A partial backordering approach to inventory control

We study the effects of using a partial backordering approach to controlling inventories under deterministic and stochastic demands, respectively. With a deterministic demand, our model is built with the objective of minimizing the total cost of ordering, holding, backordering and lost sales. The conditions for the partial backordering policy to be feasible are identified and a pair-wise comparison among the no-backordering, complete backordering, and partial backordering doctrines is conducted. In the stochastic case, we focus on a make-to-stock system with a Poisson demand and exponential production time, which allows us to establish a queuing model to examine the cost-effectiveness of using partial backorders. The conditions under which the partial backordering policy outperforms the complete backordering policy are identified.     

The Manufacturer's Incentive to Reduce Lead Times

Although, ceteris paribus, reducing lead times may be desirable from an overall system perspective, an upstream party (e.g., a manufacturer) may have strong disincentives to offer shorter lead times, even if this came at no cost. We consider a setting in which the downstream party has the ability to exert a costly effort to increase demand (e.g., through sales promotions, advertising, etc.) during the selling season, and compare two situations: one where there is zero lead time (i.e., all demand can be satisfied after observing the demand realization), and one where orders need to be made before demand is realized. We identify two interacting effects that may inhibit shorter lead times. A so‐called “safety stock effect” can be observed when a lower risk of stocking out under short lead times induces the downstream party to alter her order quantity. A second effect, termed as “effort effect,” arises if shorter lead times impact the downstream party's optimal sales effort, and, as a consequence, lead to different order quantities. We provide a formal characterization of both effects, insight into how these effects interact, and show under which conditions the manufacturer has an incentive to offer shorter lead times.     

Stackelberg竞争下考虑消费者转移和平台差异的供应商运营策略

考虑消费者转移和平台差异对需求的影响,构建不同渠道结构和销售契约下的决策模型;基于水平和垂直Stackelberg博弈,获得8种情形下的最优决策。探究佣金率、平台差异和消费者转移率对均衡结果的影响,获得最优的销售契约和渠道配置策略。研究表明:代理模式可以降低产品的销售价格,提升销量;垄断市场中,平台更愿意采取转售模式,而供应商更偏好代理模式;竞争环境下,优势平台一定条件下总是更偏好转售模式,而竞争平台销售模式偏好随着佣金率逐渐变化;供应商的渠道选择与消费者转移量有关,当转移量较高时,供应商偏向双渠道结构,反之亦然。     

Connected Substitutes and Invertibility of Demand

We consider the invertibility (injectivity) of a nonparametric nonseparable demand system. Invertibility of demand is important in several contexts, including identification of demand, estimation of demand, testing of revealed preference, and economic theory exploiting existence of an inverse demand function or (in an exchange economy) uniqueness of Walrasian equilibrium prices. We introduce the notion of “connected substitutes” and show that this structure is sufficient for invertibility. The connected substitutes conditions require weak substitution between all goods and sufficient strict substitution to necessitate treating them in a single demand system. The connected substitutes conditions have transparent economic interpretation, are easily checked, and are satisfied in many standard models. They need only hold under some transformation of demand and can accommodate many models in which goods are complements. They allow one to show invertibility without strict gross substitutes, functional form restrictions, smoothness assumptions, or strong domain restrictions. When the restriction to weak substitutes is maintained, our sufficient conditions are also “nearly necessary” for even local invertibility.     

Computer-aided formulation and optimal control of an integrated production system for competitive products

An integrated production, inventory and advertising system has been formulated based on control engineering principles. In this system, the customer's demand is satisfied from stock, which in turn is replenished from production facilities. But sales reflect the advertising effort, present as well as past. The past effort has been considered as a delay with an assumed delay period. The problem is formulated as a state and observation model. Out of this an augmented model of state and parameters is formed. The parameters describing the model are estimated from MAP discrete filter algorithm. It is shown that sales of product 1 or product 2 at (k + l)th period is effected by sales and advertising of product 1 or product 2 at the kth period, and also by sales and advertising of product 2 or product 1 at (k-θ)th period, where 6 is the delay.     

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.     

考虑促销因素的医药电商平台需求预测研究

医药电商平台需求预测涉及到药品自身属性及电商平台推出的各种促销活动,本文针对以上影响药品销量的因素提出了时间序列-机器学习组合模型对医药电商平台进行需求预测。传统研究促销因素的需求预测文献将促销阶段商品销量拆分为常规销量和促销增量的线性组合,本文首先拟合各药品促销阶段的常规销量,根据各药品常规销量时间序列数据及服用周期,使用SARIMA模型拟合药品的常规销量预测值,并将常规销量预测值与商品促销特征数据一同输入XGBoost模型进行集成学习预测。本文使用国内某医药电商平台真实销售数据测试组合模型的有效性,结果显示组合预测模型的预测效果相比其他三种传统预测模型更优。此外,本文验证了不同折扣力度下组合预测模型的有效性,以及促销变量在预测模型中的有效性,同时研究了数据共享策略在需求预测中的应用场景,结果显示预测模型在引入促销变量和采用数据共享策略后都能显著降低模型的预测误差。     

融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法研究

挖掘特定产品的需求模式无法从整体掌握该类产品的市场特征;短生命周期体验品因缺乏历史销售数据,并且销售总量波动性极大,尤其需要从整体掌握销售总量与产品属性间关系的需求特征规律,但又难以挖掘,亟待提出适用于该类产品的需求特征模式挖掘方法。基于按销售总量分区后各区的需求特征的规律性,提出了一种按销售总量分区、以已有产品介绍集和销售总量为源信息、适用于新产品开发前使用、融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法。该方法包括基于内容分析法的产品属性挖掘方法和基于关联分析的产品属性关系模式挖掘方法。前者可以得到较全面的产品属性;后者能够构建不同销售总量区间内产品集的属性关系模式,得到各区间的产品属性关系网,获得高销售总量区间具备,但中、低区间不具备的属性关系模式,从而获得需求特征模式。通过不断更新产品介绍集和销售总量并迭代挖掘,该方法能够动态挖掘需求特征模式。最后利用2013至2016年国产犯罪和爱情类电影数据验证了该方法的可行性,并得到了这两类电影的产品属性及近年的需求特征模式,可用于指导这两类电影的创作。     

部分短缺量拖后下基于顾客敏感度的补货策略适用范围研究

顾客对缺货数量和等待时间的敏感度影响着部分短缺量拖后率。基于此,根据顾客对缺货数量和等待时间的敏感度以及成本结构,对部分短缺量拖后下不同补货策略的适用范围进行了研究,得出以下结论:(s,S)连续性检查策略适用于单位缺货和丢单成本较高的库存系统;(t,S)周期性检查策略和(t,s,S)混合策略适用于单位缺货和丢单成本较低的库存系统;在单位缺货和丢单成本较低的库存系统中,当顾客对缺货数量较敏感时,(t,s,S)混合策略的运作成本更低,否则(t,S)周期性检查策略更适用,而顾客对等待时间的敏感度对补货策略适用范围的影响不明显。     

平台模式下农民合作社与供应链协调研究——以农户主导视角

针对众创平台模式下农民专业合作社与供应链协调问题。在农地流转背景下提出了一种农户主导供应链的多阶段斯坦科尔伯格博弈模型,设计了一种集中决策模式和四种分散决策模式,考虑了风险传递、决策优先权、边际成本和风险成本问题,构建了农场主、合作社、小农户和平台以农户数量（或合作比例）、土地流转收益、增产率和期望销售目标等共同影响的利润最大化模型,得到了最优土地流转收益和最大化利润所满足的方程;发现农户数量（或合作比例）、收益分配率、销售目标等因素都会对最优解产生影响。通过数值模拟,发现农场主（或合作社）优先权可以提高农场主（或合作社）利润;在一定条件下,合作社模式可以实现供应链协调;在农场主（或合作社）模式中平台和小农户利润通常是较低的;在供应链协调和小农户利润不降低的前提下,可以通过有效途径对平台利润差额进行补偿以保证合作社模式持续均衡。     

一类两级DC/STORES库存系统模型与控制

本文研究了一类具有N个一级百货店和一个二级配送中心、顾客需求服从负二项分布考虑部分失去销售(PLS)的两级库存系统。文中引入了负二项分布,克服了需求服从泊松或正态分布时在高需求率条件下所建模型的缺陷,构建了系统模型,给出了系统优化控制策略,为系统的协调控制提供了有效的支持。     

Single-Stage and Serial Production Line Systems with Unreliable Machines and General Probability Distributions

This paper presents stochastic models for analyzing production to stock and production on demand systems with arbitrary demand, production, machine breakdown and repair processes, and a finite buffer. Expressions are developed for the transition probabilities among the different states, the proportion of transitions that take the process to a particular state, the probabilities that the process is at a particular state, and the expected state residence times. Also, expressions are developed for some quantities of interest, like average inventory, percentage of lost sales, and expected waiting time. Applications are discussed for various demand, production, machine breakdown, and repair processes and numerical examples are presented. Guidelines are given for utilizing the models in evaluating the economic performance of production systems. Using an approximation procedure, the models are used for modeling multi-stage serial production to stock and production on demand lines. Simulation results indicated that the quality of the approximation is very good. The multi-stage models are useful in practical situations, because they allow the use of different probability distributions for the various processes (production, machine failure, etc.) of each stage.     

Price,Rebate, and Returns Supply Contracts for Coordinating Supply Chains with Price‐Dependent Demands

Channel rebates and returns policies are common mechanisms for manufacturers to entice retailers to increase their order quantities and sales ultimately. However, when the underlying demand depends on the retail price, it has been known that channel coordination cannot be achieved if only one of these mechanisms is deployed. In this article, we show that a policy that combines the use of wholesale price, channel rebate, and returns can coordinate a channel with both additive and multiplicative price‐dependent demands. In addition to determining the sufficient conditions for the contract parameters associated with the equilibrium policy, we show that multiple equilibrium policies for channel coordination exist. We further explore how the equilibrium policy can be adjusted to achieve Pareto improvement. Other issues such as the maximum amount of expected profit that the manufacturer can share under the coordinated channel, the structural properties of the contracts under both the additive and multiplicative price‐dependent demand functions are also discussed.     

Impact of information exchange on supplier forecasting performance

Forecasts of demand are crucial to drive supply chains and enterprise resource planning systems. Usually, well-known univariate methods that work automatically such as exponential smoothing are employed to accomplish such forecasts. The traditional Supply Chain relies on a decentralized system where each member feeds its own Forecasting Support System (FSS) with incoming orders from direct customers. Nevertheless, other collaboration schemes are also possible, for instance, the Information Exchange framework allows demand information to be shared between the supplier and the retailer. Current theoretical models have shown the limited circumstances where retailer information is valuable to the supplier. However, there has been very little empirical work carried out. Considering a serially linked two-level supply chain, this work assesses the role of sharing market sales information obtained by the retailer on the supplier forecasting accuracy. Weekly data from a manufacturer and a major UK grocery retailer have been analyzed to show the circumstances where information sharing leads to improved forecasting accuracy. Without resorting to unrealistic assumptions, we find significant evidence of benefits through information sharing with substantial improvements in forecast accuracy.     

A WTP‐Choice Model: Empirical Validation,Competitive and Centralized Pricing

Willingness To Pay (WTP) of customers plays an anchoring role in pricing. This study proposes a new choice model based on WTP, incorporating sequential decision making, where the products with positive utility of purchase are considered in the order of customer preference. We compare WTP‐choice model with the commonly used (multinomial) Logit model with respect to the underlying choice process, information requirement, and independence of irrelevant alternatives. Using WTP‐choice model, we find and compare equilibrium and centrally optimal prices and profits without considering inventory availability. In addition, we compare equilibrium prices and profits in two contexts: without considering inventory availability and under lost sales. One of the interesting results with WTP‐choice model is the “loose coupling” of retailers in competition; prices are not coupled but profits are. That is, each retailer should charge the monopoly price as the collection of these prices constitute an equilibrium but each retailer's profit depends on other retailers' prices. Loose coupling fails with dependence of WTPs or dependence of preference on prices. Also, we show that competition among retailers facing dependent WTPs can cause price cycles under some conditions. We consider real‐life data on sales of yogurt, ketchup, candy melt, and tuna, and check if a version of WTP‐choice model (with uniform, triangle, or shifted exponential WTP distribution), standard or mixed Logit model fits better and predicts the sales better. These empirical tests establish that WTP‐choice model compares well and should be considered as a legitimate alternative to Logit models for studying pricing for products with low price and high frequency of purchase.     

Forecast Updating and Supplier Coordination for Complementary Component Purchases

We study a supply chain where an original equipment manufacturer (OEM) buys subassemblies, comprised of two complementary sets of components, from a contract manufacturer (CM). The OEM provides a demand forecast at the time when the CM must order the long lead‐time set of components, but must decide whether or not to provide updated forecasts as a matter of practice. Forecast updates affect the CM's short lead‐time purchase decision, and the anticipation of updates may also affect the long lead‐time purchase decision. While the OEM and CM both incur lost sales costs, the OEM can decide whether or not to share the overage costs otherwise fully borne by the CM. We investigate when the OEM is better served by committing to provide updated forecasts and/or committing to share overage costs. For a distribution‐free, two‐stage forecast‐update model, we show that (1) the practice of providing forecast updates may be harmful to the OEM and (2) at the OEM's optimal levels of overage risk sharing, the CM undersupplies relative to the supply chain optimal quantity. For a specific forecast‐update model, we computationally investigate conditions under which forecast updating and risk sharing are in the best interest of the OEM.     

Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.     

爱尔朗型按订单装配系统最优生产——库存控制策略研究

本文研究具有复杂装配结构的爱尔朗型按订单装配(ATO)系统的组件生产与库存优化控制问题。系统涉及多种组件,一个最终产品和多类客户需求。在此系统中,各种组件的生产时间服从爱尔朗分布,各类客户的需求为泊松到达过程。针对不同客户需求类型:产品需求与独立组件需求且同为销售损失型,建立基于马尔可夫决策过程(MDP)的平均总成本模型,应用动态规划方法求解最优策略。仿真模拟方法实现最优策略,并通过数值实验分析多生产阶段和系统参数对最优策略的影响。研究结果表明,爱尔朗型生产时间ATO系统的最优策略为状态依赖型策略,即组件的生产与库存分配由动态基础库存水平值和动态库存配给水平值控制。对于任一组件,其基础库存水平值和库存配给水平值均随着生产阶段的增加而降低,且生产阶段对基础库存水平值和平均总成本的影响较显著。     

Managing Perishables with Time and Temperature History

We address the use and value of time and temperature information to manage perishables in the context of a retailer that sells a random lifetime product subject to stochastic demand and lost sales. The product's lifetime is largely determined by the temperature history and the flow time through the supply chain. We compare the case in which information on flow time and temperature history is available and used for inventory management to a base case in which such information is not available. We formulate the two cases as Markov Decision Processes and evaluate the value of information through an extensive simulation using representative, real world supply chain parameters.     

Cross‐Section Regression with Common Shocks

This paper considers regression models for cross‐section data that exhibit cross‐section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of cross‐section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t, Wald, and F statistics are found to have asymptotic standard normal, χ², and scaled χ² distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units.