Value of Information in Closed Loop Supply Chains

We explore the value of information (VOI) in the context of a firm that faces uncertainty with respect to demand, product return, and product recovery (yield). The operational decision of interest in matching supply with demand is the quantity of new product to order. Our objective is to evaluate the VOI from reducing one or more types of uncertainties, where value is measured by the reduction in total expected holding and shortage costs. We start with a single period model with normally distributed demands and returns, and restrict the analysis to the value of full information (VOFI) on one or more types of uncertainty. We develop estimators that are predictive of the value and sensitivity of (combinations of) different information types. We find that there is no dominance in value amongst the different types of information, and that there is an additional pay‐off from investing in more than one type. We then extend our analysis to the multi‐period case, where returns in a period are correlated with demands in the previous period, and study the value of partial information (VOPI) as well as full information. We demonstrate that our results from the single period model (adapted for VOPI) carry‐over exactly. Furthermore, a comparison with uniformly distributed demand and return show that these results are robust with respect to distributional assumptions.     

Pre-distribution vs. post-distribution for cross-docking with transshipments

Cross-docking operation is a logistics service mode aims to remove the storage and picking up functions of a warehouse, and coordinate goods loading between delivery vehicles and shipping vehicles. Transshipment is another logistic technique that allows goods to be conveyed from an overstocked store to a nearby understocked store, to avoid backordering or loss of sale. This paper models and analyzes two typical cross-docking operations: pre-distribution cross-docking operations (Pre-C) and post-distribution cross-docking operations (Post-C) when transshipments among retail stores are applied. The different operational performances are investigated and compared. The analytical results show that, considering the inventory cost, transshipment cost and operations cost at the cross-dock, the suitability of Pre-C and Post-C are highly sensitive to operations environment factors such as the uncertainty of demand, the unit operations cost at the cross-dock, and the unit inventory holding and shortage cost.     

Managing Retail Shelf and Backroom Inventories When Demand Depends on the Shelf‐Stock Level

Inventory displayed on the retail sales floor not only performs the classical supply function but also plays a role in affecting consumers’ buying behavior and hence the total demand. Empirical evidence from the retail industry shows that for some types of products, higher levels of on‐shelf inventory have a demand‐increasing effect (“billboard effect”) while for some other types of products, higher levels of on‐shelf inventory have a demand‐decreasing effect (“scarcity effect”). This suggests that retailers may use the amount of shelf stock on display as a tool to influence demand and operate a store backroom to hold the inventory of items not displayed on the shelves, introducing the need for efficient management of the backroom and on‐shelf inventories. The purpose of this study is to address such an issue by considering a periodic‐review inventory system in which demand in each period is stochastic and depends on the amount of inventory displayed on the shelf. We first analyze the problem in a finite‐horizon setting and show under a general demand model that the system inventory is optimally replenished by a base‐stock policy and the shelf stock is controlled by two critical points representing the target levels to raise up/drop down the on‐shelf inventory level. In the infinite‐horizon setting, we find that the optimal policies simplify to stationary base‐stock type policies. Under the billboard effect, we further show that the optimal policy is monotone in the system states. Numerical experiments illustrate the value of smart backroom management strategy and show that significant profit gains can be obtained by jointly managing the backroom and on‐shelf inventories.     

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.     

Economic Evaluation of Systems that Expedite Inventory Information

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. Such situations arise when it takes a while to process the demand data, count the inventory, and pass the results to the IM. We show that the optimal total inventory‐related cost decreases when the length of the information delay decreases. The amount of the decrease is an important datum for an IM interested in considering whether or not to invest in reducing the delay. The investment is required to finance design and acquisition of an information (collection and dissemination) system that can reduce the information delay. Such systems include phone calls, business meetings, and the use of information collection mechanisms such as radiofrequency identification tags.     

考虑策略性消费者的生鲜农产品定价和库存决策

消费者的策略性行为使零售商的生鲜农产品的定价和库存决策面临更大挑战。本文基于报童模型,综合考虑消费者的策略性行为,对生鲜农产品价值下降进行离散化处理。刻画策略性消费者的决策行为,构建零售商的单阶段和两阶段定价及库存决策模型,分析了产品价值剩余率对消费者行为、零售商最优定价、最优库存水平以及零售商利润的影响机理。研究发现,在单阶段模型中零售商最优价格和最优库存水平均随产品价值剩余率的递增而递增;而在两阶段模型中,第二阶段最优价格随价值剩余率的变化趋势可能存在阈值。     

The Value of Supplier Information to Improve Management of a Retailer's Inventory

The distribution of lead time demand is essential for determining reorder points in inventory systems. Usually, the distribution of lead time demand is approximated directly. However, in some cases it may be worthwhile to take the demand per unit time and lead time into account, particularly when specific information is available. This paper deals with the situation where a supplier, who produces on order in fixed production cycles, provides information on the status of the coming production run. The retailer can use this information to gain insight into the lead-time process. A fixed order (s_vQ) strategy is presented, with a set of reorder points s_v depending on the time t until the first possible delivery, which is determined by the information of the supplier. A Markov model that analyzes a given (s_vQ) strategy is used to quantify the value of the information provided by the supplier. Some numerical examples show that the approach may lead to considerable cost savings compared to the traditional approach that uses only one single reorder point, based on a two-moments approximation. Using this numerical insight, the pros and cons of a more frequent exchange of information between retailers and suppliers can be balanced.     

The cost of using stationary inventory policies when demand is non-stationary

Non-stationary stochastic demands are very common in industrial settings with seasonal patterns, trends, business cycles, and limited-life items. In such cases, the optimal inventory control policies are also non-stationary. However, due to high computational complexity, non-stationary inventory policies are not usually preferred in real-life applications. In this paper, we investigate the cost of using a stationary policy as an approximation to the optimal non-stationary one. Our numerical study points to two important results: (i) Using stationary policies can be very expensive depending on the magnitude of demand variability. (ii) Stationary policies may be efficient approximations to optimal non-stationary policies when demand information contains high uncertainty, setup costs are high and penalty costs are low.     

A Specialized Inventory Problem in Banks: Optimizing Retail Sweeps

Deposits held at Federal Reserve Banks are an essential input to the business activity of most depository institutions in the United States. Managing these deposits is an important and complex inventory problem for two reasons. First, Federal Reserve regulations require that depository institutions hold certain amounts of such deposits at the Federal Reserve Banks to satisfy statutory reserve requirements against customers' transaction accounts (demand deposits and other checkable deposits). Second, some inventory of such deposits is essential for banks to operate one of their core lines of business: furnishing payment services to households and firms. Because the Federal Reserve does not pay interest on such deposits used to satisfy statutory reserve requirements, banks seek to minimize their inventory of such deposits. In 1994, the banking industry introduced a new inventory management tool for such deposits, the retail deposit sweep program, which avoids the statutory requirement by reclassifying transaction deposits as savings deposits. This is an interesting inventory problem for fungible items, where the conversion process is reversible. We examine two methods for operating such sweeps programs within the limits of Federal Reserve regulations, and we develop a stochastic dynamic programming model to implement one such method, the threshold method.     

The Value of Environmental Information without Control of Subsequent Decisions

The standard value of information approach of decision analysis assumes that the individual or agency that collects the information is also in control of the subsequent decisions based on the information. We refer to this situation as the “value of information with control (VOI‐C).” This paradigm leads to powerful results, for example, that the value of information cannot be negative and that it is zero, when the information cannot change subsequent decisions. In many real world situations, however, the agency collecting the information is different from the one that makes the decision on the basis of that information. For example, an environmental research group may contemplate to fund a study that can affect an environmental policy decision that is made by a regulatory organization. In this two‐agency formulation, the information‐acquiring agency has to decide, whether an investment in research is worthwhile, while not being in control of the subsequent decision. We refer to this situation as “value of information without control (VOI‐NC).” In this article, we present a framework for the VOI‐NC and illustrate it with an example of a specific problem of determining the value of a research program on the health effects of power‐frequency electromagnetic fields. We first compare the VOI‐C approach with the VOI‐NC approach. We show that the VOI‐NC can be negative, but that with high‐quality research (low probabilities of errors of type I and II) it is positive. We also demonstrate, both in the example and in more general mathematical terms, that the VOI‐NC for environmental studies breaks down into a sum of the VOI‐NC due to the possible reduction of environmental impacts and the VOI‐NC due to the reduction of policy costs, with each component being positive for low environmental impacts and high‐quality research. Interesting results include that the environmental and cost components of the VOI‐NC move in opposite directions as a function of the probability of environmental impacts and that VOI‐NC can be positive, even though the probability of environmental impacts is zero or one.     

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.     

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.     

基于不同保鲜方式的生鲜品定价订货决策

本文考虑两种不同保鲜方式下生鲜品的不同变质率以及相互需求替代,构建了联合定价和订货模型。研究无库存约束、单一库存约束和双库存约束三种情形下零售商对不同保鲜方式生鲜品的定价和订货决策。探讨市场容量、生鲜变质率、需求替代率以及最大订货量对最优决策的影响。研究显示：市场容量越大、需求替代率越高,生鲜品零售商的获利空间越大;提高保鲜投入和保鲜努力,降低高(低)变质率保鲜方式生鲜品的变质率可以提高两种保鲜方式生鲜品的销售价格和高(低)变质率保鲜方式生鲜品的销售量;高(低)变质率保鲜方式生鲜品单一库存约束会提高该生鲜品的最优价格和低(高)变质率保鲜方式生鲜品的最优订货量;双库存约束时,两种保鲜方式生鲜品的最优价格都会提高,最优订货量为最大订货量。     

Markdown Budgets for Retail Buyers: Help or Hindrance?

For many retailers, markdown decisions are taken by retail buyers whose compensation is based on sales revenue so their objective is to maximize it through the season. This implies that the buyers' objectives are not perfectly aligned with the overall profitability the firm. Many retailers set markdown budgets prior to the season to control margin erosion and increase profitability. Markdown budget constrains the buyers on the amount of discounts that they can apply on a given inventory of merchandise and sets a limit on the dollar value of markdowns for the season. While markdown budgets may be useful in preventing excessive discounts, they can have a detrimental effect on the buyers' ability to respond to poor market and remove distressed inventory. We investigate the effectiveness of this practice in aligning the incentives of buyers with that of the firm, and provide guidance on how these budgets should be established ahead of time. We consider a firm with a fixed inventory of a seasonable item, and a single chance to mark the price down. The retailer knows only the demand distribution at the beginning of the season, but the market information is revealed during the season to the buyer. We first characterize the buyer's markdown policy and understand the circumstances under which this can be different from the retailer's markdown policy. We use our model to determine the optimal markdown budget and quantify its effectiveness considering different factors such as the level of demand uncertainty, initial markup, and market's responsiveness to markdowns.     

Emergency inventory ordering assuming Poisson demands

Abstract

Most business and industrial firms use some procedure for emergency ordering. If independent demand for low-volume, high-cost items is Poisson distributed, subsequent analyses reveal that emergency ordering may be less expensive than carrying extra safety stock. Emergency ordering reduces the out-of-stock cost as well as the required safety stock. Total cost is sensitive to the lead time required for emergency orders. The results are particularly applicable to maintenance inventory. Under the assumptions of this study, the conclusion may be drawn that an emergency ordering system may approach the theoretical minimum for operating an inventory system.     

Service Package Switching in Hotel Revenue Management Systems

Revenue Management Systems (RMS) are commonly used in the hotel industry to maximize revenues in the short term. The forecasting‐allocation module is a key tactical component of a hotel RMS. Forecasting involves estimating demand for service packages across all stayover nights in a planning horizon. A service package is a unique combination of physical room, amenities, room price, and advance purchase restrictions. Allocation involves parsing the room inventory among these service packages to maximize revenues. Previous research and existing revenue management systems assume the demand for a service package to be independent of which service packages are available for sale. We develop a new forecasting‐allocation approach that explicitly accounts for this dependence. We compare the performance of the new approach against a baseline approach using a realistic hotel RMS simulation. The baseline approach reflects previous research and existing industry practice. The new approach produces an average revenue increase of at least 16% across scenarios that reflect existing industry conditions.     

Supply Chain Management of Fresh Products with Producer Transportation*

This article considers a class of fresh‐product supply chains in which products need to be transported by the upstream producer from a production base to a distant retail market. Due to high perishablility a portion of the products being shipped may decay during transportation, and therefore, become unsaleable. We consider a supply chain consisting of a single producer and a single distributor, and investigate two commonly adopted business models: (i) In the “pull” model, the distributor places an order, then the producer determines the shipping quantity, taking into account potential product decay during transportation, and transports the products to the destination market of the distributor; (ii) In the “push” model, the producer ships a batch of products to a distant wholesale market, and then the distributor purchases and resells to end customers. By considering a price‐sensitive end‐customer demand, we investigate the optimal decisions for supply chain members, including order quantity, shipping quantity, and retail price. Our research shows that both the producer and distributor (and thus the supply chain) will perform better if the pull model is adopted. To improve the supply chain performance, we propose a fixed inventory‐plus factor (FIPF) strategy, in which the producer announces a pre‐determined inventory‐plus factor and the distributor compensates the producer for any surplus inventory that would otherwise be wasted. We show that this strategy is a Pareto improvement over the pull and push models for both parties. Finally, numerical experiments are conducted, which reveal some interesting managerial insights on the comparison between different business models.     

THE VALUE OF SKU RATIONALIZATION IN PRACTICE (THE POOLING EFFECT UNDER SUBOPTIMAL INVENTORY POLICIES AND NONNORMAL DEMAND)

Several approaches to the widely recognized challenge of managing product variety rely on the pooling effect. Pooling can be accomplished through the reduction of the number of products or stock‐keeping units (SKUs), through postponement of differentiation, or in other ways. These approaches are well known and becoming widely applied in practice. However, theoretical analyses of the pooling effect assume an optimal inventory policy before pooling and after pooling, and, in most cases, that demand is normally distributed. In this article, we address the effect of nonoptimal inventory policies and the effect of nonnormally distributed demand on the value of pooling. First, we show that there is always a range of current inventory levels within which pooling is better and beyond which optimizing inventory policy is better. We also find that the value of pooling may be negative when the inventory policy in use is suboptimal. Second, we use extensive Monte Carlo simulation to examine the value of pooling for nonnormal demand distributions. We find that the value of pooling varies relatively little across the distributions we used, but that it varies considerably with the concentration of uncertainty. We also find that the ranges within which pooling is preferred over optimizing inventory policy generally are quite wide but vary considerably across distributions. Together, this indicates that the value of pooling under an optimal inventory policy is robust across distributions, but that its sensitivity to suboptimal policies is not. Third, we use a set of real (and highly erratic) demand data to analyze the benefits of pooling under optimal and suboptimal policies and nonnormal demand with a high number of SKUs. With our specific but highly nonnormal demand data, we find that pooling is beneficial and robust to suboptimal policies. Altogether, this study provides deeper theoretical, numerical, and empirical understanding of the value of pooling.     

Strategic Management of Distressed Inventory

It is well known that maximizing revenue from a fixed stock of perishable goods may require discounting prices rather than allowing unsold inventory to perish. This behavior is seen in industries ranging from fashion retail to tour packages and baked goods. A number of authors have addressed the markdown management problem in which a seller seeks to determine the optimal sequence of discounts to maximize the revenue from a fixed stock of perishable goods. However, merchants who consistently use markdown policies risk training customers to “wait for the sale.” We investigate models in which the decision to sell inventory at a discount will change the future expectations of customers and hence their buying behavior. We show that, in equilibrium, a single‐price policy is optimal if all consumers are strategic and demand is known to the seller. Relaxing any of these conditions can lead to a situation in which a two‐price markdown policy is optimal. We show using numerical simulation that if customers update their expectations of availability over time, then optimal sales limit policies can evolve in a complex fashion.