部分延期付款下的制造商决策与供应链协调

在考虑资金约束的情况下,零售商可以先支付给制造商一部分货款,其余部分延期支付,这相当于制造商融资给零售商.作为允许延期付款的附加条件,制造商要收取一定的融资利息,同时可以将零售商的订货批量约束在确定的数量.在报童模型的框架下,建立了预期利润模型.通过对模型的讨论,使用迭代算法确定了不同零售商保留利润下的制造商最优决策.当零售商的保留利润大于一定数值时,通过对订货批量的约束,供应链实现了协调.最后,用算例验证了结论.     

基于ACC支付模式的供应链金融模型研究

在零售商存在资金不足和顾客需求随机情况下，以报童模型为基础，针对变质品构建了供应链金融问题和ACC支付模式（提前支付Advance、现金支付Cash和信用支付Credit）相结合的零售商订货模型，即零售商与供应商、融资机构合作条件下的模型。研究表明，零售商会随着最优订货批量和最低期望成本的增加和降低趋势，进而调整订货量，从而规避供大于求的风险，最终降低总的期望成本。同时供应商可以适当调整支付比例，让零售商来分担坏账和商品变质风险。     

多个订货商的两层供应链Stackelberg协调博弈分析

本文研究一个生产商和多个不同成本结构订货商的两层供应链协调系统,其中每个订货商全年需求量、订货周期、订货次数以及每次的订购量等都有所不同。通过适当的价格折扣策略,给出了双方合作下的stackelberg协调博弈模型,使得整个供应链整体运作得以改善,最后给出实例分析。     

3PL企业融资服务模式下考虑损失规避零售商的供应链运营和协调策略

针对第三方物流（3PL）企业融资服务模式，研究了具有损失规避行为的零售商面临资金约束情形的供应链运营策略和协调问题。在本文中，分析了分散情形下3PL企业的最优融资策略和损失规避零售商的最优订货策略。以及集中情形下供应链的最优订货策略，通过比较两种情形下的最优订货量，得到了可以使资金约束供应链实现整体协调的转移支付契约。研究结果表明：当零售商初始资金较高时，3PL企业融资服务模式下零售商的订货量超过其在资金充足时的订货量；当零售商初始资金满足在一定范围内时，3PL企业融资服务模式可以使3PL企业和零售商获得帕累托改善，但不能直接协调资金约束供应链；当3PL企业和零售商签订的转移支付契约满足一定条件时，供应链可以实现整体协调。     

基于税盾效应的供应链贸易信用融资优化决策研究

针对中小企业融资困境的现实，本文基于贸易信用融资模式，引入税盾效应，建立了由单一核心供应商和单一经销商组成的含税盾的贸易信用模型，探讨有融资需求的经销商进行订货、核心供应商定价的决定策略，探究不同计息方式的价值实现和融资模式优选。通过stackbelberg博弈、比较分析和算例验证，研究发现以订货量确定为基准，融资需求出现的时点对供应链企业计息方式的选择均有影响。税法关于利息抵扣上限、贸易信用贷款利率、订货量等均影响经销商计息方式的选择，融资额度影响其订货策略；生产成本的差额、定价策略影响供应商的计息方式。     

延期支付条件下考虑坏账影响的三阶段经济订货模型

研究零售商处于强势地位的三级供应链结构中,考虑到顾客坏账率的影响,分析二阶延期支付策略下零售商的最优订货策略。分两种情形讨论了坏账影响下零售商的利息收入与利息支出,建立了相应的决策模型。通过模型的分析求解,得出零售商的最优订货周期和最优订货批量的判定方法,并进一步分析了各参数对零售商最优决策的影响。 最后,通过数值分析对有关结论进行了验证。     

基于提前订购策略的供需双方动态博弈模型研究

在两级供应链环境下,相对于传统的即时订购策略,通过以较低批发价提供一个提前订购的机会,给予购买商两次订货机会,那么供应商和购买商的利润可以实现帕累托改进.同时如果供应商在提供提前订购机会的同时收取一个固定费用,则可以进一步优化模型.建立了一个具体的模型,用逆向归纳法计算出了均衡解,并对不同策略下的均衡结果进行了对比分析.     

信息非对称条件下的质量预防决策分析

研究了不同信息条件下供应链的质量预防问题,在文献[1,2]建立的有关质量签约问题的优化模型基础上,把供应商作为委托人和购买商作为代理人建立了供应链的质量预防决策委托代理模型。其中,质量预防水平和供应商要求购买商的前向支付是供应商的决策变量,质量评价水平是购买商的决策变量。本文重点研究了了非对称信息下供应链的质量预防和前向支付问题,考虑了购买商质量评价信息隐匿情况,运用极大值原理推导了供应商的质量预防和前向支付的最优解。最后结合一个农机公司拖拉机销售问题进行了仿真计算和分析,对比了不同信息环境下的决策结果。     

供应链融资模式下零售商的订货与定价研究

已有零售商订货与定价问题的研究大都忽略了零售商的初始资金,本文在考虑零售商初始资金的情况下,研究零售商面对初始资金不足时,如何借助外部的融资政策做出最优的订货与定价决策.因此,本文分别讨论了零售商在无融资服务,供应链中核心制造商担保下的外部融资服务及核心制造商提供商业信用的内部融资服务下的订货与定价问题,并建立了相应的...     

基于顾客策略行为和缺货损失的供应链定价与订购决策

顾客基于理性预期决定购买时机的策略行为造成供应链决策困难,影响到供应链利润。针对单生产商、单零售商组成、具有高支付意愿与低支付意愿两类顾客的供应链,利用均衡分析方法和理性预期理论建立了考虑缺货损失下高支付意愿顾客分别为策略型顾客与短视型顾客时供应链的分散式决策二层规划模型和集中式决策模型,对该四种模型进行了求解与对比分析、以及数值仿真和敏感性分析,算例表明了模型的合理性和结论的有效性。研究表明:高WTP顾客为策略型顾客时,集中式供应链与分散式供应链相比,其订货量高、但零售价低,故利润反而较低;高支付意愿顾客为策略型顾客时分散式供应链和集中式供应链的利润小于高支付意愿顾客为短视型顾客下的情形;四种供应链模式下的订货量随单位缺货损失的增加而增加,但供应链的利润降低。限量销售、回购剩余产品、提高顾客心目中的产品价值等策略可有效降低顾客策略行为对供应链的影响。本文将促进基于顾客策略行为的供应链理论研究与管理实践。     

概率预期下在线报童问题的最小风险策略

报童问题是库存管理中一个基本模型。已有的报童模型主要利用均值-方差方法和期望效用目标方法进行风险的描述和度量。这些方法假设需求分布信息已知,而实际中需求分布信息往往难以完全刻画。本文使用概率预期作为刻画不完全需求分布信息的格式,基于在线风险补偿的思想,为需求分布信息不完全的报童问题建立了最小风险模型。使用该模型设计了最小风险策略,使报童可以根据自己设定的不同收益和未来概率预期选择最优订购量。     

报童问题的最优竞争比策略及其风险补偿模型

报童问题是库存管理中一个重要的模型。对报童问题在不同的目标函数和约束条件下的扩展模型已有了大量的研究。已有研究假设产品的需求分布已知,然而,在实际中完全刻画需求分布信息常常是很困难的,因此,部分信息下的报童模型研究近年越来越受到重视。在竞争比分析方法下,对报童问题进行了讨论,建立概率预期下风险补偿模型用于解决部分信息的报童问题。在确定性预期和基本概率预期下,分别为具有风险偏好的报童设计了最优订购策略,使报童可以根据自己不同的风险容忍度和未来预期选择订购策略。     

损失厌恶的报童-预期理论下的报童问题新解

本文在决策理论——预期理论框架下,重新对报童问题进行了讨论,给出了损失厌恶的报童的最优订货量的计算公式,此外还进行了必要的比较静态分析。     

A Data‐Driven Inventory Control Policy for Cash Logistics Operations: An Exploratory Case Study Application at a Financial Institution

A number of market changes are impacting the way financial institutions are managing their automated teller machines (ATMs). We propose a new class of adaptive data‐driven policies for a stochastic inventory control problem faced by a large financial institution that manages cash at several ATMs. Senior management were concerned that their current cash supply system to manage ATMs was inefficient and outdated, and suspected that using improved cash management could reduce overall system cost. Our task was to provide a robust procedure to tackle the ATM's cash deployment strategies. Current industry practice uses a periodic review system with infrequent parameter updates for cash management based on the assumption that demand is normally distributed during the review period. This assumption did not hold during our investigation, warranting a new and robust analysis. Moreover, we discovered that forecast errors are often not normally distributed and that these error distributions change dramatically over time. Our approach finds the optimal time series forecaster and the best‐fitting weekly forecast error distribution. The guaranteed optimal target cash inventory level and time between orders could only be obtained through an optimization module that was embedded in a simulation routine that we built for the institution. We employed an exploratory case study methodology to collect cash withdrawal data at 21 ATMs owned and operated by the financial institution. Our new approach shows a 4.6% overall cost reduction. This reflects an annual cost savings of over $250,000 for the 2,500 ATM units that are operated by the bank.     

基于供应链金融的银行贷款价值比研究

供应链金融是金融业面向供应链的创新服务。考虑单一供应商和单一零售商构成的二级供应链,产品市场为典型的报童模型,零售商以其与制造商签订的产品采购合同向银行申请抵押贷款。将供应链上下游企业的决策引入银行基于下侧风险控制模式的贷款价值比决策模型,刻画供应链的决策对采购合同抵押价值的影响。首先确定了贷款价值比的解析式,然后讨论了其与批发价格、回购率等供应链决策的关系,并确定了相应的取值区间。     

Financial Innovation and the Transactions Demand for Cash

We document cash management patterns for households that are at odds with the predictions of deterministic inventory models that abstract from precautionary motives. We extend the Baumol–Tobin cash inventory model to a dynamic environment that allows for the possibility of withdrawing cash at random times at a low cost. This modification introduces a precautionary motive for holding cash and naturally captures developments in withdrawal technology, such as the increasing diffusion of bank branches and ATM terminals. We characterize the solution of the model, which qualitatively reproduces several empirical patterns. We estimate the structural parameters using micro data and show that quantitatively the model captures important economic patterns. The estimates are used to quantify the expenditure and interest rate elasticity of money demand, the impact of financial innovation on money demand, the welfare cost of inflation, and the benefit of ATM ownership.     

On the Need for Rethinking Current Practice that Highlights Goal Achievement Risk in an Enterprise Context

This article addresses the issue of how performance and risk management can complement each other in order to enhance the management of an enterprise. Often, we see that risk management focuses on goal achievements and not the enterprise risk related to its activities in the value chain. The statement “no goal, no risk” is a common misconception. The main aim of the article is to present a normative model for describing the links between performance and risk, and to use this model to give recommendations on how to best structure and plan the management of an enterprise in situations involving risk and uncertainties. The model, which has several novel features, is based on the interaction between different types of risk management (enterprise risk management, task risk management, and personal risk management) and a structure where the enterprise risk management overrules both the task and personal risk management. To illustrate the model we use the metaphor of a ship, where the ship is loaded with cash‐generating activities and has a direction over time determined by the overall strategic objectives. Compared to the current enterprise risk management practice, the model and related analysis are founded on a new perspective on risk, highlighting knowledge and uncertainties beyond probabilities.     

操纵性应计利润模型检测盈余管理能力的实证分析

本文运用统计模拟方法,通过测试各模型产生第一类错误和第二类错误的频率,比较基本琼斯模型、修正琼斯模型等七种常用的截面操纵性应计利润模型在中国资本市场的检验效果.检测结果发现,基本琼斯模型、修正琼斯模型和无形资产琼斯模型相对较优,它们所犯第一类错误和第二类错误的频率较小,但在收入操纵检测上,修正琼斯模型检验能力更突出;现金流量琼斯模型、非线性琼斯模型检验盈余管理的能力较强,但存在较为严重的第一类错误,易夸大上市公司盈余管理的程度;前瞻性修正琼斯模型计算复杂,第一类错误明显,检验盈余管理能力相对修正琼斯模型没有明显提高;收益匹配琼斯模型在费用操纵的检验能力上明显较弱.综合考虑,本文认为在中国证券市场上,分年度分行业回归的截面修正琼斯模型在模型的设定和盈余管理的检验能力方面表现更佳,建议未来的盈余管理实证研究以该方法为主.     

Modeling Price Volatility Based on a Genetic Programming Approach

Business profitability is highly dependent on risk management strategies to hedge future cash flow uncertainty. Commodity price shocks and fluctuations are key risks for companies with global supply chains. The purpose of this paper is to show how artificial intelligence (AI) techniques can be used to model the volatility of commodity prices. More specifically, the authors introduce a new model – LIQ‐GARCH – that uses genetic programming to forecast volatility. The newly generated model is then used to forecast the volatility of the following three indexes: the Commodity Research Bureau (CRB) index, the West Texas Intermediate (WTI) oil futures prices and the Baltic Dry Index (BDI). The empirical model performance tests show that the newly generated model in this paper is considerably more accurate than the traditional GARCH model. As a result, this model can help businesses to design optimal risk management strategies and to hedge themselves against price uncertainty.