Manufacturing & Service Operations Management current issue

Joining Longer Queues: Information Externalities in Queue Choice

Veeraraghavan, S., Debo, L. — 2009-09-29

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.

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

Deo, S., Corbett, C. J. — 2009-09-29

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.

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

Shulman, J. D., Coughlan, A. T., Savaskan, R. C. — 2009-09-29

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.

Consumer Returns Policies and Supply Chain Performance

Su, X. — 2009-09-29

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.

Inventory, Discounts, and the Timing Effect

Ahn, H.-S., Gumus, M., Kaminsky, P. — 2009-09-29

We introduce and analyze a model that explicitly considers the timing effect of intertemporal pricing—the concept, found in practice, that demand during a sale is increasing in the time since the last sale. We present structural results that characterize the interaction between the decision to hold a sale and the inventory-ordering decision. We show that the optimal inventory-ordering policy is a state-dependent base-stock policy; however, the optimal pricing policy can be quite complicated due to both the value and the cost of holding inventory and delaying sales. In our computational analysis, we find that compared to a fixed-price policy, we see an average gain in profit of almost 5% from optimally varying promotion and inventory decisions accounting for intertemporal demand, and we find that this potential profit gain increases as demand variability decreases. We also develop a heuristic based on deterministic pricing and find that it performs well relative to the optimal policy.

The Consistent Vehicle Routing Problem

Groer, C., Golden, B., Wasil, E. — 2009-09-29

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.

Managing Service Systems with an Offline Waiting Option and Customer Abandonment

Kostami, V., Ward, A. R. — 2009-09-29

Many service providers offer customers the choice of either waiting in a line or going offline and returning at a dynamically determined future time. The best-known example is the FASTPASS^® system at Disneyland. To operate such a system, the service provider must make an upfront decision on how to allocate service capacity between the two lines. Then, during system operation, he must provide estimates of the waiting times for both lines to each arriving customer. The estimation of offline waiting times is complicated by the fact that some offline customers do not return for service at their appointed time. We show that when demand is large and service is fast, for any fixed-capacity allocation decision, the two-dimensional process tracking the number of customers waiting in a line and offline collapses to one dimension, and we characterize the one-dimensional limit process as a reflected diffusion with linear drift. The analytic tractability of this one-dimensional limit process allows us to solve for the capacity allocation that minimizes average cost when there are costs associated with customer abandonments and queueing. We further show that in this limit regime, a simple scheme based on Little's Law to dynamically estimate in line and offline wait times is effective.

Strategic Safety Stocks in Supply Chains with Evolving Forecasts

Schoenmeyr, T., Graves, S. C. — 2009-09-29

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

Argon, N. T., Ziya, S. — 2009-09-29

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.

Going Bunkers: The Joint Route Selection and Refueling Problem

Besbes, O., Savin, S. — 2009-09-29

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.