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We are interested in whether preventing resale of tickets benefits the capacity providers for sporting and entertainment events. Common wisdom suggests that ticket resale is harmful to event organizers' revenues, and event organizers have tried to prevent resale of tickets. For instance, Ticketmaster recently proposed paperless (nontransferrable) ticketing, which would severely limit the opportunity to resell tickets. We consider a model that allows resale from both consumers and speculators with different transaction costs for each party. Surprisingly, we find that this wisdom is incorrect when event organizers use fixed pricing policies; in fact, event organizers benefit from reductions in consumers' (and speculators') transaction costs of resale. Even when multiperiod pricing policies are used, we find that an event organizer may still benefit from ticket resale if his capacity is small. Although paperless ticketing is suggested as a way to reduce ticket resale and prevent speculators from buying tickets, our results suggest that it may reduce the capacity providers' revenues in many situations. Instead, we propose ticket options as a novel ticket pricing mechanism. We show that ticket options (where consumers would initially buy an option to buy a ticket and then exercise at a later date) naturally reduce ticket resale significantly and result in significant increases in event organizers' revenues. Furthermore, since a consumer only risks the option price (and not the whole ticket price) if she cannot attend the event, options may face less consumer resistance than paperless tickets. This paper was accepted by Serguei Netessine, operations management .

We consider a two-location production/inventory model where each location makes production decisions and is subject to uncertain capacity. Each location optimizes its own profits. Transshipment (at a cost) is allowed from one location to another. We focus on the question of whether one can globally set a pair of coordinating transshipment prices, i.e., payments that each party has to make to the other for the transshipped goods, that induce the local decision makers to make inventory and transshipment decisions that are globally optimal. A recent paper suggests, for a special case of our model, that there always exists a unique pair of coordinating transshipment prices. We demonstrate through a counterexample that this statement is not correct and derive sufficient and necessary conditions under which it would hold. We show that in some conditions, coordinating prices may exist for only a narrow range of problem parameters and explore conditions when this can happen. Finally, we study the effects of demand and capacity variability on the magnitude of coordinating transshipment prices.

In this paper, we study a conditional upgrade strategy that has recently emerged in the travel industry. After a consumer makes a reservation for a product (e.g., a hotel room), she is asked whether she would like to upgrade her product to a higher-quality (more expensive) one at a discounted price. The upgrade, however, is not fulfilled immediately. The firm fulfills upgrades at check-in if higher-quality products are still available, and the upgrade fee is charged to the consumer if and only if she actually gets upgraded. Consumers decide which product type to book and whether to accept an upgrade offer or not based on the anticipated upgrade probability. We find that conditional upgrades create value by improving demand–supply matching for the firm. The firm can use the conditional upgrade channel to flexibly manage capacity allocations and reoptimize demand segmentation. For a firm that takes product prices as given, offering conditional upgrades is effective in compensating for the firm’s lack of ability in setting prices optimally. For a firm that has the ability to optimize product prices, conditional upgrades generate higher revenues than dynamic pricing. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2783 . This paper was accepted by Martin Lariviere, operations management.

In this paper, we address the problem of admission control and sequencing in a production system that produces two classes of products. The first class of products is made-to-stock, and the firm is contractually obliged to meet demand for this class of products. The second class of products is made-to-order, and the firm has the option to accept (admit) or reject a particular order. The problem is motivated by suppliers in many industries who sign contracts with large manufacturers to supply them with a given product and also can take on additional orders from other sources on a make-to-order basis. We model the joint admission control/sequencing decision in the context of a simple two-class M / M /1 queue to gain insight into the following problems: 1. How should a firm decide (a) when to accept or reject an additional order, and (b) which type of product to produce next? 2. How should a firm decide what annual quantity of orders to commit to when signing a contract to produce the make-to-stock products?

We propose a nonparametric data-driven algorithm called DDM for the management of stochastic periodic-review multiproduct inventory systems with a warehouse-capacity constraint. The demand distribution is not known a priori and the firm only has access to past sales data (often referred to as censored demand data). We measure performance of DDM through regret, the difference between the total expected cost of DDM and that of an oracle with access to the true demand distribution acting optimally. We characterize the rate of convergence guarantee of DDM. More specifically, we show that the average expected T -period cost incurred under DDM converges to the optimal cost at the rate of O ( T −1/2 ). Our asymptotic analysis significantly generalizes approaches used in Huh and Rusmevichientong (2009) for the uncapacitated single-product inventory systems. We also discuss several extensions and conduct numerical experiments to demonstrate the effectiveness of our proposed algorithm.

We study a supply chain consisting of one supplier and one OEM (original equipment manufacturer). The OEM faces stochastic demand for a final product that requires assembly of two major components, one of which is procured exclusively from the supplier. In the absence of competition, the supplier is able to make a take-it-or-leave-it offer to the OEM in the form of a menu of price-quantity contracts. The OEM possesses private information across two dimensions: (1) demand forecasts about the final product, and (2) production cost of the in-house component. Both pieces of information are relevant to the total supply chain profit, thus affecting the supplier's optimal offer. By initially assuming an exogenous information structure, we characterize the supplier's optimal contract menu for a simple case and demonstrate that more dimensions of asymmetric information are not always preferable for the OEM but could be beneficial for the supply chain. We subsequently examine whether this preference for one less dimension of private information implies disclosure of private information to the supplier when the information structure is endogenized. Our results indicate that if OEMs that are indifferent between disclosing and keeping information private choose to disclose it, disclosure of any verifiable information from all OEMs is always an equilibrium, whereas nondisclosure might fail to be an equilibrium. We also consider the possibility of the OEM and the supplier contracting at the ex-ante stage, i.e., before the OEM observes his private information. When both dimensions of the OEM's private information are verifiable and the cost of disclosing information is small enough, an ex-ante agreement on information disclosure is always possible; otherwise its feasibility depends on the problem parameters.

The rising cost of prescription drugs is a concern in the United States. To manage drug costs, insurance companies induce patients to choose less-expensive medications by making them pay higher copayments for more-expensive drugs, especially when multiple drug options are available to treat a condition. However, drug manufacturers have responded by offering copay coupons—coupons intended to be used by those already with prescription drug coverage. Recent empirical work has shown that such coupons significantly increase insurer costs without much benefit to patients, who incur lower out-of-pocket expenses with coupons but may eventually see higher costs passed to them. As a result, there is pressure from the insurance industry and consumer advocacy groups to ban copay coupons. In this paper we analyze how copay coupons affect patients, insurance companies, and drug manufacturers, while addressing the question of whether insurance companies would in fact always benefit from a copay coupon ban. We find that copay coupons tend to benefit drug manufacturers with large profit margins relative to other manufacturers, while generally, but not always, benefiting patients; insurer costs tend to increase with coupons from high-price drug manufacturers and decrease with coupons from low-price manufacturers. Although often helping drug manufacturers and increasing insurer costs, we also identify situations in which copay coupons benefit both patients and insurers. Thus, a blanket ban on copay coupons would not necessarily benefit insurance companies. In addition to the policy implications of our work, we make concrete managerial recommendations to insurers. We discuss how they should set formulary selection policies taking into account the fact that drug manufacturers may offer coupons; and we suggest how they can benefit from subsidizing coupons from drug manufacturers with low-price drugs, or from having drug manufacturers compete on price, to receive a favorable formulary position (i.e., copay). This paper was accepted by Yossi Aviv, operations management.

We study a multiperiod network revenue management problem where a seller sells multiple products made from multiple resources with finite capacity in an environment where the underlying demand function is a priori unknown (in the nonparametric sense). The objective of the seller is to simultaneously learn the unknown demand function and dynamically price the products to minimize the expected revenue loss. For the problem where the number of selling periods and initial capacity are scaled by k > 0 , it is known that the expected revenue loss of any non-anticipating pricing policy is Ω ( k ) . However, there is a considerable gap between this theoretical lower bound and the performance bound of the best-known heuristic control in the literature. In this paper, we propose a nonparametric self-adjusting control and show that its expected revenue loss is O ( k 1 / 2 + ϵ ⁡ log k ) for any arbitrarily small ϵ > 0 , provided that the underlying demand function is sufficiently smooth. This is the tightest bound of its kind for the problem setting that we consider in this paper, and it significantly improves the performance bound of existing heuristic controls in the literature. In addition, our intermediate results on the large deviation bounds for spline estimation and nonparametric stability analysis of constrained optimization are of independent interest and are potentially useful for other applications. The online appendix is available at https://doi.org/10.1287/moor.2018.0937 .

We study a standard dynamic pricing problem where the seller (a monopolist) possesses a finite amount of inventories and attempts to sell the products during a finite selling season. Despite the potential benefits of dynamic pricing, many sellers still adopt a static pricing policy because of (1) the complexity of frequent reoptimizations, (2) the negative perception of excessive price adjustments, and (3) the lack of flexibility caused by existing business constraints. In this paper, we develop a family of pricing heuristics that can be used to address all these challenges. Our heuristic is computationally easy to implement; it requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time. Moreover, to guarantee a strong revenue performance, the heuristic only needs to adjust the prices of a small number of products and do so infrequently. This property helps the seller focus his effort on the prices of the most important products instead of all products. In addition, in the case where not all products are equally admissible to price adjustment (due to existing business constraints such as contractual agreement, strategic product positioning, etc.), our heuristic can immediately substitute the price adjustment of the original products with the price adjustment of similar products and maintain an equivalent revenue performance. This property provides the seller with extra flexibility in managing his prices. This paper was accepted by Noah Gans, stochastic models and simulation .

This paper studies the effect of introducing a new vertical differentiation strategy, paying for an upgrade to a premium product after purchasing the base product, on the price dispersion of the base product arising from existing price discrimination strategies. In particular, we examine how a major U.S. airline’s price dispersion in the coach cabin changes after introducing the option to upgrade to a new type of premium economy seating within the coach cabin. We first provide a theoretical analysis that highlights two competing pressures that the new premium economy seating upgrades created on coach class prices. On the one hand, the airline benefits from lowering its prices because by allowing more customers to purchase in the first place, it increases the probability of selling upgrades (admission effect). On the other hand, for some customers, the value of flying with the airline increases because of the upgrade availability, therefore the airline may find it optimal to increase its prices (valuation effect). In the second part of the paper, we conduct an empirical investigation of the impact of upgrade introduction on coach class prices, based on a proprietary transaction-level data set from a major U.S. airline company. The empirical analysis tests the main predictions of our theoretical model and examines further nuances. The results show that the introduction of the premium economy seating upgrades is associated with an increase in the price dispersion and revenues in the coach class, the admission effect is stronger than the valuation effect on the low end of the price distribution, and the opposite is true on the high end of the price distribution. Finally, we discuss implications of our results for firm revenues and consumer welfare. This paper was accepted by Serguei Netessine, operations management.