Probability Of Disruption Research Articles

Short and long-term mineral resource scarcity impacts for a car manufacturer: The case of electric traction motors

The importance of metals for modern society and future trends puts pressure on companies to handle issues concerning potential mineral resource scarcity (i.e. deficiency in quantity compared with demand). Companies see the need to handle such potential scarcity both in the short-term (is the availability constrained for our current products?) and the long-term (is our current use affecting the availability for future generations?). This study aims to examine the use of complementary methods for short and long-term scarcity in a company context, through a case study on permanent magnet electric traction motors, to provide both empirical and methodological insights. To mitigate long-term scarcity impacts, the results point to copper, neodymium and to some extent dysprosium as priority. These metals contribute to a large share of such impacts both due to themselves and their companion metals. In the short-term, neodymium and dysprosium, which are often regarded as critical (i.e. high supply disruption probability and high vulnerability to supply disruption), were found to be substitutable in the electric motor, reducing their criticality. Instead, the electric motor was most vulnerable to a potential supply disruption of iron and silicon because of no or low substitutability in electrical steel. Methodologically, these perhaps unexpected results, demonstrate that criticality requires a more context-specific assessment than often applied, especially regarding substitutability. By using complementary methods, decision-making about potential mineral resource scarcity impacts in company contexts could become more comprehensive and distinctly address both short and long-term scarcity impacts. • Use of distinct and complementary methods for mineral resource scarcity in company. • Electric motors are most vulnerable to supply disruption of iron and silicon • Electric motors not as dependent on neodymium and dysprosium as can be expected. • Copper, neodymium and their companion metals contribute most to depletion impacts. • Methodological improvements of company-level criticality assessment pointed out.

The (non) impact of education on marital dissolution

Despite the relevant role attributed to education on marital outcomes, literature does not show a generalized consensus regarding a positive or negative effect from education on marital decisions. In this paper the impact of education on marriage dissolution is analysed exploiting a change in the length of compulsory education in Mexico in 1993 as an instrument for education. The federal government increased compulsory education from completion of primary school, sixth grade, to completion of secondary school, ninth grade, at a national level. In the first part of the analysis, the probit models reveal that education is significant and negatively related to the probability of marital breakdown. An additional year of education is associated with a decrease between 0.6 and 0.9 percentage points in the probability of marital disruption for the 2002-2012 period. However, the results using the instrumental variables methodology indicate that an additional year of schooling has no effect on the probability of marriage dissolution. This finding demonstrates that the relationship between education and divorce is not causal and suggests that although higher levels of education are an undeniable trait observed in non-broken marriages, it is not education by itself one of the mechanisms leading to better marriage outcomes.

SPOTTER: Assessing supply disruption impacts along the supply chain within Life Cycle Sustainability Assessment

The present article introduces the SPOTTER approach, a novel way to assess supply disruption impacts along the supply chain in the short-term (i.e. the next 5 years) and the medium-term (i.e. in 5 to 15 years) within the Life Cycle Sustainability Assessment framework. This approach allows finding the more important supply risks within global supply chains and over different time horizons, which leads to fine-tuned insights that can help creating resilient supply networks. To this end, the SPOTTER approach assesses overall supply disruption impacts for complete supply chains and supply disruption hotspots separately in the short- and medium-term. The overall impacts and hotspots are evaluated from global and country-specific supply disruption events and aggregated into the two categories of 'cost variability' and 'limited availability'. Scores of these impacts and hotspots are calculated by multiplying resource, material and product flows with respective characterization factors, which are case-specific and are defined by diverse indicators of supply disruption probability and vulnerability. The article provides an overview of the SPOTTER approach and its rationale by explaining the selection and use of indicators to define characterization factors and by describing the procedure to calculate the overall impact and hotspot scores. Furthermore, it presents an implementation procedure for the practical application of the approach. Finally, it highlights the data availability and methodological issues that currently affect the implementation practicability and the representativeness of the SPOTTER approach to describe global supply risks in detail and refers to the need for future research and development on this topic.

Visiting parents in times of COVID-19: The impact of parent-adult child contacts on the psychological health of the elderly

Using the 8th wave of the SHARE and the SHARE Corona Survey, we investigated whether the disruption of parent–adult child contacts due to social distancing restrictions increased the symptoms of depression among old age individuals during the first wave of the COVID-19 pandemic. We model the relationship between the disruption of parent–adult child contacts and the mental health of the elderly using a recursive simultaneous equation model for binary variables. Our findings show that the likelihood of disruption of parent–adult child contacts was higher with adult children who do not live with or close to their parents (i.e., in the same household or in the same building) for whom contact disruption increases about 15 %. The duration of restrictions to movement and lockdowns also has a positive and significant effect on parent-child contact disruption: an additional week of lockdown significantly increases the probability of parent-child contact disruption, by about 1.5 %. The interventions deemed essential to reduce the spread of the pandemic, such as the “stay-at-home” order, necessarily disrupted personal parent–child contacts and the social processes that facilitate psychological well-being, increasing the probability of suffering from a deepening depressed mood by about 17 % for elderly parents.

Dual-Source Procurement Strategy of Cross-Border E-Commerce Supply Chain considering Members’ Risk Attitude

The risk attitude of decision-makers will significantly affect the decision-making of enterprise risk management. Specifically, high risk represents the potential premise of high return for risk preference decision-makers, and for risk-averse decision-makers, the increase of risk degree will stimulate decision-makers’ aversion to uncertainty and turn to seek safer business strategies. Although there are many pieces of literature on the risk preference of decision-makers, they usually only assume the risk attitude of one party and rarely consider the risk attitude of suppliers and retailers in the scenario of cross-border e-commerce at the same time. Therefore, under the background of supply disruption, for the cross-border e-commerce supply chain composed of cross-border suppliers, overseas suppliers, overseas retailers, and consumers, combined with the risk attitude preference of enterprise subjects, this paper constructs the mean-variance model dominated by overseas retailers and reversely solves the risk-aversion attitude of a single cross-border supplier. When a single overseas retailer maintains a risk-aversion attitude and both cross-border suppliers and overseas retailers hold a risk-neutral or risk-aversion attitude, the pricing of products in different channels is analyzed. Finally, an example is given to analyze the impact of supply disruption probability, risk-aversion coefficient, channel distribution coefficient, and other parameters on purchase price, market demand, target profit, and utility. It is of great practical significance for improving the stability of cross-border e-commerce supply chain system and reducing revenue loss to study how different degrees of risk-aversion attitudes of cross-border suppliers and overseas retailers affect enterprise procurement pricing strategy, target profit, and utility in case of supply disruption.

공급사슬 중단 및 수율 불확실성을 고려한 최적 공급업체 수와 주문량의 결정 및 보완주문 전략에 관한 연구

In this study, we developed an optimal sourcing strategy considering compensation orders to mitigate the supply chain disruption risks and yield uncertainty. Additionally, we proposed an approximation method that can perform quick calculations to ascertain the optimal number of suppliers and initial order quantity. Finally, by using a numerical example, we confirmed that if there is a disruption probability, it is desirable to distribute the risk by dividing the order into small portions from multiple suppliers. This study proposes an optimal procurement strategy under disruption risks and serves as a guide to establishing a procurement strategy that maximizes a firm's profit in the long term.

A Channel to Form Fast-spinning Black Hole–Neutron Star Binary Mergers as Multimessenger Sources

After the successful detection of a gravitational-wave (GW) signal and its associated electromagnetic (EM) counterparts from GW170817, neutron star–black hole (NSBH) mergers have been highly expected to be the next type of multimessenger source. However, despite the detection of several NSBH merger candidates during the GW third observation run, no confirmed EM counterparts from these sources have been identified. The most plausible explanation is that these NSBH merger candidates were plunging events mainly because the primary black holes (BHs) had near-zero projected aligned spins based on GW observations. In view of the fact that neutron stars (NSs) can be easily tidally disrupted by BHs with high projected aligned spins, we study an evolution channel to form NSBH binaries with fast-spinning BHs, the properties of BH mass and spin, and their associated tidal disruption probability. We find that if the NSs are born first, the companion helium stars would be tidally spun up efficiently, and would thus finally form fast-spinning BHs. If BHs do not receive significant natal kicks at birth, these NSBH binaries that can merge within Hubble time would have BHs with projected aligned spins χ z ≳ 0.8 and, hence, can certainly allow tidal disruption to happen. Even if significant BH kicks are considered for a small fraction of NSBH binaries, the projected aligned spins of BHs are χ z ≳ 0.2. These systems can still be disrupted events unless the NSs are very massive. Thus, NS-first-born NSBH mergers would be promising multimessenger sources. We discuss various potential EM counterparts associated with these systems and their detectability in the upcoming fourth observation run.

Managing disruption risk in competing multitier supply chains

AbstractThis paper studies the sourcing problem of two competing manufacturers in two supply chains. Each supply chain consists of a Tier 0 manufacturer, a Tier 1 supplier, and a pair of Tier 2 suppliers with different disruption probabilities. The two manufacturers are engaged in Cournot competition. The two Tier 1 suppliers that serve different manufacturers may procure from different or the same Tier 2 suppliers. We investigate the impact of upstream supply chain structures on the manufacturers' performance by comparing two representative configurations: the paralleled supply chain configuration, which has no overlap in Tier 2 suppliers, and the overlapped supply chain configuration, which has the same Tier 2 suppliers. We derive the manufacturers' equilibrium sourcing strategies in the two configurations. When the more reliable Tier 2 supplier is quite reliable or the more unreliable Tier 2 supplier is very unreliable, both manufacturers offer a low wholesale price to induce their Tier 1 suppliers for single sourcing. When the more reliable Tier 2 supplier is not sufficiently reliable and the more unreliable Tier 2 supplier is not that unreliable, both manufacturers offer a high wholesale price to induce their Tier 1 suppliers for dual sourcing. There are also certain conditions under which one manufacturer chooses the high‐price sourcing strategy and the other one chooses the low‐price sourcing strategy. Besides, we demonstrate that competition decreases the manufacturers' incentives to induce dual sourcing. In a more competitive market, the manufacturers prefer less overlap in their upper suppliers. Finally, we conduct several extensions to show the robustness of our findings.

Fourth-party logistics network design under uncertainty environment

A novel fourth-party logistics (4PL) network design problem under uncertainty environment is studied in the current work. Demand uncertainty and two types of disruptions, facility and third-party logistics (3PL) disruptions, are simultaneously considered. To minimize the total cost, a two-stage stochastic programming model is presented, which is further approximated as a mixed-integer linear programming model using the sample average approximation (SAA) method. As a large number of disruption and demand scenarios generated in the approximation process lead to challenges in model solving, the scenario reduction (SR), SAA, dual decomposition and Lagrangian relaxation (DDLR) approaches are integrated to present an SR-DDLRSAA algorithm. The effectiveness of our model and algorithm is illustrated by adopting numerical instances and real-life cases. A comparative analysis indicates that the impact of disruptions on the 4PL network is closely related to the values of the disruption probability and the fluctuation degree of uncertain demand. Furthermore, we extend the proposed basic model by considering demand uncertainty and three types of disruptions, namely supply, facility, and 3PL disruptions, and analyze the impact of supply disruption on the 4PL network.

Probability, adaptability and time: some research-practice paradoxes in supply chain resilience and viability modelling

Modelling of resilience and viability became crucial and visible research avenues for decision-making support in case of supply chain disruptions. This article discusses some research-practice paradoxes stemming from typical problem settings and assumptions in supply chain resilience and viability modelling. We show that the categories of probability, adaptability, and time are major determinants of resilience and viability modelling in supply chains. As an example, we discuss how probability-based supply chain design models can be enhanced through inclusion of adaptability and connectivity characteristics of the network elements with consideration of time. We deduce some open questions the elaboration on which can provide novel and substantial contributions in future. In particular, we stress the importance of furthering our understanding of reliable suppliers, disruption probabilities, disruption time and ripple effect estimation, value-creation perspective of resilience, and viability of intertwined supply networks.

Risk-Averse Co-Decision for Lower-Carbon Product Family Configuration and Resilient Supplier Selection

Due to global pandemics, political unrest and natural disasters, the stability of the supply chain is facing the challenge of more uncertain events. Although many scholars have conducted research on improving the resilience of the supply chain, the research on integrating product family configuration and supplier selection (PCSS) under disruption risks is limited. In this paper, the centralized supply chain network, which contains only one major manufacturer and several suppliers, is considered, and one resilience strategy (i.e., the fortified supplier) is used to enhance the resilience level of the selected supply base. Then, an improved stochastic bi-objective mixed integer programming model is proposed to support co-decision for PCSS under disruption risks. Furthermore, considering the above risk-neutral model as a benchmark, a risk-averse mixed integer program with Conditional Value-at-Risk (CVaR) is formulated to achieve maximum potential worst-case profit and minimum expected total greenhouse gases (GHG) emissions. Then, NSGA-II is applied to solve the proposed stochastic bi-objective mixed integer programming model. Taking the electronic dictionary as a case study, the risk-neutral solutions and risk-averse solutions that optimize, respectively, average and worst-case objectives of co-decision are also compared under two different ranges of disruption probability. The sensitivity analysis on the confidence level indicates that fortifying suppliers and controlling market share in co-decision for PCSS can effectively reduce the risk of low-profit/high-cost while minimizing the expected GHG emissions. Meanwhile, the effects of low-probability risk are more likely to be ignored in the risk-neutral solution, and it is necessary to adopt a risk-averse solution to reduce potential worst-case losses.

Measuring raw-material criticality of product systems through an economic product importance indicator: a case study of battery-electric vehicles

PurposeThe concept of criticality concerns the probability and the possible impacts of shortages in raw-material supply and is usually applied to regional economies or specific industries. With more and more products being highly dependent on potentially critical raw materials, efforts are being made to also incorporate criticality into the framework of life cycle sustainability assessment (LCSA). However, there is still some need for methodological development of indicators to measure raw-material criticality in LCSA.MethodsWe therefore introduce ‘economic product importance’ (EPI) as a novel parameter for the product-specific evaluation of the relevance and significance of a certain raw material for a particular product system. We thereby consider both the actual raw-material flows (life cycle inventories) and the life cycle cost. The EPI thus represents a measure for the material-specific product-system vulnerability (another component being the substitutability). Combining the product-system vulnerability of a specific product system towards a certain raw material with the supply disruption probability of that same raw material then yields the product-system specific overall criticality with regard to that raw material. In order to demonstrate our novel approach, we apply it to a case study on a battery-electric vehicle.ResultsSince our approach accounts for the actual amounts of raw materials used in a product and relates their total share of costs to the overall costs of the product, no under- or over-estimation of the mere presence of the raw materials with respect to their relevance for the product system occurs. Consequently, raw materials, e.g. rare earth elements, which are regularly rated highly critical, do not necessarily reach higher criticality ranks within our approach, if they are either needed in very small amounts only or if their share in total costs of the respective product system is very low. Accordingly, in our case study on a battery-electric vehicle product system, most rare earth elements are ranked less critical than bulk materials such as copper or aluminium.ConclusionOur EPI approach constitutes a step forward towards a methodology for the raw-material criticality assessment within the LCSA framework, mainly because it allows a product-specific evaluation of product-system vulnerability. Furthermore, it is compatible with common methods for the supply disruption probability calculation — such as GeoPolRisk, ESP or ESSENZ — as well as with available substitutability evaluations. The practicability and usefulness of our approach has been shown by applying it to a battery-electric vehicle.

Developing Hospital Resilient Supply Chain Scenario through Cross-Impact Analysis Method

Developing Hospital Resilient Supply Chain Scenario through Cross-Impact Analysis Method

No Detectable Kilonova Counterpart is Expected for O3 Neutron Star–Black Hole Candidates

Abstract We analyze the tidal disruption probability of potential neutron star–black hole (NSBH) merger gravitational-wave (GW) events, including GW190426_152155, GW190814, GW200105_162426, and GW200115_042309, detected during the third observing run of the LIGO/Virgo Collaboration and the detectability of kilonova emission in connection with these events. The posterior distributions of GW190814 and GW200105_162426 show that they must be plunging events, and hence no kilonova signal is expected from these events. With the stiffest NS equation of state allowed by the constraint of GW170817 taken into account, the probability that GW190426_152155 and GW200115_042309 can make tidal disruption is ∼24% and ∼3%, respectively. However, the predicted kilonova brightness is too faint to be detected for present follow-up search campaigns, which explains the lack of electromagnetic (EM) counterpart detection after triggers of these GW events. Based on the best-constrained population synthesis simulation results, we find that disrupted events account for only ≲20% of cosmological NSBH mergers, since most of the primary BHs could have low spins. The associated kilonovae for those disrupted events will still be difficult for LSST to discover after GW triggers in the future because of their low brightness and larger distances. For future GW-triggered multimessenger observations, potential short-duration gamma-ray bursts and afterglows are more probable EM counterparts of NSBH GW events.

Emergency decision-making model of suppliers with updating information in cases of sudden accidents

In view of uncertainties caused by sudden accidents (SAs) and affecting retailers’ demand in many districts, it is difficult for suppliers to determine when and how many products to procure/produce. Considering a supply chain consisting of two types of competing suppliers and multi-retailer, this work studies the suppliers’ optimal emergency procurement/production decision (EPD) with information updating. Firstly, a probability evolution model with information updating to describe the probability of the retailers’ procurement behaviour and the occurrence probability of supply disruption (SD) is inferred. Secondly, suppliers’ EPDs regarding retailers’ procurement behaviour and occurrence probability of SD are discussed and a real-time updated emergency decision-making model (EDM) is proposed based on Stackelberg game and Bayesian inference. Thirdly, the value of information updating and the critical factors that affect the suppliers’ optimal EPD are quantitatively analysed. Numerical examples are finally provided to verify the EDM. Results indicate that information is the premise and foundation for the suppliers to deal with SA effectively; suppliers can easily determine when and how many products to procure/produce based on the proposed EDM; it is demonstrated that for any chosen supplier strategy, there exists a corresponding optimal procurement/production quantity for the suppliers that maximises the expected profits. Moreover, the suppliers’ EPD with information updating is affected by cost parameters, with the rank of information collection cost coefficient, unit procurement/production cost, unit sales price, unit holding cost and unit shortage cost, from apparently to slightly.

Robust and resilient joint periodic maintenance planning and scheduling in a multi-factory network under uncertainty: A case study

The continuity of activity and competition in the global business market has urged organizations to move from centralized to decentralized structures and develop multi-factor production (MFP) networks. The design and implementation of a maintenance system are essential due to two reasons: first, increasing the life cycle of the equipment, and second, decreasing the probability of disruption in MFP networks. This study proposes a bi-objective optimization model for maintenance planning and scheduling in an MFP network. In the proposed model, how to perform, the performing agent of the maintenance operation, and the maintenance periods are determined based on the failure function in planning and scheduling phases, respectively. Besides, two strategies are proposed for MFP network resilience under disruption occurrence. The objective functions are minimizing maintenance costs and maximizing reliability. The augmented epsilon constraint method is utilized to obtain the Pareto front and trade off the objectives. Because of the parameters' inherent uncertainty, an effective robust programming approach is employed to control the uncertainty of input parameters and the conservatism level of output decisions. The CPLEX Solver can globally solve the proposed model in small and medium instances. A heuristic method based on the genetic algorithm is proposed for solving in the large-scaled sample. Finally, the proposed model is implemented for a case study of CNG stations, in which output results approve the model's applicability.

Crossing Minimal Edge‐Constrained Layout Planning using Benders Decomposition

We present a new crossing number problem, which we refer to as the edge‐constrained weighted two‐layer crossing number problem (ECW2CN). The ECW2CN arises in layout planning of hose coupling stations at BASF, where the challenge is to find a crossing minimal assignment of tube‐connected units to given positions on two opposing layers. This allows the use of robots in an effort to reduce the probability of operational disruptions and to increase human safety. Physical limitations imply maximal length and maximal curvature conditions on the tubes as well as spatial constraints imposed by the surrounding walls. This is the major difference of ECW2CN to all known variants of the crossing number problem. Such as many variants of the crossing number problem, ECW2CN is [Formula: see text]‐hard. Because the optimization model grows fast with respect to the input data, we face out‐of‐memory errors for the monolithic model. Therefore, we develop two solution methods. In the first method, we tailor Benders decomposition toward the problem. The Benders subproblems are solved analytically and the Benders master problem is strengthened by additional cuts. Furthermore, we combine this Benders decomposition with ideas borrowed from fix‐and‐relax heuristics to design the Dynamic Fix‐and‐Relax Pump (DFRP). Based on an initial solution, DFRP improves successively feasible points by solving dynamically sampled smaller problems with Benders decomposition. Because the optimization model is a surrogate model for its time‐dependent formulation, we evaluate the obtained solutions for different choices of the objective function via a simulation model. All algorithms are implemented efficiently using advanced features of the GuRoBi‐Python API, such as callback functions and lazy constraints. We present a case study for BASF using real data and make the real‐world data openly available.

Mitigation strategies against supply disruption risk: a case study at the Ford Motor Company

Supply chains are exposed to different risks, which can be mitigated by various strategies based on the characteristics and needs of companies. In collaboration with Ford, we develop a decision support framework to choose the best mitigation strategy against supply disruption risk, especially for companies operating with a small supplier base and low inventory levels. Our framework is based on a multistage stochastic programming model which incorporates a variety of plausible strategies, including reserving backup capacity from the primary supplier, reserving capacity from a secondary supplier, and holding backup inventory. We reflect disruption risk into the framework through decision makers’ input on the time to recover and the disruption probability. Our results demonstrate that relying on the strategy which is optimal when there is no disruption risk can increase the expected total cost substantially in the presence of disruption risk. However, this increase can be reduced significantly by investing in the mitigation strategy recommended by our framework. Our results also show that this framework removes the burden of estimating the time to recover and the disruption probability precisely since there is often a small loss associated with using another strategy that is optimal in the neighbourhood of the estimated values.

Winner determination for logistics service procurement auctions under disruption risks and quantity discounts

Disruption risks and quantity discounts have not been explicitly studied in traditional winner determination problems. This paper investigates an innovative winner determination problem with accidental disruptions and quantity discounts for a fourth party logistics provider in a logistics service procurement auction. An effective hybrid strategy that integrates a fortification policy with an outside option policy is developed to mitigate possible disruptions. Integrating the hybrid strategy with a piecewise-linear discount function, a stochastic nonlinear winner determination model is constructed. Utilizing a linearization technique, such a nonlinear winner determination model can be reformulated as a mixed integer linear program. To obtain nearly optimal solutions, a scenario-based approximation method is developed, including a reduced scenario technique and a dual decomposition Lagrangian relaxation technique. Numerical experiments are conducted to illustrate the effectiveness and applicability of the proposed model and method. When the discount parameter decreases or the disruption probability increases, the fortification policy becomes more important for the logistics system. An interesting result indicates that both the auctioneer and bidders may benefit from considering the discount policy. Managerial implications are drawn for fourth party logistics providers to run a reliable logistics system in a cost-effective way.

What Controls the Probability Distribution of Local Wave Activity in the Midlatitudes?

AbstractThis paper examines probability distributions of local wave activity (LWA), a measure of the jet stream's meander, and factors that control them. The observed column‐mean LWA distributions exhibit significant seasonal, interhemispheric, and regional variations but are always positively skewed in the extratropics, and their tail often involves disruptions of the jet stream. A previously derived one‐dimensional (1D) traffic flow model driven by observed spectra of transient eddy forcing qualitatively reproduces the shape of the observed LWA distribution. It is shown that the skewed distribution emerges from nonlinearity in the zonal advection of LWA even though the eddy forcing is symmetrically distributed. A slower jet and stronger transient and stationary eddy forcings, when introduced independently, all broaden the LWA distribution and increase the probability of spontaneous jet disruption. A quasigeostrophic two‐layer model also simulates skewed LWA distributions in the upper layer. However, in the two‐layer model both transient eddy forcing and the jet speed increase with an increasing shear (meridional temperature gradient), and their opposing influence leaves the frequency of jet disruptions insensitive to the vertical shear. When the model's nonlinearity in the zonal flux of potential vorticity is artificially suppressed, it hinders wave‐flow interaction and virtually eliminates reversal of the upper‐layer zonal wind. The study underscores the importance of nonlinearity in the zonal transmission of Rossby waves to the frequency of jet disruptions and associated weather anomalies.