The Dynamic Newsvendor Model with Correlated Demand

The classic newsvendor model was developed under the assumption that period‐to‐period demand is independent over time. In real‐life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast‐based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all‐purpose models like the moving‐average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE‐optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.      

An Entropy‐Based Methodology for Valuation of Demand Uncertainty Reduction

We propose a distribution‐free entropy‐based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology's use in three different settings: (i) a newsvendor valuing knowledge of expected demand, (ii) a short life cycle product supply manager considering the adoption of a quick response strategy, and (iii) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.     

On the benefits of operational flexibility in a distribution network with transshipment

We consider a centralized distribution network with multiple retailers who receive replenishment inventory to satisfy customer demand of the local markets. The operational flexibility of the network is defined as the opportunity that one retailer's excess inventory can be transferred to satisfy other retailers’ unmet customer demand due to stock-outs. A general modeling framework is developed to optimize retailers’ order quantities under any possible flexibility level of a stylized two-stage distribution network. We apply the framework to formulate and solve the transshipment problem of a distribution network with three retailers. Six typical flexibility levels are investigated to make the comparison study on the firm's profit performance under three ordering quantity policies: average demand, newsvendor order quantity, and optimal order quantity. We find that the operational flexibility and system optimization are complements to the firm's performance. The ordering policy with newsvendor ordering quantity can perform fairly well with moderate flexibility level when compared with the optimized ordering policy with full flexibility.     

需求分布规律变化情况下的报童订货策略

在需求分布规律变化情况下，报童在进行订货决策时会因为错误判断需求分布规律而导致期望库存成本增加。为了解决这一问题，本文集成传统历史需求信息和非传统需求信息以正确地认知需求分布规律，在此基础上决策订货量。假设需求服从均值不同、方差相同的两种类型的正态分布，每一种正态分布的概率已知。利用信号检测理论构建基于历史需求信息与需求分布概率的报童最优订货策略，并与只基于需求分布概率的直觉规则订货策略进行对比。结果表明：只要排除需求分布概率很大或很小两种极端情况，最优订货策略比直觉规则订货策略在控制期望库存成本方面的作用更明显，即利用历史需求信息可以有效修正报童对实际需求分布的检测结果，从而提高实际订货决策的准确性。研究结果对传统历史需求信息和非传统需求信息的集成以及需求信息交换等有一定的管理学启示和应用价值。     

Dynamic Pricing in e‐Services under Demand Uncertainty

High volatility of the e‐services market, due to increasing competition, low life cycle of products, and easy availability of information about competing service offerings to customers, makes the demand for service offerings quite uncertain. Revenue management in such markets calls for real‐time techniques to learn the demand and its dependence on both the price and the service level associated with the service offering. We assume firms reply on exploratory approaches for demand estimation, in which firms experiment with different service offerings in order to simultaneously learn the demand while doing business. Such exploration and learning process can be costly without supervision. As reported by Rothschild (Journal of Economic Theory, 9 185‐202, 1974), traditional Bayesian dynamic control approaches may conclude with suboptimal offerings. We propose a novel demand learning approach that is guaranteed to converge to the optimal offering. The approach combines simulated annealing algorithm with Bayesian learning. We further present intelligent techniques that adaptively reduce the effort of exploration on suboptimal service offerings so as to improve the long‐run average profit.     

A Newsvendor Who Chooses Informational Effort

We study a newsvendor who can acquire the services of a forecaster, or, more generally, an information gatherer (IG) to improve his information about demand. When the IG's effort increases, does the average ex ante order quantity rise or fall? Do average ex post sales rise or fall? Improvements in information technology and in the services offered by forecasters provide motivation for the study of these questions. Much depends on our model of the IG and his efforts. We study an IG who sends a signal to a classic single‐period newsvendor. The signal defines the newsvendor's posterior probability distribution on the possible demands and the newsvendor uses that posterior to calculate the optimal order. Each of the possible posteriors is a scale/location transform of the same base distribution. When the IG works harder, the average scale parameter drops. Higher IG effort is always useful to the newsvendor. We show that there is a critical value of order cost. For costs on one side of this value more IG effort leads to a higher average ex ante order and for costs on the other side to a lower average order. But for all costs, more IG effort leads to higher average ex post sales. We obtain analogous results for a “regret‐averse” newsvendor who suffers a penalty that is a nonlinear function of the discrepancy between quantity ordered and true demand.     

A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

We propose a tractable, data‐driven demand estimation procedure based on the use of maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare class “Littlewood” problem in a setting where the firm has access to only potentially censored sales observations; this is also known as the repeated newsvendor problem. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the “spiral down” effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.     

Trial or No Trial: Supplying Costly Signals to Improve Profits*

Many software and video game firms offer free trials with limited content to help buyers assess the likely value of the goods that they may purchase. This article examines fundamental issues related to the incentives and risks for a monopoly by providing a trial. Assuming that a seller can control the mix of components in a trial, we introduce a new mechanism for buyers’ inference of using a trial. We find that a trial may enable the seller to segment the market and charge a higher price to high‐valuation buyers, but can also cause a decline in demand. Moreover, the seller forfeits partial value of a full product through providing a free trial, so the benefit is offset by this cannibalization loss. In addition to the size and content of a trial, the distribution of buyers’ prior belief also affects a trial's ability to convey information. We show that a trial can provide more information if the prior belief is more concentrated in the tails of the distribution.     

The Newsvendor Model with Consumer Search Costs

We study the newsvendor problem when consumers are heterogeneous either in their valuations of the newsvendor's product, in their valuations of an outside option available to them, or in both valuations. In this context, we observe that the outside option, which represents the value that a given consumer associates with choosing not to purchase the newsvendor's product, may be interpreted as a search cost. Taking into consideration whether consumers' valuations differ on either one dimension of heterogeneity or on both dimensions, we develop a framework for classifying newsvendor models that incorporate demand‐management effects. In particular, we show that this framework includes both the newsvendor model with price‐dependent demand and the newsvendor model with endogenous demand as special cases. In addition to making a conceptual contribution by developing and drawing insights from this framework, we make technical contributions by providing more general sufficient conditions under which the underlying optimization problems are well behaved.     

Best Nonparametric Bounds on Demand Responses

This paper uses revealed preference inequalities to provide the tightest possible (best) nonparametric bounds on predicted consumer responses to price changes using consumer‐level data over a finite set of relative price changes. These responses are allowed to vary nonparametrically across the income distribution. This is achieved by combining the theory of revealed preference with the semiparametric estimation of consumer expansion paths (Engel curves). We label these expansion path based bounds on demand responses as E‐bounds. Deviations from revealed preference restrictions are measured by preference perturbations which are shown to usefully characterize taste change and to provide a stochastic environment within which violations of revealed preference inequalities can be assessed.     

Mean–variance analysis of the newsvendor model with stockout cost

We study the risk-averse newsvendor model with a mean–variance objective function. We show that stockout cost has a significant impact on the newsvendor's optimal ordering decisions. In particular, with stockout cost, the risk-averse newsvendor does not necessarily order less than the risk-neutral newsvendor. We illustrate this finding analytically for the case where the demand follows the power distribution.     

The Dynamics of Domestic Gray Markets and Its Impact on Supply Chains

Gray markets are created by unauthorized retailers selling manufacturer's branded products. Similar to international gray markets, domestic gray markets are a growing phenomenon whose impact on supply chains is not clear. We consider a supply chain with one manufacturer and several authorized retailers who face a newsvendor problem and a domestic gray market. While a gray market provides an opportunity for retailers to clear their excess inventory (inventory‐correction effect), it also can be a threat to their demand (demand‐cannibalization effect). We first characterize the emerging equilibrium by assuming an MSRP environment. Comparing a decentralized and centralized system, we show that a wholesale pricing contract is quite efficient in a gray market environment; we explain the underlying mechanism and note some of the operational decisions that could hurt that efficiency. We show that the gray market price determines the degree of both the negative effects of demand‐cannibalization and the positive effects of inventory correction, which in turn determines the net impact of gray markets on the retailer's stocking choice and, ultimately, the manufacturer's profit. We then study the authorized retailers' problem as a price‐setting newsvendor. We observe that the gray market creates price competition between the authorized and unauthorized retailers, causing a drop in the primary market price. However, this price competition can be counteracted by the authorized retailers' stocking decision. Finally, we extend our model to consider the cases where the demand can be correlated across retailers.     

Demand Fluctuations in the Ready‐Mix Concrete Industry

I investigate the role of demand shocks in the ready‐mix concrete industry. Using Census data on more than 15,000 plants, I estimate a model of investment and entry in oligopolistic markets. These estimates are used to simulate the effect of eliminating short‐term local demand changes. A policy of smoothing the volatility of demand has a market expansion effect: The model predicts a 39% increase in the number of plants in the industry. Since bigger markets have both more plants and larger plants, a demand‐smoothing fiscal policy would increase the share of large plants by 20%. Finally, the policy of smoothing demand reduces entry and exit by 25%, but has no effect on the rate at which firms change their size.     

Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation

The widely used estimator of Berry, Levinsohn, and Pakes (1995) produces estimates of consumer preferences from a discrete‐choice demand model with random coefficients, market‐level demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward‐looking demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real‐data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large‐dimensional problems with many markets.     

Business model diversification: Demand relatedness,entry sequencing,and curvilinearity in the diversification-performance relationship

This study integrates research on business model diversification (BMD) and demand-side theory to examine the relationship of BMD to performance and the sequencing of business model additions. We begin by explaining and demonstrating that the overall degree of BMD has an inverted U-shaped relationship with firm performance. We next highlight the particular role that demand relatedness plays in BMD. We first provide evidence that the inverted U-shaped relationship flattens in times of financial shocks, consistent with arguments that the benefits of BMD from consumers’ willingness-to-pay for simultaneous use of multiple business models may diminish during shocks. Second, we argue that firms tend to sequence the addition of new business models based on demand relatedness, and we provide evidence that the degree of demand relatedness between a core and a target business model enhances the likelihood of diversification into that target business model.     

Demand switching criteria for multiple products: An inventory cost analysis

In this paper, we consider the problem of demand switching and show how a firm can take advantage of the risk-pooling effect to gain more profit. We examine the case of three products under various switching criteria; a model based on the heuristic approach is developed to determine the switching paths and the corresponding switching rates that yield the optimal profit. A constrained model with limited amount of the switched demand is also developed. In general, the profit increases as a result of higher profit margin or smaller demand variation and correlation. Our result indicates that the profit does not necessarily increase as the switching rate increases; in some cases the profit may even decrease as a result of demand switching. Numerical examples are also included to illustrate the derived models. The developed analytical approach may help practitioners to gain more insight in demand switching and facilitate inventory related decision-making process as well.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

供应链金融视角下利率市场化的鲁棒分析模型

本文从供应链金融的视角出发,在需求信息缺失的情况下研究了银行贷款利率的制定对供应链及其节点企业运营状况的影响。在利率市场化的背景下,构建了银行参与的由供应商和零售商组成的供应链三方博弈模型。运用鲁棒的报童方法和极小极大后悔方法刻画了银行决定利率、供应商决定批发价格和零售商决定订货量的三方博弈情景,并获得其博弈均衡。研究表明:在部分信息下银行的参与能有效提高供应链绩效;供应商自有资金量的多少是判断利率政策(政府调控与利率市场化)对供应链效益影响的重要指标;利率市场化并不能完全解决银行业占取实体经济利益的现状,适度的政府调控是不可或缺的。     

混合分销渠道结构下短生命周期产品供应链库存策略分析

随着竞争的加剧,产品生命周期日渐缩短。信息和网络技术的不断进步,使得网络作为一种特殊的分销渠道出现。传统分销渠道和网上直销渠道并存的混合分销渠道结构给理论研究和管理实践提出了新的挑战,本文针对混合分销渠道结构下短生命周期产品供应链,运用报童问题的框架,分析了两种不同运作模式下生产商和零售商库存策略,并通过数值实验研究了需求不确定性对生产商和零售商最优库存策略的影响。最后,根据数值实验的计算结果,总结了本研究的管理启示。