Willingness to Pay for Shifting Inventory Risk: The Role of Contractual Form

In order to reduce their inventory risk, firms can attempt to contract with their suppliers for shorter supply lead‐times, with their buyers for longer demand lead‐times, or both. We designed a controlled laboratory experiment to study contracts that shift a focal firm's inventory risk to its supply chain partners and address two questions. First, is it more effective if the cost of shifting inventory risk is framed as a fixed fee or in per‐unit cost terms? We find that, generally, our participants are willing to pay more to avoid supply–demand mismatches than the expected costs from such mismatches. This tendency to overpay is mitigated under fixed fee schemes. Second, does it matter whether the option to reduce inventory risk is the outcome of either increased responsiveness from the upstream supplier or advanced demand information from the downstream buyer? Our results suggest that this difference, when only a matter of framing, has no significant effect on willingness‐to‐pay.     

On the Benefits of Risk Pooling in Inventory Management

We analyze the benefits of inventory pooling in a multi‐location newsvendor framework. Using a number of common demand distributions, as well as the distribution‐free approximation, we compare the centralized (pooled) system with the decentralized (non‐pooled) system. We investigate the sensitivity of the absolute and relative reduction in costs to the variability of demand and to the number of locations (facilities) being pooled. We show that for the distributions considered, the absolute benefit of risk pooling increases with variability, and the relative benefit stays fairly constant, as long as the coefficient of variation of demand stays in the low range. However, under high‐variability conditions, both measures decrease to zero as the demand variability is increased. We show, through analytical results and computational experiments, that these effects are due to the different operating regimes exhibited by the system under different levels of variability: as the variability is increased, the system switches from the normal operation to the effective and then complete shutdown regimes; the decrease in the benefits of risk pooling is associated with the two latter stages. The centralization allows the system to remain in the normal operation regime under higher levels of variability compared to the decentralized system.     

The Value of Information for Managing Retail Inventory Remotely

An important difference between both manufacturing and wholesaling vs. retail is the information available concerning inventory. Typically, far less information characterizes retail. Here, an extreme environment of information shortfall is examined. The environment is technically termed “unattended points of sale,” but colloquially called vending machines. Once inventory is loaded into a machine, information on demand and inventory level is not observed until the scheduled reloading date. Technological advances and business process changes have drawn attention to the value of information (VOI) in retail inventory in many venues. Moreover, technology is now available that allows unattended points of sale to report inventory information. Capturing the value of this information requires changes in current business practice. We demonstrate the value of capturing information analytically in an environment with restrictive demand assumptions. Experiments in an environment with realistic demand assumptions and parameter values show that the VOI depends greatly on operating characteristics and can range from negligible effects to increasing profitability 30% or more in actual practice.     

On the Benefit of Inventory‐Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price‐sensitive demand, focusing on the benefit of the inventory‐based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.     

考虑成本和收益净现值的确定性存储决策模型研究

净现值是企业决策中需要考虑的重要宏观经济因素.本文建立了考虑成本和收益净现值的连续时间有限时段确定性库存系统的最优存储和定价决策模型,证明了给定价格下最优策略中任意两个相邻订货周期之间的递推关系,分析了该关系的解析性质,并得出订货周期长度的上下限.在此基础上提出求解最优存储策略和最优价格的两步优化算法.最后通过数值算例对本文模型及结果做出说明.     

需求替代情形下反应能力的价值分析

本文通过对竞争报童模型加以拓展以研究需求替代情形下企业运用反应能力产生的价值。文中考虑了两种不同的需求结构:第一种中企业总需求是竞争双方库存量的分段线性函数,第二种中企业总需求的均值是双方库存水平任意形式的函数。根据需求结构的不同,建立了不同的库存竞争模型并提出了相应的均衡库存策略。基于这些均衡结论,进一步探讨了反应能力的价值。分析表明运用反应能力能在降低企业库存水平的同时提高顾客服务水平。此外,在不同的需求结构下,运用反应能力产生的价值均随可用反应能力以及缺货惩罚成本递增,而随单位反应能力的使用成本递减。     

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.     

管理者风险偏好未知情况下的PBF与信息价值损失

在以往研究基础上,进一步分析了投资管理者风险偏好未知情况下PBF对管理者个人信息价值利用的影响,讨论了两种典型PBF结构的选择条件和相应的最佳结构设计,分析了管理者信号精度较低时采用PBF的意义。     

The Failure of Practical Intuition: How Forward‐Coverage Inventory Targets Cause the Landslide Effect

Seasonal demand for products is common at many companies including Kraft Foods, Case New Holland, and Elmer's Products. This study documents how these, and many other companies, experience bloated inventories as they transition from a low season to a high season and a severe drop in service levels as they transition from a high season to a low season. Kraft has termed this latter phenomenon the “landslide effect.” In this study, we present real examples of the landslide effect and attribute its root cause to a common industry practice employing forward days of coverage when setting inventory targets. While inventory textbooks and academic articles prescribe correct ways to set inventory targets, forward coverage is the dominant method employed in practice. We investigate the magnitude and drivers of the landslide effect through both an analytical model and a case study. We find that the effect increases with seasonality, lead time, and demand uncertainty and can lower service by an average of ten points at a representative company. While the logic is initially counterintuitive to many practitioners, companies can avoid the landslide effect by using demand forecasts over the preceding lead time to calculate safety stock targets.     

Managing the Reverse Channel with RFID‐Enabled Negative Demand Information

We analyze the inventory decisions of a manufacturer who has ample production capacity and also uses returned products to satisfy customer demand. All returned items go through an evaluation process, at the end of which the decision of disposal, direct reselling, or rework is made for each unit according to a predetermined procedure. We quantify the value of information/visibility on the reverse channel for the manufacturer by making comparisons among three approaches: No information‐naive; no visibility‐enlightened; and full visibility. We find the value of visibility increases with the comparative length of the reverse channel and volume, volatility, and usability of returns. Furthermore, the smarter the manufacturer, the less benefit visibility brings to the system. By this analysis, we quantify the visibility savings of radio frequency identification (RFID) in the reverse channel as a candidate enabler technology. We also provide numerical examples to show that practical approximations in inventory management may have acceptable penalties to the manufacturer with visibility.     

Unlocking the Value of RFID

RFID (Radio‐Frequency Identification) technology has shown itself to be a promising technology to track movements of goods in a supply chain. As such, it can give unprecedented visibility to the supply chain. Such visibility can save labor cost, improve supply chain coordination, reduce inventory and increase product availability. Industry reports and white papers are now filled with estimates and proclamations of the benefits and quantified values of RFID. Early adopters are now rallying more and more followers. However, most such claims are educated guesses at best and are not substantiated, that is, they are not based on detailed, model‐based analysis. This paper argues that there is a huge credibility gap of the value of RFID, and that a void exists in showing how the proclaimed values are arrived at, and how those values can be realized. The paper shows that this credibility gap must be filled with solid model analysis, and therefore presents a great opportunity for the Production and Operations Management (POM) research community. The paper reviews some of the ongoing research efforts that attempt to close the credibility gap, and suggests additional directions for further strengthening the POM's contribution to help industry realize the full potentials of RFID.     

A Simple Formula for Calculating the "Mass Density" of a Lognormally Distributed Characteristic: Applications to Risk Analysis

Statements such as "80% of the employees do 20% of the work" or "the richest 1% of society controls 10% of its assets" are commonly used to describe the distribution or concentration of a variable characteristic within a population. Analogous statements can be constructed to reflect the relationship between probability and concentration for unvarying quantities surrounded by uncertainty. Both kinds of statements represent specific usages of a general relationship, the "mass density function," that is not widely exploited in risk analysis and management. This paper derives a simple formula for the mass density function when the uncertainty and/or the variability in a quantity is lognormally distributed; the formula gives the risk analyst an exact, "back-of-the-envelope" method for determining the fraction of the total amount of a quantity contained within any portion of its distribution. For example, if exposures to a toxicant are lognormally distributed with σ_{in x}= 2, 50% of all the exposure is borne by the 2.3% of persons most heavily exposed. Implications of this formula for various issues in risk assessment are explored, including: (1) the marginal benefits of risk reduction; (2) distributional equity and risk perception; (3) accurate confidence intervals for the population mean when a limited set of data is available; (4) the possible biases introduced by the uncritical assumption that extreme "outliers" exist; and (5) the calculation of the value of new information.     

Economic Costs of Misinforming About Risk: The EDB Scare and the Media

This study reports results of an analysis of consumer responses to news reports of grain-product contamination by the pesticide ethylene dibromide (EDB). The results demonstrate that it is possible to quantify market disruption related to the dissemination of risk information. Implications include the need for increased awareness among risk managers that public perceptions, regardless of their objective accuracy, can induce real economic costs. Such costs should be considered in designing regulatory and information policies.     

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the U.S. Coast Guard

This article describes a methodology for risk-informed benefit–cost analyses of homeland security research products. The methodology is field-tested with 10 research products developed for the U.S. Coast Guard. Risk-informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full-cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.     

A value of information framework for assessing the trade-offs associated with uncertainty,duration, and cost of chemical toxicity testing

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.     

The Design of Feature‐Limited Demonstration Software: Choosing the Right Features to Include

Today, software supports many important tasks in a variety of industries. In the specialized nature of these environments, a common problem faced by software vendors is to correctly signal the true value of a software product to the end users. For example, telecommunications equipment manufacturers design complex software for important functions like provisioning new users in the network. These software products automate various functions that would otherwise need to be done manually. In order to enable potential customers—telecommunications providers—to evaluate and recognize the full value of the product, equipment vendors often provide a free, feature‐limited version of the product to the customer. As the specific features included in the feature‐limited version influence whether the full product is purchased or not, it is essential that the features included in the feature‐limited version be selected judiciously. While the importance of identifying the best set of features has been well recognized, there has been little research to date that systematically addresses this fundamental business decision. This study fills this gap in the literature by providing an objective approach to the design of demonstration software. We illustrate the benefits of our approach through a case study involving the design of a feature‐limited demo for a wireless telecommunications equipment manufacturer.