Disruption Costs Research Articles

The costs of maritime supply chain disruptions: The case of the Suez Canal blockage by the ‘Ever Given’ megaship

In March 2021, the six-day blockage of the Suez Canal, caused by the grounding of Evergreen's ‘Ever Given’ containership, created chaos in global trade. The 400-m giant lodged horizontally in a 265-m wide stretch of the canal and the efforts to dislodge and refloat it were unprecedented, involving dredging, towing and lightering. The accident marked one of the most severe disruptions at a key chokepoint in the international shipping network. Using ship voyage data, this research introduces a model to quantify the economic losses of a carrier's containership fleet, caused by such a disruption. The studied impacts include ship costs, environmental costs, and inventory-carrying costs. The model is applied to Maersk Line's East-West network, with 69 vessels (0.84m TEUs) affected by the blockage, either by having to reroute via the Cape of Good Hope or by the delays caused during and after the blockage. The results point to an additional 44,574 tonnes of CO2 produced by the extended trips and extra waiting times of the Maersk ships. The total losses incurred amount to $88.79m, comprising ship costs of $8.04m, environmental costs of $4.46m and, most strikingly of all, inventory-carrying costs of $76.29m, stemming from the high value of goods onboard ($26.5bn). Ship deviations also resulted in revenue losses for the Suez Canal Authority (SCA) of $5.86m, from Maersk crossings alone. Additionally, the research findings shed light on the vulnerabilities of maritime supply chains, particularly concerning prolonged roundtrips, changes to port call patterns, and extended cargo delivery times.

Integrating machine learning and deep learning for enhanced supplier risk prediction

The importance of anticipating and preventing disruptions is underscored by the increased operational complexity and vulnerability caused by advancements in supply chain management (SCM). This has spurred interest in integrating machine learning (ML) and deep learning (DL) into supply chain risk management (SCRM). In this paper, we introduce a tailored method using ML and DL to improve SCRM by predicting supplier failures, thus boosting efficiency and resilience in SC operations. Our method involves five phases focused on classifying and predicting supplier failures in non-conforming deliveries. This involves forecasting failure quantities and estimating total disruption costs. Initially, data from an automotive company is selected, and appropriate potential features and algorithms are selected, performance metric aligns with case study objectives, facilitating method evaluation are used such as: Precision, recall, F1-score, and accuracy metrics assess classification models, while Mean Squared Error (MSE) is used for regression tasks. Finally, an experimental design optimizes models, assessing success rates of various algorithms and their parameters within the chosen feature space. Experimental results underscore the success of our methodology in model development. In the classification task, the Random Forest (RF) classifier achieved 86% accuracy. When combined with the Gradient Boosting classifier, the ensemble exhibited enhanced accuracy, highlighting the complementary strengths of both algorithms and their synergistic impact, surpassing the performance of RF, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). Noteworthy is the performance in regression tasks, where Linear Regression, ANN, and RF Regressor displayed exceptionally low MSE compared to other models.

Creep slope estimation for assessing adhesion in the wheel/rail contact

AbstractThe UK rail network is subject to costly disruption due to the operational effects of adhesion variation between the wheel and rail. Causes of this are often environmental introduction of contaminants that require a wide‐scale approach to risk mitigation such as defensive driving or rail‐head maintenance. It remains an open problem to monitor the real‐time status of the network to optimise resources and approaches in response to adhesion problems. This article presents an on‐vehicle monitoring method designed to estimate the coefficient of friction by processing data from on‐board sensors of typical rail passenger vehicles. This approach uses a multi‐body physics analysis of a target vehicle to create estimators for both creep force and creep, allowing a curve fitting approach to estimate the coefficient for friction from the creep curves.

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study

The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11 % additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.

Unleashing synergy: exploring the impact of experiential learning and component technology on integration implementation strategy in cross-border technological acquisitions

PurposeDrawing on organizational design theory and organizational learning theory, this paper aims to examine component technology (CT) and the interaction between CT and experiential learning (EL) effects on the degree of integration (DI) of cross-border technological acquisitions.Design/methodology/approachUsing a sample of 267 firms consisting of 229 acquirer firms who started cross-border technological acquisitions from developed economies and 38 acquirer firms who initiated cross-border technological acquisitions from emerging economies over the period of 1993–2016, this study adopts a value chain framework to measure the acquirers’ acquisition integration degree for the investigation of the effects of CT and the interaction between CT and EL.FindingsFirst, this paper finds CT in cross-border technological acquisitions exerting a positive influence on the acquirer firm’s likelihood of the DI implementation, in line with the organizational design theory. Second, in view of organizational learning theory, this study finds EL and the combined effect of CT and EL to have an inverse influence on the DI.Practical implicationsThe results imply that the moderating role of EL significantly optimizes decision choices for an acquirer firm for integration implementation strategies in the form of DI, such as full integration (structural integration), partial integration and no integration (structural separation), which appears to be crucial for cross-border technological acquisitions.Originality/valueThis study contributed to international business strategies by shedding light on the importance of the DI for an acquirer firm that undertakes a cross-border technological acquisition with a CT target firm. This study explains why structural integration might be necessary in cross-border technological acquisitions regardless of the costs of disruption it imposes, as well as the contexts in which it becomes less important or unnecessary. The study disclosed that the increase in the likelihood of DI because of CT depends on the EL of the acquisition company in the host country environment and fluctuates with the prior acquisition knowledge and EL of the host country. Combining two cross-border technological acquisition’s literature streams, such as CT and EL, this study enlightens the importance of organizational learning theory and theory of organization design strategic direction making on acquisition integration implementation strategies.

Perception of undergraduate students of Nigeria’s premier university on the use of emergency remote teaching during Covid-19

This study reports preliminary findings on undergraduate students’ perception of the Emergency Remote Teaching (ERT) at the University of Ibadan, Nigeria, during Covid-19. Proportional to size sampling was used in obtaining the sample size of 366, while respondents were selected using convenience sampling. Data collection was through a structured questionnaire containing both open and closed ended questions and data was collected on the factors - attitude, affect, motivation; perceived behavioural control (accessibility, self-efficacy, ease of use,); and cognitive engagement. Findings showed that students’ positive attitude towards face-to-face learning was significantly higher than towards ERT and correlation analysis showed a relationship between preference for face-to-face learning and struggling with ERT. Also, the average difference in students’ motivation for learning before and after commencement of ERT was not statistically significant. Students reported more frequent use of educational technologies after ERT commenced than before and the difference was statistically significant. Increase was reported in all the self-efficacy and cognitive engagement constructs. Positive changes reported were mainly acquisition of additional hobbies as well as business, social and technology skills. Challenges to online learning included poor power supply, cost of data subscription, network disruptions and distractions from family and friends.

Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks

The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This combination produces higher performance in detecting known DDoS attacks using supervised ensemble and for zero-day DDoS attacks using an unsupervised ensemble. The unsupervised ensemble, which employs novelty and outlier detection, is effective in identifying prior unseen attacks. The ensemble framework is tested using three well-known benchmark datasets, NSL-KDD, UNSW-NB15, and CICIDS2017. The results show that ensemble classifiers significantly outperform single-classifier-based approaches. Our model with combined supervised and unsupervised ensemble models correctly detects up to 99.1% of the DDoS attacks, with a negligible rate of false alarms.

Assessing the trade–connectivity linkages in South Asia in post-pandemic period: an empirical investigation

The study unravels the relationship between trade and connectivity and presents a narrative to re-energise the South Asian regional economic integration in the post-pandemic period. In addition, the study also examines the effect on conflicts and institutional factors in South Asian trade. The study uses the bilateral gravity analysis in 2SLS (Two-Stage Least Squares) framework to address the endogeneity issues. The study finds that South Asia may gain enormously if they reduce the conflicts across the border and secure improvement in both hard and soft infrastructure. Given that the trade is a dynamic concept, connectivity disruption costs cause havoc to the region, which then promotes dis-integration. Facilitating business beyond the border in post-pandemic requires an enhanced trade facilitation and connectivity in the region. The renewed and shared agenda of the South Asian regional cooperation in post-pandemic should, therefore, aim to reduce both intra- and inter- regional trade facilitation gaps as well as to expand the connectivity. The process of South Asian regional integration has to contribute to narrowing the gaps by providing resources for the development of trade infrastructure. Therefore, South Asia has to enhance its own connectivity and trade facilitation arrangement to take forward the agenda of South Asian integration.

THE COST OF TRADE DISRUPTIONS AT DIFFERENT STAGES OF DEVELOPMENT

AbstractWe study trade disruptions at different stages of development in a two‐country, three‐sector model of Spain and United Kingdom from 1850 to 2000. The impact of trade disruptions depends on trade openness and the productivity gap between countries. A trade collapse today (more openness, less gap) comparable to the Inter‐War Trade Collapse (IWTC) decreases the capital stock threefold (12% instead of 4%) and lifetime consumption fourfold (1.58% instead of 0.37%). Capital accumulation amplifies the cost of trade disruptions. The IWTC promoted Spanish industrialization, while the opposite would be true today.

Mitigating closed-loop supply chain risk through assessment of production cost, disruption cost, and reliability

Modern supply chains are affected by high disruption risks owing to their production complexity and globalization characteristics. In addressing supply chain disruption risks, we observed mutual conflict among reliability, disruption costs, and production costs. Therefore, the relationship among production costs, disruption costs, and reliability must be balanced to accommodate supply chain disruptions more effectively. This study establishes a four-tier closed-loop supply chain multi-objective optimization model based on a propagation structure. This study is the first to unify these three objectives for supply chain optimization. Additionally, the artificial bee colony (ABC) algorithm and gradient descent method are combined for the solution. To demonstrate the effectiveness of this method, the classical ABC algorithm is compared with a simulated annealing algorithm through a case study in the semiconductor industry. Our research findings indicate that considering the industrial applicability of the semiconductor industry, increasing supply chain reliability and disruption cost weights, and assigning overall supply chain orders from a holistic perspective can effectively address supply chain disruption risk.

The potential for disruption to the entire inventory management function schedule

In the framework of working capital, inventories hold the most strategic position among business enterprises. It comprises the most substantial portion of current assets in the majority of businesses. The effective management of inventory within the context of working capital is particularly concerning because inventories obstruct about two-thirds of the current assets of cement manufacturers. In the event of a calamitous event, the initiatives worsen supply chain disruptions, thereby introducing a novel compromise between resilience in the face of disruptions and effectiveness in the course of regular operations. When making stocking decisions, operational disruptions are taken into account. These are different from demand uncertainties in terms of the risks they pose because they last longer and stop production flow. As a consequence, operational disruptions can be considerably more catastrophic, despite their low probability of occurring. We use stochastic simulation to find insights that can be used in managing inventory when there is a risk of disruption. These insights come from combining the newsvendor and order-up-to models, which look at the costs of uncertain demand, with catastrophe models, which look at both the cost of a supply disruption and the cost of recovery. These insights pertain to the management function schedule, which is the subject of discussion in this paper.

Interdependent security games in a unidirectional network.

We consider directed tree networks with a single source, where there exists a positive probability of a disruptive event at any node. Such networks model security considerations in pipelines as well as in unidirectional digital networks. If a disruptive event occurs at a certain node, that node and its downstream nodes incur economic losses. Users thus have an incentive to invest in upstream locations as well as their own sites to reduce the probability of a disruptive event. The initial model we develop to reduce the expected investment plus disruption costs is a multiplicative model for which closed-form solutions cannot be obtained in general. We overcome this problem with an additive model that we show closely approximates the initial formulation. This model reduces the security problem to a public goods setting where we minimize the total expected cost at each node. The users then need to share these costs in an equitable fashion, which gives rise to a set of cooperative games. For the case where disutilities to all users are identical, the Shapley value can be computed efficiently, along the lines of an Airport Game. We also treat the case where risk reduction and disutility vary across the network. Finally, we prove that the cooperative game is concave in this general case, which guarantees that the core of the game is nonempty and that the Shapley value is an element of the core.

Assessing the Regional Economic Ripple Effect of Flood Disasters Based on a Spatial Computable General Equilibrium Model Considering Traffic Disruptions

With growing regional economic integration, transportation systems have become critical to regional development and economic vitality but vulnerable to disasters. However, the regional economic ripple effect of a disaster is difficult to quantify accurately, especially considering the cumulated influence of traffic disruptions. This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect. A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system, and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster. The model is applied to the 50-year return period flood in 2020 in Hubei Province, China. The results show the following. First, when traffic disruption costs are considered, the total output loss of non-affected areas is 1.81 times than before, and non-negligible losses reach relatively remote zones of the country, such as the Northwest Comprehensive Economic Zone (36% of total ripple effects). Second, traffic disruptions have a significant hindering effect on regional trade activities, especially in the regional intermediate input—about three times more than before. The industries most sensitive to traffic disruptions were transportation, storage, and postal service (5 times), and processing and assembly manufacturing (4.4 times). Third, the longer the distance, the stronger traffic disruptions’ impact on interregional intermediate inputs. Thus, increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.

Microchips and sneakers: Bilateral trade, shifting power, and interstate conflict.

Strong commercial ties promote peace as states shun the opportunity costs of economic disruption. However, trade also enriches and empowers states, rendering them more capable of enforcing long-term settlements. Given economic disruption does not last forever, countries can be incentivized to trade short-term economic losses for long-term political or territorial gains. This trade-off can restrict or even reverse the pacifying effect of commerce as it renders states incapable of committing to existing peaceful deals. I argue the scope condition hinges on the existing power imbalance and the security externalities of trade, defined as states' abilities to translate trade gains into (potential) military power. For countries where the existing power gap is not extreme, the impact of bilateral strategic trade is contingent upon a country's trade externality relative to its opponent's. Although increased bilateral trade can be peace-promoting when the relative externality is small, the pacifying effects can dissipate as a relatively weaker state becomes more capable of exploiting trade gains. Building on recent work in network analysis, I propose a new measurement of trade externalities to test the above theory and find supporting results.

Optimizing the Planning of Airport Airside Expansion Projects to Minimize Air Traffic Disruptions and Construction Cost

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects.

A cost-based criterion for implementing data-driven disruption predictors

Plasma disruptions are a serious impediment to tokamak operations, and accordingly there has been a lot of attention given to the topic of predicting disruptions in order to avoid or more effectively mitigate them. While existing prediction goals are based on a combination of engineering constraints and desired operating outcomes, it does not follow that a predictor must meet these strict requirements to offer a benefit. Rather, a cost-based criterion should provide the necessary justification to implement a disruption predictor. A general model for such a cost-based criterion is derived here. This model is used to demonstrate that, with realistic assumptions about the cost of disruptions compared to the cost of unnecessary discharge terminations, existing disruption prediction techniques already meet the minimum criterion to reduce operating costs.

From Editor-in-Chief’s Keyboard (January-2023)

Hello 2023!!! The year with greater complexity and excitement.Are we prepared for all the transitions, advancements, and instabilities? Numerous academicians and industry professionals have forecast that the economy will deteriorate as a result of the war crisis, the covid-19 outbreak, the rising cost of raw materials, climate change, and technological disruption. Not even Our DREAM Journal is exempt. My colleagues and I must work quickly to ensure that all issues and creative work innovations adhere to the newest trends and versions. Through revolutionary culture to out-of-this-world innovations, this issue honours innovators and their insatiable desire to challenge the status quo. The alternative paradigm for virtual tourism and digital marketing was emphasised in an article by Stefanus Rumangkit and Antonius Satria Hadi (page 01-11). Website design, social media marketing, search engine optimization, email marketing, and affiliate marketing are the components of digital marketing. Many variables, including online destination knowledge, online destination authenticity, entertainment, enjoyment, and international virtual heritage tourism, exist within this conceptual framework. Because of this ongoing transformation, digital marketing has a solid and exciting future. As the digital marketplace continues to expand, it is expected that the remaining 23% of businesses will follow suit, bringing the total number of businesses using industry-standard content marketing tactics to nearly 77%. Excited right?? Check out more on the introduction to our issue for the year 2023. Enjoy your reading and life to the fullest! Editor-in-Chief ASSOC. PROF. DR. ILHAM SENTOSA

Estimating the cost of training disruptions on marathon performance.

Completing a marathon usually requires at least 12-16 weeks of consistent training, but busy lifestyles, illness or injury, and motivational issues can all conspire to disrupt training. This study aims to investigate the frequency and performance cost of training disruptions, especially among recreational runners. Using more than 15 million activities, from 300,000 recreational runners who completed marathons during 2014-2017, we identified periods of varying durations up to 16 weeks before the marathon where runners experienced a complete cessation of training (so-called training disruptions). We identified runners who had completed multiple marathons including: (i) at least one disrupted marathon with a long training disruption of 7 days; and (ii) at least one undisrupted marathon with no training disruptions. Next, we calculated the performance cost of long training disruptions as the percentage difference between these disrupted and undisrupted marathon times, comparing the frequency and cost of training disruptions according to the sex, age, and ability of runner, and whether the disruptions occurred early or late in training. Over 50% of runners experienced short training disruptions up to and including 6 days, but longer disruptions were found to be increasingly less frequent among those who made it to race-day. Runners who experience longer training disruptions ( 7 days) suffer a finish-time cost of 5-8% compared to when the same runners experienced only short training disruptions (<7 days). While we found little difference (<5%) in the likelihood of disruptions-when comparing runners based on sex, age, ability, and the timing of a disruption-we did find significant differences in the the cost of disruptions (10-15%) among these groups. Two sample -tests indicate that long training disruptions lead to a greater finish-time cost for males (5%) than females (3.5%). Faster runners also experience a greater finish-time cost (5.4%) than slower runners (2.6%). And, when disruptions occur late in training (close to race-day), they are associated with a greater finish-time cost (5.2%) than similar disruptions occurring earlier in training (4.4%). By parameterising and quantifying the cost of training disruptions, this work can help runners and coaches to better understand the relationship between training consistency and marathon performance. This has the potential to help them to better evaluate disruption risk during training and to plan for race-day more appropriately when disruptions do occur.

Behavioral "nudges" in the electronic health record to reduce waste and misuse: 3 interventions.

Electronic health records (EHRs) offer decision support in the form of alerts, which are often though not always interruptive. These alerts, though sometimes effective, can come at the cost of high cognitive burden and workflow disruption. Less well studied is the design of the EHR itself-the ordering provider's "choice architecture"-which "nudges" users toward alternatives, sometimes unintentionally toward waste and misuse, but ideally intentionally toward better practice. We studied 3 different workflows at our institution where the existing choice architecture was potentially nudging providers toward erroneous decisions, waste, and misuse in the form of inappropriate laboratory work, incorrectly specified computerized tomographic imaging, and excessive benzodiazepine dosing for imaging-related sedation. We changed the architecture to nudge providers toward better practice and found that the 3 nudges were successful to varying degrees in reducing erroneous decision-making and mitigating waste and misuse.