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A total minimum commitment contract is a supply contract under which a firm commits to buying a minimum quantity of a product from its supplier during the contract duration (e.g., one year). Such contracts are widely used in industries, because they provide the buyer with flexibility in terms of the timing and size of each individual order and the supplier with a guaranteed total order volume. Previous studies on such contracts are primarily concerned with the firm's inventory decisions, but none of them has considered the coordination of inventory and pricing decisions. In this paper, we fill this gap by studying dynamic inventory and pricing problems under a commitment contract. Under a general demand model with backlogging and zero lead time, we prove that the optimal policy is a committed-inventory-position-dependent base-stock list-price policy and characterize its structural properties. We also conduct an extensive numerical study to derive further managerial insights. In particular, we find that dynamic pricing can substantially improve the firm's profitability and enables it to select contracts with large committed quantities and price discount rates, and ignoring the commitment in joint pricing and ordering decisions can result in substantial profit loss. Finally, we partly extend our results to a lost-sales model and a backlogging model with positive lead times.

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.

Warehouse solutions in emerging countries near suppliers have been adopted to achieve cost efficiency in offshore procurement. Previous academic literature ignores logistics operations costs in supply chain contracting. In this paper, we study contracting between a retailer in a developed country and a supplier in an emerging country while incorporating total landed cost (including logistics operations costs) in contracting. The retailer faces stochastic demand and stochastic lead time and determines the warehousing strategy. She can hold cycle stock and safety stock at the retail location (developed country warehousing) or allow the supplier to hold second‐tier cycle stock at a warehouse in the emerging country in addition to her holding inventory at the retail location (emerging country warehousing). We study the retailer’s optimal warehousing strategy in a centralized supply chain and a decentralized supply chain with the retailer as the Stackelberg leader. When emerging country warehousing is adopted in the decentralized supply chain, the retailer and supplier could achieve a win–win or a win–lose outcome compared to developed country warehousing. Our results show that the retailer’s warehousing strategies in the centralized and decentralized supply chains could be different. We further study the retailer’s optimal warehousing strategy under the Nash bargaining framework where the wholesale price and logistics decisions are negotiated simultaneously given the warehousing strategy. We compare this contract to that where logistics costs are excluded from contract negotiation. Our results show that by including logistics costs into contract negotiation the retailer and supplier could achieve lose–win, win–win and win–lose outcomes.

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Furthermore, we study the case where the retailer works with the manufacturer to expand capacity by the revenue and cost sharing contract, and characterize the conditions under which Pareto optimality and coordination can be achieved. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic.

We study a periodic‐review assemble‐to‐order (ATO) system with multiple components and multiple products, in which the inventory replenishment for each component follows an independent base‐stock policy and stochastic product demands are satisfied according to a First‐Come‐First‐Served rule. We assume that the replenishment for various component suffers from lead time uncertainty. However, the decision maker has the so‐called advance supply information (ASI) associated with the lead times and thus can take advantage of the information for system optimization. We propose a multistage stochastic integer program that incorporates ASI to address the joint optimization of inventory replenishment and component allocation. The optimal base‐stock policy for the inventory replenishment is determined using the sample average approximation algorithm. Also, we provide a modified order‐based component allocation (MOBCA) heuristic for the component allocation. We additionally consider a special case of the variable lead times where the resulting two‐stage stochastic programming model can be characterized as a single‐scenario case of the proposed multistage model. We carry out extensive computational studies to quantify the benefits of integrating ASI into joint optimization and to explore the possibility of employing the two‐stage model as a relatively efficient approximation scheme for the multistage model.

More frequent and stronger flood hazards in the last two decades\ud have caused considerable environmental and socio-economic losses\ud in many regions of the Amazon basin. It is therefore critical to\ud advance predictions for flood levels, with adequate lead times, to\ud provide more effective and earlier warnings to safeguard lives and\ud livelihoods. Water-level variations in large, low-lying, free-flowing\ud river systems in the Amazon basin, like the Negro River, follow\ud large-scale precipitation anomalies. This offers an opportunity to\ud predict maximum water levels using observed antecedent rainfall.\ud This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at\ud Manaus, occurring between May–July. We develop forecast models\ud using multiple linear regression methods, to produce forecasts that\ud can be issued in March, February and January. Potential predictors\ud include antecedent catchment rainfall and water levels, large-scale\ud modes of climate variability and the long-term linear trend in water\ud levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than\ud existing models at similar lead times. All models lose performance\ud at longer lead times, as expected. However, our forecast models can\ud issue skilful operational forecasts in March or earlier. We show the\ud forecasts for the Negro River maximum water level at Manaus for\ud 2020 and 2021.

Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-up dating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.

Calibration and downscaling of ensemble GCM forecasts is becoming increasingly important for hydrological and agricultural modelling in support of the management and protection of valuable natural resources. Moreover, skilful and reliable daily forecast sequences are required to drive decision support models that operate on a daily time step. While downscaling of daily GCM outputs has been developed extensively in climate impacts studies, much less attention has been paid to the downscaling of ensemble GCM forecasts, which has the confronting aspect of low and diminishing skill with increasing lead time. Evidence is building that simple bias‐correction methods that do not model correlation between forecasts and observations, nor attempt to correct spatial, temporal and inter‐variable correlations, produce downscaled forecasts that perform poorly in applications models. Thus downscaling GCM forecasts requires inclusion of a full calibration component to reduce bias, improve reliability, capture skill where it is available and remove negative skill. In this study, we propose a new methodology for coupling a full GCM forecast calibration with empirical methods to (a) instil correct temporal, spatial and inter‐variable correlations in ensembles and (b) perform a multivariate downscaling of ensembles to daily sequences at multiple sites. Through a case study application, the proposed methodology is shown to produce skilful monthly–seasonal forecasts of rainfall, temperature and solar radiation at regional spatial scales. It also produces realistic and coherent multivariate daily sequences at multiple sub‐grid locations. The new methodology can be applied to more effectively integrate climate forecasts into hydrological and crop models and to support proactive decision‐making in agriculture and natural resources management.

We consider a firm which faces a Poisson customer demand and uses a base-stock policy to replenish its inventories from an outside supplier with a fixed lead time. The firm can use a preorder strategy which allows the customers to place their orders before their actual need. The time from a customer's order until the date a product is actually needed is called commitment lead time. The firm pays a commitment cost which is strictly increasing and convex in the length of the commitment lead time. For such a system, we prove the optimality of bang-bang and all-or-nothing policies for the commitment lead time and the base-stock policy, respectively. We study the case where the commitment cost is linear in the length of the commitment lead time in detail. We show that there exists a unit commitment cost threshold which dictates the optimality of either a buy-to-order (BTO) or a buy-to-stock strategy. The unit commitment cost threshold is increasing in the unit holding and backordering costs and decreasing in the mean lead time demand. We determine the conditions on the unit commitment cost for profitability of the BTO strategy and study the case with a compound Poisson customer demand.

We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items.

Forecast-Informed Reservoir Operations (FIRO) enables modern weather forecasting technology to be incorporated into Water Control Plans. FIRO provides water managers with more lead time to selectiv...

Global flood forecasting systems rely on predefining flood thresholds to highlight potential upcoming flood events. Existing methods for flood threshold definition are often based on reanalysis datasets using a single threshold across all forecast lead times, such as in the Global Flood Awareness System. This leads to inconsistencies between how the extreme flood events are represented in the flood thresholds and the ensemble forecasts. This paper explores the potential benefits of using river flow ensemble reforecasts to generate flood thresholds that can deliver improved reliability and skill, increasing the confidence in the forecasts for humanitarian and civil protection partners. The choice of dataset and methods used to sample annual maxima in the threshold computation, both for reanalysis and reforecast, is analysed in terms of threshold magnitude, forecast reliability, and skill for different flood severity levels and lead times. The variability of threshold magnitudes, when estimated from the different annual maxima samples, can be extremely large, as can the subsequent impact on forecast skill. Reanalysis‐based thresholds should only be used for the first few days, after which ensemble‐reforecast‐based thresholds, that vary with forecast lead time and can account for the forecast bias trends, provide more reliable and skilful flood forecasts.

A novel technique to produce reasonable worst‐case rainfall scenarios from ensemble forecasts is presented. This type of scenario is relevant for predicting the risk of localized, intense rainfall events with a duration between 15 min and several hours. Such rainfall events can cause surface‐water (pluvial) flooding. Producing useful forecasts of these events at lead times of more than a few hours is challenging due to the precision and accuracy in rainfall intensity, duration and location that is required. The technique described here addresses these challenges by constructing appropriate scenarios using a neighbourhood technique in combination with ensemble forecasting. It is similar to the distance‐dependent depth–duration analysis described in earlier studies, but it introduces an additional post‐processing step based on probability distribution functions of rainfall accumulation near a location of interest. This additional step makes the reasonable worst‐case scenarios less dependent on grid‐scale behaviour, and helps to generate scenarios with a consistent interpretation. The method is used to compare forecasts with a lead time of 6–36 hr to radar data for several case studies that occurred in Yorkshire. These comparisons also introduce new techniques to present maps of the reasonable worst‐case rainfall accumulation at each location.

Significant hindcast skill for the 3-month standardized precipitation index (SPI$_{3M}$) has been so far limited to one lead month. To increase that lead time, we propose to exploit well-known El N...

Ecological restoration has widely variable outcomes from successes to partial or complete failures, and there are diverse perspectives on the factors that influence the likelihood of success. However, not much is known about how these factors are perceived, and whether people's perceptions match realities. We surveyed 307 people involved in the restoration of native vegetation across Australia to identify their perceptions on the factors influencing the success of restoration projects. We found that weather (particularly drought and flooding) has realized impacts on the success of restoration projects, but is not perceived to be an important risk when planning new projects. This highlights the need for better recognition and management of weather risk in restoration and a potential role of seasonal forecasting. We used restoration case studies across Australia to assess the ability of seasonal forecasts provided by the Predictive Ocean Atmosphere Model for Australia, version M24 (POAMA-2) to detect unfavorable weather with sufficient skill and lead time to be useful for restoration projects. We found that rainfall and temperature variables in POAMA-2 predicted 88% of the weather issues encountered in restoration case studies apart from strong winds and cyclones. Of those restoration case studies with predictable weather issues, POAMA-2 had the forecast skill to predict the dominant or first-encountered issue in 67% of cases. We explored the challenges associated with uptake of forecast products through consultation with restoration practitioners and developed a prototype forecast product using a local case study. Integrating seasonal forecasting into decision making through (1) identifying risk management strategies during restoration planning, (2) accessing the forecast a month prior to revegetation activities, and (3) adapting decisions if extreme weather is forecasted, is expected to improve the establishment success of restoration.

Aims\ud Utilisation of temporary nursing staff is contentious and expensive. Using e-rostering data from 77 hospital units, this research investigates whether longer roster lead-times reduce temporary staff usage. \ud \ud Background\ud It is commonly assumed that longer roster approval lead-times, the time from when a roster is approved, to when it is worked, result in better, more cost-effective rosters. Consequently, many hospitals target lead-times of six weeks, a figure recommended for the UK National Health Service (NHS) in a recent governmental review. This contrasts with the minimum lead-time advocated by New South Wales Ministry of Health, which advises a shorter lead-time of two weeks. Using data from 77 hospital units, this paper explores this assumed relationship.\ud \ud Design\ud Using data extracted from the e-rostering system of an NHS Acute Foundation Trust, this study uses linear regression analysis to explore the relationship between roster approval lead-time and temporary staff usage. The data were captured over a period of nine months from 15th February 2016 to 23rd October 2016, a total of 693 rosters. \ud \ud Results/Findings\ud This research suggests that late roster approval may contribute to as much as 37% of temporary staff usage, while approval 4-6 weeks prior to the roster being worked reduces this to approximately 15%. However, this is only relevant under specific conditions. Importantly, this should be considered before mandating lead times across all units.\ud \ud Conclusions\ud This research implies that the optimum approval lead-time lies between four to six weeks, however, given other challenges, achieving this in practice may prove difficult.

Surprisingly, survival from a diagnosis of lung cancer has been found to be longer for those who experienced a previous cancer than for those with no previous cancer. A possible explanation is lead-time bias, which, by advancing the time of diagnosis, apparently extends survival among those with a previous cancer even when they enjoy no real clinical advantage. We propose a discrete parametric model to jointly describe survival in a no-previous-cancer group (where, by definition, lead-time bias cannot exist) and in a previous-cancer group (where lead-time bias is possible). We model the lead time with a negative binomial distribution and the post-lead-time survival with a linear spline on the logit hazard scale, which allows for survival to differ between groups even in the absence of bias; we denote our model Logit-Spline/Negative Binomial. We fit Logit-Spline/Negative Binomial to a propensity-score matched subset of the Surveillance, Epidemiology, and End Results-Medicare linked data set, conducting sensitivity analyses to assess the effects of key assumptions. With lung cancer-specific death as the end point, the estimated mean lead time is roughly 11 months for stage II with overall survival, it is roughly 3.4 months in stage I&II. For patients with higher-stage lung cancers, the mean lead time is 1 month or less for both outcomes. Accounting for lead-time bias reduces the survival advantage of the previous-cancer group when one exists, but it does not nullify it in all cases.

This paper investigates the case of shipments of containers with avocados from farmers in Africa to grocery store shelves in the European Union. We find 3 predominant challenges to containerized shipping that effectively become trade barriers: international trade cost, lead time uncertainty, and security risks. We use activity theory to describe, understand, and analyze the shipping activity in the international trade ecosystem with focus on physical objects and their related information. We find that the shipment becomes problematic and can be characterized as a runaway object in the heterogeneous and multiple organizational setting of international trade. Our analysis of shipping reveals (1) inefficient collaboration across loosely coupled activity systems and (2) fragmented information infrastructures. We propose the solution of Shipping Information Pipeline, a shared information infrastructure, thus facilitating collaboration in containerized shipping and contributing to lowering trade barriers. Shipping Information Pipeline can significantly improve containerized shipping resulting in estimated potential benefits of up to 4.7% growth in global GDP.

Seasonal mean soil moisture predictability ( x ‐axis) versus forecast skill ( y ‐axis) in terms of anomaly correlation at different lead time. Different colors represent different models. All statistics are calculated by using NMME hindcasts during 1982–2010.

Determining a rational planned lead time (PLT) is a critical and difficult problem in production planning, especially for a remanufacturing system. This paper considers an optimisation problem of t ...

This study extends the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10-m wind speed over a grid. The AnEn has been widely used to generate short-term probabilistic predictions of meteorological variables, air quality, wind and solar power, and to effectively downscale reanalysis fields. It is based on a historical data set including observations paired with corresponding deterministic predictions. For each forecast lead time and location, the AnEn is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. So far, the AnEn has been used to generate predictions at specific locations with available observations. Here, it is extended over a two-dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations. The forecast range is extended up to six days ahead to evaluate the performance of the AnEn in both the short and medium-range. The proposed approach allows to extend the AnEn to applications requiring gridded probabilistic products that are often generated with dynamical ensemble models. The AnEn forecasts are generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic and analysis model. Also, the ECMWF Ensemble Prediction System (EPS) is used for comparison. Given that the AnEn predictions are generated independently at any locations and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique called Schaake Shuffle (SS) is then applied to the ensemble members to recover the spatio-temporal correlation. The AnEn outperforms a calibrated version of EPS for the first two days of the prediction. During the third forecast day the AnEn remains competitive with the calibrated EPS, while the latter is more skillful from 72 to 144 hours ahead. The AnEn uses about 1/6 of the computational resources necessary to generate the real-time EPS prediction.

Service level agreements (SLAs) are widely adopted performance-based contracts in operations management practice, and fill rate is the most common performance metric among all the measurements in SLAs. Traditional procedures characterizing the order-up-to level satisfying a specified fill rate implicitly assume an infinite performance review horizon. However, in practice, inventory managers are liable to maintain and report fill rates over a finite performance review horizon. This horizon discrepancy leads to deviation between the target fill rate and actual achieved fill rate. In this study, we first examine the behavior of the fill rate distribution over a finite horizon with positive lead time. We analytically prove that the expected fill rate assuming an infinite performance review horizon exceeds the expected fill rate assuming a finite performance review horizon, implying that there exists some inventory “waste” (i.e., overstocking) when the traditional procedure is used. Based on this observation and the complexity of the problem, we propose a simulation-based algorithm to reduce excess inventory while maintaining the contractual target fill rate. When the lead time is significant relative to the length of the contract horizon, we show that the improvement in the inventory system can be over 5%. Further, we extend our basic setting to incorporate the penalty for failing to meet a target, and show how one can solve large-scale problems via stochastic approximation. The primary managerial implication of our study is that ignoring the performance review horizon in an SLA will cause overstocking, especially when the lead time is large. This article is protected by copyright. All rights reserved.

The standardized precipitation index (SPI) is an important yet easy-to-calculate means to describing wet or dry conditions in very different climates. In this work, we develop a new scheme for improved one-month lead-time forecasts of this index over Russia. As a basic seasonal forecasting model, we utilize the semi-implicit semi-Lagrangian vorticity-divergence (SL–AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences. Based on hindcast simulations of this model, we demonstrate its relatively poor skills in obtaining direct one-month lead-time SPI forecasts in the region of interest. In order to improve the accuracy of these forecasts, we use mean sea-level pressure and 500 hPa geopotential height fields from model output of the same SL–AV hindcasts to identify informative predictors for the local SPI values, based on the observation that the cross-correlation structure between the three different fields reveals relevant interdependencies between precipitation, mean sea-level pressure and 500 hPa geopotential height in different regions. Using this information in terms of regression models for obtaining both, deterministic and probabilistic forecasts provides a significant improvement of the SPI forecast skills, pointing to the potential for implementing the proposed scheme in operational one-month lead-time precipitation forecasts.

We consider inventory management of a firm that orders periodically from a distantly located supplier and faces random delays in shipment. We analyze various response strategies that the firm may employ to mitigate the risk of demand–supply mismatch resulting from shipment delays. We categorize these strategies along two dimensions: operational flexibility, defined by the flexible resource employed; and lead-time visibility, defined by the availability of information on shipment delays. For each of the response strategies, we provide analytical characterization of the optimal inventory policy. By comparing the optimal policies across response strategies, we offer insights into the effect of operational flexibility and lead-time visibility on firm's inventory performance. Using an extensive set of numerically solved examples, we provide cost comparison across these strategies—we show that while the operational flexibility mitigates the adverse effect of long lead-times, the lead-time visibility provides protection from lead-time uncertainty. Further, operational flexibility and lead-time visibility enhance each other's values, hence must be employed together to gain their full benefits.

As markets become more competitive and dynamic, manufacturing plants are undergoing transitions toward flexible, agile, and low costs operations. Appropriate coordination within the supply chain is an important factor in manufacturing systems' performance. In this study, the impact of preemptive control action in advance of an upcoming demand change on the economic performance of a cryogenic air separation unit is investigated. The effects of various factors are explored through optimization formulations utilizing a high-fidelity collocation based dynamic process model. This includes the amount of lead time, choice of manipulated inputs, direction of demand change, and liquid product market conditions. Plant performance is evaluated and analyzed through a comprehensive multipart case study. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3845–3859, 2017

While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal (S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

Radio frequency identification (RFID) helps improve supply chain efficiency by providing item-level identification and real-time information. Today, barcode continues to be the main identification technology for precast construction applications. In this research, we investigate the data-driven mechanisms and benefits of utilizing RFID in knowledge-based precast construction supply chains. With computer-aided self-learning capability, we simulate three models for manual-, barcode-, and RFID-enabled precast construction supply chain. The results of 100 precast wall-panel construction in a two-echelon precast construction supply chain reveal that the knowledge-based RFID system could generate 62.0% saving of operational costs, which is 29.0% higher than that of a barcode-based system. As a result, the computer-aided adaptive learning mechanism based on RFID is verifiable to improve the overall operational performance by reducing lead time, operational errors, and costs. Due to the lack of existing literature of data technology utilization in the precast construction industry, our findings in this research could improve the decision making regarding technology selection, as well as help with the operationalization of RFID and transformation to intelligent precast construction management in big data environment.

China's worst flooding since 1998 occurred over the Yangtze River Valley from 30 June to 6 July 2016. This study investigated the event using a new method – the spectral nudging and update cycle (SN + UIC) – in the regional Weather Research and Forecasting model, fuller use of small-scale features by using multi-scale blending. The SN + UIC was found to be successful in improving the prediction of this persistent severe rainfall event; and the larger the magnitude and longer the lead time, the more obvious the improvement. It was also found that the use of this new method decreased the root-mean-square error for related meteorological variables.

We show that ensemble forecasts of extreme European windstorms can be improved up to a lead time of 36–48 hr by sub-selecting ensemble members based on their performance at very short lead times (up to 12 hr). This applies to both the ensemble mean position of the cyclone centre and the ensemble windstorm footprint over the continent. A number of ensemble forecasts, including those from the ECMWF Ensemble Prediction System, are initialised every 12 hr and disseminated several hours after initialisation; therefore our approach has the potential to provide improved forecasts in an operational context. The analysis is performed on GEFS reforecast data.

We study two firms that compete on price and lead-time decisions in a common market. We explore the impact of decentralizing these decisions, as made by the marketing and production departments, respectively, with either marketing or production as the leader. We compare scenarios in which none, one, or both of the firms are decentralized to see whether decentralization can be the equilibrium strategy. We find that under intense price competition, with intensity characterized by the underlying parameters of market demand, firms may suffer from a decentralized structure, particularly under high flexibility induced by high capacity, where revenue-based sales incentives motivate sales/marketing to make aggressive price cuts that often erode profit margins. In contrast, under intense lead-time competition, a decentralized strategy with marketing as the leader can not only result in significantly higher profits, but also be the equilibrium strategy. Moreover, decentralization may no longer lead to lower prices or longer lead-times if the production department chooses capacity along with lead-time.

The prediction of precipitation by two ocean–atmosphere ensemble systems is compared with observations and each other over a broad range of time scales. The systems are the 2015 version of the Australian Bureau of Meteorology's POAMA with a T47 atmosphere, and the 2011 version of the European Centre for Medium Range Weather Forecasts’ (ECMWF) monthly system with a variable atmospheric resolution of T639 to T319. To facilitate the comparison across a seamless range of time scales, verification against observations is performed using data averaged over time windows equal in length to the forecast lead time, from 1 day to 4 weeks. In addition to this ‘actual’ skill, potential skill is computed by taking one ensemble member as truth and computing how well the other members forecast that member. Overall, ECMWF shows higher actual skill than POAMA across all time scales and in both the tropics and extratropics, as expected given its greater sophistication. ECMWF is particularly more skilful than POAMA in the tropics for the shorter leads. Consistent between the two systems, however, is that as lead time and averaging window are simultaneously increased the near-equatorial skill remains approximately constant, whereas it drops in all other latitude bands. As a result, both systems show much higher skill in the tropics than extratropics for the one week time scale and beyond, with that skill concentrated over the equatorial Pacific. Although potential skill in both systems is almost everywhere higher than their actual skill, there remains a strong similarity in the spatial patterns of potential and actual skill for the longer time scales. Within-model comparisons of potential and actual skill show largest differences for POAMA in the tropics at short lead times, and largest differences for ECMWF in the southern hemisphere high latitudes (50-70°S). The implications of these findings are discussed.

Flood forecasts and warnings are intended to reduce flood-related property damages and loss of human life. Considerable research has improved flood forecasting accuracy (e.g., more accurate prediction of the occurrence of flood events) and lead time. However, the delivery of improved forecast information alone is not necessarily sufficient to reduce flood damage and loss of life, as people have varying responses and reactions to flood warnings. This study develops an agent-based modeling framework that evaluates the impacts of heterogeneity in human behaviors (i.e., variation in behaviors in response to flood warnings), as well as residential density, on the benefits of flood warnings. The framework is coupled with a traffic model to simulate evacuation processes within a road network under various flood warning scenarios. The results show the marginal benefit associated with providing better flood warnings is significantly constrained if people behave in a more risk-tolerant manner, especially in high-density residential areas. The results also show significant impacts of human behavioral heterogeneity on the benefits of flood warnings, and thus stress the importance of considering human behavioral heterogeneity in simulating flood warning-response systems. Further study is suggested to more accurately model human responses and behavioral heterogeneity, as well as to include more attributes of residential areas to estimate and improve the benefits of flood warnings.

New techniques have recently been developed to quantify the location-dependent spatial agreement between ensemble members, and the spatial spread-skill relationship. In this paper a summer of convection permitting ensemble forecasts are analysed to better understand the factors influencing location-dependent spatial agreement of precipitation fields and the spatial spread-skill relationship over the UK. The aim is to further investigate the agreement scale method, and to highlight the information that could be extracted for a more long-term routine model evaluation. Overall, for summer 2013, the UK 2.2km-resolution ensemble system was found to be reasonably well spread spatially, although there was a tendency for the ensemble to be over confident in the location of precipitation. For the forecast lead times considered (up to 36 hrs) a diurnal cycle was seen in the spatial agreement and in the spatial spread-skill relationship: the forecast spread and error did not increase noticeably with forecast lead time. Both the spatial agreement, and the spatial spread-skill, were dependent on the fractional coverage and average intensity of precipitation. A poor spread-skill relationship was associated with a low fractional coverage of rain and low average rain rates. The times with a smaller fractional coverage, or lower intensity, of precipitation were found to have lower spatial agreement. The spatial agreement was found to be location dependant, with higher confidence in the location of precipitation to the northwest of the UK.

Anomaly detection is the characterization of a normal behavior of a system or process and identification of any deviation from such normal behavior. Anomaly detection of critical systems provides an important financial and client competitive advantage because it gives the decision maker a lead time and flexibility to manage the health of the system. Fuel systems are complex and mission critical systems that require high operational availability because of the high costs associated with the services they provide. In complex systems, it is not uncommon to monitor a quality-related response, which relies on the functional form between several variables using a nonlinear relationship. We present in this paper a new monitoring framework for smart fuel systems utilizing outlying observations detection and monitoring using c-chart. The traditional control charts based on Hotelling's T2 statistic were deficient in detecting smart fuel systems anomalies and a new approach was necessary to isolate faulty profiles. The proposed methodology requires a simple quality performance test that can be performed once assembly is completed to assure readiness for client use or completion of a job. The results were tested and validated using scaled data that mimic an actual system. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health from an inspection check to anticipate and avoid failures. Copyright © 2016 John Wiley & Sons, Ltd.

We consider a decentralized supply chain containing a risk-averse supplier and a risk-neutral retailer with lead time-sensitive and price-sensitive demands. A Stackelberg game is employed to model the lead time quote and pricing decision process between the two members under the conditional value-at-risk criterion. A unique equilibrium is obtained. Using the corresponding centralized mode as a benchmark, we find that a less risk-averse supplier is better to cooperate and share risk with the retailer to improve the entire supply chain's efficiency. With a uniformly distributed realized lead time, the impact of the supplier's risk aversion on the decisions can be characterized by a few threshold values of the late delivery penalty cost. In particular, when the unit delay penalty cost exceeds a certain level, a more risk-averse supplier will counter-intuitively quote a shorter lead time by risking a higher delay penalty cost. Copyright © 2016 John Wiley & Sons, Ltd.

Sourcing from multiple suppliers with different characteristics is common in practice for various reasons. This paper studies a dynamic procurement planning problem in which the firm can replenish inventory from a fast and a slow supplier, both with uncertain capacities. The optimal policy is characterized by two reorder points, one for each supplier. Whenever the pre-order inventory level is below the reorder point, a replenishment order is issued to the corresponding supplier. Interestingly, the reorder point for the slow supplier can be higher than that of the fast even if the former has a higher cost, lower reliability, and smaller capacity than the latter, suggesting the possibility of ordering exclusively from an inferior slow supplier in the short term. Moreover, the firm may allocate a larger portion of the long-term total order quantity to the slow supplier than to the fast, even if the former does not possess any cost or reliability advantage over the latter. Such phenomena, different from the observations made in previous studies, happen when the demand is uncertain and the supply is limited or unreliable. Our observations highlight the importance of incorporating both demand uncertainty and supplier characteristics (i.e., cost, lead time, capacity and uncertainty) in a unified framework when formulating supplier selection and order allocation strategies.

Buyer-supplier relationships are regarded as a source of competitive advantage, as suppliers can contribute valuable but imperfectly tradable resources. To coordinate these resources in the course of joint value creation and mitigate the risks of opportunism, buyers are required to establish transactional and relational governance mechanisms. While recent research showed that these mechanisms are intertwined with each other, little is known about the effects of different governance mechanisms in relationships with captive suppliers. Especially in case of captive suppliers, which are highly dependent on the buyer, the governance approach of the buyer affects the relationship and the supplier on a long-term base. Based on unique data of 101 suppliers in the aviation industry, we show that transactional and relational governance mechanisms exert different effects on the efficiency and effectiveness of relationships with captive suppliers. While transactional governance is primarily suited to foster buyer-supplier efficiency (e.g. cost or lead time reduction), relational governance strengthens buyer-supplier effectiveness (e.g. product customization or joint innovation). Additionally, the choice of a governance mechanism indirectly affects the strategic innovation orientation of captive suppliers. Focusing on buyer-supplier effectiveness stimulates strategic innovation orientation of captive suppliers; high buyer-supplier efficiency leads to opposite effects. Our paper emphasizes that misalignment of governance mechanisms and the objective of buyer-supplier relationships can limit strategic innovation orientation.