Showing papers in "Manufacturing & Service Operations Management in 2009"

Consumer Returns Policies and Supply Chain Performance

Abstract: This paper develops a model of consumer returns policies. In our model, consumers face valuation uncertainty and realize their valuations only after purchase. There is also aggregate demand uncertainty, captured using the conventional newsvendor model. In this environment, consumers decide whether to purchase and then whether to return the product, whereas the seller sets the price, quantity, and refund amount. Using our model, we study the impact of full returns policies (e.g., using 100% money-back guarantees) and partial returns policies (e.g., when restocking fees are charged) on supply chain performance. Next, we demonstrate that consumer returns policies may distort incentives under common supply contracts (such as manufacturer buy-backs), and we propose strategies to coordinate the supply chain in the presence of consumer returns. Finally, we explore several extensions and demonstrate the robustness of our findings.

Dynamic Pricing and Inventory Control of Substitute Products

Abstract: We study dynamic pricing and inventory control of substitute products for a retailer who faces a long supply lead time and a short selling season. Within a multinomial logit model of consumer choice over substitutes, we develop a stochastic dynamic programming formulation and derive the optimal dynamic pricing policy. We prove that dynamic pricing converges to static pricing as inventory levels of all variates approach the number of remaining selling periods (assuming at most one customer arrival within each period). Our extensive numerical study of the effects of time and inventory depletion on the optimal pricing reveals two fundamental underlying driving forces of the complex price behavior: the level of inventory scarcity and the quality difference among products. We also compare the performance of three restricted pricing strategies: static, unified dynamic, and mixed dynamic pricing. We find that full-scale dynamic pricing is of great value in the presence of inventory scarcity, and initial inventory decisions are quite robust in the pricing scheme employed in the selling season. Based on the above insights, we propose a computationally efficient approach to the initial inventory decision, which delivers close-to-optimal inventory levels for all testing cases.

The Influence of Psychological Safety and Confidence in Knowledge on Employee Knowledge Sharing

Abstract: This research empirically examines the influence of psychological safety on knowledge sharing among coworkers in manufacturing and service operations contexts. Reconciling conflicting findings in the literature, we demonstrate that whereas psychological safety is an important antecedent of knowledge sharing, the relationship between psychological safety and knowledge sharing is moderated by the level of confidence that employees have in what they know. The greater this confidence, the lesser is the importance of psychological safety in facilitating knowledge sharing. Linking this result to social network theory, we find that psychological safety increases with the frequency of communication among coworkers and that the confidence of employees in their knowledge is related to the codifiability of the knowledge involved. We further investigate direct and indirect antecedents of psychological safety. This research offers insights into actions that managers can take to enhance psychological safety and, consequently, motivate their employees to share knowledge.

Optimal Restocking Fees and Information Provision in an Integrated Demand-Supply Model of Product Returns

Abstract: Product returns cost U.S. companies more than $100 billion annually. The cost and scale of returns management issues necessitate a deeper understanding of how to deal with product returns. We develop an analytical model that describes how consumer purchase and return decisions are affected by a seller's pricing and restocking fee policy. Taking into account the consumers' strategic behavior, we derive the seller's optimal policy as a function of consumer preferences, consumer uncertainty about product attributes, consumer hassle cost for returns, and the effectiveness of the seller's forward and reverse channel capability. We allow for two sources of consumer uncertainty and show how the seller may use its price and restocking fee as a means of targeting a segment of consumers who know their product consumption utilities. We find that even if it is possible to eliminate returns costlessly through the provision of information about the fit between consumer preferences and product characteristics, returns can nevertheless be part of an optimal product sales process. That is, we identify conditions under which it is (or is not) optimal to provide product fit information to consumers. We show that the marginal value of information to the seller is decreasing in the operational efficiency of the seller's forward and reverse logistics process as well as the level of product uncertainty. We identify the impact of multiple product options and sources of consumer uncertainty on the model's results. The analysis generates testable hypotheses about how consumer-level and seller-level parameters affect the return policies observed in the marketplace.

The Consistent Vehicle Routing Problem

Abstract: In the small package shipping industry (as in other industries), companies try to differentiate themselves by providing high levels of customer service. This can be accomplished in several ways, including online tracking of packages, ensuring on-time delivery, and offering residential pickups. Some companies want their drivers to develop relationships with customers on a route and have the same drivers visit the same customers at roughly the same time on each day that the customers need service. These service requirements, together with traditional constraints on vehicle capacity and route length, define a variant of the classical capacitated vehicle routing problem, which we call the consistent VRP (ConVRP). In this paper, we formulate the problem as a mixed-integer program and develop an algorithm to solve the ConVRP that is based on the record-to-record travel algorithm. We compare the performance of our algorithm to the optimal mixed-integer program solutions for a set of small problems and then apply our algorithm to five simulated data sets with 1,000 customers and a real-world data set with more than 3,700 customers. We provide a technique for generating ConVRP benchmark problems from vehicle routing problem instances given in the literature and provide our solutions to these instances. The solutions produced by our algorithm on all problems do a very good job of meeting customer service objectives with routes that have a low total travel time. In the paper “The Consistent Vehicle Routing Problem,” published in Manufacturing & Service Operations Management, ePub ahead of print December 4, 2008, http://msom.journal.informs.org/cgi/content/abstract/msom.1080.0243v1, the authors have amended the original text published online to correct an oversight in conveying the real-world problem studied in this article.

Impact of Supply Learning When Suppliers Are Unreliable

Abstract: Dual sourcing and inventory are two prevalent and widely studied strategies firms use to manage yield risk. A pervasive but implicit assumption in the literature is that a firm knows its suppliers' yield distributions with certainty. This is a strong assumption in many circumstances. A firm is more likely to have a forecast of a supplier's yield distribution and to update that forecast based on its experiences with the supplier. We introduce and analyze a Bayesian model of “supply learning” (i.e., distribution updating) and investigate how supply learning influences both sourcing and inventory strategies in dual-sourcing and single-sourcing models, respectively. In the case of Bernoulli all-or-nothing yield distributions, we completely characterize the firm's optimal sourcing and inventory decisions for the supply-learning model. Among other results, we prove that for a given expected supplier reliability (i.e., the mean of the firm's forecast for the probability of successful delivery) an increase in the reliability forecast uncertainty increases the attractiveness of a supplier, but it reduces the firm's desire to invest in inventory to protect against future supply failures. We extend our analysis to allow for general yield distributions, multiple sourcing (i.e., more than two suppliers), and inventory carryover in the dual-sourcing model.

Cournot Competition Under Yield Uncertainty: The Case of the U.S. Influenza Vaccine Market

Abstract: This paper is inspired by the recurring mismatch between demand and supply in the U.S. influenza vaccine market. Economic theory predicts that an oligopolistic market with unregulated but costly entry will experience excess entry and oversupply, not the undersupply observed in the market for influenza vaccine in recent years. In this paper, we examine the interaction between yield uncertainty, a key characteristic of many production processes, including that for influenza vaccine, and firms' strategic behavior. We find that yield uncertainty can contribute to a high degree of concentration in an industry and a reduction in the industry output and the expected consumer surplus in equilibrium. We use parameter values loosely based on the U.S. influenza vaccine market to numerically illustrate the impact of yield uncertainty.

A Stochastic Inventory Model with Trade Credit

Abstract: Suppliers routinely sell goods to retailers on credit. Common credit terms are tantamount to a schedule of declining discounts (escalating penalties) that depend on how long the retailer takes to pay off the supplier's loan. However, issues such as which stocking policies are optimal in the presence of supplier-provided credit have been investigated only when demand is assumed deterministic. Nearly all stochastic inventory models assume either time-invariant finance charges or charges that may vary with time but not with the age of the credit. In this article we present a discrete time model of the retailer's operations with random demand, which is used to prove that the structure of the optimal policy is not affected by credit terms, although the value of the optimal policy parameter is. This is followed by a continuous time model, which leads to an algorithm for finding the optimal stock level. We also model the supplier's problem and calculate the optimal credit parameters in numerical experiments.

Long-Term Contracts Under the Threat of Supplier Default

Abstract: Contracting with suppliers prone to default is an increasingly common problem in some industries, particularly automotive manufacturing. We model this phenomenon as a two-period contracting game with two identical suppliers, a single buyer, deterministic demand, and uncertain production costs. The suppliers are distressed at the start of the game and do not have access to external sources of capital; hence, revenues from the buyer are crucial in determining whether default occurs. The production cost of each supplier is the sum of two stochastic components: a common term that is identical for both suppliers (representing raw materials costs, design specifications, etc.) and an idiosyncratic term that is unique to a given supplier (representing inherent firm capability). The buyer chooses a supplier and then decides on a single-or two-period contract. Comparing models with and without the possibility of default, we find that, without the possibility of supplier failure, the buyer always prefers short-term contracts over long-term contracts, whereas this preference is typically reversed in the presence of failure. Neither of these contracts coordinates the supply chain. We also consider dynamic contracts, in which the contract price is partially tied to some index representing the common component of production costs (e.g., commodity prices of raw materials such as steel or oil), allowing the buyer to shoulder some of the risk from cost uncertainty. We find that dynamic long-term contracts allow the buyer to coordinate the supply chain in the presence of default risk. We also demonstrate that our results continue to hold under a variety of alternative assumptions, including stochastic demand, allowing the buyer the option of subsidizing a bankrupt supplier via a contingent transfer payment or loan and allowing the buyer to unilaterally renegotiate contracts. We conclude that the possibility of supplier default offers a new reason to prefer long-term contracts over short-term contracts.

Demand-Supply Mismatches and Stock Market Reaction: Evidence from Excess Inventory Announcements

Abstract: This paper documents that excess inventory announcements, an indication of demand-supply mismatch, are associated with an economically and statistically significant negative stock market reaction. The results are based on a sample of 276 excess inventory announcements made during 1990--2002. Over a two-day period (the day of the announcement and the day before the announcement) the mean (median) stock market reaction ranges from -6.79% to -6.93% (-4.51% to -4.79%), depending on the benchmark used to estimate the market reaction. The percent of sample firms that experience negative market reaction ranges from 73% to 74%. When excess inventory is at the announcing firm's customers, the market reaction is more negative than when the excess inventory is at the announcing firm. The stock market reaction is less negative for excess inventory announcements made by larger firms but is more negative for firms with higher growth prospects and with higher debt-equity ratios.

Using Imperfect Advance Demand Information in Production-Inventory Systems with Multiple Customer Classes

Abstract: We consider a make-to-stock supplier that operates a production facility with limited capacity. The supplier receives orders from customers belonging to several demand classes. Some of the customer classes share advance demand information with the supplier by announcing their orders ahead of their due date. However, this advance demand information is not perfect because the customer may decide to order prior to or later than the expected due date or may decide to cancel the order altogether. Customer classes vary in their demand rates, expected due dates, cancellation probabilities, and shortage costs. The supplier must decide when to produce and, whenever an order becomes due, whether or not to satisfy it from on-hand inventory. Hence, the supplier is faced with a joint production-control and inventory-allocation problem. We formulate the problem as a Markov decision process and characterize the structure of the optimal policy. We show that the optimal production policy is a state-dependent base-stock policy with a base-stock level that is nondecreasing in the number of announced orders. We show that the optimal inventory-allocation policy is a state-dependent multilevel rationing policy, with the rationing level for each class nondecreasing in the number of announced orders (regardless of whether the class provides advance information). From numerical results, we obtain several insights into the value of advance demand information for both supplier and customers.

Benefits of Reevaluating Real-Time Order Fulfillment Decisions

Abstract: When a customer orders online, an online retailer assigns the order to one or more of its warehouses and/or drop-shippers to minimize procurement and transportation costs based on the available current information. However, this assignment is necessarily myopic because it cannot account for any subsequent customer orders or future inventory replenishment. We examine the benefits of periodically reevaluating these real-time assignments. We construct near-optimal heuristics for the reassignment for a large set of customer orders to minimize the total number of shipments. Finally, we present evidence of significant saving opportunities by testing the heuristics on order data from a major online retailer.

Second Sourcing vs. Sole Sourcing with Capacity Investment and Asymmetric Information

Abstract: We study the decision of a manufacturer (the buyer), expecting new sourcing opportunities in the future, in selecting between sole-and second-sourcing strategies for a noncommodity component. In a sole-sourcing strategy, the buyer commits to sourcing from a single supplier (the incumbent) over the entire horizon. In a second-sourcing strategy, the buyer keeps the option open to source from a new supplier (the entrant) in the future. Supplier costs are private information, and the incumbent's cost may change in the future because of what it has learned. The buyer is relatively sure about current demand but uncertain about future demand. A supplier has to invest in capacity to produce the inputs for the buyer. With future private cost information, the incumbent earns rent in the future, and this prospective rent influences the incumbent's decision early in the horizon. On one hand, a second-sourcing strategy allows the buyer to take advantage of alternative sourcing opportunities, lowering her future cost. This benefit to the buyer is referred to as the option value of second sourcing. On the other hand, the future supplier competition in second-sourcing hurts the incumbent's future profit. The expectation of a lower future profit in second sourcing induces the incumbent to ask for a higher price at the beginning of the horizon. This causes more initial sourcing cost for the buyer in second sourcing than in sole sourcing, and is referred to as the cost of future supplier competition of second sourcing. The overall benefit of second sourcing relative to sole sourcing is influenced by the demand distribution and capacity cost. If the demand increases over time with positive probability, the incumbent's initial capacity may not be able to cover all future demand. If the capacity is cheap, the entrant may serve as an exclusive supplier, ousting the incumbent. In this case, the option value of second sourcing is high. If the capacity is expensive, the entrant may serve as a supplementary supplier by receiving only the demand in excess of the incumbent's installed capacity. In this case, the cost of future supplier competition is low and the option value is still significant. Thus second sourcing is better than sole sourcing not only when the capacity cost is low, but also when it is high (under the condition that demand increases over time with positive probability and the entrant's cost is relatively low). For intermediate capacity cost, the cost of future supplier competition dominates the option value; hence, sole sourcing is preferred. We also find that second sourcing is more attractive when the buyer expects the future demand to be higher or more volatile. Finally, more initial incumbent capacity strengthens the incumbent's competitiveness against the entrant, reducing the cost of future supplier competition. As a result, we find that second sourcing may lead to overinvestment of the initial capacity.

Scheduling Flexible Servers with Convex Delay Costs in Many-Server Service Systems

Abstract: In a recent paper we introduced the queue-and-idleness ratio (QIR) family of routing rules for many-server service systems with multiple customer classes and server pools. A newly available server serves the customer from the head of the queue of the class (from among those the server is eligible to serve) whose queue length most exceeds a specified proportion of the total queue length. Under fairly general conditions, QIR produces an important state-space collapse as the total arrival rate and the numbers of servers increase in a coordinated way. That state-space collapse was previously used to delicately balance service levels for the different customer classes. In this sequel, we show that a special version of QIR stochastically minimizes convex holding costs in a finite-horizon setting when the service rates are restricted to be pool dependent. Under additional regularity conditions, the special version of QIR reduces to a simple policy: linear costs produce a priority-type rule, in which the least-cost customers are given low priority. Strictly convex costs (plus other regularity conditions) produce a many-server analogue of the generalized-cμ (Gcμ) rule, under which a newly available server selects a customer from the class experiencing the greatest marginal cost at that time.

Joining Longer Queues: Information Externalities in Queue Choice

Abstract: A classic example that illustrates how observed customer behavior impacts other customers' decisions is the selection of a restaurant whose quality is uncertain. Customers often choose the busier restaurant, inferring that other customers in that restaurant know something that they do not. In an environment with random arrival and service times, customer behavior is reflected in the lengths of the queues that form at the individual servers. Therefore, queue lengths could signal two factors---potentially higher arrivals to the server or potentially slower service at the server. In this paper, we focus on both factors when customers' waiting costs are negligible. This allows us to understand how information externalities due to congestion impact customers' service choice behavior. In our model, based on private information about both the service-quality and queue-length information, customers decide which queue to join. When the service rates are the same and known, we confirm that it may be rational to ignore private information and purchase from the service provider with the longer queue when only one additional customer is present in the longer queue. We find that, due to the information externalities contained in queue lengths, there exist cycles during which one service firm is thriving whereas the other is not. Which service provider is thriving depends on luck; i.e., it is determined by the private signal of the customer arriving when both service providers are idle. These phenomena continue to hold when each service facility has multiple servers, or when a facility may go out of business when it cannot attract customers for a certain amount of time. Finally, we find that when the service rates are unknown but are negatively correlated with service values, our results are strengthened; long queues are now doubly informative. The market share of the high-quality firm is higher when there is service rate uncertainty, and it increases as the service rate decreases. When the service rates are positively correlated with unknown service values, long queues become less informative and customers might even join shorter queues.

Data Set---Choice-Based Revenue Management: Data from a Major Hotel Chain

Abstract: In this paper, we describe data collected from five U.S. properties of a major hotel chain that can be used to benchmark the performance of choice-based revenue management (RM) algorithms. The process used to collect this data illustrates subtle complexities involved in extracting product availability information from current RM systems and sheds new light on practical issues that need to be addressed to successfully implement choice-based RM systems. The data described in this paper is publicly available at the journal's website at http://msom.pubs.informs.org .

Certification in the Indian Offshore IT Services Industry

Abstract: Third-party process certification programs such as the ISO 9001 and capability maturity model (CMM) have been widely adopted in recent years. In this study we employ three competing theoretical frameworks---signaling, efficiency gains, and institutional theory---to analyze the motivations for a firm to acquire quality certification and the performance implications thereafter. We test these hypotheses in the context of CMM certification based on data from the Indian offshore IT services industry between 1997 and 2002. Our results indicate that more cost-effective firms and export-oriented firms are more likely to seek out and acquire certification. In addition, CMM-certified firms show significant improvements in exports, but not on the firm's cost structure. Furthermore, our findings suggest that CMM certification helps indicate firm capabilities to potential customers and thus appear to be most consistent with signaling explanations of certification rather than the efficiency gains or institutional theories.

Real-Time Delay Estimation Based on Delay History

Abstract: Motivated by interest in making delay announcements to arriving customers who must wait in call centers and related service systems, we study the performance of alternative real-time delay estimators based on recent customer delay experience. The main estimators considered are: (i) the delay of the last customer to enter service (LES), (ii) the delay experienced so far by the customer at the head of the line (HOL), and (iii) the delay experienced by the customer to have arrived most recently among those who have already completed service (RCS). We compare these delay-history estimators to the standard estimator based on the queue length (QL), commonly used in practice, which requires knowledge of the mean interval between successive service completions in addition to the QL. We characterize performance by the mean squared error (MSE). We do an analysis and conduct simulations for the standard GI/M/s multiserver queueing model, emphasizing the case of large s. We obtain analytical results for the conditional distribution of the delay given the observed HOL delay. An approximation to its mean value serves as a refined estimator. For all three candidate delay estimators, the MSE relative to the square of the mean is asymptotically negligible in the many-server and classical heavy-traffic (HT) limiting regimes.

Note---Pricing and Inventory Control for a Perishable Product

Abstract: In this note, we study the concurrent determination of pricing and inventory replenishment decisions for a perishable product in an infinite horizon. Demands in consecutive periods are independent and influenced by prices charged in each period. In particular, we treat price as a decision variable to maximize the total discounted profit. We analyze the optimal solution-structure of a two-period lifetime problem and from insights gained in numerical experiments, develop a base-stock/list-price heuristic policy for products with arbitrary fixed lifetimes. Experiments show this policy to be effective.

Strategic Safety Stocks in Supply Chains with Evolving Forecasts

Abstract: We examine the placement of safety stocks in a supply chain for which we have an evolving demand forecast. Under assumptions about the forecasts, the demand process, and the supply chain structure, we show that safety-stock placement for such systems is effectively equivalent to the corresponding well-studied problem for systems with stationary demand bounds and base-stock policies. Hence, we can use existing algorithms to find the optimal safety stocks. We use a case study with real data to demonstrate that there are significant benefits from the inclusion of the forecast process when determining the optimal safety stocks. We also conduct a computational experiment to explore how the placement and size of the safety stocks depend on the nature of the forecast evolution process.

Priority Assignment Under Imperfect Information on Customer Type Identities

Abstract: In many service systems, customers are not served in the order they arrive, but according to a priority scheme that ranks them with respect to their relative “importance.” However, it may not be an easy task to determine the importance level of customers, especially when decisions need to be made under limited information. A typical example is from health care: When triage nurses classify patients into different priority groups, they must promptly determine each patient's criticality levels with only partial information on their conditions. We consider such a service system where customers are from one of two possible types. The service time and waiting cost for a customer depends on the customer's type. Customers' type identities are not directly available to the service provider; however, each customer provides a signal, which is an imperfect indicator of the customer's identity. The service provider uses these signals to determine priority levels for the customers with the objective of minimizing the long-run average waiting cost. In most of the paper, each customer's signal equals the probability that the customer belongs to the type that should have a higher priority and customers incur waiting costs that are linear in time. We first show that increasing the number of priority classes decreases costs, and the policy that gives the highest priority to the customer with the highest signal outperforms any finite class priority policy. We then focus on two-class priority policies and investigate how the optimal policy changes with the system load. We also investigate the properties of “good” signals and find that signals that are larger in convex ordering are more preferable. In a simulation study, we find that when the waiting cost functions are nondecreasing, quadratic, and convex, the policy that assigns the highest priority to the customer with the highest signal performs poorly while the two-class priority policy and an extension of the generalized cμ rule perform well.

The Impact of Service Operations Failures on Customer Satisfaction: Evidence on How Failures and Their Source Affect What Matters to Customers

Abstract: Research in consumer psychology shows that customers seek reasons for service failures and that attributions of blame moderate the effects of failure on the level of customer satisfaction. This paper extends research on service operations failures by hypothesizing that attributions of blame also affect what matters to the customer during service failures. Specifically, we hypothesize that the relative weights that customers assign to key service elements in reaching an overall assessment of customer satisfaction are affected by customer attributions of blame for service failures. We use the U.S. airline industry as a quasi-experimental research setting to investigate the components of customer satisfaction for three samples of customers who experience (1) routine service, (2) flight delays of external (i.e., weather) origin, and (3) flight delays of internal origin. Although the level of customer satisfaction is lower for all service failures, we find that the key components of satisfaction differ between delayed and routine flights only when customers blame the service provider for the failure. Specifically, when delays are of external origin satisfaction is lower than for routine flights, but there is virtually no difference in the weight that customers assign to the components of customer satisfaction (including employee interactions). In contrast, when delays are of internal origin, satisfaction is lower than for either routine flights or flights delayed by external factors, and employee interactions have a significantly diminished role in customer satisfaction evaluations. Contrary to the popular view that employee interactions take on a greater role in determining customer satisfaction during service failures, we find that the opposite is true if the customer attributes blame to the service provider. Our findings highlight the important role of customer attributions during service failures and present more nuanced evidence on the role of employee-customer interactions in mitigating the effects of service failures on customer satisfaction.

Multimarket Facility Network Design with Offshoring Applications

Abstract: Moving production to low-wage countries may reduce manufacturing costs, but it increases logistics costs and is subject to foreign trade barriers, among others. This paper studies a manufacturer's multimarket facility network design problem and investigates the offshoring decision from a network capacity investment perspective. We analyze a firm that manufactures two products to serve two geographically separated markets using a common component and two localized final assemblies. The common part can be transported between the two markets that have different economic and demand characteristics. Two strategic network design questions arise naturally: (1) Should the common part be produced centrally or in two local facilities? (2) If a centralization strategy is adopted, in which market should the facility be located? We present a transportation cost threshold that captures costs, revenues, and demand risks, and below which centralization is optimal. The optimal location of commonality crucially depends on the relative magnitude of price and manufacturing cost differentials but also on demand size and uncertainty. Incorporating scale economies further enlarges the centralization's optimality region.

Going Bunkers: The Joint Route Selection and Refueling Problem

Abstract: Managing shipping vessel profitability is a central problem in marine transportation. We consider two commonly used types of vessels---“liners” (ships whose routes are fixed in advance) and “trampers” (ships for which future route components are selected based on available shipping jobs)---and formulate a vessel profit maximization problem as a stochastic dynamic program. For liner vessels, the profit maximization reduces to the problem of minimizing refueling costs over a given route subject to random fuel prices and limited vessel fuel capacity. Under mild assumptions about the stochastic dynamics of fuel prices at different ports, we provide a characterization of the structural properties of the optimal liner refueling policies. For trampers, the vessel profit maximization combines refueling decisions and route selection, which adds a combinatorial aspect to the problem. We characterize the optimal policy in special cases where prices are constant through time and do not differ across ports and prices are constant through time and differ across ports. The structure of the optimal policy in such special cases yields insights on the complexity of the problem and also guides the construction of heuristics for the general problem setting.

The Value of Component Commonality in a Dynamic Inventory System with Lead Times

Abstract: Component commonality has been widely recognized as a key factor in achieving product variety at low cost. Yet the theory on the value of component commonality is rather limited in the inventory literature. The existing results were built primarily on single-period models or periodic-review models with zero lead times. In this paper, we consider a continuous-review system with positive lead times. We find that although component commonality is in general beneficial, its value depends strongly on component costs, lead times, and dynamic allocation rules. Under certain conditions, several previous findings based on static models do not hold. In particular, component commonality does not always generate inventory benefits under certain commonly used allocation rules. We provide insight on when component commonality generates inventory benefits and when it may not. We further establish some asymptotic properties that connect component lead times and costs to the impact of component commonality. Through numerical studies, we demonstrate the value of commonality and its sensitivity to various system parameters in between the asymptotic limits. In addition, we show how to evaluate the system under a new allocation rule, a modified version of the standard first-in-first-out rule.

Sensitivity of Optimal Capacity to Customer Impatience in an Unobservable M/M/S Queue (Why You Shouldn't Shout at the DMV)

Abstract: This paper employs sample path arguments to derive the following convexity properties and comparative statics for an M/M/S queue with impatient customers. If the rate at which customers balk and renege is an increasing, concave function of the number of customers in the system (head count), then the head-count process and the expected rate of lost sales are decreasing and convex in the capacity (service rate or number of servers). This result applies when customers cannot observe the head count, so that the balking probability is zero and the reneging rate increases linearly with the head count. Then the optimal capacity increases with the customer arrival rate but is not monotonic in the reneging rate per customer. When capacity is expensive or the reneging rate is high, the optimal capacity decreases with any further increase in the reneging rate. Therefore, managers must understand customers' impatience to avoid building too much capacity, but customers have an incentive to conceal their impatience, to avoid a degradation in service quality. If the system manager can prevent customers from reneging during service (by requiring advance payment or training employees to establish rapport with customers), the system's convexity properties are qualitatively different, but its comparative statics remain the same. Most important, the prevention of reneging during service can substantially reduce the total expected cost of lost sales and capacity. It increases the optimal capacity (service rate or number of servers) when capacity is expensive and reduces the optimal capacity when capacity is cheap.

Managing Inventory Over a Short Season: Models with Two Procurement Opportunities

Abstract: Motivated by many recent applications reported in the literature, we examine the impact of a second procurement opportunity on inventory management of products with short selling seasons. In our framework, the first order is placed at the start of the preseason and delivered at the start of the selling season; the second order is placed at or after the start of the selling season for subsequent delivery. Under this framework, the decision maker must make three interrelated choices: the first order quantity, when to place the second order, and the second order quantity. Our focus is on elucidating the optimal policy structure for the three interrelated decisions. By casting our models as sequential decision-making problems, we are able to reduce the optimization problems into sequential and embedded searches for the concerned decision variables that allow us to identify the conditions on the economic parameters and demand distribution to effectively facilitate the search for the optimal solutions.

Managing an Assemble-to-Order System with Returns

Abstract: We consider a multiproduct assemble-to-order (ATO) system, in which inventory is kept only at the component level and the finished products are assembled in response to customer demands. In addition to stochastic demand for finished products, the system experiences stochastic returns of subsets of components, which can then be used to satisfy subsequent demands. The system is managed over an infinite horizon using a component-level base-stock policy. We identify several ways in which returns complicate the behavior of the system, and we demonstrate how to handle these additional complexities when calculating or approximating key order-based performance metrics, including the immediate fill rate, the fill rate within a time window, and average backorders. We also present a method for computing a near-optimal base-stock policy. We use these results to address managerial questions on both operational and product-design levels. For example, we find that tracking product-based (as opposed to component-based) return information appears to provide much less value than tracking product-based demand information, and we explore the impact of the number of products, component lead times, and different patterns of component returns (joint versus independent returns, returns of common versus dedicated components) on the value of component commonality.

Incentives for Quality Through Endogenous Routing

Abstract: We study how rework routing together with wage and piece-rate compensation can strengthen incentives for quality. Traditionally, rework is assigned back to the agent who generates the defect (in a self-routing scheme) or to another agent dedicated to rework (in a dedicated routing scheme). In contrast, a novel cross-routing scheme allocates rework to a parallel agent performing both new jobs and rework. The agent who passes quality inspection or completes rework receives the piece rate paid per job. We compare the incentives of these rework-allocation schemes in a principal-agent model with embedded quality control and routing in a multiclass queueing network. We show that conventional self-routing of rework cannot induce first-best effort. Dedicated routing and cross-routing, however, strengthen incentives for quality by imposing an implicit punishment for quality failure. In addition, cross-routing leads to workload-allocation externalities and a prisoner's dilemma, thereby creating the greatest incentives for quality. Firm profitability depends on demand levels, revenues, and quality costs. When the number of agents increases, the incentive effect of cross-routing reduces monotonically and approaches that of dedicated routing.

Partitioning of Servers in Queueing Systems During Rush Hour

Abstract: This paper is motivated by two phenomena observed in many queueing systems in practice. The first is the partitioning of server capacity among different customers based on their service time requirements. The second is rush hour demand where a large number of customers arrive over a short period of time followed by few or no arrivals for an extended period thereafter. We study a system with multiple parallel servers and multiple customer classes. The servers can be partitioned into server groups, each dedicated to a single customer class. The system operates under a rush hour regime with a large number of customers arriving at the beginning of the rush hour period. We show that this allows us to reduce the problem to one that is deterministic and for which closed-form solutions can be obtained. We compare the performance of the system with and without server partitioning during rush hour and address three basic questions. (1) Is partitioning beneficial to the system? (2) Is it equally beneficial to all customer classes? (3) If it is implemented, what is an optimal partition? We evaluate the applicability of our results to systems where customers arrive over time using (1) deterministic fluid models and (2) simulation models for systems with stochastic interarrival times.