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Lead Time (lead + time)
Selected AbstractsDemand and Production Management with Uniform Guaranteed Lead TimePRODUCTION AND OPERATIONS MANAGEMENT, Issue 4 2005Uday S. Rao Recently, innovation-oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make-to-order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead-time guarantees. We show that the optimal lead time has a closed-form solution with a newsvendor-like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation. [source] Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non-linear deterministic approachEARTH SURFACE PROCESSES AND LANDFORMS, Issue 6 2005Bellie Sivakumar Abstract As the Mississippi River plays a major role in fulfilling various water demands in North America, accurate prediction of river flow and sediment transport in the basin is crucial for undertaking both short-term emergency measures and long-term management efforts. To this effect, the present study investigates the predictability of river flow and suspended sediment transport in the basin. As most of the existing approaches that link water discharge, suspended sediment concentration and suspended sediment load possess certain limitations (absence of consensus on linkages), this study employs an approach that presents predictions of a variable based on history of the variable alone. The approach, based on non-linear determinism, involves: (1) reconstruction of single-dimensional series in multi-dimensional phase-space for representing the underlying dynamics; and (2) use of the local approximation technique for prediction. For implementation, river flow and suspended sediment transport variables observed at the St. Louis (Missouri) station are studied. Specifically, daily water discharge, suspended sediment concentration and suspended sediment load data are analysed for their predictability and range, by making predictions from one day to ten days ahead. The results lead to the following conclusions: (1) extremely good one-day ahead predictions are possible for all the series; (2) prediction accuracy decreases with increasing lead time for all the series, but the decrease is much more significant for suspended sediment concentration and suspended sediment load; and (3) the number of mechanisms dominantly governing the dynamics is three for each of the series. Copyright © 2005 John Wiley & Sons, Ltd. [source] Long-term Hydrological Forecasting in Cold Regions: Retrospect, Current Status and ProspectGEOGRAPHY COMPASS (ELECTRONIC), Issue 5 2009Alexander N. Gelfan The influence of long-term snow accumulation on the runoff conditions several months afterwards is a distinct hydrological characteristic of cold regions, which creates opportunities for long-term (seasonal and subseasonal) hydrological forecasting in these regions. We consider evolution of the long-term forecasting approaches from the deterministic data-based index methods to the hydrological model-based ensemble approaches. Of key interest in this review are the methods developed and used in operational practice in Russia and in the USA, with the emphasis being placed on the methods used in Russia, which may be less familiar to international hydrological society. Following a description of the historical context, we review recent developments that place emphasis on problems relating to the uncertainty of the weather conditions for the lead time of the forecast. We conclude with a personal view of the prospects for the future development of long-term hydrological forecasting techniques. [source] Screening for local and regional cancer recurrence in patients curatively treated for laryngeal cancer: Definition of a high-risk group and estimation of the lead timeHEAD & NECK: JOURNAL FOR THE SCIENCES & SPECIALTIES OF THE HEAD AND NECK, Issue 5 2007Savitri C. Ritoe MD Abstract Background. All patients treated for laryngeal cancer are offered the same follow-up schedule to detect asymptomatic locoregional recurrences. In this study, we evaluated the prognostic profile of patients for cancer recurrence and estimated the lead time. Methods. A cohort study was performed between 1990 and 1995. Cox proportional hazards model was used to analyze the prognostic factors. The effect of altering the follow-up for asymptomatic recurrence detection was determined after estimating the lead time. Results. The variables cT classification, smoking, and histologic grade proved to be prognostic factors. The risk of locoregional failure was 15% in the low-risk group versus 29% in the high-risk group. The estimated lead time was 2 to 4 weeks. Conclusion. Risk profiles for locoregional relapse were defined. Intensifying the follow-up schedule is not advisable because the lead time is very short. An excessively high number of routine visits would have to be performed to increase the detection rate for asymptomatic recurrences. © 2006 Wiley Periodicals, Inc. Head Neck, 2007 [source] El Niño Southern Oscillation link to the Blue Nile River Basin hydrologyHYDROLOGICAL PROCESSES, Issue 26 2009Wossenu Abtew Abstract The objective of this study is to evaluate the relationships of El Niño Southern Oscillation (ENSO) indices and the Blue Nile River Basin hydrology using a new approach that tracks cumulative ENSO indices. The results of this study can be applied for water resources management decision making to mitigate drought or flood impacts with a lead time of at least few months. ENSO tracking and forecasting is relatively easier than predicting hydrology. ENSO teleconnections to the Blue Nile River Basin hydrology were evaluated using spatial average basin rainfall and Blue Nile flows at Bahir Dar, Ethiopia. The ENSO indices were sea surface temperature (SST) anomalies in region Niño 3·4 and the Southern Oscillation Index (SOI). The analysis indicates that the Upper Blue Nile Basin rainfall and flows are teleconnected to the ENSO indices. Based on event correspondence and correlation analysis, high rainfall and high flows are likely to occur during La Niña years and dry years are likely to occur during El Niño years at a confidence level of 90%. Extreme dry and wet years are very likely to correspond with ENSO events as given above. The great Ethiopian famine of 1888,1892 corresponds to one of the strongest El Niño years, 1888. The recent drought years in Ethiopia correspond to strong El Niño years and wet years correspond to La Niña years. In this paper, a new approach is proposed on how to classify the strength of ENSO events by tracking consecutive monthly events through a year. A cumulative SST index value of ,5 and cumulative SOI value of , ,7 indicate strong El Niño. A cumulative SST index value of ,,5 and cumulative SOI index of ,7 indicate strong La Niña. Copyright © 2009 John Wiley & Sons, Ltd. [source] APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2006Moon Seong Rang ABSTRACT: The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea. [source] Long lead time flood warnings: reality or fantasy?METEOROLOGICAL APPLICATIONS, Issue 1 2009B. W. Golding Abstract This paper reviews recent advances in weather forecasting capability in the United Kingdom and their implications for increasing the lead time with which flood warnings can be issued. The events of summer 2007 have highlighted the vulnerability of parts of the United Kingdom to flooding and the need for long lead time flood warnings to enable the protection of people and critical infrastructure. Historically, computer weather forecasting models have been unable to forecast at the scales of importance for flood warning, and so the warning processes have been forced to rely on measurements on the ground. Examples are presented to demonstrate that new forecasting technologies, currently being implemented, enable warnings to be produced much earlier, provided they are couched in probabilistic terms and interpreted appropriately. Crown Copyright © 2009. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd [source] A selective newsvendor approach to order managementNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 8 2008Kevin Taaffe Abstract Consider a supplier offering a product to several potential demand sources, each with a unique revenue, size, and probability that it will materialize. Given a long procurement lead time, the supplier must choose the orders to pursue and the total quantity to procure prior to the selling season. We model this as a selective newsvendor problem of maximizing profits where the total (random) demand is given by the set of pursued orders. Given that the dimensionality of a mixed-integer linear programming formulation of the problem increases exponentially with the number of potential orders, we develop both a tailored exact algorithm based on the L-shaped method for two-stage stochastic programming as well as a heuristic method. We also extend our solution approach to account for piecewise-linear cost and revenue functions as well as a multiperiod setting. Extensive experimentation indicates that our exact approach rapidly finds optimal solutions with three times as many orders as a state-of-the-art commercial solver. In addition, our heuristic approach provides average gaps of less than 1% for the largest problems that can be solved exactly. Observing that the gaps decrease as problem size grows, we expect the heuristic approach to work well for large problem instances. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2008 [source] Capacity expansion with lead times and autocorrelated random demandNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2003Sarah M. Ryan Abstract The combination of uncertain demand and lead times for installing capacity creates the risk of shortage during the lead time, which may have serious consequences for a service provider. This paper analyzes a model of capacity expansion with autocorrelated random demand and a fixed lead time for adding capacity. To provide a specified level of service, a discrete time expansion timing policy uses a forecast error-adjusted minimum threshold level of excess capacity position to trigger an expansion. Under this timing policy, the expansion cost can be minimized by solving a deterministic dynamic program. We study the effects of demand characteristics and the lead time length on the capacity threshold. Autocorrelation acts similarly to randomness in hastening expansions but has a smaller impact, especially when lead times are short. However, the failure either to recognize autocorrelation or to accurately estimate its extent can cause substantial policy errors. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003 [source] On the (S , 1, S) lost sales inventory model with priority demand classesNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2002R. Dekker Abstract In this paper an inventory model with several demand classes, prioritised according to importance, is analysed. We consider a lot-for-lot or (S , 1, S) inventory model with lost sales. For each demand class there is a critical stock level at and below which demand from that class is not satisfied from stock on hand. In this way stock is retained to meet demand from higher priority demand classes. A set of such critical levels determines the stocking policy. For Poisson demand and a generally distributed lead time, we derive expressions for the service levels for each demand class and the average total cost per unit time. Efficient solution methods for obtaining optimal policies, with and without service level constraints, are presented. Numerical experiments in which the solution methods are tested demonstrate that significant cost reductions can be achieved by distinguishing between demand classes. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 593,610, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10032 [source] Inventory cost impact of order processing priorities based on demand uncertaintyNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 4 2002Ananth. Abstract We evaluate an approach to decrease inventory costs at retail inventory locations that share a production facility. The retail locations sell the same product but differ in the variance of retail demand. Inventory policies at retail locations generate replenishment orders for the production facility. The production facility carries no finished goods inventory. Thus, production lead time for an order is the sojourn time in a single server queueing system. This lead time affects inventory costs at retail locations. We examine the impact of moving from a First Come First Served (FCFS) production rule for orders arriving at the production facility to a rule in which we provide non-preemptive priority (PR) to orders from retail locations with higher demand uncertainty. We provide three approximations for the ratio of inventory costs under PR and FCFS and use them to identify conditions under which PR decreases retail inventory costs over FCFS. We then use a Direct Approach to establish conditions when PR decreases retail inventory costs over FCFS. We extend the results to orders from locations that differ in the mean and variance of demand uncertainty. The analysis suggests that tailoring lead times to product demand characteristics may decrease system inventory costs. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 376,390, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10016 [source] Mass Customization in the Automotive Industry: Comparing Interdealer Trading and Reconfiguration Flexibilities in Order FulfillmentPRODUCTION AND OPERATIONS MANAGEMENT, Issue 5 2010Philip G. Brabazon Mass customization has been viewed as desirable but difficult to achieve in the volume automotive sector. Here we consider flexibility in automotive order fulfillment systems to enhance the ability to satisfy customers with their desired vehicle variants within acceptable delivery lead times. Two types of flexibility are compared in a Virtual-Build-to-Order system,reconfiguration in the planning pipeline and interdealer trading. A representative simulation model is used to investigate the impact of the two types of flexibility across a wide spectrum of product variety levels. The impacts on major stakeholders in the system,the producer, dealers, and customers,are considered. The study shows that both types of flexibilities can bring significant benefits in terms of reductions in lead time and inventory holding. The level of product variety strongly influences the observed effects,an important finding in the mass customization context. Upstream reconfiguration flexibility brings greater benefits than downstream trading flexibility. Reconfiguration tends to dominate trading as a fulfillment mechanism when both are in operation. The findings have implications for the design and management of automotive order fulfillment systems in improving their ability to offer mass customization. The study has relevance for companies in other sectors with high levels of variety that seek to combine efficiency, speed, and flexibility in order fulfillment. [source] Demand and Production Management with Uniform Guaranteed Lead TimePRODUCTION AND OPERATIONS MANAGEMENT, Issue 4 2005Uday S. Rao Recently, innovation-oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make-to-order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead-time guarantees. We show that the optimal lead time has a closed-form solution with a newsvendor-like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation. [source] A Global Model for Forecasting Political InstabilityAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 1 2010Jack A. Goldstone Examining onsets of political instability in countries worldwide from 1955 to 2003, we develop a model that distinguishes countries that experienced instability from those that remained stable with a two-year lead time and over 80% accuracy. Intriguingly, the model uses few variables and a simple specification. The model is accurate in forecasting the onsets of both violent civil wars and nonviolent democratic reversals, suggesting common factors in both types of change. Whereas regime type is typically measured using linear or binary indicators of democracy/autocracy derived from the 21-point Polity scale, the model uses a nonlinear five-category measure of regime type based on the Polity components. This new measure of regime type emerges as the most powerful predictor of instability onsets, leading us to conclude that political institutions, properly specified, and not economic conditions, demography, or geography, are the most important predictors of the onset of political instability. [source] Secular Trends in the Incidence of Female Breast Cancer in the United States, 1973,1998THE BREAST JOURNAL, Issue 2 2004Kiumarss Nasseri DVM Abstract: , Statistical modeling suggests a causal association between the rapid increase in the incidence of female breast cancer (FBC) in the United States and the widespread use of screening mammography. Additional support for this suggestion is a shift in the stage at diagnosis that consists of an increase in early stage diagnosis followed by a decrease in late-stage diagnosis. This has not been reported in the United States. The objective of this study was to examine the secular trends in the incidence of FBC in search of empirical support for this shift. FBC cases in the Surveillance, Epidemiology, and End Results (SEER) database from 1973 through 1998 were dichotomized into early and late detection based. Early detection included all the in situ and invasive cases with local spread. Late detection included cases with regional spread and distant metastasis. Joinpoint segmented regression modeling was used for trend analysis. Early detection in white and black women followed a similar pattern of significant increase in the early 1980s that continued through 1998 with slight modification in 1987. The expected shift in stage was noticed only for white women when the incidence of late detection in them began to decline in 1987. The incidence of late detection in black women has remained stable. These results provide further support for the previously implied causal association between the use of screening mammography and the increased incidence of FBC in the United States. It also shows that the expected stage shift appeared in white women 50,69 years of age after an estimated detection lead time (DLT) of about 5 years. This is the first estimate of DLT in the United States that is based on actual data. The subsequent increase in late detection in white women since 1993 may be due to changes in case management and the increased use of sentinel lymph node biopsy (SLNB) rather than changes in the etiology or biology of FBC., [source] Medium-range multimodel ensemble combination and calibrationTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 640 2009Christine Johnson Abstract As part of its contribution to The Observing System Research and Predictability Experiment (THORPEX), the Met Office has developed a global, 15 day multimodel ensemble. The multimodel ensemble combines ensembles from the European Centre for Medium-Range Weather Forecasts (ECMWF), Met Office and National Centers for Environmental Prediction (NCEP) and is calibrated to give further improvements. The ensemble post-processing includes bias correction, model-dependent weights and variance adjustment, all of which are based on linear-filter estimates using past forecast-verification pairs, calculated separately for each grid point and forecast lead time. Verification shows that the multimodel ensemble gives an improvement in comparison with a calibrated single-model ensemble, particularly for surface temperature. However, the benefits are smaller for mean-sea-level pressure (mslp) and 500 hPa height. This is attributed to the higher degree of forecast-error similarity between the component ensembles for mslp and 500 hPa height than for temperature. The results also show only small improvements from the use of the model-dependent weights and the variance adjustment. This is because the component ensembles have similar levels of skill, and the multimodel ensemble variance is already generally well calibrated. In conclusion, we demonstrate that the multimodel ensemble does give benefit over a single-model ensemble. However, as expected, the benefits are small if the ensembles are similar to each other and further post-processing gives only relatively small improvements. © Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd. [source] Boreal winter predictions with the GEOS-2 GCM: The role of boundary forcing and initial conditionsTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 567 2000Yehui Chang Abstract Ensembles of atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulations are analysed to assess the controlling influences of boundary forcing and memory of the initial conditions. Both the forecasts and simulations are carried out with version 2 of the Goddard Earth Observing System (GEOS-2) GCM forced with observed sea surface temperatures (SSTs). While much of the focus is on the seasonal time-scale (January-March; 1981,95) and the Pacific North American (PNA) region, we also present results for other regions, shorter time-scales, and other known modes of variability in the northern hemisphere extratropics. Forecasts of indices of some of the key large-scale modes of variability show that there is considerable variability in skill between different regions of the northern hemisphere. The eastern North Atlantic region has the poorest long-lead forecast skill, showing no skill beyond about 10 days. Skilful seasonal forecasts are primarily confined to the wave-like El Niño Southern Oscillation (ENSO) response emanating from the tropical Pacific. In the northern hemisphere, this is similar to the well-known PNA pattern. Memory of the initial conditions is the major factor leading to skilful extratropical forecasts of lead time less than one month, while boundary forcing is the dominant factor at the seasonal time-scale. Boundary forcing contributes to skilful forecasts at sub-seasonal time-scales only over the PNA region. The GEOS-2 GCM produces average signal-to-noise ratios which are less than 1.0 everywhere in the extra-tropics, except for the subtropical Pacific where they approach 1.5. An assessment of the sampling distribution of the forecasts suggests the model's ENSO response is very likely too weak. These results show some sensitivity to the uncertainties in the estimates of the SST forcing fields. In the North Pacific region, the sensitivity to SST forcing manifests itself primarily as changes in the variability of the PNA response, underscoring the need for an ensemble approach to the seasonal-prediction problem. [source] An integrated inventory model with controllable lead time and distribution-free demandAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010Shu-Lu Hsu Abstract The impact of lead time reduction on an integrated periodic review inventory system comprising a single vendor and multiple buyers with a step crashing cost function and service-level constraints is studied. The probability distribution of demand during the protection period for each buyer is unknown, but the mean and the variance are given. Each production lot of the vendor can be delivered in a number of shipments to all buyers. A minimax distribution-free procedure with Lagrange multipliers is applied to determining the lead time, the common shipment cycle time, the target levels of replenishments and the number of shipments per production cycle so that the expected total system cost is minimized. Numerical experiments along with sensitivity analysis were performed to illustrate the effects of parameters on the decision and the total system cost. Copyright © 2009 John Wiley & Sons, Ltd. [source] Ensemble forecasting using TIGGE for the July,September 2008 floods in the Upper Huai catchment: a case studyATMOSPHERIC SCIENCE LETTERS, Issue 2 2010Yi He Abstract We present a case study using the TIGGE database for flood warning in the Upper Huai catchment (ca. 30 672 km2). TIGGE ensemble forecasts from 6 meteorological centres with 10-day lead time were extracted and disaggregated to drive the Xinanjiang model to forecast discharges for flood events in July-September 2008. The results demonstrated satisfactory flood forecasting skills with clear signals of floods up to 10 days in advance. The forecasts occasionally show discrepancies both in time and space. Forecasting quality could potentially be improved by using temporal and spatial corrections of the forecasted precipitation. Copyright © 2010 Royal Meteorological Society [source] The frontline and the ivory tower: A case study of service and professional-driven curriculumAUSTRALIAN JOURNAL OF RURAL HEALTH, Issue 3 2009Sue Lenthall Abstract Objective:,To describe the development of a postgraduate, multidisciplinary program designed to meet the needs of remote health professionals, present formative evaluation findings and to offer an analysis of the difficulties and lessons learnt. Design:,Case study. Setting:,University Department of Rural Health in a remote region. Participants:, University staff, students and stakeholders involved in the development of the remote health practice program. Results:,Formative evaluation suggests that a curriculum driven by service and professional groups, such as the Flinders University Remote Health Practice program, is able to better prepare remote health practitioners and improve their effectiveness. Difficulties in development included a lack of recognition by some university academics of the value of practitioner knowledge and a reluctance to accept a clinical component in a masters program. Lessons learnt included the importance of: (i) respect for practitioner knowledge; (ii) explicit and appropriate values; (iii) high-quality academics with strong service links; (iv) appropriate length of lead time; (v) institutional links between university and both relevant professional organisations and health services; (vi) a receptive university; (vii) location; and (viii) ongoing engagement with services and professional responsive development. Conclusion:,The success of the program was due in large part to the relationship with professional bodies and close links with remote health services. We have described a number of lessons learnt from this experience that can be useful to other educational groups developing or revising their educational programs. [source] The Value of Production Schedule Integration in Supply ChainsDECISION SCIENCES, Issue 4 2001Lee Krajewski Abstract This study explores the value of integrated production schedules for reducing the negative effects of schedule revisions in supply chains involving buyer and supplier firms. A stochastic cost model is developed to evaluate the total supply chain cost with integrated purchasing and scheduling policies. The model minimizes the costs associated with assembly rate adjustment, safety stock, and schedule changes for all supply chain members. Through experimentation, the paper examines the impact of several environmental factors on the value of schedule integration. This study finds that schedule integration can lead to overall cost savings in a supply chain, but some firms may have to absorb costs in excess of those they would incur with independent scheduling. Environments with high inventory holding costs and long supplier lead times may not find it beneficial to adopt an integrated schedule. Forecast effectiveness plays a critical role in realizing the benefits of schedule integration. The paper concludes with suggestions for future research. [source] Temporal Elements in the Spatial Extension of Production NetworksGROWTH AND CHANGE, Issue 4 2006JOHAN WOXENIUS ABSTRACT The spatial extension of production networks presents a significant challenge to managers accustomed to reducing lead times by geographically contracting supply chains. This paper extends the theory on time in transportation by defining the elements of transport time, order time, timing, punctuality, and frequency and elaborating on their characteristics. Structured along these elements, it analyses the consequences of extending production networks from within a mature economic region, mainly the EU-15, U.S., and Japan, first to adjacent and then to nearby and finally distant low-cost regions. Distance obviously affects the transport quality in all time dimensions. Except for air parcel services that globally match what road transport offers within an economic region, the longer the distance, the lower the time-related performance. Distant, low-cost regions, meaning China and India, also imply a polarisation between air and sea transport at opposite ends of the time, cost, and capacity scales. This supply gap restricts the types of products traded. The conceptual framework is illustrated in the setting of a global vehicle manufacturer spatially extending its sourcing. It demands that sequenced sub-assemblies and small, cheap, and generic components are delivered from the vicinity of each assembly plant. Batched components can be sourced from adjacent regions, but deliveries from longer distances imply storage at pick-up points to fulfil their time requirements. Hence, the suppliers must offer the manufacturing firm deliveries as if they produce relatively close to the assembly plants. [source] Statistical prediction of global sea-surface temperature anomaliesINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2003A. W. Colman Abstract Sea-surface temperature (SST) is one of the principal factors that influence seasonal climate variability, and most seasonal prediction schemes make use of information regarding SST anomalies. In particular, dynamical atmospheric prediction models require global gridded SST data prescribed through the target season. The simplest way of providing those data is to persist the SST anomalies observed at the start of the forecast at each grid point, with some damping, and this strategy has proved to be quite effective in practice. In this paper we present a statistical scheme that aims to improve that basic strategy by combining three individual methods together: simple persistence, canonical correlation analysis (CCA), and nearest-neighbour regression. Several weighting schemes were tested: the best of these is one that uses equal weight in all areas except the east tropical Pacific, where CCA is preferred. The overall performance of the combined scheme is better than the individual schemes. The results show improvements in tropical ocean regions for lead times beyond 1 or 2 months, but the skill of simple persistence is difficult to beat in the extratropics at all lead times. Aspects such as averaging periods and grid size were also investigated: results showed little sensitivity to these factors. The combined statistical SST prediction scheme can also be used to improve statistical regional rainfall forecasts that use SST anomaly patterns as predictors. Copyright © Crown Copyright 2003. Published by John Wiley & Sons, Ltd. [source] Small-sample confidence intervals for multivariate impulse response functions at long horizonsJOURNAL OF APPLIED ECONOMETRICS, Issue 8 2006Elena Pesavento Existing methods for constructing confidence bands for multivariate impulse response functions may have poor coverage at long lead times when variables are highly persistent. The goal of this paper is to propose a simple method that is not pointwise and that is robust to the presence of highly persistent processes. We use approximations based on local-to-unity asymptotic theory, and allow the horizon to be a fixed fraction of the sample size. We show that our method has better coverage properties at long horizons than existing methods, and may provide different economic conclusions in empirical applications. We also propose a modification of this method which has good coverage properties at both short and long horizons. Copyright © 2006 John Wiley & Sons, Ltd. [source] Field-Scale Application of Three Types of Neural Networks to Predict Ground-Water Levels,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 5 2007Tirusew Asefa Abstract:, In this paper, a field-scale applicability of three forms of artificial neural network algorithms in forecasting short-term ground-water levels at specific control points is presented. These algorithms are the feed-forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground-water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground-water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field-scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored. [source] APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2006Moon Seong Rang ABSTRACT: The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea. [source] Use of medium-range ensembles at the Met Office 2: Applications for medium-range forecastingMETEOROLOGICAL APPLICATIONS, Issue 3 2002M V Young The term ,medium range' is taken to refer to forecasts for lead times ranging from about 2 or 3 days ahead up to about 10 days ahead. A wide variety of numerical model products are available to the forecaster nowadays, and one of the most important of these is the ECMWF Ensemble Prediction System (EPS). This paper shows how forecasters at the Met Office use these products, in particular the EPS, in an operational environment in the production of medium-range forecasts for a variety of customers, and illustrates some of the techniques involved. Particular reference is made to the PREVIN post-processing system for the EPS which is described in the companion paper by Legg et al. (2002). Forecast products illustrated take the form of synoptic charts (produced primarily via Field Modification software), text guidance and other graphical formats. The probabilistic approach to forecasting is discussed with reference to various examples, in particular the application of the EPS in providing early warnings of severe weather for which risk assessment is increasingly important. A central theme of this paper is the vital role played by forecasters in interpreting the output from the models in terms of the likely weather elements, and using the EPS to help assess confidence levels for a particular forecast as well as possible alternative synoptic evolutions. Verification statistics are presented which demonstrate how the EPS helps the forecaster to add value to the wide range of individual deterministic model products and that furthermore, the forecaster can improve upon many probabilistic products derived directly from the ensemble. Copyright © 2002 Royal Meteorological Society. [source] Capacity expansion under a service-level constraint for uncertain demand with lead timesNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 3 2009Rahul R. Marathe Abstract For a service provider facing stochastic demand growth, expansion lead times and economies of scale complicate the expansion timing and sizing decisions. We formulate a model to minimize the infinite horizon expected discounted expansion cost under a service-level constraint. The service level is defined as the proportion of demand over an expansion cycle that is satisfied by available capacity. For demand that follows a geometric Brownian motion process, we impose a stationary policy under which expansions are triggered by a fixed ratio of demand to the capacity position, i.e., the capacity that will be available when any current expansion project is completed, and each expansion increases capacity by the same proportion. The risk of capacity shortage during a cycle is estimated analytically using the value of an up-and-out partial barrier call option. A cutting plane procedure identifies the optimal values of the two expansion policy parameters simultaneously. Numerical instances illustrate that if demand grows slowly with low volatility and the expansion lead times are short, then it is optimal to delay the start of expansion beyond when demand exceeds the capacity position. Delays in initiating expansions are coupled with larger expansion sizes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009 [source] Production planning with resources subject to congestionNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2009Jakob Asmundsson Abstract A fundamental difficulty in developing effective production planning models has been accurately reflecting the nonlinear dependency between workload and lead times. We develop a mathematical programming model for production planning in multiproduct, single stage systems that captures the nonlinear dependency between workload and lead times. We then use outer linearization of this nonlinear model to obtain a linear programming formulation and extend it to multistage systems. Extensive computational experiments validate the approach and compare its results to conventional models that assume workload-independent planning lead times. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009 [source] Outbound supply chain network design with mode selection and lead time considerationsNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 3 2007Erdem Eskigun Abstract We present a large-scale network design model for the outbound supply chain of an automotive company that considers transportation mode selection (road vs. rail) and explicitly models the relationship between lead times and the volume of flow through the nodes of the network. We formulate the problem as a nonlinear zero-one integer program, reformulate it to obtain a linear integer model, and develop a Lagrangian heuristic for its solution that gives near-optimal results in reasonable time. We also present scenario analyses that examine the behavior of the supply chain under different parameter settings and the performance of the solution procedures under different experimental conditions. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007 [source] | |||||||||||||||||||||||||