Search results

The purpose of this paper is to provide groundwork for an emerging theory of supply chain robustness – which has been conceptualized as a dimension of supply chain resilience – through reviewing and synthesizing related yet disconnected studies. The paper develops a formal definition of supply chain robustness to build a framework that captures the dimensions, antecedents and moderators of the construct as discussed in the literature.

The authors apply a systematic literature review approach. In order to reduce researcher bias, they involve a team of academics, librarians and managers.

The paper first, provides a formal definition of supply chain robustness; second, builds a theoretical framework of supply chain robustness that augments both causal and descriptive knowledge; third,shows how findings in this review support practice; and fourth,reveals methodological insights on the use of journal rankings in reviews.

At this stage, managers may benefit from seeing these relationships as clues derived from the literature. The paper is fundamentally a call for researchers to conduct quantitative testing of such relationships to derive more reliable understanding and practical applications.

Rather than presenting empirical findings, this paper reveals to managers that visibility, risk management orientation and reduced network complexity have been the main predictive antecedents of supply chain robustness (as discussed in the academic literature). This provides a potentially important signal as to where to invest resources.

The study is the first to develop a formal definition of supply chain robustness and to establish a comprehensive theoretical framework for understanding the construct.

In light of the COVID-19 pandemic, all business sectors have critical needs. They face multiple challenges to restructuring their operations to build a resilient, cost-effective and sustainable supply chain. Therefore, this paper aims to investigate the practice and the research gaps related to supply chains.

This research paper is influenced by a literature review of the past decade. This review paper incorporates industry challenges of the COVID-19 pandemic, including future steps toward developing resilient supply chains in the new normal economy. The research provides a detailed framework for designing cost-effective survivable supply chains that withstand disruptions for the long term.

The proposed research focuses on the effects of the COVID-19 pandemic on supply chains and attempts to bridge pre and post COVID-19 research and practice gaps. Post-COVID-19 resilient supply chains need to be transformed into survivable supply chains. The survivability of the supply chain can be achieved by combining both supply chain resilience and supply chain viability measures. To the best of the authors’ belief, this is the first study that grounds a theory to provide interconnection of five critical supply chain concepts to manage supply chain risk. This study is uniquely positioned to develop a theoretical framework to design a cost-effective, resilient and sustainable supply chain by establishing the interconnection among these concepts in supply chains. This framework helps practitioners to implement the key strategies at the operational, tactical and strategic levels that enhance maturity in supply chains.

The research findings are based on secondary reports such as industry reports, cases, research papers and expert opinions. The authors tried to consult with many companies. However, they were reluctant to share the recovery plan information from COVID. Also, as COVID still exists in many places in Canada, the authors could not gather every intended information from the companies. However, the authors have successfully shared the outcomes of this research with a reputed retail company in Canada. They recognized the importance of survivability in supply chains. Going forward, business organizations need to design cost-effective, sustainable and survivable supply chains.

The study attempts to unify current research dealing with supply chain resilience. The study concludes with the limitations of the current research. It highlights the prospects of future research and bridges the supply chain practice gaps from the challenges faced by industries due to COVID-19. The study contributes to the literature by identifying gaps to bridge the supply chain practice and reiterating new research directions to develop a cost-effective, survivable and sustainable supply chain.

As globalisation makes supply networks more complex, the risk of material disruptions increases. Many factors have been considered as affecting the reliability of supply networks. However, no empirical research has been carried out to assess and evaluate the impact of each of these factors on the reliability of supply networks. This paper aims to address this issue.

A gap in the literature was identified around the evaluation of the impact of supply network design characteristics on reliability. This gap is addressed by performing a full factorial experimental design considering all the factors described in the literature, and then analysing (by using analysis of variance and linear regression models), thousands of theoretical and extreme structures of supply networks, thus allowing the analysis of the influence of each factor on the overall network resilience.

Results show that network density, node criticality and complexity are significant factors in reducing the reliability of supply networks. In particular, node complexity (i.e. the total number of nodes in the network) was found to have the strongest negative effect on network reliability, while the strongest positive factor was sources criticality (i.e. the level of redundancy of suppliers).

The identification of these factors and their relative impacts on network reliability can serve as a guide for the design of more reliable networks, and to know which are the most important to consider when designed distribution networks.

The paper identifies, from the literature, key factors affecting supply network reliability and evaluates their relative impact. Given the number of factors identified, an extensive Monte Carlo simulation is used for the first time, by considering simple and very complex networks, to allow the testing of the role of each factor in supply network reliability.

The purpose of this paper is to develop a decision tool that enables supply chain (SC) architects to design resilient SC networks (SCNs). Two resilience design determinants are considered: SC density and node criticality. The effect of considering these determinants on network structures is highlighted based on the ability to resist disruptions and how SC performance is affected.

A mixed-integer non-linear programming model is proposed as a proactive strategy to develop resilient structures; design determinants are formulated and considered as constraints. An upper limit is set for each determinant, and resistance capacity and performance of the developed structures are evaluated. These upper limits are then changed until SC performance stabilizes in case of no disruption.

Resilient SCN structures are achieved at relatively low design determinants levels on the expense of profit and without experiencing shortage in case of no disruption. This reduction in profit can be minimized on setting counter values for the two determinants; relatively higher SC density with lower node criticality or vice versa. At very low SC density levels, the design model will reduce the number of open facilities largely leading to only one facility open at each echelon; therefore, shortage occurs and vulnerability to disruption increases. On the other hand, at high determinants levels, SC vulnerability also increases as a result of having more geographically clustered structures with higher inbound and outbound flows for each facility.

In this paper, a novel proactive decision tool is adopted to design resilient SCNs. Previous literature used metrics for SC density and node criticality to assess resilience; in this research, determinants are incorporated directly as constraints in the design model. Results give insight to SC architects on how to set determinant values to reach resilient structures with minimum performance loss in case of no disruption.

The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

The purpose of this paper is to explore the current trends, implications and challenges of information systems (IS) related to omni-channel logistics.

An exploratory survey study is conducted with 23 Swedish retail companies transforming to omni-channel logistics. The study investigates the retailers’ current situations regarding logistics IS as well as their perceptions of the future development.

From the perspective of leading Swedish retailers, omni-channel requirements drive the implementation of new IS to support effective and efficient material handling across the network and in the respective nodes. The shifting roles and increase in the number of handlings nodes will require flexible IS platforms that can support multiple flows and integrated inventory. The major increase in the implementation of new, critical functionalities is related to real-time, multi-criteria decision making on order allocation to different handling nodes. More advanced IS functionality is also required in material-handling nodes to support the increased degree of automation and continuous improvements with the aim to shorten order-to-delivery lead times. A number of challenges are identified that must be addressed during the transformation to omni-channel logistics, especially related to the growing complexity and decentralization of networks, tougher lead-time requirements and larger product assortments.

To support further theory development, 11 propositions related to trends and a schematic framework conceptualizing implications and challenges are submitted for testing in future research.

The study highlights several aspects related to logistics IS that are important for practitioners to consider as they undergo the transition to omni-channels. It provides insights into IS functionalities that are likely to grow in use and criticality for supporting material handling and inventory management in increasingly complex and decentralized networks. In particular, the authors stress the need to implement functionality that works across previously separated handling nodes and decision areas. Managers can also use the propositions to reflect on what the near future holds and as input for their own scenario analyses.

Previous research has primarily focused on technology that supports the front-end customer experience. This study is original in that it explores the trends, implications and challenges for logistics IS in omni-channels – an area that has not been explored in detail previously. It also studies both perceived and expected changes over time related to the transformation toward omni-channel logistics.

Severe disruptions to maritime supply chains, including port closures, congestion and shortages in shipping capacity, have occurred during the COVID-19 pandemic. This paper’s purpose is to explore flexibility-based countermeasures that enable actors in maritime supply chains to mitigate the effects of disruptions with different characteristics.

Semi-structured interviews were conducted with shipping lines, shippers, forwarders and ports. Data on the COVID-19 pandemic's effects and countermeasures were collected and compared with data regarding the 2016–2017 Gothenburg port conflict.

Spatial, capacity, service and temporal flexibility emerged as the primary countermeasures, whilst important characteristics of disruptions were geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. Spatial flexibility was exercised in both disruptions by switching to alternative ports. During the COVID-19 pandemic, ensuring capacity flexibility included first removing and then adding vessels. Shipping lines exercising service flexibility prioritised certain cargo, which made the spot market uncertain and reduced flexibility for forwarders, importers and exporters that changed carriers or traffic modes. Experience with disruptions meant less surprise and better preparation for spatial flexibility.

Understanding how actors in maritime supply chains exercise flexibility-based countermeasures amid disruptions with different characteristics can support preparedness for coming disruptions.

Comparing flexibility-based measures in a pandemic versus port conflict provides insights into the important characteristics of disruptions and the relevance of mitigation strategies. The resilience of maritime supply chains, although underexamined compared with manufacturing supply chains, is essential for maintaining global supply chain flows.

Supply chains need to balance competing objectives; in addition to efficiency, supply chains need to be resilient to adversarial and environmental interference and robust to uncertainties in long-term demand. Significant research has been conducted designing efficient supply chains and recent research has focused on resilient supply chain design. However, the integration of resilient and robust supply chain design is less well studied. The purpose of the paper is to include resilience and robustness into supply chain design.

The paper develops a method to include resilience and robustness into supply chain design. Using the region of West Africa, which is plagued with persisting logistical issues, the authors develop a regional risk assessment framework and then apply categorical risk to the countries of West Africa using publicly available data. A scenario reduction technique is used to focus on the highest risk scenarios for the model to be tractable. Next, the authors develop a mathematical model leveraging this framework to design a resilient supply network that minimizes cost while ensuring the network functions following a disruption. Finally, the authors examine the network's robustness to demand uncertainty via several plausible emergency scenarios.

The authors provide optimal sets of transshipment hubs with varying counts from 5 through 15 hubs. The authors determine there is no feasible solution that uses only five transshipment hubs. The authors' findings reinforce those seven transshipment hubs – the solution currently employed in West Africa – is the cheapest architecture to achieve resilience and robustness. Additionally, for each set of feasibility transshipment hubs, the authors provide connections between hubs and demand spokes.

While, at the time of this research, three other manuscripts incorporated both resilience and robustness of the authors' research unique solved the problem as a network flow instead of as a set covering problem. Additionally, the authors establish a novel risk framework to guide the required amount of redundancy, and finally the out research proposes a scenario reduction heuristic to allow tractable exploration of 512 possible demand scenarios.

The current outbreak of COVID-19 is an unprecedented event in air transportation. In this study, we investigate the impact of COVID-19 on global air transportation through the lens of complex networks different at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks, discovering hidden patterns on flight frequency reduction. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.