On Analytic Empirical Bayes Estimation of Failure Rates

The estimation of plant accident rates and component failure rates is addressed within the framework of a parametric empirical Bayes approach. The observables, the numbers of failures recorded in various similar systems, obey the Poisson probability law. The parameters of a common gamma prior distribution are determined by a special moment matching method such that the results are consistent with classical (fiducial) confidence limits. Relations between Bayesian, classical, and Stein's estimation are discussed. The theory of the method is fully developed, although the suggested procedure itself is relatively simple. Solutions exist and they are in allowed ranges for all practical cases, including small samples and clustered data. They are also unbiased for large samples. Numerical examples are analyzed to illustrate the method and to allow comparisons with other methods.     

A Method for Projecting Age-Specific Mortality Rates for Certain Causes of Death

A method is presented for projecting mortality rates for certain causes on the basis of observed rates during past years. This method arose from a study of trends in age-specific mortality rates for respiratory cancers, and for heuristic purposes it is shown how the method can be developed from certain theories of cancer induction. However, the method is applicable in the more common situation in which the underlying physical processes cannot be modeled with any confidence but the mortality rates are approximable over short time intervals by functions of the form a exp (bt) , where b may vary in a continuous, predictable fashion as the time interval is varied. It appears from applications to historical data that this projection method is in some cases a substantial improvement over conventional curve-fitting methods and often uncovers trends which are not apparent from observed data.     

Individual Prior Information in a Physiological Model of 2H8-Toluene Kinetics: An Empirical Bayes Estimation Strategy

Physiologically-based toxicokinetic (PBTK) models are widely used to quantify whole-body kinetics of various substances. However, since they attempt to reproduce anatomical structures and physiological events, they have a high number of parameters. Their identification from kinetic data alone is often impossible, and other information about the parameters is needed to render the model identifiable. The most commonly used approach consists of independently measuring, or taking from literature sources, some of the parameters, fixing them in the kinetic model, and then performing model identification on a reduced number of less certain parameters. This results in a substantial reduction of the degrees of freedom of the model. In this study, we show that this method results in final estimates of the free parameters whose precision is overestimated. We then compared this approach with an empirical Bayes approach, which takes into account not only the mean value, but also the error associated with the independently determined parameters. Blood and breath ²H₈-toluene washout curves, obtained in 17 subjects, were analyzed with a previously presented PBTK model suitable for person-specific dosimetry. Model parameters with the greatest effect on predicted levels were alveolar ventilation rate Q_PC, fat tissue fraction V_FC, blood-air partition coefficient K_b, fraction of cardiac output to fat Q_a/co and rate of extrahepatic metabolism V_max-p. Differences in the measured and Bayesian-fitted values of Q_PC, V_FC and K_b were significant (p < 0.05), and the precision of the fitted values V_max-p and Q_a/co went from 11 ± 5% to 75 ± 170% (NS) and from 8 ± 2% to 9 ± 2% (p < 0.05) respectively. The empirical Bayes approach did not result in less reliable parameter estimates: rather, it pointed out that the precision of parameter estimates can be overly optimistic when other parameters in the model, either directly measured or taken from literature sources, are treated as known without error. In conclusion, an empirical Bayes approach to parameter estimation resulted in a better model fit, different final parameter estimates, and more realistic parameter precisions.     

供应链金融视角下利率市场化的鲁棒分析模型

本文从供应链金融的视角出发,在需求信息缺失的情况下研究了银行贷款利率的制定对供应链及其节点企业运营状况的影响。在利率市场化的背景下,构建了银行参与的由供应商和零售商组成的供应链三方博弈模型。运用鲁棒的报童方法和极小极大后悔方法刻画了银行决定利率、供应商决定批发价格和零售商决定订货量的三方博弈情景,并获得其博弈均衡。研究表明:在部分信息下银行的参与能有效提高供应链绩效;供应商自有资金量的多少是判断利率政策(政府调控与利率市场化)对供应链效益影响的重要指标;利率市场化并不能完全解决银行业占取实体经济利益的现状,适度的政府调控是不可或缺的。     

A Linear Model for Managing the Risk of Antimicrobial Resistance Originating in Food Animals

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.     

A Structural model for the impact of supply chain relationship traits on project performance in construction

The objectives of this study are to discover the supply chain relationship traits in construction and their impact on the project performance. Using factor analysis technique, the supply chain relationship traits were determined to be collaboration in the supply chain, support and commitment to the supply chain, and sharing the benefits and risks. A two-step SEM model was then established to test the hypothesised relationship between the supply chain relationship traits and the project performance. The findings indicated that the supply chain relationship traits have a significant positive impact on the project performance. This study contributes to the body of knowledge relating to the construction supply chain management. The findings may help the parties to identify the attributes that may improve the relationships among them and result in better project performance.     

Analysis of Effects of Operational Execution on Repeat Purchasing for Heterogeneous Customer Segments

Many retailers are increasingly turning to home delivery as a new arena of operational competition. This study controlled for industry by investigating the online home delivery grocery business, and an analysis of 1,919 customers of home delivery grocers identified four groups of online customers based on reasons for selecting this service. These four groups were next linked to operational execution in terms of service, product, and Internet quality, and found to vary in predicable ways. Subsequent to the initial data collection, five month's of post hoc longitudinal purchasing history was collected on the four groups of online customers to determine the relative profitability. Finally, as a follow‐on analysis, the study used regression to predict future consumer purchases based upon operational execution. Time savings and service quality emerged as the two most important independent variables in terms of future buying from such online home delivery services.     

养老服务集成商的服务质量决策分析

服务质量是影响养老服务产业发展的重要因素之一。作为实现养老服务产业化发展的良好载体,养老服务供应链模式应运而生,研究其核心企业——养老服务集成商的质量决策具有重要意义。为此,以养老服务集成商为研究对象,在给定服务提供商服务质量的情况下,研究不同市场需求规模下单个养老服务集成商以及多个服务集成商量在相互竞争学习时,养老服务集成商如何通过博弈获得最优质量决策的问题;在此基础上,分析质量风险影响下养老服务集成商在面对服务提供商时的服务质量决策。为养老服务集成商在实际运作中提供有效的管理决策支持,也为养老服务产业化发展提供重要的理论基础。     

基于信任的供应链伙伴关系维系管理方法研究

在契约的基础上进行基于信任的伙伴关系维系管理是保证节点企业间融洽合作关系的有效策略之一.本文首先分析了供应链伙伴关系管理实践中需要注意的问题以及信任与供应链伙伴关系的内在联系;在供应商开发程序的基础上融入信任诊断、协商沟通和改进供应链伙伴关系提高绩效的过程,提出了一种基于信任诊断的供应链伙伴关系开发程序进行多层伙伴关系维系的方法,包括基于信任的伙伴关系诊断层、伙伴关系协商沟通层和伙伴关系修正层.最后给出了支持该方法不同组成元素的企业实际运作例证.     

基于股票收益的操作风险资本估计——自上而下方法

操作风险的度量方法包括自下而上和自上而下两种方式,自上而下的方法由于风险敏感度低而较少被银行和学术界关注,但其具有对数据要求低、容易验证的优点.构建一个基于上市银行股票收益的自上而下的操作风险度量模型,利用2000年～2006年中国5家上市银行面板收益数据和其他相关数据,对中国上市银行的操作风险进行实际度量,发现了在中国上市银行的股票收益中操作风险所占的平均比率,并得到相应的操作风险资本估计;用估计的操作风险经济资本与用基本指标法计算的操作风险经济资本进行比较,发现两种方法结果大致相当,这表明按照<巴塞尔新资本协议>规定的基本指标法确定风险资本基本能满足抵御操作风险的要求.度低而较少被银行和学术界关注,但其具有对数据要求低、容易验证的优点.构建一个基于上市银行股票收益的自上而下的操作风险度量模型,利用2000年～2006年中国5家上市银行面板收益数据和其他相关数据,对中国上市银行的操作风险进行实际度量,发现了在中国上市银行的股票收益中操作风险所占的平均比率,并得到相应的操作风险资本估计;用估计的操作风险经济资本与用基本指标法计算的操作风险经济资本进行比较,发现两种方法结果大致相当,这表明按照<巴塞尔新资本协议>规定的基本指标法确定风险资本基本能满足抵御操作风险的要求.     

Risk Analysis of Chemical, Biological, or Radionuclear Threats: Implications for Food Security

If the food sector is attacked, the likely agents will be chemical, biological, or radionuclear (CBRN). We compiled a database of international terrorist/criminal activity involving such agents. Based on these data, we calculate the likelihood of a catastrophic event using extreme value methods. At the present, the probability of an event leading to 5,000 casualties (fatalities and injuries) is between 0.1 and 0.3. However, pronounced, nonstationary patterns within our data suggest that the "reoccurrence period" for such attacks is decreasing every year. Similarly, disturbing trends are evident in a broader data set, which is nonspecific as to the methods or means of attack. While at the present the likelihood of CBRN events is quite low, given an attack, the probability that it involves CBRN agents increases with the number of casualties. This is consistent with evidence of "heavy tails" in the distribution of casualties arising from CBRN events.     

产品再生系统的供应链管理研究进展

产品再生系统通过对废旧产品的循环再利用减少了最终垃圾的数量,进而减少了对环境的负面影响,由于良好的生态效益和潜在的经济效益,该系统在近几年受到了理论界和企业界的普遍关注.相对于传统系统,产品再生系统涉及到了正向物流与逆向物流活动,其研究范畴更为广泛与复杂.本文主要从供应链管理视角,对产品再生系统的物流网络设计、生产规划、库存控制和信息流管理四个领域的研究成果进行了总结,同时也对未来的研究方向和热点进行了展望.     

The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and long‐standing problem in hospital management. The majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel semi‐Markov model (SMM)‐based clustering scheme, as opposed to clustering by patient attributes as in previous literature. Our methodology is validated by simulation and then applied to real patient data from a partner hospital where we demonstrate that it outperforms a suite of well‐established clustering methods. Furthermore, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM‐clustering is a novel approach applicable to any temporal‐spatial stochastic data that is prevalent in many industries and application areas.     

不利天气影响下“公司+农户”型订单契约设计

不利天气影响农业生产并使订单农业中各成员在履约过程中遭遇不可控风险,针对这一问题,构建了由风险厌恶的农户与风险中性的公司组成的两级农产品供应链随机利润模型,在条件风险价值(CVaR)准则下,建立了具有风险厌恶特性农户的CVaR决策模型,比较分析了不利天气对集中式与分散式决策模式下最优决策的影响。研究发现不利天气降低了供应链绩效而农户的风险厌恶特性则加剧了双边际效应,对此,设计了一种与天气指数和农户风险厌恶度相关的改进收益共享契约。研究表明:公司可依据不利天气影响的结果来设计订单契约以激励双方成为"收益共享、风险共担"的统一体,此外,在实施该订单契约后可实现农产品供应链的完美共赢协调,这弥补了分散式决策下的效率损失并增强了供应链系统的稳健性。     

Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry

The aim of this study was to identify and analyse the key success factors behind successful achievement of environment sustainability in Indian automobile industry supply chains. Here, critical success factors (CSFs) and performance measures of green supply chain management (GSCM) have been identified through extensive literature review and discussions with experts from Indian automobile industry. Based on the literature review, a questionnaire was designed and 123 final responses were considered. Six CSFs to implement GSCM for achieving sustainability and four expected performance measures of GSCM practices implementation were extracted using factor analysis. interpretive ranking process (IRP) modelling approach is employed to examine the contextual relationships among CSFs and to rank them with respect to performance measures. The developed IRP model shows that the CSF ‘Competitiveness’ is the most important CSF for achieving sustainability in Indian automobile industry through GSCM practices. This study is one of the few that have considered the environmental sustainability practices in the automobile industry in India and their implications on sectoral economy. The results of this study may help the mangers/SC practitioners/Governments/Customers in making strategic and tactical decisions regarding successful implementation of GSCM practices in Indian automobile industry with a sustainability focus. The developed framework provides a comprehensive perspective for assessing the synergistic impact of CSFs on GSCM performances and can act as ready reckoner for the practitioners. As there is very limited work presented in literature using IRP, this piece of work would provide a better understanding of this relatively new ranking methodology.     

政府激励政策下人造板绿色供应链谈判-协调机制研究

当前我国人造板产业面临效率低下、资源浪费和环境污染等问题,上下游企业缺乏合作与协调。本文界定了人造板绿色供应链系统,运用契约理论和纳什谈判理论,分别构建了政府激励政策下人造板绿色供应链集中优化决策、分散均衡决策和谈判协调决策模型,从而建立了相应的收益分享-成本分担契约谈判协调机制,并基于相关行业和企业经验数据,与传统人造板供应链进行了对比数值分析。研究结果表明:(1)收益分享-成本分担契约谈判-协调机制能够很好地实现人造板绿色供应链的协调运营,提高资源效率,降低环境负影响和提升运营绩效。(2)人造板绿色供应链管理模式下供应链及其成员的最优利润均高于传统管理模式,谈判协调决策情形下供应链及其成员的最优利润均高于分散均衡决策情形。(3)制定有"门槛"的增值税即征即退政策和适当的环境税政策,自建经济林场、选用经济型枝桠材,强化技术研发和工艺改进,有助于提高人造板供应链运营绩效。     

A Deliberative Method for Ranking Risks (II): Evaluation of Validity and Agreement among Risk Managers

A deliberative method for ranking risks was evaluated in a study involving 218 risk managers. Both holistic and multiattribute procedures were used to assess individual and group rankings of health and safety risks facing students at a fictitious middle school. Consistency between the rankings that emerged from these two procedures was reasonably high for individuals and for groups, suggesting that these procedures capture an underlying construct of riskiness. Participants reported high levels of satisfaction with their groups' decision-making processes and the resulting rankings, and these reports were corroborated by regression analyses. Risk rankings were similar across individuals and groups, even though individuals and groups did not always agree on the relative importance of risk attributes. Lower consistency between the risk rankings from the holistic and multiattribute procedures and lower agreement among individuals and groups regarding these rankings were observed for a set of high-variance risks. Nonetheless, the generally high levels of consistency, satisfaction, and agreement suggest that this deliberative method is capable of producing risk rankings that can serve as informative inputs to public risk-management decision making.     

A framework for managing sustainable palm oil supply chain operations: a case of Indonesia

The challenges faced by the palm oil industry in Indonesia lie in the supply chain risk management, specifically in the integration of decision-making at the operational level. Problems at this level have become more apparent while the industry continues to face pressures as a consequence of the strict conditions imposed by, predominantly, international trading communities, and in some cases, by the environmental issues from domestic and overseas. As part of an effort to address the underlying problems, this paper aims to propose a conceptual framework that can be used to effectively manage the palm oil supply chain, by integrating risk assessment, performance measurement and supply chain optimisation, which will be compiled into a decision-making tool. The perceived benefits from the implementation of this framework include more realistic target planning, clearer prediction of risks and better visibility of the operating performance.     

A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products

Uniform product deliveries are required in the ceramic, horticulture and leather sectors because customers require product homogeneity to use, present or consume them together. Some industries cannot prevent the lack of homogeneity in products in their manufacturing processes; hence, they cannot avoid non-uniform finished products arriving at their warehouses and, consequently, fragmentation of their stocks. Therefore, final uniform product amounts do not match planned production ones, which frequently makes serving previous committed orders with homogeneous quantities impossible. This paper proposes a model-driven decision support system (DSS) to help the person in charge of delivery management to reallocate the available real inventory to orders to satisfy homogenous customer requirements in a collaborative supply chain (SC). The DSS has been validated in a ceramic tile collaborative SC.     

A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus

There is a lack of knowledge about which risk factors are more important in West Nile virus (WNV) transmission and risk magnitude. A better understanding of the risk factors is of great help in developing effective new technologies and appropriate prevention strategies for WNV infection. A contribution analysis of all risk factors in WNV infection would identify those major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness and efficiency in developing WNV control and prevention strategies. Neural networks have many generally accepted advantages over conventional analytical techniques, for instance, ability to automatically learn the relationship between the inputs and outputs from training data, powerful generalization ability, and capability of handling nonlinear interactions. In this article, a neural network model was developed for analysis of risk factors in WNV infection. To reveal the relative contribution of the input variables, the neural network was trained using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections emerges. The significant risk factors can be identified by analyzing this skeletal network. The proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The results demonstrate the effectiveness of the proposed approach.