Disruption Recovery Research Articles

Green Container Shipping Schedule Design Considering Collaboration and Disruption Recovery

Green Container Shipping Schedule Design Considering Collaboration and Disruption Recovery

A Machine Learning Approach for Solution Space Reduction in Aircraft Disruption Recovery

A Machine Learning Approach for Solution Space Reduction in Aircraft Disruption Recovery

A prediction-based supply chain recovery strategy under disruption risks

ABSTRACT This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance.

The effects of tie strength and data integration with supply base on supply disruption ambiguity and its impact on inventory turnover

PurposeIn this paper, the authors introduce supply disruption ambiguity as the inability of a sourcing firm to attach probability point estimates to the occurrence of and to the magnitude of loss from supply disruptions. The authors drew on the “ambiguity in decision-making” literature to define this concept formally, connected it to relevant supply disruption information deficit, positioned it relative to supply chain risk assessment and hypothesized and tested its negative associations with both supply base ties and inventory turnover.Design/methodology/approachThe authors analysed survey data from 171 North American manufacturers and archival data for a subset (88 publicly listed) of these manufacturers via Ordinary Least Squares (OLS) estimation after ensuring that methodological concerns with survey research have been addressed. They used appropriate controls and employed the heteroskedasticity-based instrumental variable (HBIV) approach to ensure that inferences from our results are not unduly influenced by endogeneity.FindingsStrong supply base ties decrease supply disruption ambiguity, which, in turn, increases inventory turnover. Moreover, strong supply base ties and data integration with the supply base have indirect and positive effects on inventory turnover. As sourcing firms strengthen ties and integrate data exchange with their supply base, their inventory turnover improves from access to information relevant to detect and diagnose supply disruptions effectively.Originality/valueResearch on supply disruption management has paid more attention to the “disruption recovery” stage than to the “disruption discovery” stage. In this paper, the authors add novel insights regarding the recognition and diagnosis aspects of the “disruption discovery” stage. These novel insights reveal how and why sourcing firms reduce their overall ambiguity associated with detecting and assessing losses from supply disruptions through establishing strong ties with their supply base and how and why reducing such ambiguity improves inventory turnover performance.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

Practising circular economy performance in Malaysia: managing supply chain disruption and technological innovation capability under industry 4.0

ABSTRACT In response to environmental awareness and financial return, manufacturing firms are increasingly concerned about practicing circular economy performance (CEP). The lack of comprehensive evidence on the integrated technology capability in IR4.0 driven based supply chain management literature has motivated this study to investigate how the company manages the disruption of Industry 4.0 technology and its impact on CEP. Data were obtained from 130 Malaysian manufacturing companies. Data were analyzed using structural equation modelling using PLS-SEM. The results showed CEP's positive and significant effect on managing supply chain disruption and technological innovation capability (TIC). Positive relationships prove that CEP has a considerable influence on the manufacturing industry. The mediating results found that the TIC has played a complimentary mediation effect to support the nexus of managing supply chain disruption, supply chain disruption recovery and CEP. Supply chain managers are encouraged to control interference problems and improve effective communication and teamwork.

The role of supply chain collaboration in disruption recovery: a logistics services perspective

The role of supply chain collaboration in disruption recovery: a logistics services perspective

Strategic supply chain alignment: The role of third-party logistics service providers during disruption recovery

Background: Global supply chain complexity and increased logistics outsourcing have made global supply chains more vulnerable to supply chain disruptions (SCDs). The proliferation of network partners has changed the role of outsourced logistics providers to be more strategic. However, this changing role comes with increased responsibility for the coordination and alignment of supply chain partners during supply chain disruption recovery (SCDR). Successful strategic supply chain alignment (SSCA) may improve overall supply chain performance during SCDR by aligning the recovery efforts of supply chain partners.Objectives: This study aimed to investigate the role of third-party logistics service providers (3PLs) in SSCA during SCDR in South Africa.Method: This research employed a generic qualitative design using purposive sampling techniques. Data were collected from five logistics triads that included 3PL, supplier and customer firms using semi-structured interviews.Results: Third-party logistics service providers play various roles in SSCA during SCDR namely transactional, relational, dependency, resilience and more advanced roles. In addition, 3PLs utilise a range of approaches to achieve SSCA during SCDR including using collaborative planning, transparent communication policies and platforms, performance measurement and supply chain visibility.Conclusion: This study expands on current literature by identifying the value-adding roles of 3PLs in SSCA during SCDR and the use of various approaches to achieve SSCA during SCDR in the South African context. For managers, the findings provide insight into the roles of 3PLs and the approaches used to achieve SSCA during SCDR that could increase overall supply chain performance.

A multi-stage supply chain disruption mitigation strategy considering product life cycle during COVID-19.

The global pandemic of COVID-19 has caused severe damage to the supply chain such that manufacturers may face long-term supply disruptions. In this paper, a disruption recovery strategy of a supply chain system is investigated from the perspective of product change, in which the life cycle and design change time of a new product are both considered in order to minimize the losses of manufacturer after disruptions. A mixed-integer linear programming (MILP) model is presented to address the disruption recovery problem for this multi-period, multi-supplier, and multi-stage supply chain system. A two-stage heuristic algorithm is designed to solve the problem. Experimental results show that the proposed disruption mitigation strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the impact of product life cycle in the selection of new product design planning. A sensitivity analysis is performed to ensure the applicability of the model in the actual environment, which illustrates the effect of different parameter changes on the results. This work can help manufacturers establish an optimal recovery strategy whenever the supply chain system experiences supply disruptions.

Managing reputational risk during supply chain disruption recovery: A triadic logistics outsourcing perspective

Background: The coronavirus disease 2019 (COVID-19) pandemic has opened the world’s eyes to the impact that supply chain disruptions have on our society. Supply chain disruptions can result in various long-term effects, of which reputational risk is one of the biggest. A good reputation can create value for all stakeholders of a firm, however it can also expose a firm to risk. Reputational risk has been extensively studied in supply chain management; however, the management of reputational risk during supply chain disruption recovery (SCDR) has been neglected. Objective: This study explores reputational risk management during SCDR, between a logistics triad consisting of third party logistics providers (3PLs), their upstream suppliers and downstream customers within a South African context. Method: A generic qualitative design was employed to collect data from five logistics triads using semi-structured interviews. Results: The study found that reputational risk has a predominantly positive influence on the SCDR process. Furthermore, the study expands on existing literature by identifying additional approaches to manage corporate reputation during SCDR not evident in literature. These approaches include the use of control centres and involvement of the key account manager. Conclusion: This study creates awareness for the importance of reputational risk during SCDR and also provides managers with valuable insight into how reputational risk should be managed during SCDR.

A production inventory model for the deteriorating goods under COVID-19 disruption risk

The rapid onset of the COVID-19 epidemic has brought the manufacturing process to a halt. The problem is especially serious for deteriorating products because demand for these items is not consistent and the product's worth has diminished with time. Many deteriorating product industries are now looking for an appropriate and effective disruption recovery plan to help them recover. However, a survey of the literature suggests that there has been little research done on developing an effective inventory production model for deteriorating products exposed to COVID-19 pandemic risks. This research intends to develop a disruption recovery model that considers demand as a time-dependent quadratic function to find out the optimum number of orders. Two different heuristic algorithms named: Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) have been employed to solve the model and it has been found that WOA performs better in terms of convergence. The numerical findings indicate that the price inclination rate for the component price and selling price played a pivotal role to maximize net profit. It is expected that by employing the proposed model of this research, the industry managers will be greatly benefitted to obtain quick recovery from the COVID-19 disruption risk for the deteriorating goods and retain financial stability.

Green Container Shipping Schedule Design Considering Collaboration and Disruption Recovery

Green Container Shipping Schedule Design Considering Collaboration and Disruption Recovery

A Collaborative Berth Planning Approach for Disruption Recovery

Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).

Sequential recovery of disrupted facilities

For a bipartite graph with disrupted facilities, we confront the key concern that disrupted facilities can only be recovered one after one due to resource shortage. The objective of this paper is to design a sequential recovery schedule such that the summation of waiting time and transportation cost of all customers to regain service is minimized. For such a sequential disruption recovery problem, we consider two situations: sequential disruption recovery for uncapacitated facilities (SDRUF) and sequential disruption recovery for capacitated facilities (SDRCF). We model the corresponding optimization problems as integer linear programs, and propose two heuristic algorithms for each situation, based on both Lagrangian relaxation and greedy method, to generate feasible solutions. In the end, computational experiments are carried out to implement the proposed heuristic algorithms. The computational results show that the two heuristic algorithms are efficient and effective for solving the SDRUF and the SDRCF problems, and act as good alternatives for each other based on problem size.

Agri-food supply chain network disruption propagation and recovery based on cascading failure

In recent years, some extreme weather and public health events have led to frequent disruptions in agri-food supply chain networks in China, and scholars recognize the importance of this problem, but there are relatively few studies on the disruption propagation. Firstly, we construct the agri-food supply chain networks and the weak tie networks, and innovatively introduce weak ties into the disruption propagation of the agri-food supply chain networks. Secondly, we analyze the impact of two strategies, strengthening existing business relationships and establishing new business relationships, on the disruption propagation of agri-food supply chain networks under extreme natural disasters or unexpected wholesale market closures. Based on the results, the impact of disruption recovery on supply–demand relationships in agri-food supply chain network is analyzed. This research provides decision-making reference against supply chain disruptions and related problems.

Quantifying supply chain disruption: a recovery time equivalent value at risk approach

ABSTRACT The global pandemic COVID-19 has disrupted supply chains in many industries all over the world. If not well contained and managed at an early stage, such unprecedented disruptions may lead to even more serious consequences in the era of supply chain reglobalization. The attendant challenge for research and practice is on how to readily measure and quantify the disruption risk. To address the plethora of concerns, this article presents a recovery time equivalent (RTE) disruption risk measurement model using Value at Risk (VaR). We consider a disruption recovery model comprising abrupt, normal, fast, and slow modes. To demonstrate the practical relevance of our study, we establish a case study with a multinational corporation from the IT sector. Decision makers and supply chain managers can use the model to conduct ‘what-if' analyses on their supply chain vulnerabilities and risks for a more proactive business continuity planning and contingency management.

Managing Supply Chain Disruption Recovery: The Role of Organizational Justice

AbstractSupply chain disruptions negatively impact the economy and individual organizations. However, as many companies recover from disruptions (COVID-19 being a recent example), less attention has been paid to how these events impact business-to-business (B2B) relationships characteristic of supply chains. Drawing on justice theory, we examine how the suppliers’ management of the recovery process affects behavioral reactions in the context of supply chain relationships. This study empirically examines the role that partners’ recovery process, honesty, effort, availability, and outcome fairness play in influencing satisfaction, future business volumes, and word-of-mouth. Using ordinary least squares (OLS) regression to test survey data, results indicate that how recovery processes are managed positively affects satisfaction, reduces the likelihood of future business loss, and affects the propensity to communicate negatively about a relationship partner. Unexpectedly, outcome fairness (distributive justice) has a significant positive impact on negative word-of-mouth, indicating that increased perceptions of distributive justice actually increase negative behavioral outcomes in certain settings. We conduct exploratory post hoc serial mediation analysis to further examine this finding and uncover a potential extension to the boundaries of a justice lens. These post hoc results, which generally confirm the OLS results, drive opportunity for better understanding of complaining behavior and navigating the tricky environment associated with managing B2B relationships in a post- disruption environment.

Dynamics of disruption and recovery in air transportation networks

Flight delays occur in the air transportation system when disruptive events such as weather, equipment outage, or congestion create an imbalance between system capacity and demand. These cycles of disruptions and subsequent recoveries can be viewed from a dynamical systems perspective: exogenous inputs (convective weather, airspace restrictions, etc.) disrupt the system, inducing delays and inefficiencies from which the system eventually recovers. We study these disruption and recovery cycles through a state-space representation that captures the severity and spatial impact of airport delays. In particular, using US airport delay data from 2008 to 2017, we first identify representative disruption and recovery cycles. These representative cycles provide insights into the common operational patterns of disruptions and recoveries in the system. We also relate these representative cycles to specific off-nominal events such as airport outages, and elucidate the differing disruption–recovery pathways for various off-nominal events. Finally, we explore temporal trends in terms of when and how the system tends to be disrupted, and the subsequent recovery.

A supply chain disruption recovery strategy considering product change under COVID-19

A recent global outbreak of Corona Virus Disease 2019 (COVID-19) has led to massive supply chain disruption, resulting in difficulties for manufacturers on recovering their supply chains in a short term. This paper presents a supply chain disruption recovery strategy with the motivation of changing the original product type to cope with that. In order to maximize the total profit from product changes, a mixed integer linear programming (MILP) model is developed with combining emergency procurement on the supply side and product changes by the manufacturer as well as backorder price compensation on the demand side. The model uses a heuristic algorithm based on ILOG CPLEX toolbox. Experimental results show that the proposed disruption recovery strategy can effectively reduce the profit loss of manufacturer due to late delivery and order cancellation. It is observed that the impact of supply chain disruptions is reduced. The proposed model can offer a potentially useful tool to help the manufacturers decide on the optimal recovery strategy whenever the supply chain system experiences a sudden massive disruption.

The Effects of Different Supply Chain Integration Strategies on Disruption Recovery: A System Dynamics Study on the Cheese Industry

Long and complex supply chains are vulnerable to disruptions. One way to solve this problem is to successfully manage supply chain integration (SCI). A system dynamics simulation is thus applied to study a cheese supply chain with three individual firms: a producer, a logistics service provider (LSP), and a retailer. Our purpose is to study the effects of SCI strategies with different dimensional focuses, i.e., information integration (Scenario 1), relational integration (Scenario 2), and operational integration (Scenario 3), on the recovery of three types of disruptions, i.e., a producer capacity disruption, an LSP capacity disruption, and a demand disruption. Tests of parameter scenarios are further applied to provide solutions for supply chains using different strategies. Our results indicate that Scenario 3 is the best practice, regardless of any type of disruption, while Scenario 1 usually achieves the worst performance. This is consistent with an evolutionary perspective of supply chain integration: information integration gives firms a competitive advantage as the first step. Working as partners to share the most appropriate information leads to greater benefits. We extend this perspective by showing that further elimination of information delay helps the supply chain achieve the best performance.