. First, it aims to empirically conceptualize whether a wide array of fragmented demand planning activities, performed in supply chains, can be logically categorized into actionable sets of practices, which then form a broader conceptualization of the demand planning process. Second, regarding certain contextual factors, our research seeks to investigate the contribution of demand planning, as a higher-order construct, to mitigating disruptions induced by operational risks in supply chains.

In this study, PLS-SEM was used to estimate the reflective-formative nature of the model. The results of PLS-SEM were additionally complemented by the assessment of the predictive power of our model. Finally, to reveal possible contingency effects, the multigroup analysis (MGA) was conducted.

The study suggests that demand planning process (DPP) is a second-order construct that is composed of four sets of practices, including goal setting, data gathering, demand forecasting, communicating the demand predictions and synchronizing supply with demand. The study also reveals that the demand planning practices, only when considered together, as a higher-order factor, significantly contribute to mitigating disruptions driven by operational risks. Finally, the research shows that the strength of the impact of demand planning on disruptions is contextually dependent.

While the study makes some important contributions, the obtained findings ought to be considered within the context of limitations. First, the study only investigates disruptions driven by operational risks, ignoring the negative consequences of environmental risks (terrorist attacks, natural disasters, etc.), which may have a far more negative impact on supply chains. Second, the sample is mostly composed of medium and large companies, not necessarily representative of demand planning performed by the entire spectrum of companies operating in the market.

The study shows that to effectively mitigate disruptions induced by operational risks, the demand planning practices should be integrated into a higher-order construct. Likewise, our research demonstrates that the intensity of demand planning process is contingent upon a number of contextual factors, including firm size, demand variability and demand volume.

The study indicates that to mitigate disruptions of operational risk, demand planning as a higher-order dynamic capability can be referred to the concept of organizational learning, which contributes to forming a critical common ground, ensuring the balance between formal and informal dynamic routines.

The paper depicts that to fully deal with disruptions, the demand planning practices need to be integrated and categorized into the dedicated higher-order. This may lead to forming demand planning as a higher-order dynamic capability that provides a more rapid and efficient rebuttal to any disruptions triggered by operational risks.

The purpose of this paper is to explore the effects of demand planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering demand, supply, control and process risks) can be absorbed or amplified through the application of specific demand planning practices in supply chains.

The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation.

The findings suggest that although one may not observe uniformity and standardization in the role of demand planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some demand planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk.

The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of demand planning might be very helpful for managers when dealing with demand and control risks.

The study simultaneously examines a more detailed bundle of practices forming the demand planning process. The research attempts to investigate the link between the demand planning process and operational risk consequences, derived from all sources (supply, demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the demand planning process. The study demonstrates that the demand planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

Recent trends of outsourcing in global competition make the firms vulnerable to operational risks. The purpose of this paper is to illustrate how firms implement supply chain strategies to reduce operational risks, especially risk exposure involving catastrophic events.

Drawn on risk management and supply chain research, the concepts of operational risk and the underlying demand and supply uncertainties are delineated. Then, based on literature review and numerical demonstrations, the authors evaluate the effectiveness of supply chain strategies in reducing operational risks. The paper examines the benefit of these strategies and illustrate how to setup risk pooling and dual sourcing programs.

Employing the strategies of risk pooling and dual sourcing, an agile and diversified supply chain can be built to cope with the demand or supply uncertainties and in turn reduce the operational risks.

Leaders in any organization should consider operational and supply risk critically when planning their competitive strategy. They could foster creative solutions in supply chain strategies and essentially enhance competitiveness.

The paper highlights the tension between outsourcing trend in pursuit of competitive advantage and risk exposure to catastrophic events. This paper fulfils a practical need for better understanding of how supply chain strategies could be implemented to reduce operational risks.

The purpose of this study is to investigate the contingencies of supply chain risk management (SCRM) in manufacturing multinational corporations (MNCs) by exploring the moderating role of international asset dispersion in the performance effect of SCRM, as well as the counteraction effect of supply chain integration (SCI).

Multi-country survey data from a sample of 378 responses collected by the sixth round International Manufacturing Strategy Survey were analyzed. Hierarchical regression analysis was conducted to test the proposed hypotheses.

The results demonstrate that SCRM improves the operational effectiveness of manufacturing MNCs but this performance effect is attenuated by international asset dispersion. Nevertheless, external integration can counteract the negative effect of international asset dispersion and ensure the efficacy of SCRM practices.

The results of this study provide suggestions for supply chain managers of manufacturing MNCs to better conduct SCRM practices under the condition of dispersed international assets. Besides, supply chain managers can leverage external integration to ensure the efficacy of SCRM practices.

Although there are plenty of studies on the relationship between SCRM and operational performance, prior findings are inconsistent. This study sheds light on the SCRM-operational performance relationship by considering how a manufacturing MNC’s international asset dispersion can influence the efficacy of SCRM practices and how SCI can attenuate the negative effect of international asset dispersion.

To counteract the effects of global competition, many organizations have extended their enterprises by forming supply chain networks. However, as organizations increase their dependence on these networks, they become more vulnerable to their suppliers' risk profiles. The purpose of this paper is to present a methodology for modeling and evaluating risk profiles in supply chains via Bayesian networks.

Empirical data from 15 casting suppliers to a major US automotive company are analyzed using Bayesian networks. The networks provide a methodological approach for determining a supplier's external, operational, and network risk probability, and the potential revenue impact a supplier can have on the company.

Bayesian networks can be used to develop supplier risk profiles to determine the risk exposure of a company's revenue stream. The supplier risk profiles can be used to determine those risk events which have the largest potential impact on an organization's revenues, and the highest probability of occurrence.

A limitation to the use of Bayesian networks to model supply chain risks is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to formulate supply chain risk management strategies and tactics which mitigate overall supply chain risks.

The methodology used in this study can be used by organizations to reduce supply chain risks which yield numerous societal benefits.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop targeted approaches to minimize the occurrence of supply chain risk events.

The purpose of this paper is to scrutinize the performance effects of supply chain risk management (SCRM). Besides financial performance, two aspects of operational performance are examined: operational efficiency and flexibility. Moreover, the authors explore the moderating role of supplier integration in the relationship between SCRM and operational performance.

A survey-based methodology was adopted. Based on the data from an international survey, this study applied the structural equation modeling and latent moderated structural equations approach to test the hypotheses.

The results indicate that SCRM positively influences both operational efficiency and flexibility, and has an indirect effect on financial performance. In addition, supplier integration enhances the impact of SCRM on operational flexibility, but does not moderate the relationship between SCRM and operational efficiency.

This study extends the existing literature by providing a comprehensive analysis of the performance effects of SCRM. It also provides managerial insights on both risk management and supplier integration.

Proactive risk assessment suggests that risk assessment should emphasize the consequences that it might cause and the opportunities it might create for firms. Hence, this study aims to validate risk impact on supply chain performance in the context of the Vietnamese construction sector. Also, a complex network, in which multiple risk factors mutually affect, impede or promote each other, is developed to assist managers in tackling unpredictable risks proactively. In particular, the authors investigate whether certain risks could be considered either challenges or opportunities for businesses in turbulent times to improve SC performance.

The construction industry is the focal study context as it is one of the most essential industries in charge of providing accommodations, infrastructures and employment for society. 289 valid responses used in this research are from a large-scale survey result, supported by a Japanese government project promoting sustainable socio-economic development in Vietnam.

From the study findings, the authors find that external risk brings opportunities for supply chain performance. Meanwhile, demand risk, when it occurs, can reduce the danger level of operational risk, which is an interesting finding of this research. It is evident that when multiple risk factors mutually affect, impede or promote each other, it provides a more meaningful examination of mutually interconnected supply chain risks.

Practitioners should perceive risks as an opportunity than a threat. This study contributes to preventing risks and guaranteeing an effective and efficient supply chain by tackling unpredictable risks in a disruptive period. Moreover, data on validating research models collected during the Covid-19 pandemic and Ukraine and Russia conflicts reflect the topicality of this study.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.