The literature establishes (and practice confirms) that sellers can benefit from allowing consumers to purchase in advance of the date of actual consumption (e.g., concert tickets, sporting events, etc.). Because of this advance purchasing, consumers can find themselves either with a ticket that they no longer want, or without a ticket that they wish to have. In the past, scalpers would facilitate transactions among these consumers, for a fee. Sellers historically disliked those practices and actively worked to prevent them. In fact, we obtain a stark finding: an unfettered and efficient reselling market eliminates all of the benefits of advance selling, which justifies sellers' historic hostility to reselling. But now ticket exchanges are common, growing, and even embraced by sellers. What changed? We present a theory that demonstrates reselling is actually beneficial for sellers under one crucial condition-the seller must have some control over the reselling process, thereby allowing the seller to earn something from each transaction through commission fees. The old-fashioned paper ticket did not give such control, but technology now enables electronic tickets, which do. In fact, a seller cannot earn more than what it receives from a properly designed reselling market (i.e., reselling is optimal for the seller), especially for popular events with limited capacity. When market demand is uncertain, the optimal commission should vary with the demand state, which may be challenging to implement in practice. Fortunately, seller revenue is strong even with a single commission rate, and captures nearly all of the achievable revenue if combined with a cap on collected commissions. However, while authorized reselling increase overall social welfare because it corrects the allocation errors inherent in advance selling, consumer surplus is always greater with an uncontrolled reselling market. In sum, our results explain why the seller's view towards reselling has shifted dramatically, we illuminate the welfare tradeoffs this shift entails, and provide simple and effective commission structures.

Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human-machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI's promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human-AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.

An open debate in platform design is who should control pricing: the platform (centralized pricing) or its service providers (decentralized pricing). We show that a key trade-off is between regulating competition and enabling price tailoring. Centralized pricing allows the platform to manage competition, but it faces information asymmetry as it cannot observe agent costs. Decentralized pricing lets agents adjust prices to their costs, but without oversight, competition can become too strong (prices too low) or too weak (prices too high). For commission-based platforms, either form of price control can prevail depending on market conditions, implying that neither dominates. However, a relatively simple tweak-adopting an affine fee structure based on posted prices or quantities served-allows the platform to decentralize pricing control without sacrificing optimality. This flexibility further supports agent classification as independent contractors, offering platforms a valuable strategic option for how to structure their workforce.

We study a delivery problem in which geographically dispersed demands are served from a central depot. The task is to choose a staffing level (number of servers) and an operating policy (when are servers dispatched and with which demands are they dispatched) to minimize the average response time to serve demand within a cost budget. The basic tradeoffs are plainly evident - dispatches to distant locations require more time, serving more demand on each dispatch amortizes the effort across more units, and it takes time to assemble and serve dispatches with numerous units. Nevertheless, the delivery problem harbors remarkable complexity due to the effects of congestion (queueing) as well as the high dimensional and complex nature of the state transitions (where demands are waiting and servers are located). We first develop a novel universal lower bound on performance which is anchored around the first order task of managing the workload in the system. We use insights from the lower bound to develop policy-type specific estimates of achievable performance. This enables us to isolate the power of two fundamental features of policies: how they batch in time (the dispatch quantities) and how/if they batch in space (the spatial scope of deliveries). Our analysis demonstrates that $i.)$ there are simple, static, operating policies that perform remarkably well, and $ii.)$ some systems that do not batch in space, such as robotic systems used in e-commerce fulfillment centers, are vulnerable to poor results when fast response times are required (e.g., two-hour rather than two-day).

The literature establishes (and practice confirms) that sellers can benefit from allowing consumers to purchase in advance of the date of actual consumption (e.g., concert tickets, sporting events, etc.). Because of this advance purchasing, consumers can find themselves either with a ticket that they no longer want, or without a ticket that they wish to have. In the past, scalpers would facilitate transactions among these consumers, for a fee. Sellers historically disliked those practices and actively worked to prevent them. In fact, we obtain a stark finding: an unfettered and efficient reselling market eliminates all of the benefits of advance selling, which justifies sellers' historic hostility to reselling. But now ticket exchanges are common, growing, and even embraced by the sellers. What changed? We present a theory that demonstrates reselling is actually beneficial for sellers under one crucial condition - the seller must have some control over the reselling process, thereby allowing the seller to earn something from each transaction through licensing fees to third-party sellers. The old-fashioned paper ticket did not give such control, but technology now enables electronic tickets, which do. In fact, a seller cannot earn more than what it receives from a properly designed reselling market (i.e., reselling is optimal for the seller), especially for popular events with limited capacity. Furthermore, speculators do not disrupt the market: i.e., the seller has no need for scalpers nor should fear them. In sum, our results explain why the seller's view towards reselling has shifted dramatically.

Florida, an important state in presidential elections in the United States, has received considerable media coverage in recent years for long lines to vote. Do some segments of the population receive a disproportionate share of the resources to serve the voting process, which could encourage some or dissuade others from voting? We conduct the first empirical panel data study to examine whether minority and Democrat voters in Florida experience lower poll worker staffing, which could lengthen the time to vote. We do not find evidence of a disparity directly due to race. Instead, we observe a political party effect—all else equal, a 1% increase in the percentage of voters registered as Democrat in a county increases the number of registered voters per poll worker by 3.5%. This effect appears to be meaningful—using a voting queue simulation, a 5% increase in voters registered as Democrat in a county could increase the average wait time to vote from 40 minutes (the approximate average wait time to vote in Florida in 2012 and the highest average wait time across all states in that election per the Cooperative Congressional Election Study) to about 115 minutes. This paper was accepted by Vishal Gaur, operations management.

Online service platforms that enable customers to connect with a large population of independent servers have been successfully developed in many sectors, including transportation, lodging, and delivery, among others. We ask a basic, yet fundamentally important, question - who should set the prices on the platform? The platform or the servers? In addition to regulatory implications for the classification of the workers on the platform as either employees or contractors, this choice influences the degree of competition among servers, and in turn determines both the amount of supply available and the overall attractiveness of the platform to consumers. We find that when the platform uses a simple commission contract to earn revenue, the price delegation decision depends on the importance of regulating competition among the large population of servers relative to the value of allowing servers to tailor their prices to their privately known costs. The same tradeoff exists in fully disintermediated platforms, such as those enabled with blockchain technology. However, merely adding appropriate linear quantity discounts or surcharges to the basic commission contract maximizes the platform's revenue and allows all participants to enjoy the benefits of both centralized and decentralized control of prices.

What is the relationship between inventory and sales? Clearly, inventory could increase sales: expanding inventory creates more choice (options, colors, etc.) and might signal a popular/desirable product. Or, inventory might encourage a consumer to continue her search (e.g., on the theory that she can return if nothing better is found), thereby decreasing sales (a scarcity effect). We seek to identify these effects in U.S. automobile sales. Our primary research challenge is the endogenous relationship between inventory and sales—e.g., dealers influence their inventory in anticipation of demand. Hence, our estimation strategy relies on weather shocks at upstream production facilities to create exogenous variation in downstream dealership inventory. We find that the impact of adding a vehicle of a particular model to a dealer’s lot depends on which cars the dealer already has. If the added vehicle expands the available set of submodels (e.g., adding a four-door among a set that is exclusively two-door), then sales increase. But if the added vehicle is of the same submodel as an existing vehicle, then sales actually decrease. Hence, expanding variety across submodels should be the first priority when adding inventory—adding inventory within a submodel is actually detrimental. In fact, given how vehicles were allocated to dealerships in practice, we find that adding inventory actually lowered sales. However, our data indicate that there could be a substantial benefit from the implementation of a “maximize variety, minimize duplication” allocation strategy: sales increase by 4.4% without changing the total number of vehicles at each dealership. This paper was accepted by Vishal Gaur, operations management.

Operations management has evolved since the founding of M&SOM: new departments have been created in our journals, new tracks have been established in our conferences, and new methodologies have been adopted in our research. Are these changes good for the field? To some, they seem detrimental, yielding a fragmented community that does not always speak the same language nor interact in any meaningful way. Others celebrate our expanded diversity and the new areas of research that it opens up. We argue that neither group is entirely wrong, nor entirely correct. Like the latter, we argue that we must contribute to a growing set of domains using all possible tools of inquiry. But sharing the concern of the former, we view fragmentation as a symptom of a problem. To get out of its rut, to have greater impact, the field needs to ask questions that are important and provide answers that are interesting. In particular, we should (i) avoid the trap of specificity (excellent answers to narrowly defined questions), (ii) expand our horizon beyond our (relatively) small field (connect and actively engage with diverse audiences), and (iii) be bold to pioneer new areas of inquiry. Operations management is at the heart of many of the big issues in society today, and we should be (and can be) central to the conversation.

Every firm has a business model, which is the collection of strategic decisions that determine how the firm generates a sustainable enterprise through the creation of enough value (its supply model) and the extraction of a sufficient portion of that value (its revenue model). Innovative business models—for example, fast fashion (e.g., Zara), e-tailing (e.g., Amazon), and ride-sharing (e.g., Uber)—are capable of offering new products and services that generate considerable consumer utility and transform industries. This paper develops a research framework for understanding business models and how business models have evolved over time. Links are made to the existing literature (primarily in pricing and operations), and simple models are developed to unify and clarify existing research findings. Through this framework, it is possible (i) to identify the few design decisions that explain the success of these diverse firms with otherwise seemingly disparate models, and (ii) to speculate on potential future business-model innovations. This paper was accepted by Teck Ho, operations management.

Online retailers often offer free shipping threshold policies: customers who purchase more than a threshold amount are not charged an additional fee for shipping. This paper provides a data-driven analytical model to (i) assess the profitability of a retailer’s current shipping threshold policy and (ii) identify the best freeshipping threshold policy for a retailer. The model is estimated from actual transaction and product return data. The model explicitly accounts for changes in customer shopping behavior due to a free shipping threshold, including strategically adding items to a shopping basket to receive free shipping, which we call orderpadding, and the subsequent adjustment in product return decisions. Roughly speaking, according to our model, a retailer that offers a free shipping threshold policy should set the threshold slightly abovethe average shopping basket amount. We calibrate our model to data from an online apparel retailer and determine that its decision to offer a lower free shipping threshold reduced its profitability considerably.This result is robust to a number of assumptions regarding the impact on long-run sales and possible price adjustments. We conclude that free shipping threshold policies are profitable only under a limited set of restrictive conditions.

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It has been shown that a monopolist can use advance selling to increase profits. This paper documents that this may not hold when a firm faces competition. With advance selling a firm offers its service in an advance period, before consumers know their valuations for the firms' services, or later on in a spot period, when consumers know their valuations. We identify two ways in which competition limits the effectiveness of advance selling. First, while a monopolist can sell to consumers with homogeneous preferences at a high price, this homogeneity intensifies price competition, which lowers profits. However, the firms may nevertheless find themselves in an equilibrium with advance selling. In this sense, advance selling is better described as a competitive necessity rather than as an advantageous tool to raise profits. Second, competition in the spot period is likely to lower spot period prices, thereby forcing firms to lower advance period prices, which is also not favorable to profits. Rational firms anticipate this and curtail or eliminate the use of advance selling. Thus, even though a monopolist fully exploits the practice of advance selling, rational firms facing competition either mitigate it or avoid it completely.The online appendix is available at https://doi.org/10.1287/mksc.2016.1006.

Combinatorial allocation involves assigning bundles of items to agents when the use of money is not allowed. Course allocation is one common application of combinatorial allocation, in which the bundles are schedules of courses and the assignees are students. Existing mechanisms used in practice have been shown to have serious flaws, which lead to allocations that are inefficient, unfair, or both. A recently developed mechanism is attractive in theory but has several features that limit its feasibility for practice. This paper reports on the design and implementation of a new course allocation mechanism, Course Match, that is suitable in practice. To find allocations, Course Match performs a massive parallel heuristic search that solves billions of mixed-integer programs to output an approximate competitive equilibrium in a fake-money economy for courses. Quantitative summary statistics for two semesters of full-scale use at a large business school (the Wharton School of Business, which has about 1,700 students and up to 350 courses in each semester) demonstrate that Course Match is both fair and efficient, a finding reinforced by student surveys showing large gains in satisfaction and perceived fairness.

Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity. The e-companion is available at https://doi.org/10.1287/msom.2017.0618 .