Disruption Management Research Articles

Cost Optimization in Disruption Conditions: A Case Study in Small Medium Enterprise

Purpose: The objective of this study was to design a cost optimization model that offers production improvement for SMEs. Theoretical framework: Several studies related to production system disruption management have been conducted, with the majority focusing on large companies. However, small and medium enterprises (SMEs) have limitations compared to large companies. Repairability is considered for cost optimization. Design/methodology/approach: This research designed a cost optimization model that offers production improvement with repairability process for SME. Findings: There is a need for repairability given the disruption caused by defective products in SMEs. There is a clear difference in total profit between the current state without repairability and proposed conditions with repairability. SMEs suffer massive losses in the absence of repairs, assuming they do not consider repairing defective products with a production defect rate of approximately 15%. The current state produces many downgraded products. However, repairability still needs to be improved to increase profits. Research, Practical & Social implications: The study implied that there is a need to consider repairability for product defects at SMEs, especially those with a 15% product defect rate. The use of the proposed model optimizes profit and is designed to increase production capacity based on product improvements. Repairability was considered in this research, considering that SMEs are more susceptible to disruptions compared to large companies. Originality/value: The novelty of this paper is adding process repairability to the cost optimization model for SMEs in the textile sector, then considering the product downgrade under the conditions in SMEs.

A general metro timetable rescheduling approach for the minimisation of the capacity loss after random line disruption

This study proposes a generic metro timetable rescheduling method for the minimization of the capacity loss that integrates spatial and temporal information on random line disruptions and time-varying characteristics of passenger flows. The proposed emergency operating rules can be immediately deployed after a random disruption using one crossover track. The spatiotemporal information of a disruption and the current state of the line are integrated into a metro disruption management (MDM) model, which considers deviated from the original schedule and the number of stranded passengers as optimization objectives. An iterative meta-heuristic for the general metro rescheduling (IMH-GMR) algorithm is developed to flexibly classify an accident and determine rescheduling solutions for the MDM model within an effective running time (e.g., 15-60 sec). Test results show that the line capacity loss is significantly reduced (94.95%) compared with the total loss caused by the accident disposal in the test scenario.

Let’s talk about bad experiences instead of forgetting them: An empirical study on the importance of memory for supply chain disruption management

Few studies have been concerned about the role of previous knowledge in supply chain disruption management. Based on the premise that supply chain resilience and robustness impact supply chain performance, this paper aims to expand this knowledge by conceptually and empirically establishing the role of supply chain memory for supply chain disruption management performance. A survey receiving 290 valid responses was conducted with supply chain managers. Quantitative data were analyzed with structural equation modeling. In addition, a mixed-methods approach was conducted to validate and explore the quantitative results. This paper contributes to theory and practice showing that experience, familiarity, and knowledge about dealing with disruptions improve supply chain resilience and robustness efficiency. The moderation effect of memory reveals that high memory leverages the impact of supply chain resilience on supply chain performance. Moreover, it brings important insights into the impact of robustness on supply chain performance since it is conditional to memory levels. The full model was able to explain 37.19% of supply chain performance.

Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic

PurposeAmid the outbreak of coronavirus disease 2019 (COVID-19), supply chains have faltered. This has influenced operational and financial performance and lead to uncertainty in supply and distribution. Therefore, systems measuring supply chain risk and disruption management performance have gained interest. This study explores barriers to supply chain performance measurement during disruptions such as COVID-19.Design/methodology/approachBased on a survey and literature review, the authors formalise the barriers and rank them using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) fuzzy Višekriterijumsko kompromisno rangiranje (VIKOR) methodology. A total of 14 experts in 11 countries were surveyed. Performance measurement is based on a balanced scorecard.FindingsFifteen barriers are identified. Based on DEMATEL analysis, the cause–effect relationships among the four scorecard aspects are explored. The customer axis is revealed as the cause, while the financial, internal business and learning/development are identified as the effect of the supply chain performance measurement. Fuzzy VIKOR calculations show that uncertainty of investment, disrupted cash flows and the bullwhip effect are the most critical barriers to measure supply chain performance during outbreaks such as the COVID-19 pandemic.Research limitations/implicationsThe study identifies and ranks general barriers; additional research is required to differentiate barriers in specific industrial sectors.Practical implicationsThe findings may help develop proactive, resilient supply chain performance strategies to overcome disruptions.Social implicationsPolicy-makers and decision-makers in industrial and service firms can explore these findings to inform strategies for robust supply chains that can resist disruption in risky environments.Originality/valueThis research addresses a knowledge gap in barriers to measure supply chain performance in post-pandemic areas. It is unclear how far firms will measure supply chain performance in terms of learning from disruption patterns, managing financial and customer demand processes in light of COVID-19. This study contributes by explaining the influences among the barriers and exploring them, offering insights from multiple stakeholders.

Credibility-based cascading approach to achieve net-zero emissions in energy symbiosis networks using an Organic Rankine Cycle

Industrial symbiosis (IS) integrates multiple industries to share resources (e.g., energy, water, by-materials) for economic and environmental reasons. Energy demand is dynamic, imposing financial and logistical barriers to achieving Circular Energy (CEn). This paper addresses this by proposing a new framework that integrates central heating networks of an Organic Rankine Cycle (ORC) system to recover unused low-temperature energy. The framework is formulated as a stochastic programming problem to capture the uncertainty of some parameters to reduce the costs arising from fluctuations in energy supply and demand and, subsequently, wasted energy. Disruption management is also applied to handle the risk of suppliers becoming unavailable due to unforeseen events. The proposed multi-objective mixed-integer linear programming model optimizes the conflicting sustainability value preferences in the IS-based CEn network. The paper presents a novel robust possibilistic programming technique to control the epistemic uncertainty of the developed mode and develops a robust variant of the augmented ε-constraint algorithm (AUGMECON-R) to obtain the Pareto optimal solutions. The results provide a new platform for reducing the undesirable effects of worst-case energy symbiosis scenarios. The results show that using ORCs in an IS network increases the economic benefits of such a system by about 20% and reduces customer dissatisfaction from energy supply disruptions by about 27%.

Disruption management-based coordinated scheduling for vessels and ship loaders in bulk ports

For ensuring the orderly operation of the port, it is vital to coordinately schedule available ship loaders and vessels that plan to enter and exit the port when ship loaders are unable to work due to faults. Therefore, this paper studies the coordination between vessels and ship loaders scheduling problem affected by failed ship loaders (VSLPB), and proposes a novel disruption management-based method to address this problem. An innovative optimization model is developed to reduce the generalized cost with the constraints of disruption management strategies (DMS), aiming to minimize the impact of failed ship loaders on the coordinated scheduling and the bulk cargo handling efficiency. For solving the VSLPB, an effective two-stage row generation (TSRG) algorithm is developed. In the first stage, the disruption conditions in the model are released to find the available ship loaders and berths for vessels affected by the failure factors. In the second stage, the optimal strategy is sought among multiple DMS to minimize the objective function value. Using the proposed method in Huanghua Coal Port as a case study, the results show that our method can effectively solve the impact of ship loader failure on the efficiency of bulk cargo handling and the efficiency of vessels entering and leaving the port. These further highlights the importance of implementing DMS, and show that the proposed method can provide an efficient and reliable solution for port production and operation to deal with disruption problems. Furthermore, the proposed method in this paper can help improve the ability of the port to resist uncertain factors, thus improving the ability of the entire supply chain to resist risks.

Catastrophe insurance and flexible planning for supply chain disruption management: a stochastic simulation case study

Catastrophe-related insurance (e.g. business interruption insurance) is an effective financing tool for global corporations to reduce economic losses caused by high impact events. Flexible operational planning is an often-used tool enabling rapid adjustment of operational plans for reducing catastrophe-related damage costs. The interaction between catastrophe insurance and flexible operations planning has rarely been studied. In this paper, we develop a stochastic programming model for a multi-echelon global supply chain network that we solve to investigate the impact of purchasing catastrophe insurance on supply chain operational planning in a catastrophe-prone environment. Computational simulations are developed for evaluating solution quality and measuring catastrophe-related damage costs. We find that it may be optimal for supply chains to scrap redundant products in catastrophes when customer demand falls below the expected level. Purchasing catastrophe insurance may encourage supply chains to scrap more products, which results in more catastrophe-related damages. From analysing supply chain costs, catastrophe-related damage costs, and operational plans, we find that a higher compensation rate of catastrophe insurance triggers more production activities being planned at the vulnerable node just before the vulnerable time period, especially for low residual value products. Finally, we give managerial insights to help reduce unnecessary damages in catastrophes.

Dealing with disruptions in railway track inspection using risk-based machine learning

Unplanned track inspections can be a direct consequence of any disruption to the operation of on-board track geometry monitoring activities. A novel response strategy to enhance the value of the information for supplementary track measurements is thus established to construct a data generation model. In this model, artificial (synthetic) data is assigned on each measurement point along the affected track segment over a short period of time. To effectively generate artificial track measurement data, this study proposes a NARX (nonlinear autoregressive with exogenous variables) model, which incorporates short-range memory dependencies in the dependent variable and integrates interdependent effects from external factors. Nonlinearities in the proposed model have been determined using an artificial neural network that allowed fast computation of a mapping function in line with the needs of effective disruption management. The risk of over fitting the data generation model, which reflected its generalisation ability, has been effectively managed through risk aversion concept. For the model evaluation, the deviation of track longitudinal level has been taken as a case study, predicted using its degradation rate and track alignment and gauge as exogenous variables. Simulation results on two datasets that are statistically different showed that the data generation model for disrupted track measurements is reliable, accurate, and easy-to-use. This novel model is an essential breakthrough in railway track integrity prediction and resilient operation management.

The impact of using digital technologies on supply chain resilience and robustness: the role of memory under the covid-19 outbreak

PurposeThis paper’s main aim is to check the mediating effect of supply chain memory in the relationship between using digital technologies and both supply chain resilience and robustness. In addition, the impact of the COVID-19 disruption was tested as a moderator of the impact of supply chain memory on supply chain resilience and robustness.Design/methodology/approachAltogether, 257 supply chain managers answered the questionnaire, and data were analysed through structural equation modelling.FindingsThis paper contributes to theory and practice by demonstrating that the experience, familiarity and knowledge to deal with disruptions partially mediate the relationship between digital technologies, resilience and robustness. Moreover, our results show that memory is less efficient for the supply chain to maintain an acceptable level of performance in case of a new extreme disruptive event like COVID-19. The full model was able to explain 36.90% of supply chain memory, 41.58% of supply chain resilience and 46.21% of supply chain robustness.Originality/valueThe study helps to understand how to develop supply chain memory, positioning digital technologies as an antecedent of it. The impact of supply chain memory on supply chain resilience and robustness is proved. Knowledge about the impact of industry 4.0 technologies on disruption management is quantitatively improved. It demonstrates that digital technologies impact resilience and robustness mainly through supply chain memory. The study proves that supply chain memory is less efficient for the chain remains effective when a non-routine disruptive event occurs, but it is still imperative to recover from it.

For an Effective Disruptions Management in Rail Transport: Case Study Tramway of Constantine, Algeria.

For an Effective Disruptions Management in Rail Transport: Case Study Tramway of Constantine, Algeria.

IMMUNER: Integrated Multimodal Mobility Under Network Disruptions

Targeting at improving door-to-door accessibility, efficiency and reducing environmental impact, recent decades have witnessed vigorous development of multimodal transport. Coupled through passenger transfer, the failure of one mode, however, is likely to cause cascading disruptions to the complete system. Therefore, to maintain safe and efficient operations, the integrated recovery of the multimodal transportation system becomes an important, timely topic, given increasingly complex schedule interactions. This study proposes a mathematical model to handle the integrated recovery problem of multimodal transportation network by minimizing the passenger travel cost and mode recovery cost under failures of multiple critical infrastructures. For a case study on air/HSR interaction in China, our model incorporates decisions in terms of aircraft recovery, railway timetable rescheduling, and passenger routing with realistic constraints (e.g. max travel time, max transfer time). A column-and-row generation based heuristic solution algorithm is developed in order to solve the complex model with given computational budgets. Results reveal that real-world test disruption scenarios can be solved within 20 minutes with a small relaxation gap. A significant amount of passenger traveling time can be reduced in contrast to a sequential recovery approach with independent reaction decisions for each transport mode. This work contributes towards the development of support tools for integrated disruption management of multimodal transportation.

COVID-19 and emerging markets: A strategic framework for pharmaceutical supply chains

The main aim of this study is to develop a strategic framework to address COVID-19-induced supply chain disruptions within the South African pharmaceutical industry. This study explores the perceptions of 25 pharmaceutical supply chain (PSC) professionals in South Africa. Using semi-structured interviews, the study elucidates strategies such as research and development (R&D), marketing strategies, collaborative supply chain disruption management, and local production that can be instrumental in overcoming PSC disruptions. Results underscore the importance of local R&D initiatives and the integration of digital technologies for enhanced resilience (Ivanov & Dolgui, 2020a; Paul & Chowdhury, 2021). Effective communication, local manufacturing, and strong relationships with suppliers and customers were highlighted as key marketing strategies. Collaborative approaches, including information sharing and joint planning, were deemed crucial, along with the potential of local production to reduce dependence on foreign suppliers (Ivanov, 2020; Socal et al., 2021). This strategic framework can significantly contribute to mitigating the impact of COVID-19 and future disruptions on PSCs in South Africa and other emerging markets. This research contributes a practical, industry-informed strategic framework that addresses a major gap in managing PSC disruptions during global crises. Further research is required to explore the specific interventions and policies that facilitate these strategies (Modisakeng et al., 2020).

Standard Disruption Management Policy at Mactan-Cebu International Airport

Standard Disruption Management Policy at Mactan-Cebu International Airport

Application of Machine Learning algorithms in Supply Chain Disruption Management: An Indian MSME Perspective

Application of Machine Learning algorithms in Supply Chain Disruption Management: An Indian MSME Perspective

A Real-Time Decision Support Approach for Managing Disruptions in Line-Haul Freight Transport Networks

Unexpected disruptions in road freight transport caused by poor weather conditions, traffic accidents, etc., are quite frequent and have negative effects on the whole supply chain. Therefore, an intelligent disruption management system is necessary to revise the transport plan directly after disruptions have occurred. The literature presents several approaches for managing disruptions in road freight transport. However, most of them focus on urban freight distributions where disruptions are often handled by vehicle rerouting. The current work, in contrast, addresses disruption management in a line-haul freight transport network that connects urban distribution systems. We present a novel hybrid approach combining a simulation model, optimization algorithms, and a cost-effectiveness analysis. When a disruption occurs, the proposed approach can be used to analyze the impacts of the disruption, identify the affected trips, and revise their plan quickly in real time. Six re-planning strategies are proposed to handle the disruptions and are evaluated in terms of cost, reliability (expressed in time delays), and CO2 emissions. Cost-effectiveness analyses are conducted to rank the obtained solutions and identify the best strategy. Moreover, we suggest a decision support system architecture, based on the proposed approach, to enable disruption management in real-time settings. Real data is used to evaluate the proposed approach in different disruption scenarios. The results provide transport planners with useful insights into possible re-planning strategies and how to identify the best cost-effective strategy to minimize the disruptions’ effects and be more economically sustainable. This work also supports carriers in the transition towards intelligent disruption management.

Teachers’ changing practices for “disruption management” in the classroom

The purpose of this article is to present the main aspects of teachers’ responses to students’ disruptive behaviours during lessons, analysed in the context of changes that are taking place in the approach to institutional practices in this area. In the modern school, effective “disruption management” is a considerable challenge for the teacher, but also a priority. Teachers are under tremendous pressure as their responses to student misbehaviour are evaluated and are a marker of their competence. Two different ways of conceptualizing practices to correct disruptive student behaviour are compared. The changes offered by the new approach to methods of reducing undesirable classroom disruptions seem very promising, but also impose new responsibilities on the teacher. The review of the literature presented in this article aims to prepare a solid ground for research in this area. Increased knowledge on how to respond to disruptive student behaviour in everyday teaching situations can also be valuable for teacher training institutions and thus contribute to improving the quality of education.

Reliable container supply chain under disruption.

In this study, we develop a reliable formulation based on a container network flow problem, along with the full implementation of an empty container management strategy in the context of an integer linear programming model. The proposed approach can play a key role in coping with disruption in the network and can offer a proactive measure for effective disruption management to maintain a stable level of reliability in supply capability. To formulate a reliable container network problem, we design the pattern of disruption, a rare and irregular uncertainty, based on binomial coefficients in the objective function. In this way, flow interruption due to disruption can be expressed in node- and arc-failures and can be properly managed. Furthermore, we provide a non-disruptive model based on a deterministic formulation derived from a bast-case scenario. Through numerical illustrations and sensitivity analyses, we conduct in-depth analyses on the impact of disruption in the container supply chain, and a benchmark model based on a bast-case scenario is used to determine disruption costs, for comparative study. In particular, the numerical experiments show that if both maritime and hinterland disruptions are not managed in advance, disruption costs derived from a benchmark model result in a significant surge according to increasing potential disruption risk. Throughout computational experiments, we also found that maritime disruption is more destructive to container supply capability than hinterland disruption is. In particular, critical findings show that when a certain level of threshold is violated, the proposed strategies are completely interrupted in a container supply chain. Therefore, proactive measures to keep up a reliability of container supply in a high-risk region are highly recommended for management side.

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.

Playing politics or playing right: Impacts of reputation-seeking on short-term disruptions management

When disruptive events cripple a firm's operational capacity, the decision-making dynamics relating to recovery management become complex. In this study, we examine how the reputation-seeking behavior of managers involved in disruptions management impacts their recovery decisions. We explore this issue from both an analytical and an empirical perspective. Our results from our analytic models demonstrate how recovery decisions made by a reputation-seeking manager differ from those by an efficiency-seeking manager (i.e., a manager who uses a cost-optimizing decision calculus). To test our theoretical findings, we collected data from senior executives and managers to obtain 281 usable responses. Our empirical analysis corroborates our theory, and shows that reputation-seeking managers, relative to their peers who are efficiency-seeking, recover capacity at a higher level. Managers who have a greater propensity for reputation seeking give higher directives to, and participate less in teamwork with, their subordinates during recovery from disruptive events. We also find that the directives stipulated by reputation-seeking managers for their subordinates (i.e., the work demanded from the subordinates) depend on the effort level of subordinates. Finally, we discuss the implications of these findings and highlight avenues for further research on the relationship between reputation-seeking behaviors and disruptions management.

Airline maintenance task rescheduling in a disruptive environment

Airline maintenance task scheduling takes place in a disruptive environment. The stochastic arrival of corrective maintenance tasks and changes in both fleet and resource availability require schedules to be continuously adjusted. An optimal schedule ensures that all tasks are executed before their due date in both an efficient (at minimum use of ground-time) and a stable (limited number of schedule changes) manner. This paper is the first study to address disruption management for the hangar maintenance task scheduling problem, proposing a practical and efficient modeling framework. The framework comprises a mixed integer linear programming model for airline maintenance task rescheduling in a disruptive environment, in which task scheduling is constrained by the availability of resources. The model’s capabilities include creating and adjusting maintenance schedules continuously and dynamically reacting to new information when this becomes available. The modeling framework was tested in a case study provided by a large airline, and its performance was compared to the current practice of the airline. The results show that the proposed approach produces more efficient and stable results. A 3% ground time decrease was achieved, while the number of schedule changes in the last days before operations was decreased by more than half.