Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review

Assessing risk associated with recreational activities in coastal areas by using a bayesian network

Taiwan is an island and therefore has a considerable amount of coastal land. Drowning or near-drowning incidents often occur in coastal recreational areas. To reduce the risk of drowning or near-drowning associated with marine recreational activities in Taiwan, this study collected data on the risk associated with marine recreational activities. It selected risk factors using a modified Delphi panel method, with an expert panel used to obtain probability values for each risk factor. A Bayesian network for risk assessment was then established. The results of this study can serve as a reference for stakeholders involved in marine recreational activities. Severe weather conditions increase wave height and current speed, resulting in an increased risk of drowning or near-drowning when coastal recreational activities occur under these conditions. Individuals who undertake marine recreational activities without safety awareness are more likely to exhibit risky behaviors. When self-rescue ability is insufficient to prevent possible danger, the probability of drowning or near-drowning is higher. Serious incidents may lead to death, and therefore, marine recreational activities should be avoided when weather conditions are poor. In addition, the safety awareness and self-rescue ability of individuals undertaking coastal recreational activities should be improved. This study did not explore emergency response measures or postincident policy management.

COVID-19 and distribution centres operations: The impacts and countermeasures

COVID-19 has wreaked havoc on supply chains. This is particularly true for distribution centres as they struggle to bounce back amid the COVID-19 outbreak. While much literature has recently emerged on supply chain disruption, studies pertaining to the impacts of COVID-19 on distribution centres and the countermeasures taken to mitigate such impacts are elusive and mute. Our study fills this important gap in the supply chain literature. This study employs a multiple-case methodology and conducts 40 semi-structured interviews with senior managers/executives from eight distribution centres in the United Arab Emirates. Our results exhibit that COVID-19 is adversely affecting the distribution centres in at least six distinct ways. For instance, distribution centres are encountering limited staff availability, inventory shortage, destabilized supply chains, excessive inventory, limited capacity and surge in demand. Results also demonstrate six corresponding strategies employed by distribution centres to mitigate the impact. For example, distribution centres enhance warehouse automation, increase hands-on inventory, reshoring manufacturing, use scalable processes and an automation retrieval system, and finally employ a picking strategy. Distribution centres can use the findings provided in this study. Particularly, they can learn how COVID-19 affects them and what corresponding strategies they should adopt to stay strong during this pandemic. This study demystifies its contribution to theory and practice alongside limitations and future research directions.

Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network

Understanding how plants respond to environmental conditions such as temperature, CO₂, humidity, and light radiation is essential for plant growth. This paper proposes an Artificial Neural Network (ANN) model to predict plant response to environmental conditions to enhance crop production systems that improve plant performance and resource use efficiency (e.g. light, fertiliser and water) in a Chinese Solar Greenhouse. Comprehensive data collection has been conducted in a greenhouse environment to validate the proposed prediction model. Specifically, the data has been collected from the CSG in warm and cold weather. This paper confirms that CSG's passive insulation and heating system was effective in providing adequate protection during the winter. In particular, the CSG average indoor temperature was 18 [Formula: see text]C higher than the outdoor temperature. The difference in environmental conditions led to a yield of 320.8g per head in the winter after 60 growing days compared to 258.9g in the spring experiment after just 35 days. Three different architectures of Bayesian Neural Networks (BNN) models have been evaluated to predict plant response to environmental conditions. The results show that the BNN network is accurate in modelling and predicting crop performance.

Nexus between drivers of COVID-19 and country risks

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.

Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions

The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting "if a shipment can be exported from one source to another", despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs.

Two years of COVID-19 pandemic: Understanding the role of knowledge-based supply chains towards resilience through bibliometric and network analyses

Coronavirus disease (COVID-19) catastrophically disrupted most of the global supply chains (SC). Knowledge-based SC can cope with the pandemic disruptions by the efficient use of data, information, knowledge, human intelligence and emerging technologies. This article aims to critically analyse the SC research during the two years of COVID-19 pandemic to understand the role of knowledge-based supply chain towards SC resilience. A review of the 281 shortlisted articles is presented, along with bibliometric and network analyses in order to create an intellectual map of the domain and to identify the emerging knowledge themes. Bibliometric analysis reveals that the knowledge focus during this short span has migrated from COVID-19 pandemic to SC risk management and finally to risk mitigation strategies. The network analysis identifies five emerging knowledge themes, namely impact of COVID-19 on SC; SC risk mitigation and resilience; supply chain viability; sustainable SC strategies; and food SC. This review also elucidates the strategies to mitigate COVID-19 disruptions for incorporating resilience in SC. Future research directions for a knowledge-based sustainable-leagile-resilient (S-leagilient) supply chain have also been propounded.

Just-in-time approach in healthcare inventory management: Does it really work?

Healthcare organizations need to efficiently use their available resources, improve their productivity, reduce operating costs, and provide high-quality services. Just in time (JIT) is an approach that has benefited the healthcare industry in these regards, improving patient outcomes by reducing waste and non-value-adding activities. As such, our main purpose in this study was to discuss the use of JIT systems in healthcare inventory management and highlight their importance, as well as explore the advantages and limitations of JIT systems in healthcare management systems. We also explored supply chain issues in healthcare during the COVID-19 pandemic and provide strategies and recommendations for improvement.

Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective

This study employs a structured literature analysis considering Industry 4.0 technologies and their adoption stages (intention, adoption, implementation, routinization, continuance, and diffusion). We identify the technology adoption stage for each technology type, which in turn supports a maturity level categorization, as well as future research suggestions and challenging open research questions. By considering an integrated view of all the adoption stages of Industry 4.0 key technologies, we reveal the key technologies and their development stages, as well as a novel maturity level categorization perspective. The proposed categorization brings valuable research insights in the form of guidelines for practitioners and decision-makers interested in gaining a deeper understanding of the maturity level of key Industry 4.0 technologies.

Digital Innovation, Data Analytics, and Supply Chain Resiliency: A Bibliometric-based Systematic Literature Review

In recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners' awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place.

Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers

Given the increasing complexity of the global supply chain, it is an important issue to enhance the agilities of enterprises that manufacture new energy materials to reduce the ripple effects of supply chains. Quality function deployment (QFD) has been applied in many areas to solve multi-criteria decision making (MCDM) problems successfully. However, there is still lack of sufficient research on the use of MCDM to develop two house-of-quality systems in the supply chain of new energy materials manufacturing enterprises to determine ripple effect factors (REFs), supply chain agility indicators (SCAIs), and industry 4.0 enablers (I4Es). This study aimed to develop a valuable decision framework by integrating MCDM and QFD; using key I4Es to enhance the agility of supply chain and reduce or mitigate its ripple effects ultimately, this study provides an effective method for new energy materials manufacturers to develop supply chains that can rapidly respond to change and uncertainty. The case study considered China’s largest new energy materials manufacturing enterprise as the object and obtained important management insights, as well as practical significance, from implementing the proposed research framework. The study found the following to be the most urgent I4Es required to strengthen the agility of supply chain and reduce the key REFs: ensuring data privacy and security, guarding against legal risks, adopting digital transformation investment to improve economic efficiency, ramming IT infrastructure for big data management, and investing and using the new equipment of Industry 4.0. When these measures are improved, the agility of the supply chain can be improved, such as long-term cooperation with partners to strengthen trust relationships, supply chain information transparency and visualization to quickly respond to customer needs, and improving customer service levels and satisfaction. Finally, REFs, such as the bullwhip effect caused by inaccurate prediction, facility failure, and poor strain capacity caused by supply chain disruption, can be alleviated or eliminated. The proposed framework provides an effective strategy for formulating I4Es to strengthen supply chain agility (SCA) and mitigate ripple effects, as well as provides a reference for supply chain management of other manufacturing enterprises in the field of cleaner production.

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

Purpose

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Design/methodology/approach

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Findings

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Originality/value

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Meta-analysis of Supply Chain Disruption Research

The purpose of this chapter is to provide insights into literature on supply chain disruption research with a specific focus on future research opportunities. A structured meta-literature review approach covering 93 literature reviews was chosen. Quantitative and qualitative content analysis and bibliographic network analysis are applied to highlight trends and research gaps. The meta-analysis shows the current and past academic discourse on supply chain disruptions. Furthermore, this research establishes a research framework and highlights future research opportunities. The research points to research topics that should be addressed in the future. The paper provides a holistic understanding of literature on supply chain disruptions in the commercial and humanitarian context.

Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies

This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations' resilience, as well as the ways in which they may do so.

Sustainable Integrated Fuzzy Optimization for Multimodal Petroleum Supply Chain Design with Pipeline System: The Case Study of Vietnam

Over the years, oil-related energy sources have played an irreplaceable role in both developed and developing countries. Therefore, the efficiency of petroleum supply chains is a key factor that significantly affects the economy. This research aimed to optimize the configuration of the uncertainty multimodal petroleum supply chain in terms of economy, energy and environment (3E assessment). This study proposes a novel integration methodology between a heuristic algorithm and exact solution optimization. In the first stage, this study determines the facilities’ potential geographical coordinates using heuristic algorithm. Then, the fuzzy min-max goal programming model (FMMGPM) was developed to find the multi-objective solutions. In particular, this model allows analysis of supply chain uncertainty through simultaneous factors such as demand, resource, cost and price. These uncertainty factors are expressed as triangular fuzzy parameters that can be analyzed in terms of both probability and magnitude. Moreover, the model is applied to the entire petroleum supply chain in Vietnam, including downstream and upstream activities. In addition, another novelty is that for the first time, pipeline systems in logistics activities are considered in Vietnam’s petroleum supply chain optimization study. The results also show the short-term and long-term benefits of developing a pipeline system for oil transportation in Vietnam’s petroleum supply chain. To evaluate the effects of uncertainty on design decisions, this study also performed a sensitivity analysis with scenarios constructed based on different magnitudes and probabilities of uncertainty.

Energy Resilience Impact of Supply Chain Network Disruption to Military Microgrids

The ability to provide uninterrupted power to military installations is paramount in executing a country’s national defense strategy. Microgrid architectures increase installation energy resilience through redundant local generation sources and the capability for grid independence. However, deliberate attacks from near-peer competitors can disrupt the associated supply chain network, thereby affecting mission critical loads. Utilizing an integrated discrete-time Markov chain and dynamic Bayesian network approach, we investigate disruption propagation throughout a supply chain network and quantify its mission impact on an islanded microgrid. We propose a novel methodology and an associated metric we term “energy resilience impact” to identify and address supply chain disruption risks to energy security. The proposed methodology addresses a gap in the literature and practice where it is assumed supply chains will not be disrupted during incidents involving microgrids. A case study of a fictional military installation is presented to demonstrate how installation energy managers can adopt this methodology for the design and improvement of military microgrids. The fictional case study shows how supply chain disruptions can impact the ability of a microgrid to successfully supply electricity to critical loads throughout an islanding event.

Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review

In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented

Logistics and supply chain management of food industry during COVID-19: disruptions and a recovery plan

An ongoing worldwide pandemic, known as Covid infection 2019 (COVID-19), influences the food supply chains significantly. In the pandemic situation, the movements of the people are restricted due to strict lock-down, and retail shops are closed. The supply of products to the customer is a challenging situation for the food supplier. These disruptions impact the food supply chain system suddenly, and the process can collapse without necessary and immediate actions. In this paper, a direct delivery channel has been used as a recovery strategy to minimize the effects of disruptions in the pandemic situation. In the recovery plan, the manufacturer appoints vendors and delivers the products directly to the customers by introducing multi-delivery channels. We optimize the recovery plan under the profit maximization criteria from the recovery window. Some numerical examples have been illustrated to justify that the developed recovery model can resist the reduction of demand and improve the profit of the system. Also, managerial insights are discussed which help the decision-makers to make an accurate and prompt decision of designing a recovery strategy during COVID-19.

Key supply chain strategies for the post-COVID-19 era: implications for resilience and sustainability

Purpose

The impacts of the novel coronavirus (COVID-19) outbreak continue to devastate supply chain operations. To attain a competitive advantage in the post-COVID-19 era, decision-makers should explore key supply chain strategies to move forward and ready their policies to be implemented when the crisis sufficiently subsides. This is a significant and practical decision-making issue for any supply chain; hence, the purpose of this study is to explore and analyse key supply chain strategies to ensure robustness and resilience in the post-COVID-19 era.

Design/methodology/approach

This study conducted an expert survey targeting practitioners and academics to explore key supply chain strategies as means of moving forward in the post-COVID-19 era. Further, the key strategies were quantitatively analysed by applying the best-worst method (BWM) to determine their priority importance in the context of the manufacturing sector.

Findings

The results revealed that supply chain resilience and sustainability practices could play a dominant role in this period. The findings of the study can assist supply chain decision-makers in their formulations of key strategies.

Originality/value

This is the first study to investigate key supply chain strategies for the post-COVID-19 era. This study will help practitioners paying attention to resilience and sustainability practices for managing the impacts of future large-scale disruptions.

Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context

In the wake of the COVID-19 pandemic, many firms lacked a strategy to cope with disruptions and maintain resiliency. In this study, we develop a measurement method to evaluate the impact of resilience strategies in a multi-stage supply chain (SC) in the presence of a pandemic. For the first time, we propose a method to deduce quantitative resilience assessment from simulation. We implement two resilience strategies, i.e., prepositioning extra-inventory and a backup supplier, and then we simulate its impact on SC resilience and financial performance. The simulation results indicate that the extra inventory leads to a higher resilience than a backup supplier but costs more for the given contextual setting. Finally, we examine the demand fulfillment and observe that the extra-inventory strategy allows for a higher service level, confirming our resilience simulations. We discuss the managerial implications of these findings on the descriptive and predictive analysis levels. Decision-makers can utilize our model and findings to develop a response plan in the occurrence of a pandemic or any long-duration high magnitude disruption. Also, scholars and managers can use our proposed method to measure SC resiliency from simulation in any disruption.

Supply chain resilience during the COVID-19: empirical evidence from an emerging economy

Purpose

Although the advances in the supply chain resilience (SCR) literature, there is a critical gap concerning this understanding in a high disruption context, such as in the case of the COVID-19. This paper aims to investigate some dimensions (agility, robustness, disruption orientation and resource reconfiguration) of the SCR during this unprecedented disruption in the Brazilian supply chain context.

Design/methodology/approach

Supported by the resource-based view, dynamic capabilities and the SCR literature, we developed a model, which in turn was analyzed and validated by partial least squares structural equation modelling.

Findings

The results revealed that while resource reconfiguration and supply chain disruption orientation positively affect SCR, we found a non-significant effect of supply chain robustness and agility.

Practical implications

The findings suggest that in a considerable disruption scenario, managers with their supply chain operations in emerging economies should prioritize the development of resources to support the disruption orientation and manage the scarce resources adequately by reconfiguring them.

Originality/value

Our study is one of the first that reported the dynamics of the SCR dimensions in an emerging economy during the COVID-19.

Risk Propagation of Concentralized Distribution Logistics Plan Change in Cruise Construction

Compared with the ordinary merchant ship building, the concentralized distribution in cruise building is more complex. Plan change is a common phenomenon in cruise building, and it is easy to lead to mismatch between production and logistics, resulting in risks such as production schedule delay and inventory backlog. In order to reduce the adverse effects of plan change on the shipyard, it is necessary to conduct an in-depth study on the risks of a centralized distribution logistics plan. Based on the analysis of the composition of the centralized distribution logistics planning system, risk factors in different plan links are identified in this paper. A system dynamic model is constructed to simulate the propagation of five basic types of planning risk, including procurement plan, warehousing plan, pallet concentralization plan, distribution plan and production plan. In the case study of HVAC (heating, ventilation and air conditioning) materials, the values of risk factors are estimated though consulting experts with questionnaire. The weight of each risk factor in each subsystem is calculated by a method combined with analytic hierarchy process and coefficient of variation method. Through the simulation experiments carried out in Vensim, it is found that both inventory backlog risk and cruise construction schedule delay risk increase with the increasement of estimated values of risk factors, which is an effective proof of the rationality of the model, and that the most sensitive risk factor for both the two kinds of risk is production planning risk.

Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic

The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0's resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions.

A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis.

Evaluation of supply chain resilience index: a graph theory based approach

Purpose

The present study aims at developing a model to quantify supply chain resilience as a single numerical value. The numerical value is called resilience index that measures the resilience capability of the case company's supply chain. The model calculates the index value based on the interactions between the enablers of supply chain resilience and its dimensions.

Design/methodology/approach

Graph theoretic approach (GTA) is used to evaluate the resilience index for the case company's supply chain. In GTA, the dimensions of resilience enablers and their interdependencies are modelled through a digraph. The digraph depicting the influence of each dimension is converted into an adjacency matrix. The permanent function value of the adjacency matrix is called the resilience index (RI).

Findings

The proposed approach has been illustrated in context of an Indian automobile organization, and value of the RI is evaluated. The best case and the worst-case values are also obtained with the help of GTA. It is noted from the model that strategic level dimension of enablers is most important in contributing towards supply chain resilience. They are followed by tactical and operational level enablers. The GTA framework proposed will help supply chain practitioners to evaluate and benchmark the supply chain resilience of their respective organizations with the best in the industry.

Originality/value

A firm can compare the RI of its own supply chain with other's supply chain or with the best in the industry for benchmarking purpose. Benchmarking of resilience will help organizations in developing strategies to compete in dynamic market scenario.

Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency

The increasing use of information technology (IT) in supply chain management and logistics is connected to corporate advantages and enhanced competitiveness provided by enterprise resource planning systems and warehouse management systems. One downside of advancing digitalization is an increasing dependence on IT systems and the negative effects of technology disruption impacts on firm performance, measured by logistics efficiency, e.g., with data envelopment analysis (DEA). While the traditional DEA model cannot deconstruct production processes to find the underlying causes of inefficiencies, network DEA (NDEA) can provide insights into resource allocation at the individual stages of operations. We apply an NDEA approach to measure the impact of IT disruptions on the efficiency of operational processes in retail logistics. We compare efficiency levels during IT disruptions, as well as ripple effects throughout subsequent days. In the first stage, we evaluate the efficiency of order picking in retail logistics. After handing over the transport units to the outgoing goods department of a warehouse, we assess the subsequent process of truck loading as a second stage. The obtained results underline the analytical power of NDEA models and demonstrate that the proposed model can evaluate IT disruptions in supply chains better than traditional approaches. Insights show that efficiency reductions after IT disruptions occur at different levels and for diverse reasons, and successful preparation and contingency management can support improvements.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts' evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

Deploying Resilience Enablers to Mitigate Risks in Sustainable Fashion Supply Chains

The complex structure of supply chains makes them vulnerable to risk, so enhancing their resilience is an important goal. In particular, fashion supply chain research has identified two important issues that need to be addressed: sustainability and risk. However, investigation of these issues is relatively sparse and has primarily been independent with little combinatory research. Therefore, it is crucial to develop a risk mitigation method that can maximize the resilience of sustainable supply chains for fashion companies. The objective of this study is to develop an integrated quality function deployment approach and to mitigate supply chain risk by deploying resilience capabilities and resilience-enhancing features, thus ultimately providing the fashion industry with a useful approach for the development of resilient, sustainable supply chains. Using a fashion company as an example, the practicability of the proposed approach is verified. To strengthen resilience and thus mitigate key risks, it is found that the most urgent tasks are to reallocate the company’s resources, to carry out the real-time monitoring of risk on the spot, to share the risk responsibility, and to establish an incentive system. When these features are strengthened, agility and adaptability can be improved, and finally, the risks of supplier delays, natural disasters, political instability, and problematic supplier materials with the greatest impact can be alleviated. This study provides a new strategy for the fashion industry for the implementation of resilient, sustainable supply chains to mitigate risks.

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications

The COVID-19 pandemic unveils unforeseen and unprecedented fragilities in supply chains (SC). A primary stressor of SCs and their subsequent shocks derives from disruption propagation (i.e., the ripple effect) through related networks. In this paper, we conceptualize current state and future research directions on the ripple effect for pandemic context. We scrutinize the existing OR (Operational Research) studies published in international journals dealing with disruption propagation and structural dynamics in SCs. Our study pursues two major contributions in relation to two research questions. First, we collate state-of-the-art research on disruption propagation in SCs and identify a methodical taxonomy along with theories displaying their value and applications for coping with the impacts of pandemics on SCs. Second, we reveal and systemize managerial insights from theory used for operating (adapting) amid a pandemic and during times of recovery, along with becoming more resistant to future pandemics. Streamlining the literature allowed us to reveal several new research tensions and novel categorizations and classifications. The outcomes of our study show that methodical contributions and the resulting managerial insights can be categorized into three levels, i.e., network, process, and control. Our analysis reveals that adaptation capabilities play the most crucial role in managing the SCs under pandemic disruptions. Our findings depict how the existing OR methods can help coping with the ripple effect at five pandemic stages (i.e., Anticipation; Early Detection; Containment; Control and Mitigation; and Elimination) following the WHO classification. The outcomes and findings of our study can be used by industry and researchers alike to progress the decision-support systems guiding SCs amid the COVID-19 pandemic and toward recovery. Suggestions for future research directions are offered and discussed.