产能有限的供应商主导的双渠道分销策略研究

在供应商产能有限的背景下,研究供应商的最优分配策略和双渠道分销的问题。根据期望利润最大化建立供应商渠道分配模型,依据纳什均衡确定供应商的最优定价、最优产能和最优分销策略。结果表明,在双渠道策略的情况下,供应商应该优先满足销售企业的销售量。若供应链的销售量恰好得到满足,供应商应该按最优产能生产。供应商的渠道选择与供应商产能、供应链各节点的盈利能力和生产成本有关。     

PRODUCT LINE AND INVENTORY STRATEGY

The problem of optimal variety in inventory heretofore has been approached on an individual item basis. This paper demonstrates the analysis of aggregate inventory through the use of the log-normal distribution. The theory and application of this distribution to inventory problems are reviewed. This distribution is then applied to the problem of optimal variety, measuring the impact on sales and cost of deletion of slow-moving items in order to determine the optimal assortment for profitability. This then leads to analysis of optimal margin determination for slow-moving items. The final part of the discussion deals with inventory characteristics of the distribution channel, utilizing the characteristics of the log-normal distribution.     

Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores

In this study, we use hourly data on store traffic, sales, and labor from 41 stores of a large retail chain to identify the extent of understaffing in retail stores and quantify its impact on sales and profitability. Using an empirical model motivated from queueing theory, we calculate the benchmark staffing level for each store, and establish the presence of systematic understaffing during peak hours. We find that all 41 stores in our sample are systematically understaffed during a 3‐hour peak period. Eliminating understaffing in these stores can result in a significant increase in sales and profitability in these stores. Also, we examine the extent to which forecasting errors and scheduling constraints drive understaffing in retail stores and quantify their relative impacts on store profits for the retailer in our study.     

Promotion Planning and Supply Chain Contracting in a High–Low Pricing Environment

Demand forecast errors threaten the profitability of high–low price promotion strategies. This article shows how to match demand and supply effectively by means of two‐segment demand forecasting and supply contracts. We find that demand depends on the path of past retail prices, which leads to only a limited number of reachable demand states. However, forecast errors cannot be entirely eliminated because competitive promotions entail some degree of random (i.e., last‐minute) pricing. A hedging approach can be deployed to distribute demand risk efficiently over multiple promotional campaigns and within the supply chain. A retailer that employs a portfolio of forward, option, and spot contracts can avoid both stockouts and excess inventories while achieving the first‐best solution and Pareto improvements. We provide an improved forecasting method as well as stochastic programs to solve for optimal production and purchasing policies such that the right amount of inventory is available at the right time. By connecting a stockpiling model of demand with the supply side, we derive insights on optimal risk management strategies for both manufacturers and retailers in a market environment characterized by frequent price promotions and multiple discount levels. We employ a data set of the German retail market for a key generator of store traffic—namely, diapers.     

OPTIMAL PLANNING: THE USE OF SALES FORECASTS

Budgets are established and decisions in a firm are made on the basis of the sales forecast. In the majority of firms this forecast is a single value estimate of sales. The decisions made on the basis of the single value estimate frequently fall short of optimal strategies because of costs that occur when actual sales differ from forecast sales. These costs, measured in terms of opportunity losses, can be minimized when considered in conjunction with a probability distribution of sales. Three methods for determining the probability distribution of sales are considered in this paper. These are regression analysis Schlaifer's approach for establishing a subjective distribution, and Bayesian regression. The use of the distribution in conjunction with linear and quadratic loss functions is illustrated.     

有限时域内部分缺货的变质品生产-定价策略研究

研究有限时域内对变质产品同时安排生产计划和制定定价策略的问题。有限时域被划分成同等长度的多个周期,产品以固定的生产率间歇生产,并以固定的速率发生变质。需求同时依赖于时间和产品价格,在每个周期内都允许缺货且短缺量部分延迟订购。目的是要寻求一个使有限时域内系统的平均利润最大化的生产计划和定价策略。在算例中,分别讨论了需求随时间增大和减小两种不同的需求模式,采用Box复合算法,通过数值计算求得相应的最优生产计划和定价策略。分析表明：对于成长期或销售季初的产品,系统应采用“小批量多批次”的生产方式;而对于衰退期或销售季末的产品,应采用“大批量小批次”的生产方式。此外,不同类型的产品在不同需求模式下的定价策略有所不同：对于有限时域较短或变质率较高的产品,处于衰退期或销售季末的定价要低于成长期或销售季初的定价;相反,对于有限时域相对较长或变质率较低的产品,在衰退期或销售季末的最优定价要高于在成长期或销售季初的定价。     

The Impact of Product Lifecycle on Capacity Planning of Closed‐Loop Supply Chains with Remanufacturing

Product recovery operations in reverse supply chains face rapidly changing demand due to the increasing number of product offerings with reduced lifecycles. Therefore, capacity planning becomes a strategic issue of major importance for the profitability of closed‐loop supply chains. This work studies a closed‐loop supply chain with remanufacturing and presents dynamic capacity planning policies developed through the methodology of System Dynamics. The key issue of the paper is how the lifecycles and return patterns of various products affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The model can be used to identify effective policies, to conduct various “what‐if” analyses, and to answer questions about the long‐term profitability of reverse supply chains with remanufacturing. The results of numerical examples with quite different lifecycle and return patterns show how the optimal collection expansion/contraction and remanufacturing contraction policies depend on the lifecycle type and the average usage time of the product, while the remanufacturing capacity expansion policy is not significantly affected by these factors. The results also show that the collection and remanufacturing capacity policies are insensitive to the total product demand. The insensitivity of the optimal policies to total demand is a particularly appealing feature of the proposed model, given the difficulty in obtaining accurate demand forecasts.     

基于提前支付且考虑银行存贷的资金约束供应链最优定价决策

针对由单一制造商和单一零售商组成的供应链，考虑制造商或零售商在资金闲置与资金约束时向银行存款与贷款的情形，研究了供应链Stackelberg定价策略。首先，给出了市场需求函数；然后，考虑销售收入的零存整取收益、利润的存款收益以及贷款损失，分别构建了制造商资金约束与零售商资金约束两种情形下基于提前支付且考虑银行存贷的供应链成员利润函数；进一步地，依据Stackelberg理论，确定了两种权力结构下供应链的最优策略；在此基础上，分析了零存整取利率、存款利率和贷款利率对供应链最优策略的影响；最后，针对不同资金约束对象与不同权力结构，对比分析了银行利率对供应链最优策略的影响差异，揭示其背后的影响机理。研究结果表明，三种银行利率均会影响供应链的最优策略，相对于供应链权力结构，其影响与供应链资金约束对象更加相关。     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

RETAIL INVENTORY CONTROL WITH LOST SALES,SERVICE CONSTRAINTS,AND FRACTIONAL LEAD TIMES

This paper describes a periodic review, fixed lead time, single-product, single-facility model with random demand, lost sales and service constraints that was developed for potential application at a Western Canadian retailer. The objective of this study was to determine optimal (s, S) policies for a large number of products and locations. To this end, we evaluate the long run average cost and service level for a fixed (s, S) policy and then used a search procedure to locate an optimal policy. The search procedure is based on an efficient updating scheme for the transition probability matrix of the underlying Markov chain, bounds on S and monotonicity assumptions on the cost and service level functions. A distinguishing feature of this model is that lead times are shorter than review periods so that the stationary analysis underlying computation of costs and service levels requires subtle analyses. We compared the computed policies to those currently in use on a test bed of 420 products and found that stores currently hold inventories that are 40% to 50% higher than those recommended by our model and estimate that implementing the proposed policies for the entire system would result in significant cost savings.     

Process Mean Determination with Quantity Discounts in Raw Material Cost*

Setting the mean (target value) for a container-filling process is an important decision for a producer when the material cost is a significant portion of the production cost. Because the process mean determines the process conforming rate, it affects other production decisions, including, in particular, the production setup and raw material procurement policies. In this paper, we consider the situation in which quantity discounts exist in the raw material acquisition cost, and incorporate the quantity-discount issue into an existing model that was developed for simultaneously determining the process mean, production setup, and raw material procurement policies for a container-filling process. The product of interest is assumed to have a lower specification limit, and the items that do not conform to the specification limit are scrapped with no salvage value. The production cost of an item is proportional to the amount of the raw material used in producing the item. A two-echelon model is formulated for a single-product production process, and an algorithm is developed for finding the optimal solution. A sensitivity analysis is performed to study the effects of the model parameters on the optimal solution.     

顾客策略行为下差异产品两阶段定价模型研究

互联网和信息技术的发展,为零售商收集顾客信息带来方便的同时,也使得消费者变得越来越具有策略性和选择性。首先,构建零售商只销售一种高质量产品的情形,研究其两阶段的定价策略,并分析产品的跨期折扣因子对产品的零售价格、销售量和零售商利润的影响。然后,考虑零售商可提供高质量和低质量两种产品的情形,顾客在第一销售期、第二销售期都可以选择两种产品。构建两阶段的定价决策模型,并分析产品的跨期折扣因子、顾客对低质量产品的接受度等参数对产品两阶段的零售价格、销售量的影响。最后,用数值算例分析了第二种模型下顾客对低质量产品的接受度等模型参数对零售商两阶段总利润的影响,然后比较了第一种和第二种模型下高质量产品的零售价格和销售量。研究结论较全面的分析了顾客策略行为对差异产品两阶段销售期的定价决策的影响,为零售商进行定价优化决策提供了十分重要的参考。     

基于“拆分-填充-分解-集成”的我国线上零售额预测研究

线上零售额的准确预测是政府制定零售政策和发展规划的依据,也是电商和物流企业确定发展战略的基础。由于我国线上零售额数据具有样本量小、波动性大、受节日影响大、存在缺失值等特征,准确预测变得十分困难。为解决这个问题,本文提出了一种“拆分-填充-分解-集成”的预测框架。具体而言,首先将数据集拆分为实物零售数据与非实物零售数据两部分。其次,分别根据实物零售与非实物零售数据不同的缺失特征对样条插值法做了改进,提出了基于“样条插值-二分调整”的分解填充法以及基于“分段线性函数拟合-样条插值”的分解填充法,对两组数据进行缺失值填充。继而基于两组数据的不同特征,分别提出“乘法分解-ARIMA-移动平均”以及“STL分解-BP神经网络-灰色波形”的预测方法对两组数据进行预测。最后将两组预测结果集成,得到我国线上零售额的预测值。实证结果表明,本文提出的预测框架能较好地捕捉我国线上零售额数据的特征,具有很高的预测精度,且较传统的缺失值填充和预测方法在性能上表现更好。本文提出的“拆分-填充-分解-集成”预测框架,丰富了现有的缺失值填充与预测方法,并为预测实践提供了解决方案。     

Supply chain configuration for diffusion of new products: An integrated optimization approach

We develop an integrated/hybrid optimization model for configuring new products’ supply chains while explicitly considering the impact of demand dynamics during new products’ diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.     

多个不确定性因素下跨国公司投资策略

利用实物期权方法,在汇率、国内外生产成本和产品价格均不确定的情况下,把跨国企业迁移生产和销售的空间决策柔性(spatial decision flexibility)模型为交换期权,分别给出了迁移生产的最优下界和迁移销售的最优上界、及其迁移决策规则,并讨论了关键参数对迁移决策规则的影响。最后,给出应用迁移生产和销售决策规则的数值模拟算例,体现了本文给出的决策规则在实际应用中具有可操作性。     

Evaluating manufacturer's buyback policies in a single-period two-echelon framework under price-dependent stochastic demand

This paper attempts to model the profitability of a secondary market, in a newsvendor setting, to a profit-maximizing manufacturer, who is offering to the retailer a buyback policy for the unsold merchandise left at the end of the selling season. With a buyback agreement, the manufacturer shares the risks of demand uncertainty, thus inducing the buyer to place larger orders. The manufacturer's risk is mitigated to some extent by the availability of an extra market to dispose off the unsold merchandise. Both parties are risk-neutral profit-maximizers and both have the same information about the final demand for the product and its uncertainty. The manufacturer's decision is to arrive at an optimal wholesale price and the buyback price. Based on this offer, the retailer in turn sets the optimal amount of merchandise to purchase, as well as the unit selling price to meet a price-dependent uncertain demand for the merchandise in question. Due to the difficulty of obtaining analytical results, we have undertaken a numerical analysis to (i) compare the optimal policies across demand functions and error structures for three situations namely the no-incentive case and the buyback policies with and without a secondary market; (ii) indicate the conditions whereby the trade incentive is beneficial to both parties; (iii) assess the efficacy of the policies using two other performance indices (probability of achieving a target profit, and pass-through ratios) alternate to profit maximization; and (iv) conjecture the conditions for successful buyback policies and the nature of the benefits from the secondary market.     

Decision support for multi-family aggregate production planning

Aggregate production planning decisions are inter mediate range decisions that can have a significant impact on both productivity and profitability. In this paper, we examine an interactive computer-based method that provides decision support for the aggregate planner. The proposed approach combines the judgement of the planner with the optimization of subproblems to arrive at an effective solution for multi-family aggregate production planning problems. In the interactive approach, the planner exercises direct control over sensitive workforce levels and production capacities. A network flow sub-problem solver is used to generate optimal production plans and inventory levels given the user-specified production capacities. Decision aids are provided to help the planner achieve a cost-effective solution that is consistent with judgement concerning workforce levels. Computational testing on five test problems indicates that very cost-effective solutions can be obtained. The results of applying the interactive method to a real-world problem are also reported.     

THE APPLICATION OF A DYNAMIC FORECASTING MODEL WITH INVENTORY CONTROL PROPERTIES

This article summarizes the application of a forecasting model. Forecasts are made of monthly sales of products which do not change in style on an annual basis. The model is an exponential smoothing model. Adjustments of the parameters of the model are made whenever the average forecast error over the previous four periods is too large to be explained solely by unassignable causes. The efficiency gained in using the model is measured by the ratio of the standard deviation of the forecast errors to the standard deviation of sales. If this ratio is less than one, then the safety stock level that is carried for a given product can be reduced if sales are forecasted with the model and the standard deviation of the forecast errors is used to determine the safety stock level. The net effect is the reduction in the cost of carrying safety stocks. The results of the proposed model are also compared to a similar set of results generated from a basic, exponential model.     

Managing the Revenue of Sales Platforms: Insights into Service Fees

Online sales platforms have grown substantially in recent years. These platforms assist sellers to conduct sales, and in return, collect service fees from sellers. We study the fee policies by considering a fee‐setting platform, on which a seller may conduct a sale with a reserve price to a group of potential buyers: the seller retains the object for sale if the final trading price is below the reserve price. The platform may charge two types of fees as in current practice: a reserve fee as a function of the seller's reserve price and a final value fee as a function of the sale's final trading price. We derive the optimality condition for fee policies, and show that the platform can use either just a final value fee or just a reserve fee to achieve optimality. In the former case, the optimal final value fee charged by the platform is independent of the number of buyers. In the latter case, the optimal reserve fee is often a decreasing, instead of increasing, function of the seller's reserve price. An increasing reserve fee may make the seller reluctant to use a positive reserve price and hurt the platform's revenue. In general, the optimal fees are nonlinear functions, but in reality, linear fees are commonly used because of their simplicity for implementation. We show that a linear fee policy is indeed optimal in the case that the seller's valuation follows a power distribution. In other cases, our numerical analysis suggests close‐to‐optimal performance of the linear policy.     

INVENTORY RATIONING AND SHIPMENT FLEXIBILITY ALTERNATIVES FOR DIRECT MARKET FIRMS

This paper investigates inventory‐rationing policies of interest to firms operating in a direct market channel. We model a single product with two demand classes, where one class requests a lower order fulfillment lead time but pays a higher price. Demand for each class follows a Poisson process. Inventory is fed by a production system with exponentially distributed build times. We study rationing policies in which the firm either blocks or backlogs orders for the lower priority customers when inventory drops below a certain level. We compare the performance of these rationing policies with a pure first‐come, first‐serve policy under various scenarios for customer response to delay: lost sales, backlog, and a combination of lost sales and backlog.