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.     

Grocery Perishables Management

In this article, we study the joint pricing and inventory control problem for perishables when a retailer does not sell new and old inventory at the same time. At the beginning of a period, the retailer makes replenishment and pricing decisions, and at the end of a period, the retailer decides whether to dispose of ending inventory or carry it forward to the next period. The objective of the retailer is to maximize the long‐run average profit. Assuming zero lead time, we propose an efficient solution approach to the problem, which is also generalized to solve three extensions to the basic model. A feature of the present study is that we consider explicitly the influence of perishability on the demand. Among the insights gathered from the numerical analysis, we find that dynamic pricing aids extending shelf life and when disposal incurs a lower cost, or even a positive salvage value, the retailer is induced to dispose earlier since the benefit of selling new inventory offsets the loss due to disposal. We also observe that the faster the perceived rate of deterioration, the lower the threshold of the ending inventory for disposal. Perhaps a bit counter‐intuitive, maximizing profits does not mean eliminating disposals or expirations.     

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.     

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.     

基于价格折扣的易腐物品供应链库存的协作控制研究

库存作为供应链上的重要环节,越来越受到企业界和学术界的关注.但是目前对于供应链库存协作控制的研究大多假设库存货物能无限期存放,很少考虑产品在存贮过程中发生的变质、减值等情况,因此现有的以一般产品为对象的供应链库存协作控制研究并不适用于易腐物品.本文以易腐物品为对象,研究如何用价格折扣的激励方式来实现对易腐物品二级供应链库存的协作控制,从而最终达到优化供应链库存的目的.     

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.     

应对突发事件的库存共享策略

以包含两个销售商的库存系统为例,构建了一个描述库存共享应对突发事件的非合作博弈模型,证明了其纳什均衡解是唯一存在的。研究结果显示库存共享策略总有可能使得销售商的期望利润得以改进。另外,比较静态分析反映了转载价格和转载成本显著影响库存共享销售商的最优订货量和最优期望利润。最后,提出了一个简单的求解模型纳什均衡解的启发式算法。     

A Field Study of RFID Deployment and Return Expectations

Radio Frequency Identification (RFID) technology promises to transform supply chain management. Building on previous research in information systems and supply chain management, this paper proposes a theoretical framework for RFID adoption and benefits, and tests the framework using data on u. s. firms. Our analysis suggests that there is a positive association between information technology (IT) application deployment and RFID adoption. We find that RFID implementation spending and partner mandate are associated with an expectation of early return on RFID investment, and a perceived lack of industry‐wide standards is associated with an expectation of delayed return on RFID investment. These results suggest that firms with broad IT application deployment and a critical mass of RFID implementation spending are more likely to report early returns from RFID deployments. This paper extends previous research to understand the relationship between organization characteristics and adoption and expected benefits of the emerging RFID technology.     

可变提前期的易逝品供应链协调

易逝品供应链作为供应链的一个重要分支日益受到关注,已成为现代商业活动中不可或缺的组成部分.在提前期与预测误差呈线性关系,提前期与赶工成本呈分段线性关系的前提下,建立了在分散决策、集中决策两种模式下可变提前期的易逝品供应链库存模型,设计了优化算法并通过数值分析比较了这两种模式下供应链整体及其成员收益的变化.进一步探讨了在合作的分散决策模式下,可变提前期的易逝品供应链实现渠道协调的可行性以及实现渠道协调的条件,并通过数值例子说明了模型的结论.     

Shelf Life Extending Packaging,Inventory Control and Grocery Retailing

Retailers of perishable goods are often faced with the choice between more expensive packaging that can extend shelf life of their products and less expensive packaging that cannot. Different choices will lead to different sales, costs, and waste, and different choices require different inventory control policies. In this paper, we study the coordination of inventory and packaging decisions in a retailing environment. Items in an active package have a longer lifetime than those in a regular package and cost more. Customers always pick items with a longer lifetime first. When items with a longer lifetime are out of stock, a portion of customers are willing to buy less fresh items as substitutes. Our study shows the optimality of a “separation” policy when the substitution rate is high. In the separation policy, as the initial inventory level increases, the optimal policy changes from using active packaging only, then to using regular packaging only, and finally to ordering nothing. These results are very specific to the way perishable inventories are depleted in retailing. Our numerical study shows that although it may not be optimal in terms of cost, the adoption of active packaging can consistently achieve substantial waste reduction.     

On the Preference to Avoid Ex Post Inventory Errors

The value of demand information underlies many supply chain strategies that aim at better matching supply and demand. This study reports on the results of a laboratory experiment designed to estimate the behavioral value of demand information. Relative to the commonly assumed benchmark of a rational risk‐neutral decision maker, we find that decision makers are consistently willing to pay too much for the option to eliminate the risk of supply not matching demand. Contrary to intuition, we show that risk aversion does not explain this result. We posit that demand information provides behavioral value because it mitigates regret from ex post inventory errors.     

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.     

Managing Production and Distribution for Supply Chains in the Processed Food Industry

We develop and evaluate a modeling approach for making periodic review production and distribution decisions for a supply chain in the processed food industry. The supply chain faces several factors, including multiple products, multiple warehouses, production constraints, high transportation costs, and limited storage at the production facility. This problem is motivated by the supply chain structure at Amy's Kitchen, one of the leading producers of natural and organic foods in the United States. We develop an enhanced myopic two‐stage approach for this problem. The first stage determines the production plan and uses a heuristic, and the second stage determines the warehouse allocation plan and uses a non‐linear optimization model. This two‐stage approach is repeated every period and incorporates look‐ahead features to improve its performance in future periods. We validate our model using actual data from one factory at Amy's Kitchen and compare the performance of our model to that of the actual operation. We find that our model significantly reduces both inventory levels and stockouts relative to those of the actual operation. In addition, we identify a lower bound on the total costs for all feasible solutions to the problem and measure the effectiveness of our model against this lower bound. We perform sensitivity analysis on some key parameters and assumptions of our modeling approach.     

基于RFID技术的供应链投资决策及协调策略研究

本文以单制造商和单零售商组成的两级供应链为研究对象,基于链上成员是否投资RFID技术,考虑四种情景:供应链成员均不投资RFID,仅零售商投资RFID,仅生产商投资RFID及供应链成员共同投资RFID。采用Newsvendor模型建立四种情景时链上成员的收益模型,得出Stackelberg均衡解和供应链成员的最优期望收益,探讨了RFID技术投资对各情景时链上成员收益的影响,得到了供应链成员共同投资RFID时的协调策略。研究表明:当RFID标签成本和零售商库存不准确率在相应的阈值内,零售商或制造商单独投资RFID技术均能提升自身收益,同时对链上其他成员的收益存在"正外部性";此外,收益共享契约能够有效地协调链上成员共同投资RFID技术后的供应链,并且该协调思想丰富并拓展了供应链投资RFID技术的协调研究。     

基于ARMA（1，1）需求的供应链历史订单量信息价值的分析

牛鞭效应和供应链成本是目前供应链管理中值得关注的两个问题,本文通过分析ARMA(1,1)需求条件下的供应链历史订单量信息对牛鞭效应和制造商平均成本的影响,得出历史订单量信息的充分利用可以减小牛鞭效应、降低供应链成本.本文研究表明,如果充分利用历史订单量信息,则需求信息共享对供应链的贡献就不会有目前所认为的那么显著,其中的部分贡献可以由历史订单量信息来承担.因此,历史订单量信息的充分利用可以在一定程度上减少为寻求供应链需求信息共享而付出的投资费用.     

Inventory Record Inaccuracy,Double Marginalization,and RFID Adoption

Most retailers suffer from substantial discrepancies between inventory quantities recorded in the system and stocks truly available to customers. Promising full inventory transparency, radio frequency identification (RFID) technology has often been suggested as a remedy to the problem. We consider inventory record inaccuracy in a supply chain model, where a Stackelberg manufacturer sets the wholesale price and a retailer determines how much to stock for sale to customers. We first analyze the impact of inventory record inaccuracy on optimal stocking decisions and profits. By contrasting optimal decisions in a decentralized supply chain with those in an integrated supply chain, we find that inventory record inaccuracy exacerbates the inefficiencies resulting from double marginalization in decentralized supply chains. Assuming RFID technology can eliminate the problem of inventory record inaccuracy, we determine the cost thresholds at which RFID adoption becomes profitable. We show that a decentralized supply chain benefits more from RFID technology, such that RFID adoption improves supply chain coordination.     

基于产品时间价值的闭环供应链库存策略研究

许多被回收产品的价值会随着时间推移迅速减少,因此如何尽可能地利用库存策略减少此类产品价值的损失就成为了非常值得研究的问题.本文将EOQ模型拓展到闭环供应链中,基于产品的时间价值推导出了允许缺货和不允许缺货两种情况下回收品和成品库存的最优策略,并讨论了产品价值的流失对库存策略的影响.     

Impact of Retailers with Knowledge of Supplier's Inventory on Supply Chain Performance

We model a supply chain consisting of a supplier and multiple retailers facing deterministic demand. We denote some retailers as strategic in the sense that given the supplier inventory information, they will implement the optimal stocking policy by incorporating such information. On the other hand, some retailers are denoted as naïve in the sense that they ignore supply information and resort to a simplistic ordering policy. Naïve retailers learn the optimal policy over time and adjust their orders accordingly. We study the dynamics of this game and investigate the impact of such strategic and naïve retailers on the cost, ordering pattern and stocking policies of all parties. We analyze the supply chain under two scenarios: the centralized supply chain where the objective is to minimize the total supply chain cost, and the decentralized supply chain where each self‐interested player minimizes its own cost in a Stackelberg game setting. We fully characterize the optimal policies under both centralized and decentralized scenarios and show that, surprisingly, the supply chain might be better off by virtue of naïve retailers. The result is driven by the fact that strategic and naïve players’ decisions shift the positioning of inventory in the supply chain with its final impact being determined by the relative costs of different retailer‐types. Our results also offer managerial insights into how access to supply information can improve supply chain performance.     

Misplaced Inventory and Radio‐Frequency Identification (RFID) Technology: Information and Coordination

Misplaced inventory is a major operational problem in many supply chains. Radio‐frequency identification (RFID) technology has been publicized as a promising solution for the misplaced inventory. Adoption of this technology has a fixed cost and variable cost of implementation, which can cause incentive issues in the supply chain. In this paper, we consider a supply chain under misplacement of inventory subject to uncertain demand. We study both centralized and decentralized cases and identify the conditions to coordinate the supply chain under implementation of RFID. We show that the incentives of the parties for investing in the technology are not perfectly aligned in the existence of the fixed cost of investment. Based on the relative payments of the parties for the fixed cost of investment, the incentives to adopt RFID can be characterized into regions, where we observe only one party or two parties benefiting from the technology when the tag price falls in a region specified in the paper. We further establish the effects of changes in mean and variance of a uniform demand on the incentives for investing in RFID and find that the incentives of the firms may indeed decrease as demand becomes more variable.     

Managing Inventory with Cash Register Information: Sales Recorded but Not Demands

Inventory inaccuracy is common in many businesses. While retailers employ cash registers to enter incoming orders and outgoing sales, inaccuracy arises because they do not record invisible demand such as spoilage, damage, pilferage, or returns. This setting results in incomplete inventory and demand information. An important inventory control problem therefore is to maximize the total expected discounted profit under this setting. Allowing for dependence between demand and invisible demand, we obtain the associated dynamic programming equation with an infinite‐dimensional state space, and reduce it to a simpler form by employing the concept of unnormalized probability. We develop an analytical upper bound on the optimal profit as well as an iterative algorithm for an approximate solution of the problem. We compare profits of the iterative solution and the myopic solution, and then to the upper bound. We see that the iterative solution performs better than the myopic solution, and significantly so in many cases. Furthermore, it gives a profit not far from the upper bound, and is therefore close to optimal. Using our results, we also discuss meeting inventory service levels.