Gérard P. Cachon's research while affiliated with University of Pennsylvania and other places

Online service platforms enable customers to connect with a large population of independent servers and operate successfully in many sectors, including transportation, lodging, and delivery, among others. We study how prices are chosen and fees are collected on the platform. The platform could assert full control over pricing despite being unaware of the servers' costs (e.g., ride sharing). Or the platform could allow unfettered price competition among the servers (e.g., lodging). This choice influences both the amount of supply available and the overall attractiveness of the platform to consumers. When the platform collects revenue via a commission or a per-unit fee, neither price delegation strategy dominates the other. However, the platform's best payment structure is simple and easy to implement - it is merely the combination of a commission and a per-unit fee (which can be negative, as in a subsidy). Furthermore, this combination enables the delegation of price control to the servers, which may assist in the classification of the servers as contractors rather than employees. A similar approach can be used to maximize profits by fully disintermediated platforms (i.e., no central owner), such as those enabled by blockchain technology.

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.

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 adjust based on prevailing demand conditions. For example, Uber uses “surge pricing” 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. Our base model places no restrictions on the platform’s dynamic pricing and waging schemes, whereas our surge pricing analogue requires wages to be a fixed fraction of dynamic prices and our traditional taxi analogue requires prices to be fixed. We show that although surge pricing is not optimal, it generally achieves nearly the optimal profit, justifying its use in practice. Despite its merits for the platform, surge pricing has been criticized due to concerns for the welfare of consumers. In our model, as labor becomes more expensive, consumers are better off with surge pricing relative to fixed pricing because they benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that both the platform and consumers can benefit from the use of surge pricing on a platform with self-scheduling capacity.

Perishable capacity is often sold before it is used (e.g., tickets sold weeks before a sporting event) which requires a price path decision (increasing or decreasing over time) and creates the opportunity to include in the pricing mechanism a recourse strategy, i.e., allowing the firm or buyer to change ownership after an initial transaction. For example, a buyer could be allowed to resell the purchased unit to another buyer (e.g., a ticket exchange), or the firm could offer to refund the buyer if the buyer prefers to relinquish the unit, or the firm could overbook, i.e., sell its capacity twice, possibly denying service to the first buyer. Previous work without recourse mechanisms demonstrates the effectiveness of an "advance selling strategy" (an increasing price path) assuming that consumers are uncertain of their preferences early in the horizon. We find that a "price skimming" strategy (a decreasing price path) can dominate advance selling when the firm is unable to precisely determine when consumers learn their preferences. No matter the price path selected, we demonstrate that recourse strategies are generally able to substantially increase the firm's profit and can at the same time increase buyer welfare. They are effective because they contribute to the two mechanisms that generate value in this system: (i) increase the probability of a transaction and (ii) increase the likelihood the buyer with the highest value gets the capacity. Reselling with sellers posting willingness-to-sell prices is best among recourse strategies even though consumers are able to sell for more than they paid. In fact, reselling either achieves or nearly achieves the firm's maximum revenue and it can also benefit consumers. We conclude that the firm should encourage reselling, especially when the firm implements an advance selling strategy.

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.