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The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

The purpose of this paper is to carry out a comprehensive review for state-of-the-art works in disruption risk management of express logistics mainly supported by air-transportation. The authors aim to suggest some new research directions and insights for express logistics practitioners to develop more robust planning in air-transportation.

The authors mainly confined the research to papers published over the last two decades. The search process was conducted in two dimensions: horizontal and vertical. In the horizontal dimension, attention was paid to the evolution of disruption management across the timeline. In the vertical dimension, different foci and strategies of disruption management are employed to distinguish each article. Three keywords were used in the full text query: “Disruption management”, “Air transportation”, and “Airline Operations” in all database searches listed above. Duplications due to database overlap, articles other than those from academic journals, and papers in languages other than English were discarded.

A total of 98 articles were studied. The authors categorized the papers into two broad categories: Reactive Recovery, and Proactive Planning. In addition, based on the problem characteristics and their application scenarios, a total of 11 sub-categories in reactive recovery and nine sub-categories in proactive planning were further identified. From the analysis, the authors identified some new categories in the air-transportation recovery. In addition, by analyzing the papers in robust planning, according to the problem characteristics and the state-of-the-art research in recovery problems, the authors proposed four new research directions to enhance the reliability and robustness of air-transportation express logistics.

This study provided a comprehensive and feasible taxonomy of disruption risk management. The classification scheme was based on the problem characteristics and the application scenarios, rather than the algorithms. One advantage of this scheme is that it enables an in-depth classification of the problem, that is, sub-categories of each class can be revealed, which provides a much wider and clearer horizon to the scientific progress in this area. This helps researchers to reveal the problem’s nature and to identify the future directions more systematically. The suggestions for future research directions also point out some critical research gaps and opportunities.

This study summarized various reasons which account for the disruption in air-transportation. In addition, the authors suggested various considerations for express logistics practitioners to enhance logistics network reliability and efficiency.

There are various classification schemes in the literature to categorize disruption management. Using different algorithms (e.g. exact algorithm, heuristics, meta-heuristics) and distinct characteristics of the problem elements (e.g. aircraft, crew, passengers, etc.) are the most common schemes in previous efforts to produce a disruption management classification scheme. However, the authors herein attempted to focus on the problem nature and the application perspective of disruption management. The classification scheme is hence novel and significant.

A recent global pandemic, known as coronavirus disease 2019 (COVID-19), affects the manufacturing supply chains most significantly. This effect becomes more challenging for the manufacturers of high-demand and most essential items, such as toilet paper and hand sanitizer. In a pandemic situation, the demand of the essential products increases expressively; on the other hand, the supply of the raw materials decreases considerably with a constraint of production capacity. These dual disruptions impact the production process suddenly, and the process can collapse without immediate and necessary actions. To minimize the impacts of these dual disruptions, we aim to develop a recovery model for making a decision on the revised production plan.

In this paper, the authors use a mathematical modeling approach to develop a production recovery model for a high-demand and essential item during the COVID-19. The authors also analyze the properties of the recovery plan, and optimize the recovery plan to maximize the profit in the recovery window.

The authors analyze the results using a numerical example. The result shows that the developed recovery model is capable of revising the production plan in the situations of both demand and supply disruptions, and improves the profit for the manufacturers. The authors also discuss the managerial implications, including the roles of digital technologies in the recovery process.

This model, which is a novel contribution to the literature, will help decision-makers of high-demand and essential items to make an accurate and prompt decision in designing the revised production plan to recover during a pandemic, like COVID-19.

Attracting commuters from driving to light rail systems has a good potential for reducing carbon emissions. However, the light rail system is interrupted by disruptions frequently, which reduces its attraction to passengers. Therefore, how to provide a quick replacement service during disruptions is of vital importance to avoid passengers change to other higher emission vehicles. The purpose of this paper is to focus on the decision analysis of the replacement tool for disruption recovery service in urban public light rail systems from the perspective of environmental effect.

The traditional approach – bus replacement service – and the new approach – taxi replacement service – which has been recently adopted by several cities, are examined individually and compared. The benefit of the light rail company is formulated by balancing between carbon emission and financial cost. The involving parties’ decision functions taking the passengers’ behaviors as well as numerous other important factors into account are formulated.

Both theoretical and numerical sensitivity analyses are conducted to shed light for light rail systems to better coping with disruptions, increasing service level, and attracting more passengers to the environmental transit system to reduce carbon emission.

It is worth mentioning that this research is a successful application for disruption recovery in a public transit system considering the environmental effect. To the authors’ knowledge, this research is one of the first of such applications in this area and can be used not only in the public light rail systems, but also in other urban public transport network components such as the subway and rail systems.

To study how supply chain decision makers gather, process and use the available internal and external information when facing supply chain disruptions.

The paper reviews relevant supply chain literature to build an information management model for disruption management. Afterwards, three case studies in the vehicle assembly sector, namely cars, trucks and aircraft wings, bring the empirical insights to the information management model.

This research characterises the phases of disruption management and identifies the information companies use to recover from a variety of disruptive events. It presents an information management model to enhance supply chain visibility and support disruption management at the operational level. Moreover, it arrives at two design propositions to help companies in the redesign of their disruption discovery and recovery processes.

This research studies how companies manage operational disruptions. The proposed information management model allows to provide visibility to support the disruption management process. Also, based on the analysis of the disruptions occurring at the operational level we propose a conceptual model to support decision makers in the recovery from daily disruptive events.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

The purpose of this paper is to investigate different combinations of collaboration strategies to deal with different types of supply chain disruptions, find the best combination, and provide targeting suggestions for investments.

A system dynamics simulation is applied to study a supply chain with three tiers: a producer, a logistics service provider (LSP), and a retailer. There are three types of disruptions to simulate: a producer capacity disruption, an LSP capacity disruption, and a demand disruption. As each tier has the option to choose whether or not to collaborate with the other two tiers, eight (2×2×2) scenarios are generated to represent different combinations of collaboration strategies.

For a producer capacity disruption, both the producer and the LSP should collaborate by providing their surge capacities, while the retailer does not have to collaborate. For an LSP capacity disruption, the producer should not provide its surge capacity, while the LSP should do so; the retailer does not have to collaborate. For a demand disruption, both the producer and the LSP should not provide their surge capacities, while the retailer should not collaborate but play shortage gaming. Targeting suggestions for investments are provided.

Through system dynamics modeling, this study allows the discussion of surge capacity to help supply chain partners and the discussion of shortage gaming when products are oversupplied, in a disruption recovery system over time.

The purpose of this paper is to present a conceptual framework on resilience types in supply chain networks.

Using a complex adaptive systems perspective as an organizing framework, the paper explores three forms of resilience: engineering, ecological and evolutionary and their antecedents and links these to four phases of supply chain resilience (SCRES): readiness, response, recovery, growth and renewal.

Resilient supply chains need all three forms of resilience. Efficiency and system optimization approaches may promote quick recovery after a disruption. However, system-level response requires adaptive capabilities and transformational behaviors may be needed to move supply chains to new fitness levels after a disruption. The three resilience types discussed are not mutually exclusive, but rather complement each other and there are synergies and tradeoffs among these resilience types.

The empirical validation of the theoretical propositions will open up new vistas for supply chain research. Possibilities exist for analyzing and assessing SCRES in multiple and more comprehensive ways.

The findings of the research can help managers refine their approaches to managing supply chain networks. A more balanced approach to supply chain management can reduce the risks and vulnerabilities associated with supply chain disruptions.

This study is unique as it conceptualizes SCRES in multiple ways, thereby extending our understanding of supply chain stability.

Are major and frequent disruptions transforming global supply chains? This study aims to investigate how multinational companies (MNCs) are responding to the phenomenon of accumulated major disruptions in recent years and plausible new paradigm of unstable conditions and environmental uncertainty from a supply chain resilience (SCRES) perspective.

Following an inductive interpretivist approach based on interpretive phenomenology, this study gathers insights from ten MNCs supply chain managers and international consultants who participated as key informants via semi-structured interviews, sharing their experience of the phenomenon. Additionally, secondary sources such as press releases, media articles and industry reports were used for data collection.

Findings include five categories of recovery actions, i.e. levelling, rationing, buffering, bridging and boundary redefining, key strategic changes in competitive priorities, internal organisation and coordination structures, and a hierarchy between SCRES characteristics, integrated in an empirically derived conceptual framework connecting these constructs. This contributes to middle-range theories within SCRES body of knowledge. The authors also identify a set of areas for future SCRES research.

Findings can support MNCs’ supply chain professionals in designing and managing resilient global supply chains, based on learnings from the recent highly disruptive environment, particularly, regarding recovery actions and resilience-building strategic changes contributing to agility and robustness in global supply chains.

Non-positivist interpretive and inductive works are scarce in SCRES research. By adopting this novel approach for this field, the authors broadened the categorisation of responses used in previous works and identified prominent strategic changes and SCRES characteristics and relations among constructs, thus bringing conceptual clarity to SCRES research within the context of the study.