Knowledge that Transforms

It is a common practice that business and economy compartments share the seating capacity of a short or medium-haul European flight, with the middle seats in the business compartment kept vacant to enhance comfort and privacy for business passengers. The two compartments are separated by a movable curtain whose position determines the capacity allocated to each compartment. By postponing the decision on curtain position until departure, airlines gain extra flexibility in their capacity allocation. We formulate an airline’s problem of dynamically determining the curtain position and the subset of fare products to offer in each compartment to maximize the combined revenue from business and economy passengers. We show that the airline should offer a business (resp., economy) fare product if and only if its fare exceeds a business (resp., economy) threshold or bid price. We then establish monotonicity properties which are useful in expediting the computation of the optimal policy. An extended model where the airline has the option to offer a free upgrade (from economy to business class) to a new arriving economy passenger is presented, and a necessary condition on when to offer upgrades is provided. Using a large-scale and real-life case study based on a proprietary dataset, we demonstrate significant revenue improvements from postponing the decision on curtain position, compared to the current practice of fixing it at the beginning of the booking horizon. Furthermore, we provide guidance for the implementation of our policy, taking into account passengers’ seat selection and substitution towards lower fare classes. We also discuss the implications of our work in practice.

We study a multi-server queueing system where a customer is satisfied (and generates a unit revenue) if their queueing time is at most a given constant. If the queueing time of the admitted customer exceeds this constant, the customer gets served, but is unsatisfied and generates no revenue. Such queueing systems arise in the context of modeling service systems where excessive delays are of concern. A key challenge is how to design an admission control policy to maximize the number of satisfied customers per unit time in the long run, assuming that we can observe the number of customers in the system at any time. We call this the binary reward structure system and show that a threshold-type admission policy is optimal. The optimal threshold policy has to be computed numerically. Hence we propose a square-root admission policy to approximate the optimal admission control policy, and compare the performance of these two policies. We derive an analytical upper bound on the performance of optimal admission control policy by deriving an optimal admission policy assuming we have full information over the queueing time of the admitted customers. This is equivalent to a queueing system where customers abandon the queue (i.e., leave without service) if their queueing time exceeds the given constant. We demonstrate that the optimal policy that includes customer abandonment, or alternatively, the optimal policy under full information, the optimal threshold policy, and the square-root admission policy, all exhibit identical performance in the asymptotic regions of the parameter space. Our numerical results indicate that the worst optimality gap of the square-root admission policy is within 3.9% of the optimal revenue, and implementing the square-root admission policy in the observable queueing system leads to a revenue loss that is at most 5.6% of the maximum possible revenue rate in the full information system. We also compare the binary reward structure with the more common linear reward structure where the system incurs holding cost per unit queueing time per customer. In addition, we also show that the analysis based on queueing time is applicable to the system time as well.

Considering the real-world situations where a customer’s purchase choices in previous stages can influence the prices she encounters in subsequent stages, this research examines the multiproduct price optimization problem under a multistage choice model. Particularly, the seller commits to a multistage pricing policy and determines product prices based on the customer’s purchase history, and the customer makes purchase decisions such that the total expected utility is maximized. We show that the pricing problem has a unique optimal solution under some mild conditions and the optimal solution satisfies a modified equal adjusted markup property. Based on the property, the problem can be solved efficiently by reducing it to a single-dimensional search problem. Moreover, the optimal pricing policy has an important property, namely, the product with a higher adjusted markup in earlier stages should always lead to lower prices in subsequent stages. We also show that compared to customers who are myopic, the seller should offer higher first-stage prices and lower second-stage prices to forward-looking customers, which will lead to a higher profit. Numerical analyses are also conducted to demonstrate the above results.

Hospital-physician integration has been increasingly considered a potential solution for the underlying challenges hospitals face as they are adapting to value-based healthcare services. We adopt an activity-based measure of integration (ABI) to investigate the relationship between integration and care outcomes, namely in-hospital mortality risk, length of stay (LOS), and 30-day readmission risk. ABI is measured based on a group of physicians who handle a specific procedure, coronary artery bypass graft (CABG), that is, CABG physicians, and is operationalized as the proportion of cases handled by the CABG physicians who single-site (or concentrate all their activity) at a focal hospital. To test hypotheses that posit a U-shaped relationship between ABI and patient care outcomes, we utilize patient-visit level information for Florida patients who underwent cardiac surgery performed by CABG physicians during 2011–2014. We find that ABI has a U-shaped relationship with both mortality risk and LOS, such that patient mortality risk and LOS are minimized at ABI tipping points of about 55%. In contrast, 30-day readmission risk continues to decrease as ABI increases. We also find that hospital teaching status and bed utilization moderate the relationship between ABI and LOS, such that the U-shaped relationship is flatter, basically linear in teaching and/or high-utilization hospitals. Our results suggest that a medium level of integration could be desirable, since a strategy of high integration trades off potentially higher patient volumes and revenues for suboptimal care outcomes. Overall, this study offers new insights for theory and practice, as the non-linear association between integration and care outcomes has not been investigated previously.

In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behaviour, we investigate (1) the role of commissions as a potential tool to influence ESRs’ pricing decisions under limited authority, (2) the impact of optimized commissions on firm profitability, and (3) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behaviour is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives.

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go “out of favor.” Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as “out-of-favor” or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics, 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising “star” product to turn into a liability (“dog”). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.

To encourage small business inclusion in public procurement, the U.S. federal government has established the set-aside program that mandates government agencies to award a portion of their contracts to small businesses. We study whether and to what extent the performance of R&D contracts awarded through this program differs from those awarded through open competition. Analyzing a large dataset of federal R&D contracts, we find that despite restricting competition to small businesses, set-aside R&D contracts experience lower schedule and cost overrun than R&D contracts awarded through open competition. Furthermore, although set-aside R&D contracts experience lower schedule and cost overrun when they are awarded to more experienced contractor firms, this benefit arises primarily from a contractor firm's experience in executing R&D contracts across different agencies compared to the firm's experience with the same agency. Finally, set-aside R&D contracts awarded early in a fiscal year experience lower schedule and cost overrun than those awarded later. Post-hoc analysis examining the underlying dimensions of different-agency experience highlights the asymmetric effects of related-agency experience and unrelated-agency experience of contractor firms on the performance of set-aside R&D contracts awarded by the Department of Defense. While related-agency experience improves contract performance, unrelated-agency experience has a detrimental effect on contract performance. These findings demonstrate that small business inclusion policies may not necessarily compromise contract performance. Importantly, they emphasize the need for federal agencies and contracting officers to consider the underlying dimensions of contractor firm experience and contract award timing to improve contract performance and taxpayer money utilization.

Self-interested customers’ form of reasoning and its consequences for system performance affect the planning decisions of service providers. We study procedurally rational customers—customers who make decisions based on a sample containing anecdotes of the system times experienced by other customers. Specifically, we consider procedurally rational customers in two-station service networks with open routing, that is, customers can choose the order in which to visit the stations. Because some actions may be less represented in the population, a given customer may not succeed in obtaining anecdotes about all possible actions. We introduce a novel sampling framework that extends the procedurally rational framework to incorporate the possibility that a customer may not receive any anecdotes for one of the actions; in this case, the customer uses a prior point estimate in lieu of the missing anecdotes. Under this framework, we study the procedurally rational equilibrium in open routing. We show first that as the sample size grows large, customers’ estimates become more accurate, and the procedurally rational equilibrium converges to the fully rational equilibrium (which is also socially optimal). We then uncover two main findings. First, we obtain bounds on the distance between the procedurally rational and fully rational equilibrium, aiding operational planning and showing the rate of convergence to the fully rational outcome as the sample size of anecdotes of each individual customer grows. Second, if customers obtain anecdotes of both actions with high probability, then the equilibrium will approximate the fully rational outcome, despite the sampling error inherent to procedural rationality.

The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequential personalized interventions that maximize the adoption probability for a new product. We model consumer behavior by a conversion funnel that captures the state of each consumer (e.g., interaction history with the firm) and allows the consumer behavior to vary as a function of both her state and firm’s sequential interventions. We show our model captures consumer behavior with very high accuracy (out-of-sample area under the curve of over 0.95) in a real-world email marketing dataset. However, it results in a very large-scale learning problem, where the firm must learn the state-specific effects of various interventions from consumer interactions. We propose a novel attribution-based decision-making algorithm for this problem that we call model-free approximate Bayesian learning. Our algorithm inherits the interpretability and scalability of Thompson sampling for bandits and maintains an approximate belief over the value of each state-specific intervention. The belief is updated as the algorithm interacts with the consumers. Despite being an approximation to the Bayes update, we prove the asymptotic optimality of our algorithm and analyze its convergence rate. We show that our algorithm significantly outperforms traditional approaches on extensive simulations calibrated to a real-world email marketing dataset.

Three-dimensional (3D) printing technology has opened up possibilities for product design collaborations between device providers and customers. To enable an environment of cocreation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors such as the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers’ impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for the fixed-fee pricing model.