带学习效应的双渠道供应链库存策略研究

针对具有学习行为的双渠道供应链问题,本文研究了两种分销渠道并存下的最优库存策略。有限计划期内,分销商通过传统销售和在线销售来满足下游顾客的需求。两种分销渠道下的销售单价为时变不减线性函数,当系统中各周期的生产订购固定成本以一定的概率具有学习效应行为时,分别建立了非变质产品生产存贮问题的混合整数约束优化模型以及易变质产品存贮问题的无约束混合整数优化模型,所建立模型的目标为极大化分销商总利润函数。对于这两类模型,通过分析其最优解的性质,利用将生产订购次数松弛为连续变量的技巧证明了最优解存在的唯一性。给出了最优策略的求解方法并比较了两类模型最优利润函数值的大小。最后通过数值算例对上述模型进行了验证,数值结果表明当供应链系统中存在学习效应行为时,该系统能够获得更多的利润。     

Managing Slow‐Moving Perishables in the Grocery Industry

We address the value of information and value of centralized control in the context of a two‐echelon, serial supply chain with one retailer and one supplier that provide a single perishable product to consumers. Our analysis is relevant for managing slow‐moving perishable products with fixed lot sizes and expiration dates of a week or less. We evaluate two supply chain structures. In the first structure, referred to as decentralized information sharing, the retailer shares its demand, inventory, and ordering policy with the supplier, yet both facilities make their own profit‐maximizing replenishment decisions. In the second structure, centralized control, incentives are aligned and the replenishment decisions are coordinated. The latter supply chain structure corresponds to the industry practices of company‐owned stores or vendor‐managed inventory. We measure the value of information and value of centralized control as the marginal improvement in expected profits that a supply chain achieves relative to the case when no information is shared and decision making is decentralized. Key assumptions of our model include stochastic demand, lost sales, and fixed order quantities. We establish the importance of information sharing and centralized control in the supply chain and identify conditions under which benefits are realized. As opposed to previous work on the value of information, the major benefit in our setting is driven by the supplier's ability to provide the retailer with fresher product. By isolating the benefit by firm, we show that sharing information is not always Pareto‐improving for both supply chain partners in the decentralized setting.     

Du pont decision support system (DSS) for expenditure budgeting

The objective of this project is to develop a decision support system (DSS) for expenditure budgeting. The system will support current expenditure budgeting according to the same principles as the fixed expenditure (capital) budgeting. The framework of the analysis is based on an ex ante version of the traditional (ex post) du Pont system presenting the profitability of the firm as a function of asset turnover and profit margin. The present approach depicts the relationships between sales and the alternative classes of expenditure, expenses and assets by means of a mathematical model expressed as a DSS in an EXCEL spreadsheet framework. The model assumes that the average absorption time and revenue lag structure for expenditure classes are constant in time. However, the monetary productivity of expenditures is a second-order parabola of the amount of expenditure. The use of the model is demonstrated in two illustrative cases (SIEMENS and ABB) using published financial statement data. The estimation of the revenue function is based on an interactive analysis and simulation with past data.     

AUTOCORRELATED CASH FLOWS AND THE SELECTION OF A PORTFOLIO OF CAPITAL ASSETS *

Capital budgeting models for analyzing real assets typically are based on a set of restrictive assumptions that influence financial managers' decisions and may prevent optimization of the firm's objectives. This research examines the common restrictive assumption that cash flows are intertemporally independent by first developing an economic state and simulation model based on a Markov process for including autocorrelated cash flows in the capital budgeting decision process and then demonstrating why managers should include autocorrelated cash flows in capital budgeting models by empirically testing the impact of assuming intertemporally independent cash flows on capital budgeting decisions. The results indicate that ignoring autocorrelated cash flows seriously limits the ability of capital budgeting models to provide optimal investment decisions. The model also is very attractive for practical application because it can be implemented with a minimum number of estimates and provides the set of input data required by a number of capital budgeting models. A discussion of the implementation of the model is included.     

RATIONING DISCRETIONARY ECONOMIC RESOURCES: A MULTIOBJECTIVE APPROACH

Prior research involving capital rationing has focused on capital budgeting issues: given a limited resource pool, which major long-lived fixed assets should be acquired? However, capital rationing can be extended to include not only new fixed-asset purchases but also expenditures for any other discretionary activity such as advertising, research and development, or maintenance. In this broader situation, measuring the contributions of certain discretionary activities to a single objective (e.g., maximize profits) or to multiple objectives often is hindered by a lack of reliable revenue estimates. This problem severely hampers the usefulness of traditional mathematical programming approaches to capital rationing. In this paper, we modify the usual multiobjective capital rationing procedures by treating each discretionary activity as an objective in itself. In this manner, we can mitigate the difficulties associated with revenue measurement problems and allow a decision maker systematically to explore the various trade-offs between competing discretionary investments.     

Grocery Perishables Management

In this article, we study the joint pricing and inventory control problem for perishables when a retailer does not sell new and old inventory at the same time. At the beginning of a period, the retailer makes replenishment and pricing decisions, and at the end of a period, the retailer decides whether to dispose of ending inventory or carry it forward to the next period. The objective of the retailer is to maximize the long‐run average profit. Assuming zero lead time, we propose an efficient solution approach to the problem, which is also generalized to solve three extensions to the basic model. A feature of the present study is that we consider explicitly the influence of perishability on the demand. Among the insights gathered from the numerical analysis, we find that dynamic pricing aids extending shelf life and when disposal incurs a lower cost, or even a positive salvage value, the retailer is induced to dispose earlier since the benefit of selling new inventory offsets the loss due to disposal. We also observe that the faster the perceived rate of deterioration, the lower the threshold of the ending inventory for disposal. Perhaps a bit counter‐intuitive, maximizing profits does not mean eliminating disposals or expirations.     

Risk-averse and risk-taking newsvendors: a conditional expected value approach

Introduction  We consider a company sourcing a product with short life cycle to stock using the framework of the newsvendor model. Traditionally, risk-neutral inventory managers are considered optimizing the expected cost or profit. But experimental findings state that the actual quantity ordered deviates from the optimal quantity derived from the classical newsvendor model. Model  Recently, the newsvendor model with objectives different from maximizing expected profit has been an active field of research. Here, we propose a newsvendor model where the inventory manager can control internal and customer-oriented performance measures. The objective function is a convex combination of conditional expected values of low and high profits, respectively. Results   We give a qualitative characterization of the optimal order quantity and the resulting performance measures in dependence of the model parameters. A risk-averse inventory manager cannot Pareto-dominate a risk-neutral or risk-taking inventory manager with respect to the expected profit and the level of product availability. Finally, the risk preferences of the inventory manager are expressed as a function of the profit value of the product with respect to the level of product availability and the probability of loss, respectively.      

固定资产投资项目国家资本金管理的总体构想

固定资产投资项目国家资本金管理制度创新是当前的主要趋势和重要内容。本文对项目国家资本金管理的原则、目标、对象、内容、方式的创新做了初步探索,提出了固定资产投资项目国家资本金管理的总体构想。     

供应链融资结构视角下的零售商订购策略研究

本文引入供应链融资结构因素,在传统库存管理模型基础上,研究零售商在面临随机市场需求情形下考虑股权和债权等融资方式的订购策略,建立允许零售商延期支付的最优订购模型,利用最优化理论与方法得到了该模型的最优解,证明了最优解的存在性和唯一性,并分析了债权融资比例、价格成本比例系数、销售价格、订货周期、回购价格、进货价格、缺货成本、库存成本、以及融资利率等因素对最优解的影响。进一步,通过数值示例深入探讨具体市场环境下零售商的最优订购量,并进行了敏感性分析。研究发现,债权融资比例、价格成本比例系数、进货价格、订购周期、库存成本与零售商订购量存在负相关关系;缺货成本、回购价格、销售价格与零售商订购量正相关。相关研究结论可以为零售商订购行为提供科学指导,并为其库存管理提供重要决策依据。     

基于随机久期缺口控制的全资产负债优化模型

资产负债管理是银行等金融机构在负债结构和总量一定的前提下,通过对资产进行优化配置,达到资产流动性、盈利性和安全性“三性”之间的平衡。本文基于CIR动态利率期限结构求解随机久期,对包括增量和存量在内的全部资产负债组合的久期缺口进行预留和约束,构建资产负债优化模型控制利率风险。本文的创新与特色有三:一是以控制CIR利率期限结构的随机久期缺口为约束条件建立非线性规划模型、对资产配置进行利率风险免疫,反映了利率随时间的动态变化,突破了Macaulay久期、FW久期等现有研究的利率随时间的变化是固定不变或平行移动的限定条件,使资产配置的利率风险免疫更加符合现实情况。二是建立了包括增量资产负债与存量资产负债的全资产负债优化配置模型,改变了现有资产负债模型大多只考虑增量资产负债、而忽略存量资产负债的弊端。三是以市场利率朝着最不利方向变动时、预留缺口损失后的资本充足率仍满足监管要求为约束条件,保证了在利率不利变动情况下损失仍在可控范围内,在利率有利变动时银行净值增加。     

信贷资产证券化模式选择与产品设计

信贷资产证券化具有盘活非流动资产、分散本性风险等优势,是公司从资本市场获得低成本融资的主要工具之一。本文假设资产池由多个相关、且均服从算术布朗运动的资产现金流组成,运用公司证券定价理论,得到了基于资产池总现金流的证券化产品价值的解析解。以商业银行为例,研究了资产证券化的动机和最佳发行方式。数值分析表明：资产证券化不仅可以盘活资本,而且可以提高资产的价值;资产池的总价值与进入资产池的各个部分现金流之间的相关系数存在单调递减的关系。最后,针对我国商业银行的资产证券化模式提出了建议。     

资金约束供应链RFID投资决策与融资优化

本文以一个供应商和一个存在库存错放的资金约束零售商组成的两级供应链为研究对象,探讨了供应链成员采用无线射频识别（Radio Frequency Identification,RFID）技术的决策及融资选择问题。基于报童模型构建了供应链成员是否采用RFID技术、以及零售商选择贸易信贷融资或银行融资四种情景下的收益模型,求解出链上各成员的最优收益并探讨了RFID采用决策及融资选择策略。研究发现：资金约束零售商通过银行借贷融资可以获得更多的融资金额,但是其选择贸易信贷融资的意愿随着自有资金的减少而增强;当零售商自有资金适中时,随着错放率的上升或RFID成本的降低,零售商从银行融资向贸易信贷融资转变;银行融资在一定程度上能够缓解零售商的库存错放问题。     

STOCHASTIC COST-VOLUME-PROFIT ANALYSIS WITH A LINEAR DEMAND FUNCTION

In this paper we incorporate a linear demand function to model the price-volume causal relationship into stochastic cost-volume-profit (CVP) analysis. We assume that the objective function is to maximize expected profit; other objective functions are also discussed and compared. A linear stochastic model follows from which probabilistic statements can be easily obtained if the random variables are assumed to be multivariate normal. The basic framework is shown to be a special case of project value maximization where project value is the cash flow of the project discounted for time and risk according to the capital asset pricing model. Moreover, an intertemporal extension that considers inventory is developed. In summary, a new approach to stochastic CVP analysis that incorporates the management decision process in an uncertain environment is developed.     

The sophistication of capital budgeting in Malaysian companies

The sophistication of capital budgeting in 70 Malaysian companies is assessed in terms of a three-stage model of the capital budgeting process with reference to the following: the percentage of capital investments for which the techniques are used, explicit assessment of risk, the method of determining the cost of capital, the method of measuring cash flows, the formal profit contribution analysis, the extent of use of management science/operations research in capital budgeting, information systems support and the planning and control of capital expenditures. The assessment reveals a general tendency in the direction of increasing sophistication in the various phases of capital budgeting in Malaysian companies.     

Allocation of Returned Products among Different Recovery Options through an Opportunity Cost–Based Dynamic Approach*

In a make‐to‐order product recovery environment, we consider the allocation decision for returned products decision under stochastic demand of a firm with three options: refurbishing to resell, parts harvesting, and recycling. We formulate the problem as a multiperiod Markov decision process (MDP) and present a linear programming (LP) approximation that provides an upper bound on the optimal objective function value of the MDP model. We then present two solution approaches to the MDP using the LP solution: a static approach that uses the LP solution directly and a dynamic approach that adopts a revenue management perspective and employs bid‐price controls technique where the LP is resolved after each demand arrival. We calculate the bid prices based on the shadow price interpretation of the dual variables for the inventory constraints and accept a demand if the marginal value is higher than the bid price. Since the need for solving the LP at each demand arrival requires a very efficient solution procedure, we present a transportation problem formulation of the LP via variable redefinitions and develop a one‐pass optimal solution procedure for it. We carry out an extensive numerical analysis to compare the two approaches and find that the dynamic approach provides better performance in all of the tested scenarios. Furthermore, the solutions obtained are within 2% of the upper bound on the optimal objective function value of the MDP model.     

Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm

The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently.     

Variable Royalty Rates for Improving Franchise Channel Coordination*

A business format franchisor obtains a major part of its revenues from franchise royalties, which are typically a fixed percentage of franchisee gross sales. When a fixed royalty rate is used and the marginal costs of operating the franchise are increasing, the franchisee does not have an incentive to increase sales beyond a certain “optimal” volume. We present a model that recommends the use of a variable franchise royalty rate for extending this optimal sales volume. For a general convex cost function, we show that a new lower rate can be applied to incremental sales beyond the original optimal level. We show that this new rate should be less than half of the original rate when a quadratic cost function is applicable. Adopting a variable royalty rate increases franchisor royalty revenues and franchisee profits.     

Optimal bid-ask price strategies

Economic analysis has begun to focus on the implications of transaction costs to trading in capital assets. Specifically, the economics of market making and the price of liquidity has received considerable attention. This paper formulates the market maker's bid-ask price decision as a semi-Markov decision process with the reward being a function of expected return and risk. Risk is intimately related to dealer inventory and hence the solution of the analysis specifies bid-ask price strategies which are inventory dependent. Numerical examples indicate the market maker's optimal bid-ask prices will tilt around the ‘assets’ equilibrium price to control inventory as well as influence expected profit.     

A flexible model for the pricing of perishable assets

We present a flexible and versatile model which addresses the problem of assigning optimal prices to assets whose value becomes zero after a fixed expiry date. (Such assets include the important example of seats on airline flights.) Our model is broad in scope, in particular encompassing the ability to deal with arrivals of customers in groups. It is highly adaptable and can be adjusted to deal with a very extensive set of circumstances.Our approach to the problem is based on elementary and intuitively appealing ideas. We model the arrival of customers (or groups of customers) according to an inhomogeneous Poisson process. We incorporate into the model time dependent price sensitivity (which may also be described as “time dependent elasticity of demand”). In this setting the solution to the asset pricing problem is achieved by setting up coupled systems of differential equations which are readily amenable to numerical solution via (for instance) a vectorised Runge-Kutta procedure. An attractive feature of our approach is that it unifies the treatment of discrete and continuous prices for the assets.     

Inventory‐Based Dynamic Pricing with Costly Price Adjustment

We study an average‐cost stochastic inventory control problem in which the firm can replenish inventory and adjust the price at anytime. We establish the optimality to change the price from low to high in each replenishment cycle as inventory is depleted. With costly price adjustment, scale economies of inventory replenishment are reflected in the cycle time instead of lot size—An increased fixed ordering cost leads to an extended replenishment cycle but does not necessarily increase the order quantity. A reduced marginal cost of ordering calls for an increased order quantity, as well as speeding up product selling within a cycle. We derive useful properties of the profit function that allows for reducing computational complexity of the problem. For systems requiring short replenishment cycles, the optimal solution can be easily computed by applying these properties. For systems requiring long replenishment cycles, we further consider a relaxed problem that is computational tractable. Under this relaxation, the sum of fixed ordering cost and price adjustment cost is equal to (greater than, less than) the total inventory holding cost within a replenishment cycle when the inventory holding cost is linear (convex, concave) in the stock level. Moreover, under the optimal solution, the time‐average profit is the same across all price segments when the inventory holding cost is accounted properly. Through a numerical study, we demonstrate that inventory‐based dynamic pricing can lead to significant profit improvement compared with static pricing and limited price adjustment can yield a benefit that is close to unlimited price adjustment. To be able to enjoy the benefit of dynamic pricing, however, it is important to appropriately choose inventory levels at which the price is revised.