The Value of Information in Decision‐Analytic Modeling for Malaria Vector Control in East Africa

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced‐based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5–21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria‐transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs.     

Managing dependency in China outsourcing relationships

Abstract

This paper examines manufacturing firms’ use of guanxi and formal control to address dependence on suppliers and achieve desired outsourcing performance in China. Using survey data collected from manufacturing firms operating in China, a structural equation model is used to test the research model. The results suggest that manufacturing firms with high dependence on suppliers build Chinese guanxi with those suppliers to overcome the dependence and enhance outsourcing performance. In addition, Chinese guanxi also appears to facilitate the use of formal control, which was also found to have a positive relationship with outsourcing performance. This paper contributes to the literature by illustrating the importance of guanxi in achieving desired outsourcing performance in the China context, particularly in the case where a manufacturer perceives high dependency on its major supplier.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

回收物流库存控制研究

本文探讨了包含回收品的库存系统。回收物流库存中既有一般生产过程的新产品,也有经再制造的回收品,使其最优库存策略非常复杂,论文推导出不同控制方式下的EOQ模型,以确定最优生产和再制造批量。此外,还对求得的最优批量进行适当的调整,以确保在循环周期中的定货次数为整数。该模型形式、结构简单易于在实践中应用。     

项目系统中的工程造价管理与控制思想探讨

我国目前工程造价管理限于“工程发包与承包价格”的管理,而且造价管理注重于计价依据、形式、主体资格等方面,相对孤立,未与工程项目其他要素联系起来,将其作为工程项目系统的一个子系统看待。项目管理的思想重在系统性(整体性)和过程性,将这两个特点引入造价管理,在工程项目造价的整体目标、系统控制、综合管理、全面构成、动态计算、过程性评价等方面能有所改变,从而在一定程度上突破现行管理模式和内容。     

基于非线性理论的企业市场价值取向战略研究

针对传统企业对市场价值的线性思维模式,利用非线性理论建立了基于Logistic方程的市场价值模型,以及企业内部市场领先战略与技术领先战略模型;通过模型分析了实施单一市场领先战略或技术领先战略企业的发展前景;提出了企业市场价值取向的双绞线均衡战略及战略动态控制要求.     

生产性企业组织学习最优控制模型及其理论分析

运用最优控制理论研究生产性企业组织学习活动的动态最优决策问题.以企业的概念性学习投资率和操作性学习投资率为决策变量,累积知识量、生产率、单位成本和废品率等为状态变量,计划期内的总利润为指标函数,建立了一个最优控制模型,其特点在于规范地描绘概念性学习和操作性学习对企业累积知识量、生产率、单位成本、质量以及企业利润的动态影响.根据生产性企业组织学习和生产经营的实际情况,提出了一些定量化表达的假设和定义.在假设和定义的基础上利用最大值原理分析了所建立的模型,获得了关于动态最优概念性学习投资策略和操作性学习投资策略性质的一些结论,将这些结论与实际问题相结合,指出了在生产性企业组织学习实践上的含义.     

基于电子市场中介的交易风险控制

电子市场中介实施控制可以有效地降低电子商务中的交易风险.在讨论客观风险、估计风险、交易者风险偏好及风险控制相互之间关系的基础上,设计了电子市场中介的交易风险控制机制,构建了控制水平选择的决策模型,讨论了模型最优解的存在性,进而分析了交易者平均风险偏好一定和变化情况下的最优控制策略.分析结果有助于电子市场中介的交易风险控制决策.     

Clickstream Data and Inventory Management: Model and Empirical Analysis

We consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non‐transactional websites, and their matching is error‐prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional clickstream state variables. Using a novel data set of matched online clickstream and offline purchasing data, we identify statistically significant clickstream variables and empirically investigate the value of clickstream tracking on non‐transactional websites to improve inventory management. We show that the noisy clickstream data is statistically significant to predict the propensity, amount, and timing of offline orders. A counterfactual analysis shows that using the demand information extracted from the clickstream data can reduce the inventory holding and backordering cost by 3% to 5% in our data set.