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.     

Supply chain risk management and its mitigation in a food industry

We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.     

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.     

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.     

Postponement in supply chain risk management: a complexity perspective

While the poor response implications of supply are often not elaborated on in the literature, postponement has recently been mentioned as a useful tool for managing supply risk and disruptions. To interpret this in a more complete manner, this paper has attempted to explore the role of postponement in supply chain risk management from a complexity perspective. After a review of the relevant literature, it first draws insights emerging from normal accident theory that addresses the system characteristics of catastrophic accidents and applies them to supply chain disruptions. This is followed by the utilisation of normal accident theory to explain the role of postponement in supply chain risk management. Building on this, this paper also investigates the complexity implications of some commonly recommended measures to mitigate supply chain disruptions. In certain circumstances, the introduction of those measures may add to the complexity of a system and thus become inherently infeasible. The paper concludes with a summary and some suggestions for further research.     

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 risk management and artificial intelligence: state of the art and future research directions

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.     

A note on supply chain risk classification: discussion and proposal

The main objective of this note is to complement the excellent work on supply chain risk classification by Rangel et al. [International Journal of Production Research, vol. 52 (7), 2014]. In this note, we consider the use of ISO/IEC (International Organisation for Standardisation/International Electrotechnical Commission) norms to support the supply chain risk classification. Its purpose is to develop, maintain and promote standards in the fields of information technology and information communications technology. Therefore, to improve on the work by Rangel et al. (2014), ISO 27036 (Information Security for Supplier Relationship) and ISO 28000 (Specification for Security Management Systems for the Supply Chain) are aligned with ISO 31000 (Risk Management–Risk Assessment Techniques). Furthermore, since supply chain risk management does not have a standardised process, these norms, particularly ISO 31000, can serve as a guide to improve its implementation.     

Sustainable supply chains for supply chain sustainability: impact of sustainability efforts on supply chain risk

Supply chain managers across the globe are finding it difficult to manage the increasingly complex supply chains despite adopting a variety of risk mitigation strategies. Firms on the other hand have also been adopting various kinds of environmental and social sustainability practices in recent times to reduce carbon footprint and improve their image on the social front. However, very few studies in the extant literature have examined the impact of sustainability practices on supply chain risk. We address this important gap in literature by empirically testing this relationship, using primary data from six manufacturing sectors and 21 different countries including developed as well as emerging markets across the globe. Our findings indicate that risk mitigation strategies do not always reduce the actual supply chain risk experienced by firms, whereas sustainability efforts help reduce supply chain risk, especially in emerging market contexts. In addition, we find that, while reactive risk mitigation strategies on their own fail to reduce supply chain risk, they are effective when used in conjunction with sustainability efforts. We also find that preventive risk mitigation efforts are only effective in mature supply chains such as the OECD countries.     

Supply chain risk and resilience: theory building through structured experiments and simulation

The research literature of supply chain risk and resilience is at a critical developmental stage. Studies have established the importance of these topics both to researchers and practitioners. They also have identified factors contributing to risk, the impact of risk and disruptions on performance, and the strategies and tactics used to build the capacity for supply chain resilience. Although these efforts can provide support for constructing a theory of risk and resilience, researchers are currently restricted in their ability to build such a theory by the difficulty of collecting the necessary data. This paper contributes to this literature development effort by summarising prior research reviews and developing a three-component framework aimed at helping researchers to build better theories. This is accomplished through combining structured experimental design with discrete-event simulations of supply chains. The result is a methodology that allows researchers to develop better understanding of the factors that link a disruption to its impact on supply chain performance through both direct and interaction effects. Following the methodology development, the paper concludes with an example using the factors of shock interarrival time, supply chain connectivity and buffer stocks to illustrate the potential for contributing to the theory-building process.     

Real options approach to evaluate postponement as supply chain disruptions mitigation strategy

The purpose of this paper is to assess the value of postponement as strategy for mitigating supply chain disruptions. To accomplish this objective, we develop a real option computational model that quantifies the value of postponement in mitigating both supply and demand disruptions by taking into account the value of managerial flexibility to decide whether exploiting or not the strategy, if and when disruptions occur, and whenever product differentiation proves valuable based on information available at that time. Numerical experiments show the importance of incorporating an option valuation method when pricing the value of postponement. This ensures managers implement postponement only when it is valuable, thus avoiding burdening the company with its initial sunk costs. By modelling the postponement implementation under different conditions, we identify the situations in which postponement performs better as supply chain disruptions mitigation strategy. We derive the operational configurations, in terms of decoupling point position, and external conditions, in terms of riskiness of the environment, which make the postponement an effective mitigation strategy.     

Supply chain coordination under information asymmetry: a review

Aligning supply chain decisions of separate entities with independent objectives can be considered to be one of the difficulties of supply chain management. This difficulty becomes worse if the supply chains are characterised by an asymmetrical distribution of information. Although considerable research has recently been devoted to supply chain coordination, less attention has been paid to different information asymmetry settings to the mechanisms underlying it. This research attempts to help fill this gap by reviewing and classifying the literature based on supply chain features, applied methodology, coordination mechanisms, and types of information asymmetry. The proposed classification is used to highlight the ongoing issues in the area and identify the direction for future research.     

Supply chain resilience: model development and empirical analysis

The purpose of this study is to develop a hierarchy-based model for supply chain resilience (SCRES), explaining the dynamics between various enablers and validating the model empirically. Literature review and a survey identified the enablers. Interpretive structural modelling (ISM) is used to analyse the levels of relationships among enablers. Based on their driving power and dependence, these enablers are also classified into different categories. Structural equation modelling is used to validate the hierarchical SCRES model and test the path analytical model. The study provides empirical justification for a framework that identifies 13 key enablers of resilient supply chain practices and describes the relationship among them using ISM. It also classifies them using Matrix of Cross Impact Multiplications Applied to Classification analysis on the basis of their driver power and dependence. The key finding is that using the proposed model, organisations can enhance their resilience potential by modifying their strategic assets. The model was tested using rigorous statistical tests including convergent validity, discriminant validity and reliability. The holistic view offered by the proposed model depicts the relationship among enablers to achieve SCRES.     

Global sensitivity analysis and aggregation of risk in multi-product supply chain networks

Manufacturing firms manage complex supply chain networks which are exposed to a plethora of hazard events. An essential part of the risk management process is the calculation of the stand-alone risk exposures of the product-specific supply chains, but also of the entire multi-product system. In this paper, first, a global sensitivity analysis of the statistical supply chain risk model is conducted. This method helps the decision-makers to understand the risk of the model they are using. Second, a methodology for risk aggregation in multi-product supply chain networks is proposed. The real-world data is used to analyse and validate the model. Supply chain managers equipped with the proposed method will better cope with the risk in supply chains for different product configurations.     

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.     

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.     

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.     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

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 disruption risk management through strategic information acquisition and sharing and risk-sharing contracts

In this paper, we develop a framework that captures the effects of information management and risk-sharing contracts in supply chain networks. In particular, we analyse the impact of strategic information acquisition and sharing on supply chain disruption risks and costs and we evaluate the supply chain performance of risk-sharing contracts. Risk-sharing contracts specify who needs to incur the costs when supply chain disruptions occur. We develop a model that consists of three tiers of multi-criteria decision-makers, manufacturers, retailers, and demand markets. We describe the behaviour or each decision-maker, derive the finite-dimensional variational inequality formulation of the equilibrium conditions of the supply chain and present numerical examples. The numerical examples highlight that it is not a priori clear which participant in the supply chain network will benefit from increased information-sharing activities. Our models indicate that the beneficiary of reduced information-sharing costs is in some cases dependent on the negotiation power of participants and that it is also dependent on the type of risk-sharing contract used. Furthermore, the numerical examples show that, in some cases, information-sharing and risk-sharing contracts are complements while in other cases they are substitutes.