Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches

Supply chains are becoming more lengthy and complex due to globalisation and vertical integrations. In this context, adopting proactive approaches is needed for dealing with changing risks and vulnerabilities for securing supply chain systems. Supply chain risks are interlinked and thus, one mitigation strategy can reduce many of other supply chain risks. For example, aggregate or pooling demand reduces forecast risks, capacity risks and inventory risks. Also, some of the risk mitigation strategies have negative influences over certain supply chain risks as adding capacity has a negative influence on capacity risks. Twelve major supply chain risk categories and 21 risk mitigation strategies with typical focus on electronics manufacturing supply chains have been identified. A combination of grey theory and digraph-matrix methodologies has been used for quantifying various supply chain risk mitigation strategies and this approach is not seen in literature till date. The proposed model was also tested taking a case study of an Indian electronics manufacturing company. Obtained results were also subject to sensitivity analysis. The net positive influence values of risk mitigation strategies proposed in this research could effectively be used by top management for ascertaining their risk mitigation strategies for better management of supply chains as a whole.     

Risks and performance in supply chain: the push effect

A risk, when it occurs, causes negative effects on outputs. Typically risks are not independent, as multiple risks occur simultaneously. These risks have links, creating a ‘push’ effect, thus increasing the severity of each and all risk(s) on outputs. This paper aims to verify the mechanism of the push effect that is a new approach in the supply chain risk management literature. In this study, two models were compared: (1) only exists in direct effects of risks on supply chain performance, i.e. the competitive model. The other, (2), contains relationships among risks that show the mechanism of the push effect, i.e. the hypothesised model. Empirical evidence found in the Vietnam construction sector proved that the hypothesised model is better suited and has greater effect on supply chain performance in terms of each and all risk(s). Comparing 55% variance of the competitive model, the hypothesised one can explain up to 73% variance of supply chain performance. These results confirm our hypotheses of the push effect. Furthermore, findings achieved from this research can be used as ‘a guideline’ for reducing the impact of this mechanism.     

Supply chain risk classification: discussion and proposal

The supply chain management philosophy has often been used by organisations to achieve a competitive advantage, but it increases the vulnerability of these supply chains (SC) to certain risks. This dialogue between competitive advantage and risk generation has increased the number of studies related to the topic of ‘supply chain risk management’. Aiming to contribute to this field of research, a literature survey was conducted on 16 risk classifications, which included 56 risk types. These risk types were sorted according to existing conceptual similarities and then related to the five management processes intrinsic in a functional SC (plan, source, make, deliver and return), which are mainly advocated by the supply chain operations reference model. This literature review also highlights the lack of consensus among the surveyed authors concerning the risk types that affect a SC, a gap which this paper seeks to close by proposing a supply chain risk classification.     

A supply chain network equilibrium model for operational and opportunism risk mitigation

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.     

Supply chain resilience for single and multiple sourcing in the presence of disruption risks

This paper investigates the use of sourcing strategies to achieve supply chain resilience under disruptions. The coping strategies considered are single and multiple sourcing, backup supplier contracts, spot purchasing, and collaboration and visibility. Collaboration and visibility, which affect suppliers’ recovery capabilities and a buyer’s warning capability, have not been similarly modelled in the past. A scenario-based mathematical model is developed such that it considers objectives under uncertainties including disruption risks and operational risks. A broad numerical study examines its output for various risk attitudes in a decision-maker, ranging from risk neutral to risk averse. The sensitivity of procurement strategies to other key parameters such as recovery and warning capabilities is examined. One of the major findings is that buyer’s warning capability plays a vital role in enhancing supply chain resilience. We seek to build on these efforts to further support disruption planning and mitigation and to obtain a deeper understanding of the relationship between supply chain characteristics and resilience.     

Supply chain risk modelling and mitigation

In today’s global competitive environment, supply chains are more susceptible to vulnerability due to the increasing occurrence of internal and external risk events. In addition, the trend associated with lean management, which involves reducing inventory, leads to more dependency of supply chain partners on each other which exacerbates risk exposure of companies in the supply chain. This creates the need for more effective management of supply chain risks. In this research, a methodology based on Bow-Tie analysis and optimisation techniques is proposed to quantify and mitigate supply chain risks. The proposed methodology takes into consideration risk interconnections, and it identifies the best combination of mitigation strategies under budget constraints. A real case study from a high-end server manufacturing environment is presented. Results from the case study showed that the proposed methodology for risk modelling and mitigation can effectively be used to quantify the risks and achieve the required risk reduction at minimum cost while considering risk correlations.     

A hybrid risks-informed approach for the selection of supplier portfolio

In conventional supplier selection approaches, cost consideration is usually emphasised and it renders a vulnerable supply chain with various risks. This article aims to develop a quantitative approach for modelling both supply chain operational risks and disruption risks to support decision-making with regard to order allocation and risk mitigation. We introduce two types of risk evaluation models: value-at-risk (VaR) and conditional value-at-risk (CVaR). Specifically, VaR is used to measure operational risks caused by improper selection and operations of a supplier portfolio to the stochastic demand, which may frequently occur but result in relatively small losses to supply chains; CVaR is used to evaluate disruption risks that are less frequent and tend to cause significant damage. After incorporating risk factors into a probability-based multi-criteria optimisation model, different methods and parameters are compared and tested to determine the factors that may influence the supplier selection process. Computational examples by simulation are presented to illustrate the approach and how decision-makers make trade-offs between costs and hybrid risks.     

Budget allocation in coping with supply chain disruption risks

Disruption management, as an important research topic, has attracted scholars’ broad attention in recent years due to the increasing exposure of disruption risks in supply chains. To date, researches in this field often focus on either prevention or mitigation measures and the budget allocation problem is paid relatively little attention. This paper therefore proposes an approach to determine the optimal budget allocation based on prevention measures in combination with mitigation measures. First, considering different disruption situations, the bow-tie is applied to developing the disruption management frameworks that integrate risk prevention and risk mitigation. Second, the corresponding optimization models are formulated to determine the optimal budget allocation plans. In order to validate the proposed approach, we compare the computation results with those obtained from the prevention approach and the mitigation approach. Also, random experiments are conducted to analyse the impacts of randomly generated disruption and response scenarios. Finally, a real-life case is provided to testify the usefulness and merits of our proposed approach. The results show that the proposed approach can help decision-makers reduce more loss caused by the disruption risks.     

Exploring political risk in offshoring engagements

This research focuses on the analysis of political risk in the context of offshoring decisions. The study uses the Repertory Grid Technique, which entails a series of semi-structured interviews exploring key political risk experiences across offshoring engagements. The research extends the spectrum of political risk analysis in the context of offshoring engagements, and explores the varied impact of political risk across business activity types. The research identifies five key political risks affecting offshoring engagements and highlights the moderating effect of specific offshoring activity types Business Process Outsourcing, Information Technology Outsourcing or Knowledge Process Outsourcing on political risk implications. The research explores the conditioning effect of activity specific exposure to political risk and enhances the explanatory ability of the Transaction Cost Economics constructs, offering a novel operationalisation of the political risk component of external uncertainty. From a practical perspective, the research highlights the need for developing managerial tools to improve monitoring and identification of risks. The key practical contribution is the development of differentiated political risk typologies that can capture the nuances of external risks in offshoring, allowing for more accurate risk assessment of offshoring decisions.     

Risk analysis of the agri-food supply chain: A multi-method approach

Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnerabilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so should be managed effectively. This study advances existing literature on identifying risk factors, defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.     

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

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.     

供应链网络社会责任风险传导研究

在构建基于核心企业多层供需供应链网络拓扑结构模型基础上,建立了社会责任风险在供应链网络中传导概念模型和数学模型,并通过对我国乳制品业实证分析验证了模型有效性。研究结果表明:供应链网络拓扑结构和节点自适应性导致风险在传导过程中不仅表现出传递性等一般特性,还具有转换性、反馈性、可控制性、非线性和复杂性等特性;供应链网络复杂性导致了供应链网络中社会责任风险涌现。     

Development of a hybrid fresh food supply chain risk assessment model

Supply chain managers and scholars recognise the importance of managing supply chain risk, especially in fresh food supply chain due to the perishable nature and short life cycle of products. Supply chain risk management consists of supply chain risk assessment, risk evaluation and formulation and implementation of effective risk response strategies. The commonly adopted qualitative methods such as risk assessment matrix to determine the level of risk have limitations. This paper proposes a hybrid model comprising both fuzzy logic (FL) and hierarchical holographic modelling (HHM) techniques where risk is first identified by the HHM method and then assessed using both qualitative risk assessment model (named risk filtering, ranking and management Framework) and fuzzy-based risk assessment method (named FL approach). The risk assessment results by the two different approaches are compared, and the overall risk level of each risk is calculated using the Root Mean Square calculation before identifying response strategies. This novel approach takes advantage of the benefits of both techniques and offsets their drawbacks in certain aspects. A case study in a fresh food supply chain company has been conducted in order to validate the proposed integrated approach on the feasibility of its functionality in a real environment.     

Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS

Risk is inherent in almost every activity of supply chain management. With the ever-increasing push for efficiency, supply chains today are getting more and more risky. Adding to the difficulty of dealing with these risks is the amount of subjectivity and uncertainty involved. This makes analytical examination of the situation very difficult, especially as the amount of information available at a particular time is not sufficient for such an analysis. Thus a supply chain risk index, which captures the level of risk faced by a supply chain in a given situation, is the need of the hour. This study is an effort towards quantifying the risks in a supply chain and then consolidating the values into a comprehensive risk index. An integrated approach, with a fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) as its important elements, has been used for this purpose. Fuzzy values in this study help in capturing the subjectivity of the situation with a final conversion to a crisp value which is much more comprehensible. A case study is used to illustrate the proposed methodology.     

农业保险对生鲜农产品供应链合作关系的影响研究

在我国当前生鲜农产品易受自然风险影响以及成员合作关系不稳定的现实下,本文通过构建自然风险和农业保险两种模式下的供应链利润模型,首先研究了两部收费制对供应链系统的协调作用及对成员合作关系的影响;然后探讨了农业保险的设定和理赔机制,对比分析了农业保险对供应链利润及成员合作关系稳定性的影响,最后通过算例验证模型的结果,并给出决策意见。研究表明：自然风险下两部收费制能协调生鲜农产品供应链,但供应链合作关系不稳定;引入农业保险不仅可以有效管理自然风险,还能增加供应链整体及各级成员的收益;随着自然灾害系数的上升,农业保险下生鲜农产品供应链合作关系稳定性也会下降,但相比较于自然风险下的供应链系统,其稳定性更好。     

Effect of firm characteristics,supplier relationships and techniques used on Supply Chain Risk Management (SCRM): an empirical investigation on French industrial firms

Recent years have witnessed a focus on managing risks in supply chains. On the one hand, there are many cases where events like natural disasters, strikes and terrorism have significantly influenced the performance of organisational supply chains and in turn their competitiveness. And on the other hand, operational activities and strategic decisions of the firms (concerning, for e.g. supply, procurement, production, delivery, commercialisation, demand management, planning, etc.) can be different than expected and so create uncertainties. Uncertainties, whether they are external or internal, impact organisations leading to increased supply chain risk. Realising the potential implications of these situations on supply chain competitiveness, an attempt has been made to define risks and their sources and to identify the management that can help reduce the negative impact of risks on supply chains. In this paper, a framework for supply chain risk management (SCRM) is proposed and is applied using the data collected from 164 French companies, in manufacturing sector. The literature review, theoretical framework and empirical research undertaken in this work have led to identifying critical success management for SCRM. The focus of this paper is the inter-organisational management of supply chain risk: the collaborative relationship (with industrial and supply partners) can be considered as an efficient way to make SCRM. The paper finishes with a summary of the findings and conclusions, along with suggestions for future research projects.     

Risk readiness and resiliency planning for a supply chain

This paper proposes supply chain (SC) risk readiness and resiliency measures and formulates a model for planning and controlling select internal business factors to create desired risk resiliency in order to avert potential risks and mitigate their after-effect. SCs may be exposed to events that affect their business operations, and primarily impact the production processes (i.e. production-related risks), or events (such as natural calamities or terrorism) that affect the way the business interacts with the market, and primarily impact the transportation and distribution processes (i.e. market-related risks). Although a business cannot control such disasters as natural calamities or terrorism, it is possible to identify and control the factors that are responsible for production-related risks and that influence several market-related risks or disasters. The proposed model and the measures will guide SCs through the process of identification, planning and controlling the internal factors that make the chain resilient to these various risks. The resiliency measures and the mixed integer programming model will also enable SCs to conduct what-if analyses of cost and performance trade-off options. A numerical example illustrates the planning in typical scenarios.     

Literature review on disruption recovery in the supply chain*

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.     

Supply chain risk management: a literature review

Risk management plays a vital role in effectively operating supply chains in the presence of a variety of uncertainties. Over the years, many researchers have focused on supply chain risk management (SCRM) by contributing in the areas of defining, operationalising and mitigating risks. In this paper, we review and synthesise the extant literature in SCRM in the past decade in a comprehensive manner. The purpose of this paper is threefold. First, we present and categorise SCRM research appearing between 2003 and 2013. Second, we undertake a detailed review associated with research developments in supply chain risk definitions, risk types, risk factors and risk management/mitigation strategies. Third, we analyse the SCRM literature in exploring potential gaps.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.