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Maintenance service plans (MSPs) are contracts for the provision of maintenance by a service provider to an equipment operator. These plans can have different payment structures and risk allocations, which induce various types of incentives for agents in the service chain. How do such structures affect service performance and service chain value? We provide an empirical answer to this question by using unique panel data covering the sales and service records of more than 700 diagnostic body scanners. We exploit the presence of a standard warranty period and employ a matching approach to isolate the incentive effects of MSPs from the confounding effects of endogenous contract selection. We find that moving the equipment operator from a basic, pay-per-service plan to a fixed-fee, full-protection plan not only reduces reliability but also increases equipment service costs. Furthermore, that increase is driven by both the operator and the service provider. Our results indicate that incentive effects arising from MSPs leads to losses in service chain value, and we provide the first evidence that a basic pay-per-service plan-under which risk of equipment failure is borne by the operator--can improve performance and reduce costs. History: Accepted by Gad Allon, operations management. Keywords: maintenance repair * contracting * fine balance matching * service value chain * healthcare, 1. Introduction Operators of capital-intensive equipment often spend a large annual budget on maintenance so as to ensure high equipment reliability. In the medical imaging equipment industry, on which our [...]

Firms often finance their inventory through debt and subsequently sell it to generate profits and service the debt. Pricing of products is consequently driven by inventory and debt servicing considerations. We show that limited liability under debt induces sellers to charge higher prices and to discount products at a slower pace. We find that these distortions result in revenue losses that compound over time, leading to some form of performance spiral down. We quantify the extent to which these inefficiencies can be mitigated by practical debt contract terms that emerge as natural remedies from our analysis, and find debt amortization or financial covenants to be the most effective, followed by debt relief and early repayment options. History: Accepted by Serguei Netessine, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2017.2862. Keywords: dynamic pricing * debt * pricing distortions * channel efficiency * firm value * spiral down, 1. Introduction Recent years have witnessed a surge in the adoption of dynamic pricing practices across a wide range of industries, fueled in no small part by the increased availability [...]

A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates [...]

Ad exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a prespecified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in ad exchanges and the implications for publishers' decisions. The presence of budgets introduces dynamic interactions among advertisers that need to be taken into account when attempting to characterize the bidding landscape or the impact of changes in the auction design. To this end, we introduce the notion of a fluid mean-field equilibrium (FMFE) that is behaviorally appealing and computationally tractable, and in some important cases, it yields a closed-form characterization. We establish that an FMFE approximates well the rational behavior of advertisers in these markets. We then show how this framework may be used to provide sharp prescriptions for key auction design decisions that publishers face in these markets. In particular, we show that ignoring budgets, a common practice in this literature, can result in significant profit losses for the publisher when setting the reserve price. Keywords : auction design; revenue management; ad exchange; display advertising; Internet; budget constraints; dynamic games; mean field; fluid approximation History: Received September 19, 2012; accepted June 6, 2014, by Dimitris Bertsimas, optimization. Published online in Articles in Advance January 27, 2015., 1. Introduction The market for display ads on the Internet, consisting of graphical content such as banners and videos on Web pages, has grown significantly in the last decade, generating [...]

We consider a multiperiod single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in doing so, the seller needs to resolve the tension between learning the unknown demand curve and maximizing earned revenues. The main question that we investigate is the following: How large of a revenue loss is incurred if the seller uses a simple parametric model that differs significantly (i.e., is misspecified) relative to the underlying demand curve? We measure performance by analyzing the price trajectory induced by this misspecified model and quantifying the magnitude of revenue losses (as a function of the time horizon) relative to an oracle that knows the true underlying demand curve. The 'price of misspecification' is expected to be significant if the parametric model is overly restrictive. Somewhat surprisingly, we show (under reasonably general conditions) that this need not be the case. Keywords: model misspecification; inference; price optimization; revenue management; myopic pricing History: Received January 6, 2012; accepted June 28, 2014, by Gerard Cachon, stochastic models and simulation. Published online in Articles in Advance January 19, 2015., 1. Introduction The famous industrial statistician George E. P. Box is widely credited for the saying All models are wrong, but some are useful. A direct read of the first [...]

We study a firm's optimal pricing policy under commitment. The firm's objective is to maximize its long-term average revenue given a steady arrival of strategic customers. In particular, customers arrive over time, are strategic in timing their purchases, and are heterogeneous along two dimensions: their valuation for the firm's product and their willingness to wait before purchasing or leaving. The customers' patience and valuation may be correlated in an arbitrary fashion. For this general formulation, we prove that the firm may restrict attention to cyclic pricing policies, which have length, at most, twice the maximum willingness to wait of the customer population. To efficiently compute optimal policies, we develop a dynamic programming approach that uses a novel state space that is general, capable of handling arbitrary problem primitives, and that generalizes to finite horizon problems with nonstationary parameters. We analyze the class of monotone pricing policies and establish their suboptimality in general. Optimal policies are, in a typical scenario, characterized by nested sales, where the firm offers partial discounts throughout each cycle, offers a significant discount halfway through the cycle, and holds its largest discount at the end of the cycle. We further establish a form of equivalence between the problem of pricing for a stream of heterogeneous strategic customers and pricing for a pool of heterogeneous customers who may stockpile units of the product. Keywords: pricing; optimization; intertemporal pricing; price commitment; price discrimination; dynamic pricing; strategic customers; stockpiling History: Received August 7, 2012, accepted August 4, 2014, by Yossi Aviv, operations management., 1. Introduction Dynamic pricing is widely used in practice by firms in a variety of industries, ranging from airlines and hotels to supermarkets and clothing outlets (Talluri and van Ryzin [...]

Many factors introduce the prospect of changes in the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, [...]

We consider a repeated newsvendor problem in which the decision maker (DM) does not have access to the underlying demand distribution. The goal of this paper is to characterize the implications of demand censoring on performance. To that end, we compare the benchmark setting in which the DM has access to demand observations to a setting in which the DM may only rely on sales data. We measure performance in terms of regret: the difference between the cumulative costs of a policy and the optimal cumulative costs with knowledge of the demand distribution. Through upper and lower bounds, we characterize the optimal magnitude of the worst-case regret for the two settings, enabling one to isolate the implications of demand censoring. In particular, the results imply that the exploration-exploitation trade-off introduced by demand censoring is fundamentally different in the continuous and discrete demand cases, and that active exploration plays a much stronger role in the latter case. We further establish that in the discrete demand case, the need for active exploration almost disappears as soon as a lost sales indicator (that records whether demand was censored or not) becomes available, in addition to the censored demand samples. Key words: demand censoring; inventory management; newsvendor; estimation; nonparametric History: Received January 29, 2010; accepted April 23, 2012, by Gerard P. Cachon, stochastic models and simulation. Published online in Articles in Advance January 28, 2013., 1. Introduction An important factor driving firms to hold inventories is uncertainty in demand. Consequently, a key input in any stochastic inventory model is a description of the demand process, [...]

We study a seller that starts with an initial inventory of goods, has a target horizon over which to sell the goods, and is subject to a set of financial milestone constraints on the revenues and sales that need to be achieved at different time points along the sales horizon. We characterize the revenue maximizing dynamic pricing policy for the seller and highlight the effect of revenue and sales milestones on its structure. The optimal policy can be written in feedback form, where the price at each point in time is selected so as to track the most stringent among all future milestones. Building on that observation, we propose a discrete-review policy that aims to dynamically track the appropriate milestone constraint and show that this simple and practical policy is near optimal in settings with large initial capacity and long sales horizons even in settings with no advance demand model information. One motivating application comes from the sales of new multiunit, residential real estate developments, where intermediate milestone constraints play an important role in their financing and construction. Key words: revenue management; dynamic pricing; market uncertainty; estimation; feedback; real estate; asymptotic analysis History: Received November 20, 2009; accepted December 16, 2011, by Gerard P. Cachon, stochastic models and simulation. Published online in Articles in Advance April 27, 2012., 1. Introduction A popular practice in many industries is to make pricing decisions so as to 'track' upcoming milestones or targets, essentially lowering prices when falling behind targets and raising [...]

The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically [...]

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 [...]