How Sourcing of Interdependent Components Affects Quality in Automotive Supply Chains

In the automotive industry, many firms source key components from different suppliers, even though the components may function interdependently. In this study, we investigate how component level interdependence impacts quality performance and analyze how various operational factors moderate this relation. We synthesize information from several case studies to model the quality challenges faced by an automotive firm. For several sub‐assemblies that go into its products, the firm sourced key components from two different suppliers. The sub‐assemblies would fail whenever a component fails, but due to interdependent operations, failure of one component could cause the failure of the other. The firm found it challenging to improve the suppliers' quality performance as it was difficult to trace the failures to specific components. Our analysis reveals that – (i) the impact of interdependence is governed by the supply chain structure: reducing the interdependence between components improves quality when suppliers provide the components, but reducing interdependence worsens quality when the firm manufactures the entire sub‐assembly; and (ii) the relation between interdependence and quality performance is moderated by factors such as penalties, production costs, and interdependence costs. Additionally, we find that quality performance is lower when the firm outsources the components than when the firm manufactures the entire sub‐assembly. We identify coordinating mechanisms that leverage incentives and penalties to bridge the quality performance gap.     

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.     

指令驱动市场中非知情交易者的最优交易策略

在非对称信息下,综合非知情交易者对资产价值的预期、私人估值及资产价值波动,构造指令驱动市场价格形成的动态模型,给出非知情交易者指令提交策略的解析解,并依此描述了非知情交易者对资产价值预期的动态更新过程.研究发现:私人估值和资产价值波动通过影响非知情交易者最优策略,使得市场进入不同均衡状态;在每个均衡状态下,非知情交易者根据资产价值的预期变化更新交易策略,进而引起知情交易者策略调整,特别地,使得知情交易者被挤出市场成为可能.进一步分析表明,私人估值、资产价值波动及知情交易者比例对市场流动性的影响依赖于每个均衡中限价指令执行风险的差异.     

基于鲁棒性的关键链二次资源冲突消除策略

以关键链方法中的二次资源冲突困境为切入点,从鲁棒调度优化角度提出一种解决策略.首先,采用定量化建模对问题进行数学描述和表达,剖析插入输入缓冲引起二次资源冲突的原理,进而采用场景分析法从复杂的冲突表象中分解出四种基本的冲突场景构成要素.其次,基于鲁棒调度优化理论,探究各种冲突子问题的有效对策并归类,据此开发出一种消除二次资源冲突的局部重调度启发式协调策略;根据策略设计基于两次调度进程和两类缓冲动态消耗的鲁棒性指标,采用鲁棒性关键链项目调度问题输出鲁棒性最大的调度方案.再次,设计仿真程序和三个测试指标:项目实际按期完工率、活动开始时间偏差绝对值之和及偏差绝对值的方差;基于ProGen随机地生成测试算例集合进行数值实验.结果发现,以鲁棒性调度方案为依据安排项目的实施过程,三个统计指标值都优于以传统关键链调度方案为依据时相对应的指标值.结论表明:基于鲁棒调度优化的二次资源冲突消除策略及设计的关键链鲁棒性指标在项目实施中具有较好的稳定性效果.     

智能创新生产力理论与中国经济发展深层思考

提出智能创新生产力概念及其理论模型,发现智能演进创新和非守恒规律及其作用原理,研究智能创新的核心生产力,即智能创新克服人的生理认知能力有限性与客观物质(能量)变化无限性之间的悖论,创造利用物质世界的无穷手段和能力,创造时间和空间,解决资源稀缺与社会需求矛盾;在此基础上发现智能创新自认知、自选择和自组织原理,破解著名的智能认知体系结构与优化代理体系结构(这是本文重要发现之一),研究智能创新的系统生产力,即智能经济相对于传统经济可以达到和实现理想的生产过程,使生产活动在开放的资源空间和动态系统中进行,虚拟智能的物理化使经济活动没有时空约束和资源稀缺约束,创新经济和社会的生产方式与组织形态.本文所提出的智能创新生产力理论模型,即智能创新的核心生产与系统生产力概念也为中国经济改革发展提出重要思考.     

VMI对供应链性能的影响分析

建立了分散式供应链和VMI模式供应链的模型,比较了两种模式供应链的性能,研究表明在缺乏有效的协作机制的情况下,VMI的优势不能得到有效的发挥,提出了进一步提高VMI模式供应链性能的有效途径,最后给出了数值算例对结论进行了验证。     

电子化供应链管理实施方法研究

首先总结e化供应链的特征,分析了e化供应链与传统供应链的区别,然后对e化供应链管理的实施模式进行了总结、分析和对比,在此基础上提出了基于Web Service的e化供应链的一体化解决方案,最后对基于该方案的e化供应链的实施过程进行了论述。     

销售商的努力影响需求变化的供应链的合约

本文首先重新考察报童模型,认识设计使供应链达到合作的合约的内在机制。在此基础上,提出一个设计供应链合约的一般模型,然后利用此模型分析在销售商的努力影响需求变化的情形下使供应链达到合作的合约的设计框架,同时提出一个在销售商的努力影响需求变化的情形下使供应链达到合作的合约。     

供应链环境下合作预测效果的分析

本文定量分析了供应链中合作预测对需求长鞭效应的减小作用,在多节点企业所组成的供应链中,合作预测对订单量的波动和需求预测误差的波动起到积极的抑制作用。本文通过理论研究和数值分析的结果表明,供应链信息共享和合作预测能够控制需求流动现象。     

大型供应链设计的基本数学模型与算法研究

随着信息技术与全球经济一体化的发展,供应链管理成为全球管理科学的研究热点。本文在分析国内外各种关于供应链设计的数学模型与算法的基础上,提出了具有普遍性意义且简单易行的MIP供应链设计的数学模型以及求解供应链问题的有界变量广义上界算法。实例计算表明,提出的模型和方法是可靠实用的。