Surges In Demand Research Articles

Analysis of Tailored Base-Surge Policies in Dual Sourcing Inventory Systems

We study a model of a firm managing its inventory of a single product by sourcing supplies from two supply sources, a regular supplier who offers a lower unit cost and a longer lead time than a second, emergency, supplier. A practically implementable policy for such a firm is a tailored base-surge (TBS) policy [Allon G, Van Mieghem JA (2010) Global dual sourcing: Tailored base-surge allocation to near- and offshore production. Management Sci. 56(1):110–124] to manage its inventory. Under this policy, the firm procures a constant quantity from the regular supplier in every period and dynamically makes procurement decisions for the emergency supplier. Allon and Van Mieghem describe this practice as using the regular supplier to meet a base level of demand and the emergency supplier to manage demand surges, and they conjecture that this practice is most effective when the lead time difference between the two suppliers is large. We confirm these statements in two ways. First, we show the following analytical result: when demand is composed of a base demand random component plus a surge demand random component, which occurs with a certain small probability, the best TBS policy is close to optimal (over all policies) in a well-defined sense. Second, we also numerically investigate the cost effectiveness of the best TBS policy on a test bed of problem instances. The emphasis of this investigation is the study of the effect of the lead time difference between the two suppliers. Our study reveals that the cost difference between the best TBS policy and the optimal policy decreases dramatically as the lead time of the regular supplier increases. On our test bed, this cost difference decreases from an average (over the test bed) of 21% when the lead time from the regular supplier is two periods (the emergency supplier offers instant delivery) to 3.5% when that lead time is seven periods.This paper was accepted by Martin Lariviere, operations management.

Supply Chain Planning for Random Demand Surges: Reactive Capacity and Safety Stock

Globalization, innovation, social media, and exposure to natural and man-made disasters have increased organizations’ need to cope with demand surges: random, significant increases in demand in an otherwise relatively stable demand environment. To build supply chain capabilities, organizations face a choice between two fundamentally different sourcing strategies – reactive capacity and safety stock. We develop a framework to guide the joint sourcing strategy that minimizes the long-run average cost under a target service level. A salient feature of our modeling framework is a novel demand model that captures important continuous-time, non-Markovian characteristics of demand surge trajectories. In addition to the total magnitude as typically modeled in the literature, we define several other metrics of surges, including duration, intensity, compactness, peak position, volatility, and frequency. The resulting optimization problem is challenging because it requires evaluating, for any sample path, whether surge demand can be satisfied at every point in time – a refined feature that traditional models do not have. To identify the optimal strategy, we first characterize the optimal production and deployment policy for any given strategy and then transform the original problem into two equivalent but more tractable problems. Finally, through stochastic comparison techniques, we show how the magnitude and predictability of surge demand characteristics (mentioned above) and the cost profiles of each strategy impact the optimal joint strategy.

Simple subcontracting rules for make-to-order shops with limited subcontractor capacity: an assessment by simulation

Companies that produce a high variety of customised products often suffer from large fluctuations in demand. Subcontracting can be an important means of overcoming resulting capacity shortages during high demand periods. A set of effective subcontracting rules, for determining which jobs to subcontract and which to produce internally, has recently been presented in the literature for this type of company. But evaluations of these rules have assumed subcontractor capacity is infinite. This study examines the impact of limited subcontractor capacity on the performance of the three best-performing subcontracting rules for make-to-order companies using a simulation model of an assembly job shop. Limiting subcontractor capacity inhibits the ability of a subcontracting rule to protect the internal shop from surges in demand, which negatively affects performance. However, significant performance differences between the rules evaluated are maintained, which underlines the importance of choosing the right subcontracting rule. Further analysis reveals that a limit on the work that can be subcontracted leads to less work being subcontracted more often, which requires adequate response by management. Meanwhile, the assumption of infinite capacity results in sporadically subcontracting a large amount of work. The results have important implications for future research and practice.

Are emergency admissions contagious?

Unexpected increases in deaths, emergency admissions, accident and emergency (A&E) attendances, GP referral and NHS staff sickness absence appear to have occurred in 2014. These increases endure for around 12 months before abating. This is a repeat of the situation documented in 2008, 2010 and 2012; although the 2010 event was more sporadic. Unprecedented A&E pressures experienced during 2014 and into 2015 appear to have been exacerbated by the 2014 event. The usual factors which have been offered as ‘explanations’ for the increase in NHS demand may be merely contributory—but not causative of—the large surges in demand which occur during these events. Spatial spread of both deaths and sickness absence can be demonstrated. The ratio of follow-up to first attendances following GP referral for an outpatient attendance, show marked step-like changes during these events apparently due to the subtle changes in the case-mix and severity of the referred patients.

Selection and allocation of manual traffic control points and personnel during emergencies.

Manual traffic control is an intersection control strategy in which law enforcement officers allocate intersection right-of-way to turning movements. Many emergency traffic management plans call for manual traffic control in response to oversaturated roadway conditions. This is because it is thought to more effectively move traffic during temporary surges in demand. The goal of this research was to evaluate the current state-of-the-practice used by the Army Corps of Engineers (ACE) in selecting intersections for manual traffic control and allocating police personnel to them during emergencies. This research uses the emergency traffic management plans developed by the ACE for nine counties in the Maryland Eastern Shore region. This area encompassing 14,318 intersections of which 74 were selected for manual traffic control during emergencies. This work sought to quantify the correlations that exist between intersection attributes and the ACE' decision to allocate officers to control them. The research findings suggest that US routes, State routes, and emergency evacuation routes are statistically significant in determining the need for police control at intersections. Also significant are intersection on contraflow corridors and intersections near grade separated interchanges. The model also determined that intersections isolated from evacuation routes and county exits were more likely to be selected for manual control, indicating that rural areas may rely on manual traffic control in the absence of multilane highway and freeways. This research also found that intersections involving evacuation routes, contraflow corridors, and grade separated interchanges may warrant additional police personnel (two or more officers) for manual traffic control.

Consumer preferences and willingness to pay for beef food safety assurance labels in the Kumasi Metropolis and Sunyani Municipality of Ghana

The demand surge for assured food safety and quality in livestock products among consumers in developing countries in particular has been phenomenal. The present paper examines consumers' preferences and willingness to pay for beef food safety assurance labels in the Kumasi Metropolis and Sunyani Municipality of Ghana. Consumers' in both Kumasi and Sunyani mainly rely on beef attributes which assures them of their safety. Key attributes include hygienic condition of the shopping environment, excellent and attractive packaging that minimizes contamination, leanness and certification of beef products for safety and quality. Therefore, guaranteed food safety information and attributes should emerge as a new index and basis for future trade in the beef industry. Preference heterogeneity exists among consumers in Kumasi Metropolis and Sunyani Municipality for verified animal health status, food safety inspection and certification and nutritional label. Hence, it is important for beef investors, government and NGO's to segment consumers into different classes when designing strategies to mitigate unsafe beef production, marketing and consumption. Higher willingness to pay exists for verified animal health stamp in both Kumasi and Sunyani compared to assured nutritional label, food and drugs board food safety certification license. Willingness to pay estimates in Kumasi were higher for assured nutritional label, food and drugs board food safety certification license compared to Sunyani. Consumer preferences for food safety inspection and certification, and nutritional label are explained by age, income and education in Sunyani Municipality whereas preferences for verified animal health status, food safety inspection and certification, and nutritional label are influenced by age, income, education and gender in Kumasi Metropolis. Albeit the impact of gender and age are negative for verified animal health status and food safety certification license in both locations. Therefore, the use of selective demographic targeting to maintain or build strong food safety and quality measures should be seen as a reality by policy makers and investors in the beef industry. Minimizing microbial, chemical and physical food contamination and incidents of food safety in Kumasi and Sunyani requires adoption of strict certification and inspections starting from the health status of animals to be slaughtered to the final product with proper labeling information for consumers, combined with strict sanitary inspections at the shopping or selling place. Also, sensitization of women on food safety practices, handling and violation of food safety is very essential in Kumasi and Sunyani.

Deployment of field hospitals in mass casualty incidents

We propose an analysis framework to determine the location and size of emergency service facilities to be established after a disaster to cope with the demand surge. We utilize a multi-period mixed integer programming (MIP) model that simultaneously optimizes capacity allocation and casualty transportation decisions in order to provide emergency transportation and medical care services to the largest number of casualties in shortest time. The objectives are to minimize the total travel and waiting time of casualties over the search-and-rescue period and the total cost of establishing new facilities. The model minimizes a weighted sum of these objectives subject to the availability of vehicles and service capacity at existing and new facilities in each period. We provide a detailed case study of a large-scale emergency due to an expected earthquake in Istanbul to demonstrate the two-level solution approach. Service requests over time and travel times are generated with respect to regional vulnerabilities and road network conditions. Solutions and sensitivity analysis reveal the resource needs and service performance to guide preparedness strategies.

PV powered smart charging station for PHEVs

A huge inrush of PHEVs is envisioned in the future. There is a growing risk that, this proliferation in the number of PHEVs will trigger extreme surges in demand while charging them during rush hours. To mitigate this impact, a smart charging station is proposed in which the charging of the PHEVs is controlled in such a way that the impact of charging during peak load period is not felt on the grid. The power needed to charge the plug in hybrids comes from grid-connected photovoltaic generation or the utility or both. The three way interaction between the PV, PHEVs and the grid ensures optimal usage of available power, charging time and grid stability. The system designed to achieve the desired objective consists of a photovoltaic system, DC/DC boost converter, DC/AC bi-directional converter and DC/DC buck converter. The output of DC/DC boost converter and input of DC/AC bi-directional converter share a common DC link. A unique control strategy based on DC link voltage sensing is proposed for the above system for efficient transfer of energy.

Effect of Phased Evacuations in Megaregion Highway Networks

Evacuation phasing or staging is a technique used to disperse traffic so that sudden, wide-ranging surges in travel demand do not overwhelm the available capacity of a road network during an emergency. Despite their many benefits, phased evacuations are not an appropriate strategy for all locations and conditions. Even though researchers have evaluated various aspects of their use, little, if any, systematic study of staged evacuations has been conducted over large multiregional areas. A research effort to simulate staged evacuations in a megaregion addresses this information gap. Megaregions are thought to be particularly vulnerable to evacuation problems because of their enormous populations, wide geographic expanses, and common locations along oceanic coasts or major inland waterways. A range of demand levels was used to investigate the effects of phased evacuation under different scenarios of road network congestion. Because mass evacuations are infrequent and a full megaregion evacuation never has been conducted, this simulation-based framework offered an opportunity to create and assess a megaregion mass evacuation in microlevel detail. Simulation results revealed that the effectiveness of phased evacuations could vary widely according to the level of demand and congestion in the network. In general, no significant improvements could be gained by phasing when the network was only marginally congested or in the absence of congestion. The methods presented can be used for other locations and for other types of hazards, but whether similar levels of effectiveness would be experienced in other locations or for other events is unknown.

A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids

As an emerging new electrical load, plug-in electric vehicles (PEVs)’ impact on the power grid has drawn increasing attention worldwide. An optimal scenario is that by digging the potential of PEVs as a moveable energy storage device, they may not harm the power grid by, for example, triggering extreme surges in demand at rush hours, conversely, the large-scale penetration of PEVs could benefit the grid through flattening the power load curve, hence, increase the stability, security and operating economy of the grid. This has become a hot issue which is known as vehicle-to-grid (V2G) technology within the framework of smart grid. In this paper, a scenario of V2G implementation within regional smart grids is discussed. Then, the problem is mathematically formulated. It is essentially an optimization problem, and the objective is to minimize the overall load variance. With the increase of the scale of PEVs and charging posts involved, the computational complexity will become tremendously high. Therefore, a double-layer optimal charging (DLOC) strategy is proposed to solve this problem. The comparative study demonstrates that the proposed DLOC algorithm can effectively solve the problem of tremendously high computational complexity arising from the large-scaled PEVs and charging posts involved.

Storm Surge to Demand Surge: Exploratory Study of Hurricanes, Labor Wages, and Material Prices

Demand surge is understood to be a socioeconomic phenomenon associated with large-scale natural disasters. It is most commonly explained as higher repair costs resulting from higher labor wages and material prices after a large- versus small-scale disaster. This paper explores this explanation by developing quantitative models for the relative cost change of sets of repairs to residential and commercial properties damaged by Atlantic hurricanes in the southeast mainland United States. Cost data from 2002–2010 show that changes in the labor component drive the changes in total repair costs and that cost changes can vary notably by year and less so by geographic region. A series of multilevel regression models is then proposed to predict the relative cost changes by considering several combinations of the following explanatory variables: the largest wind speed at a city in a hurricane season; the number of tropical storms passing near a city in a hurricane season; and relative cost changes in the first two quarters of the year. For commercial properties in Florida, the data are sufficient to develop models with only linear and interacting explanatory variables. These models, however, show a large uncertainty in expected cost changes and a systematic underprediction for larger cost changes. For residential properties in Florida and both residential and commercial properties in other states on the Atlantic and Gulf of Mexico coasts, either the data are insufficient to develop robust models or a more complex model must be proposed for relative cost changes at these property types.

An econometric analysis of the demand surge effect

Devastating natural catastrophes lead to severe damages to buildings and infrastructure which have to be eliminated in the aftermath of the event. Thus, the demand for skilled labor and materials increase sharply which in turn leads to significant price increases that should be taken into account in the forecast of catastrophe losses. This interdependency is referred to as the “Demand Surge” effect. The paper at hand presents an extensive econometric analysis and modeling of the Demand Surge effect based on data about 176 natural catastrophes in the US provided by EM-DAT. We find that Demand Surge is positively influenced by the total amount of repair work, by alternative catastrophes in the same region in close temporal proximity, and by a higher amount of insurance claims per event. Furthermore, the Demand Surge effect is more pronounced if the construction sector is in a growth stage. The contribution is a short summary of a working paper presented at the annual meeting of the German Insurance Science Association (DVfVW) in Berlin in March 2013.

To be or not to be: dilemma of Africa's economic engagement with China and other emerging economies

In this study an attempt to examine the China−Africa trade potential and the implications of exploiting that potential is made. The potential is examined using the Trade Complementarity Index and the stochastic frontier gravity model of trade. The results suggest that there is considerable potential to expand China−Africa trade. Apart from reforming trade policies, removing other behind-the-border socio-political-institutional constraints and improving trade facilitation would contribute to realization of this potential. However, the realization of this potential may entail locking African countries in primary commodity sectors (and hence de-industrialization). A non-parametric test using the ‘Galtonian regression method’ as well as a multi-country version of the Heckscher−Ohlin−Vanek trade model is employed to test this pattern. The results suggest that African countries are characterized by re-deepening specialization in the primary commodity exports sector owing to China's commodity demand surge. We also found that the indirect impacts of this demand surge are more significant. This calls for strategic policy engagement with China, India and other emerging economies such as Brazil with the aim of balancing between the temporary resource windfall gain and the future industrialization and development of the continent.

Health systems' "surge capacity": state of the art and priorities for future research.

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice. A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health. The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings. The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

An Econometric Analysis of the Demand Surge Effect

In the aftermath of a natural catastrophe, there is increased demand for skilled reconstruction labor, which leads to significant increases in reconstruction labor wages and hence insured losses. Such inflation effects are known as effects. It is important for insurance companies to properly account for these effects when calculating insurance premiums and determining economic capital. We propose an approach to quantifying the Demand Surge effect and present an econometric model for the effect that is based on 191 catastrophe events in the United States. Our model explains more than 75% of the variance of the Demand Surge effect and is thus able to identify the key drivers of the phenomenon.

Microsimulation of financial impact of demand surge on hospitals: the H1N1 influenza pandemic of fall 2009.

Microsimulation was used to assess the financial impact on hospitals of a surge in influenza admissions in advance of the H1N1 pandemic in the fall of 2009. The goal was to estimate net income and losses (nationally, and by hospital type) of a response of filling unused hospital bed capacity proportionately and postponing elective admissions (a "passive" supply response). Epidemiologic assumptions were combined with assumptions from other literature (e.g., staff absenteeism, profitability by payer class), Census data on age groups by region, and baseline hospital utilization data. Hospital discharge records were available from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS). Hospital bed capacity and staffing were measured with the American Hospital Association's (AHA) Annual Survey. Nationwide, in a scenario of relatively severe epidemiologic assumptions, we estimated aggregate net income of $119million for about 1 million additional influenza-related admissions, and a net loss of $37million for 52,000 postponed elective admissions. Aggregate and distributional results did not suggest that a policy of promising additional financial compensation to hospitals in anticipation of the surge in flu cases was necessary. The analysis identified needs for better information of several types to improve simulations of hospital behavior and impacts during demand surges.

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator.

BackgroundHealth care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics.ResultsThe AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources.ConclusionsThe AsiaFluCap Simulator is freely available software (http://www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.

The challenges of the 2004 Indian Ocean tsunami for strategic international human resource management in multinational nonprofit enterprises

In December 2004, the Indian Ocean tsunami created devastation across the Asia-Pacific region. Drawing on the resource-based view of the firm, we investigate the emerging strategic international human resource management challenges in multinational nonprofit enterprises (MNNPEs). Using a two-stage research design, we analyse open-ended survey responses, organisation documents and newspaper articles. Findings suggest that simultaneous surges in supply and demand for resources did not allow sufficient time for developing organisational capacity, adversely affecting MNNPEs’ local responses. Furthermore, the quality of human resources and the human resource systems that manage those resources form crucial aspects of an organisation's disaster management.

Pre-positioning hurricane supplies in a commercial supply chain

Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.

Is There a Link between Mitochondrial Reserve Respiratory Capacity and Aging?

Oxidative phosphorylation is an indispensable resource of ATP in tissues with high requirement of energy. If the ATP demand is not met, studies suggest that this will lead to senescence and cell death in the affected tissue. The term reserve respiratory capacity or spare respiratory capacity is used to describe the amount of extra ATP that can be produced by oxidative phosphorylation in case of a sudden increase in energy demand. Depletion of the reserve respiratory capacity has been related to a range of pathologies affecting high energy requiring tissues. During aging of an organism, and as a result of mitochondrial dysfunctions, the efficiency of oxidative phosphorylation declines. Based on examples from the energy requiring tissues such as brain, heart, and skeletal muscle, we propose that the age-related decline of oxidative phosphorylation decreases the reserve respiratory capacity of the affected tissue, sensitizes the cells to surges in ATP demand, and increases the risk of resulting pathologies.