Showing papers in "Manufacturing & Service Operations Management in 2008"

Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers

Abstract: We study the optimal pricing of a finite quantity of a fashion-like seasonal good in the presence of forward-looking (strategic) customers. We distinguish between two classes of pricing strategies: contingent and announced fixed-discount. In both cases, the seller acts as a Stackelberg leader announcing his pricing strategy, while consumers act as followers taking the seller's strategy as given and determining their purchasing behavior. In each case, we identify a subgame-perfect Nash equilibrium and show that given the seller's strategy, the equilibrium in the consumer subgame is unique and consists of symmetric threshold purchasing policies. For both cases, we develop a benchmark model in which customers are nonstrategic (myopic). We conduct a comprehensive numerical study to explore the impact of strategic consumer behavior on pricing policies and expected revenue performance. We show that strategic customer behavior suppresses the benefits of price segmentation, particularly under medium-to-high values of heterogeneity and modest rates of decline in valuations. However, when the level of consumer heterogeneity is small, the rate of decline is medium-to-high, and the seller can optimally choose the time of discount in advance, segmentation can be used quite effectively even with strategic consumers. We find that the seller cannot avoid the adverse impact of strategic consumer behavior even under low levels of initial inventory. We argue that while the seller expects customers to be more concerned about product availability at discount time, he cannot use high-price “betting” strategies as he would in the case of low inventory and myopic customers. Under certain qualifications, announced fixed-discount strategies perform essentially the same as contingent pricing policies in the case of myopic consumers. However, under strategic consumer behavior, announced pricing policies can be advantageous to the seller, compared to contingent pricing schemes. Interestingly, those cases that announced discount strategies offer a significant advantage compared to contingent pricing policies. They appear to offer only a minimal advantage in comparison to fixed-pricing policies. Finally, when the seller incorrectly assumes that strategic customers are myopic in their purchasing decisions, it can be quite costly, reaching potential revenue losses of about 20%.

Toward a Theory of Behavioral Operations

Abstract: Human beings are critical to the functioning of the vast majority of operating systems, influencing both the way these systems work and how they perform. Yet most formal analytical models of operations assume that the people who participate in operating systems are fully rational or at least can be induced to behave rationally. Many other disciplines, including economics, finance, and marketing, have successfully incorporated departures from this rationality assumption into their models and theories. In this paper, we argue that operations management scholars should do the same. We explore the theoretical and practical implications of incorporating behavioral and cognitive factors into models of operations management and suggest fruitful avenues for research in behavioral operations.

Dual Sales Channel Management with Service Competition

Abstract: We study a manufacturer’s problem of managing his direct online sales channel together with an independently owned bricks-and-mortar retail channel, when the channels compete in service. We incorporate a detailed consumer channel choice model in which the demand faced in each channel depends on the service levels of both channels as well as the consumers’ valuation of the product and shopping experience. The direct channel’s service is measured by the delivery lead time for the product; the retail channel’s service is measured by product availability. We identify optimal dual channel strategies that depend on the channel environment described by factors such as the cost of managing a direct channel, retailer inconvenience, and some product characteristics. We also determine when the manufacturer should establish a direct channel or a retail channel if he is already selling through one of these channels. Finally, we conduct a sequence of controlled experiments with human subjects to investigate whether our model makes reasonable predictions of human behavior. We determine that the model accurately predicts the direction of changes in the subjects’ decisions, as well as their channel strategies in response to the changes in the channel environment. These observations suggest that the model can be used in designing channel strategies for an actual dual channel environment. 1

On the Choice-Based Linear Programming Model for Network Revenue Management

Abstract: Gallego et al. [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. CORC Technical Report TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York.] recently proposed a choice-based deterministic linear programming model (CDLP) for network revenue management (RM) that parallels the widely used deterministic linear programming (DLP) model. While they focused on analyzing “flexible products”---a situation in which the provider has the flexibility of using a collection of products (e.g., different flight times and/or itineraries) to serve the same market demand (e.g., an origin-destination connection)---their approach has broader implications for understanding choice-based RM on a network. In this paper, we explore the implications in detail. Specifically, we characterize optimal offer sets (sets of available network products) by extending to the network case a notion of “efficiency” developed by Talluri and van Ryzin [Talluri, K. T., G. J. van Ryzin. 2004. Revenue management under a general discrete choice model of consumer behavior. Management Sci.50 15--33.] for the single-leg, choice-based RM problem. We show that, asymptotically, as demand and capacity are scaled up, only these efficient sets are used in an optimal policy. This analysis suggests that efficiency is a potentially useful approach for identifying “good” offer sets on networks, as it is in the case of single-leg problems. Second, we propose a practical decomposition heuristic for converting the static CDLP solution into a dynamic control policy. The heuristic is quite similar to the familiar displacement-adjusted virtual nesting (DAVN) approximation used in traditional network RM, and it significantly improves on the performance of the static LP solution. We illustrate the heuristic on several numerical examples.

Learning by Doing in the Newsvendor Problem: A Laboratory Investigation of the Role of Experience and Feedback

Abstract: We investigate learning by doing in the newsvendor inventory problem. An earlier study observed that decision makers tend to anchor their orders around average demand and fail to adjust sufficiently toward the expected profit-maximizing order. Principles of behavioral theory suggest some relatively simple interventions into the decision maker's experience and feedback that might improve performance, and these guide our investigation. The results imply that the institutional organization of experience and feedback may have a significant influence on whether inventory is stocked optimally.

Bounded Rationality in Newsvendor Models

Abstract: Many theoretical models adopt a normative approach and assume that decision makers are perfect optimizers. In contrast, this paper takes a descriptive approach and considers bounded rationality, in the sense that decision makers are prone to errors and biases. Our decision model builds on the quantal choice model: While the best decision need not always be made, better decisions are made more often. We apply this framework to the classic newsvendor model and characterize the ordering decisions made by a boundedly rational decision maker. We identify systematic biases and offer insight into when overordering and underordering may occur. We also investigate the impact of these biases on several other inventory settings that have traditionally been studied using the newsvendor model as a building block, such as supply chain contracting, the bullwhip effect, and inventory pooling. We find that incorporating decision noise and optimization error yields results that are consistent with some anomalies highlighted by recent experimental findings.

Newsvendor “Pull-to-Center” Effect: Adaptive Learning in a Laboratory Experiment

Abstract: In the newsvendor game, the expected-profit-maximizing order quantity is higher in the demand interval when the per-unit profit margin is high and lower in the demand interval when the per-unit profit margin is low. However, laboratory experiments show a “pull-to-center” effect: average order quantities are too low when they should be high and vice versa. We replicate this pull-to-center effect in laboratory experiments and construct an adaptive learning model that incorporates memory, reinforcement, and probabilistic choice to explain individual decisions. The intuition underlying the model's prediction is that the most recent demand observation is more likely to have been greater than the optimal order quantity if the optimal order quantity is low, in which case a recency bias tends to pull the order quantity upward. A countervailing downward pull exists if the optimal order quantity is high. The recency effect may be augmented by a reinforcement bias, which causes subjects to focus more on the profitab...

Designing Optimal Preannounced Markdowns in the Presence of Rational Customers with Multiunit Demands

Abstract: We analyze the optimal design of a markdown pricing mechanism with preannounced prices. In the presence of limited supply, buyers who choose to purchase at a lower price may face a scarcity in supply. Our focus is on the structure of the optimal markdown mechanisms in the presence of rational or strategic buyers who demand multiple units. We first examine a complete information setting where the set of customer valuations is known but the seller does not know the valuation of each individual customer (i.e., cannot exercise perfect price discrimination). We then generalize our analysis to an incomplete valuation information setting where customer valuations are drawn from known distributions. For both settings, we compare the seller's profit resulting from the optimal markdown mechanism and the optimal single price. We provide a number of managerial insights into designing profitable markdown mechanisms.

Retail Inventory Management When Records Are Inaccurate

Abstract: Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. In this paper, we consider an intelligent inventory management tool that accounts for record inaccuracy using a Bayesian belief of the physical inventory level. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory levels. We show that a probability distribution on physical inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated in a Bayesian fashion as observations are accumulated. We also demonstrate the use of this distribution as the basis for practical replenishment and inventory audit policies and illustrate how the needed parameters can be estimated using data from a large national retailer. Our replenishment policies avoid the problem of “freezing,” in which a physical inventory position persists at zero while the corresponding record is positive. In addition, simulation studies show that our replenishment policies recoup much of the cost of inventory record inaccuracy, and that our audit policy significantly outperforms the popular “zero balance walk” audit policy.

Interday Forecasting and Intraday Updating of Call Center Arrivals

Abstract: Accurate forecasting of call arrivals is critical for staffing and scheduling of a telephone call center. We develop methods for interday and dynamic intraday forecasting of incoming call volumes. Our approach is to treat the intraday call volume profiles as a high-dimensional vector time series. We propose first to reduce the dimensionality by singular value decomposition of the matrix of historical intraday profiles and then to apply time series and regression techniques. Our approach takes into account both interday (or day-to-day) dynamics and intraday (or within-day) patterns of call arrivals. Distributional forecasts are also developed. The proposed methods are data driven, appear to be robust against model assumptions in our simulation studies, and are shown to be very competitive in out-of-sample forecast comparisons using two real data sets. Our methods are computationally fast; it is therefore feasible to use them for real-time dynamic forecasting.

Mass Customization vs. Mass Production: Variety and Price Competition

Abstract: We study competition between two multiproduct firms with distinct production technologies in a market where customers have heterogeneous preferences on a single taste attribute. The mass customizer (MC) has a perfectly flexible production technology and thus can offer any variety within a product space, represented by Hotelling's linear city. The mass producer (MP) has a more focused production technology and therefore offers a finite set of products in the same space. The MP can invest in more flexible technology, which reduces its cost of variety and hence allows it to offer a larger set of products; in the extreme, the MP can emulate the MC's technology and offer infinite variety. The firms simultaneously decide whether to enter the market, and the MP chooses its degree of product-mix flexibility on entry. Next, the MP designs its product line---i.e., the number and position of its products---the MC's perfectly flexible technology makes this unnecessary. Finally, both firms simultaneously set prices. We analyze the subgame-perfect Nash equilibrium in this three-stage game, allowing firm-specific fixed and variable costs that together characterize their production technology. We find that an MP facing competition from an MC offers lower product variety than an MP monopolist to reduce the intensity of price competition. We also find that the MP can survive this competition, even if it has higher fixed cost of production technology, higher marginal cost of production, or both.

Dynamic Inventory Management with Learning About the Demand Distribution and Substitution Probability

Abstract: Awell-known result in the Bayesian inventory management literature is: If lost sales are not observed, the Bayesian optimal inventory level is larger than the myopic inventory level (one should “stock more” to learn about the demand distribution). This result has been proven in other studies under the assumption that inventory is perishable, so the myopic inventory level is equal to the Bayesian optimal inventory level with observed lost sales. We break that equivalence by considering nonperishable inventory. We prove that with nonperishable inventory, the famous “stock more” result is often reversed to “stock less,” in that the Bayesian optimal inventory level with unobserved lost sales is lower than the myopic inventory level. We also prove that making lost sales unobservable increases the Bayesian optimal inventory level; in this specific sense, the famous “stock more” result of other studies generalizes to the case of nonperishable inventory. When the product is out of stock, a customer may accept a substitute or choose not to purchase. We incorporate learning about the probability of substitution. This reduces the Bayesian optimal inventory level in the case that lost sales are observed. Reducing the inventory level has two beneficial effects: to observe and learn more about customer substitution behavior and (for a nonperishable product) to reduce the probability of overstocking in subsequent periods. Finally, for a capacitated production-inventory system under continuous review, we derive maximum likelihood estimators (MLEs) of the demand rate and probability that customers will wait for the product. (Accepting a raincheck for delivery at some later time is a common type of substitution.) We investigate how the choice of base-stock level and production rate affect the convergence rate of these MLEs. The results reinforce those for the Bayesian, uncapacitated, periodic review system.

Strategic Inventory Placement in Supply Chains: Nonstationary Demand

Abstract: The life cycle of new products is becoming shorter and shorter in all markets. For electronic products, life cycles are measured in units of months, with 6-to 12-month life cycles being common. Given these short product life cycles, product demand is increasingly difficult to forecast. Furthermore, demand is never really stationary because the demand rate evolves over the life of the product. In this paper, we consider the problem of where in a supply chain to place strategic safety stocks to provide a high level of service to the final customer with minimum cost. We extend our model for stationary demand to the case of nonstationary demand, as might occur for products with short life cycles. We assume that we can model the supply chain as a network, that each stage in the supply chain operates with a periodic review base-stock policy, that demand is bounded, and that there is a guaranteed service time between every stage and its customers. We consider a constant service time (CST) policy for which the safety stock locations are stationary; the actual safety stock levels change as the demand process changes. We show that the optimization algorithm for the case of stationary demand extends directly to determining the safety stocks when demand is nonstationary for a CST policy. We then examine with an illustrative example how well the CST policy performs relative to a dynamic policy that dynamically modifies the service times. In addition, we report on numerical tests that demonstrate the efficacy of the proposed solution and how it would be deployed.

Structural Properties of Buyback Contracts for Price-Setting Newsvendors

Abstract: This paper studies a buyback contract in the Stackelberg framework of a manufacturer (leader) selling to a price-setting newsvendor retailer (follower). Using an analytical model that focuses on a multiplicative demand form, we generalize previous results and produce new structural insights. A novel transformation technique first enables us to establish the unimodality of the profit functions for both channel partners, under relatively mild assumptions. Further analysis identifies the necessary and sufficient condition under which the optimal contract for the manufacturer (wholesale and buyback prices) is distribution free, i.e., independent of the uncertainty in customer demand. A specific instance of the above condition is also necessary and sufficient for a no-buyback contract to be optimal from the manufacturer's perspective. We then prove that the optimal performance of the decentralized channel for distribution-free buyback contracts depends only on the curvature of the deterministic demand part. In addition, some of the optimal decisions and relevant profit ratios for buyback contracts in our setting are shown to be identical to those for their deterministic price-only counterparts.

Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior

Abstract: We consider a revenue management, network capacity control problem in a setting where heterogeneous customers choose among the various products offered by a firm (e.g., different flight times, fare classes, and/or routings). Customers may therefore substitute if their preferred products are not offered. These individual customer choice decisions are modeled as a very general stochastic sequence of customers, each of whom has an ordered list of preferences. Minimal assumptions are made about the statistical properties of this demand sequence. We assume that the firm controls the availability of products using a virtual nesting control strategy and would like to optimize the protection levels for its virtual classes accounting for the (potentially quite complex) choice behavior of its customers. We formulate a continuous demand and capacity approximation for this problem, which allows for the partial acceptance of requests for products. The model admits an efficient calculation of the sample path gradient of the network revenue function. This gradient is then used to construct a stochastic steepest ascent algorithm. We show the algorithm converges in probability to a stationary point of the expected revenue function under mild conditions. The algorithm is relatively efficient even on large network problems, and in our simulation experiments it produces significant revenue increases relative to traditional virtual nesting methods. On a large-scale, real-world airline example using choice behavior models fit to actual booking data, the method produced an estimated 10% improvement in revenue relative to the controls used by the airline. The examples also provide interesting insights into how protection levels should be adjusted to account for choice behavior. Overall, the results indicate that choice behavior has a significant impact on both capacity control decisions and revenue performance and that our method is a viable approach for addressing the problem.

Design Architecture and Introduction Timing for Rapidly Improving Industrial Products

Abstract: Technological advances present firms in many industries with opportunities to substantially improve their product's capabilities in short periods of time. Customers who invest in these products may, however, react adversely to rapid improvements that make their previous versions obsolete by deferring their purchase. In industrial markets, there is an emerging trend of sequentially improving products designed to be upgraded in a modular fashion. We study the impact of product architecture and introduction timing on the launch of rapidly improving products. We find that by localizing performance improvements in a sequence of upgradable modules of the product, a firm can better manage the introduction of rapidly improving products. Specifically, we show that modular upgradability can reduce the need for slowing the pace of innovation or forgoing upgrade pricing. The additional flexibility in pricing and timing makes the modular, upgradable approach preferable to an integrated architecture, even in some situations where there may be distinct performance or cost-related disadvantages to pursuing the modular architecture. We differentiate between proprietary and nonproprietary approaches to modular upgradability and consider the implications for profits. Our central contribution in this paper is the innovative integration of product architecture with pricing and timing decisions for managing the introduction of rapidly improving products.

Newsvendors Under Simultaneous Price and Inventory Competition

Abstract: This paper extends the theory of N competitive newsvendors to the case where competition occurs simultaneously in price and inventory. The basic research questions are whether the Nash equilibrium exists in this game, whether it is unique, and how the resulting inventories and prices are affected by competition. Using a novel method, we show the quasiconcavity of the competitive newsvendor's problem and establish the existence of the pure-strategy Nash equilibrium. Through a contraction mapping approach, we develop sufficient conditions for the Nash equilibrium to be unique. We then analyze the properties of the equilibrium and compare it with the optimal solution for the (noncompeting) price-sensitive newsvendor. We prove that at a symmetric equilibrium, retail prices and safety stocks strictly increase with the proportion of a newsvendor's unsatisfied customers that switch to a competitor, but strictly decrease with the intensity of price competition. Total inventories, on the other hand, increase with the intensity of price competition. Furthermore, the competitive equilibrium never has lower safety stocks and higher retail prices (a situation that definitely hurts the customers) than the solution for noncompetitive newsvendors.

Simple Methods for Shift Scheduling in Multiskill Call Centers

Abstract: This paper introduces a new method for shift scheduling in multiskill call centers. The method consists of two steps. First, staffing levels are determined, and next, in the second step, the outcomes are used as input for the scheduling problem. The scheduling problem relies on a linear programming model that is easy to implement and has short computation times, i.e., a fraction of a second. Therefore, it is useful for different purposes and it can be part of an iterative procedure: for example, one that combines shifts into rosters.

OM Forum---Managing Customer Experiences: Perspectives on the Temporal Aspects of Service Encounters

Abstract: Aservice encounter is an experience that extends over time. Therefore, its effective management must include the control of the timing of the delivery of each of the service's elements and the enhancement of the customer's experience between and during the delivery of the various elements. This paper provides a conceptual framework that links the duration of a service encounter to behaviors that have been shown to affect profitability. Analysis of the framework reveals a wide gap between the behavioral assumptions typically made in operations research (OR) and operations management (OM) models and the state of the art in the marketing and psychology literature. The central motivations behind this paper are (1) to help the OR and OM community bridge this gap by bringing to its attention recent findings from the behavioral literature that have implications for the design of queueing systems for service firms and (2) to identify opportunities for further research.

Data Set---Real-World Multiechelon Supply Chains Used for Inventory Optimization

Abstract: This data set describes 38 multiechelon supply chains that have been implemented in practice. These chains exhibit special structure that can be used to inform and test analytical models. Although the data were not collected with the intention of econometric analysis, they may be useful in an empirical study. The data described in this paper are publicly available at the journal's website (http://msom.pubs.informs.org/ecompanion.html).

Inventory Service-Level Agreements as Coordination Mechanisms: The Effect of Review Periods

Abstract: Asupplier stocking goods for delivery to a retailer may face a (finite-horizon) service-level agreement (SLA). In this context, the SLA is a commitment by a supplier to achieve a minimum fill rate over a specified time horizon. This kind of SLA is an important, but understudied coordination mechanism. We focus on the impact of two contract parameters: the length of the review period and the magnitude of the bonus for meeting or exceeding the service-level target. For a supplier following a base stock (order-up-to) inventory policy, increasing the bonus increases optimal supplier stocking levels, whereas lengthening the review period may increase or decrease optimal stocking levels. We investigate these mechanisms in a controlled laboratory setting and find that longer review periods are generally more effective than shorter review periods in inducing higher stocking levels. As in several earlier laboratory studies, the explanation lies in the improved feedback reliability that longer review periods provid...

Competitive Customization

Abstract: We consider a duopoly market with heterogeneous customer tastes. The firms play a two-stage game. First, each firm chooses whether to invest in mass customization, which would enable it to offer customized products that increasingly match each customer's ideal product as the chosen customization level increases. A firm that chooses not to invest in mass customization serves a standard product. Second, the firms competitively price their product lines. We characterize each firm's investment in mass customization and study its dependence on competitive position, as determined by its cost efficiency and perceived quality vis-a-vis its competitor. We find that the value of mass customization critically depends on the firm's competitive position. It may not be desirable even at zero cost due to its negative effect on price competition. A firm with an overall cost and quality disadvantage never unilaterally adopts mass customization. We show that allowing firms to set different prices for each product configuration leads to a broader adoption of mass customization compared to when they are restricted to uniform prices. However, a firm's chosen customization level may be higher with uniform prices. Our analysis also helps a customizing firm determine whether to target its process improvement efforts for a lower cost or a higher customization level.

Facility Location with Stochastic Demand and Constraints on Waiting Time

Abstract: We analyze the problem of optimal location of a set of facilities in the presence of stochastic demand and congestion. Customers travel to the closest facility to obtain service; the problem is to determine the number, locations, and capacity of the facilities. Under rather general assumptions (spatially distributed continuous demand, general arrival and service processes, and nonlinear location and capacity costs) we show that the problem can be decomposed, and construct an efficient optimization algorithm. The analysis yields several insights, including the importance of equitable facility configurations (EFCs), the behavior of optimal and near-optimal capacities, and robust class of solutions that can be constructed for this problem.

Cooperation Between Multiple Newsvendors with Warehouses

Abstract: This study considers a supply chain that consists of n retailers, each facing a newsvendor problem, and m warehouses. The retailers are supplied with a single product via some warehouses. In these warehouses, the ordered amounts of goods of these retailers become available after some lead time. At the time that the goods arrive at the warehouses, demand realizations are known by the retailers. The retailers can increase their expected joint profits if they can coordinate their orders and make allocations after demand realization. For this setting, we consider an associated cooperative game between the retailers. We show that this associated cooperative game has a nonempty core. Finally, we introduce a noncooperative game, where the retailers decide on their order quantities individually, and show that the set of payoff vectors resulting from strong Nash equilibria corresponds to the core of the associated cooperative game.

Pricing Promotional Products Under Upselling

Abstract: Upselling is offering an additional product to a customer who just made a purchase. Most catalogers and online sellers, in addition to some traditional retailers, use upselling often to clear inventories of slow-moving items. We investigate the pricing and discounting questions for such an item, which we call the promotional product. In our model, an arriving customer may purchase this promotional product or one of the other products that the firm sells. If the customer purchases one of the other products, the promotional product is offered to the customer, possibly with a discount. While deciding whether to offer a discount and, if so, how big a discount to offer, the firm uses the information that the customer has just bought a certain product with a certain price. We investigate how discounting decisions depend on the inventory levels, time, type of pricing policy in use, and the relationship between the customers' reservation prices for the promotional product and the other products (negatively or positively correlated). In particular, we find that if the firm sets prices and discounts dynamically and the customers' reservation prices for the promotional product are negatively correlated with their reservation prices for the product they purchased, then customers are always offered a discount regardless of the inventory levels and time. On the other hand, if the customers' reservation prices for the promotional product are positively correlated with their reservation prices for the product they purchased, then the customer may or may not be offered a discount, depending on the inventory levels and time. Our numerical study shows that the benefit to the firm from using customer purchase information is high when the firm uses a static price, but chooses discounts dynamically. We also find that although dynamic discounting decisions bring modest improvements, setting the price dynamically seems to have a more significant effect on the firm's profits.

A Simple Staffing Method for Multiskill Call Centers

Abstract: We study a simple method for staffing in multiskill call centers. The method has short computation times and determines nearly optimal staffing levels. It is in both views competitive to other methods from the literature. Because of the fast and accurate performance of the method, many different scenarios can be analyzed, and our method can be used for both tactical and strategic capacity management decisions.

An Analysis of the Control-Algorithm Re-solving Issue in Inventory and Revenue Management

Abstract: While inventory-and revenue-management problems can be represented as Markov decision process (MDP) models, in some cases the well-known dynamic-programming curse of dimensionality makes it computationally prohibitive to solve them exactly. An alternative solution, called here the control-algorithm approach, is to use a math program (MP) to approximately represent the MDP and use its optimal solution to heuristically instantiate the parameters of the decision rules of a given set of control policies. As new information is observed over time, the control algorithm can incorporate it by re-solving the MP and revising the parameters of the decision rules with the newly obtained solution. The re-solving issue arises when one reflects on the consequences of this revision: Does the performance of the control algorithm really improve by revising its decision-rule instantiation with the solution of the re-solved MP, or should an appropriate modification of the prior solution be used? This paper analyzes the control-algorithm re-solving issue for a class of finite-horizon inventory-and revenue-management problems. It establishes sufficient conditions under which re-solving does not deteriorate the performance of a control algorithm, and it applies these results to control algorithms for network revenue management and multiproduct make-to-order production with lost sales and positive lead time.

Decision Biases in Revenue Management: Some Behavioral Evidence

Abstract: We study a problem of selling a fixed number of goods over a finite and known horizon. After presenting a procedure for computing optimal decision policies and some numerical results of a simple heuristic policy for the problem, we describe results from three experiments involving financially motivated subjects. The experiments reveal that decision makers employ decision policies of the same form of the optimal policy. However, they show systematic biases to demand too much when they have many units to sell and too little when they have few to sell, resulting in significant revenue losses.

Load-Based POLCA: An Integrated Material Control System for Multiproduct, Multimachine Job Shops

Abstract: This article proposes a supporting framework for the implementation of the material control system POLCA (paired-cell overlapping loops of cards with authorization). The POLCA system is particularly appropriate for environments that involve highly variable demand and large product variety, which force small batch (or even one-of-a-kind) production. We propose a load-based version of the POLCA control system (LB-POLCA), which determines the POLCA parameters (release authorizations, allowed workloads in the loops) according to an advanced resources planning (ARP) system that adequately captures the stochastic behavior of the production system and enables fine-tuning and high-level optimization of the manufacturing lot sizes. We also discuss the implementation of an electronic LB-POLCA system in a metal shop of Spicer Off-Highway Products Division, a subsidiary of the Dana Corporation.

Integrating the Promotional and Service Roles of Retail Inventories

Abstract: In some retail contexts, higher inventories not only improve service levels, but also stimulate demand by serving as a promotional tool (e.g., by increasing product visibility). Motivated by a building-products retailer's practice of stocking large quantities of products to stimulate demand, we study inventory management and pricing policies when demand is uncertain but increases with stocking quantity. We first characterize the profit-maximization policy for a stochastic inventory model with a general inventory-dependent demand distribution and given price, and show that demand stimulation (by inventories) has the effect of increasing the target service level beyond the classical newsvendor model's critical fractile ratio. To underscore the importance of considering both demand stochasticity and inventory influence, we consider two functionally oriented benchmark policies---a demand-driven policy and a critical fractile policy---that might, respectively, represent marketing and inventory managers' viewpoints. Our numerical analysis reveals that the optimal policy can generate considerably higher profits than the two complementary functional perspectives. Moreover, we prove that the optimal stocking quantity always exceeds the critical fractile solution and can even exceed the demand-driven stocking quantity. We also address the problem of jointly optimizing both stocking quantity and price for demand-stimulating products using a multiplicative model to represent the influence of price and stocking quantity on the demand distribution. For this model, we show that the pricing and stocking decisions can be determined sequentially, with the optimal policy setting higher prices and stock levels than both the functional policies.