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Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines nonparametric regression, game theory, and pandemic modeling to develop a data-driven competitive online nonparametric regression method. Specifically, the method takes the future COVID-19 case estimates, which can be simulated via the susceptible-infectious-removed (SIR) epidemic model as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev. In generating the calibration level, we focus on an online learning setting where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee, we derive our algorithm by minimizing regret, which is the difference between the squared ℓ2 -norm associated with labels generated by the algorithm and labels generated by an adversary and the squared ℓ2 -norm associated with labels generated by the best isotonic (nondecreasing) function in hindsight and the adversarial labels. We develop a computationally efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels (possibly non-i.i.d. and even adversarial). We demonstrate the performances of our algorithm on both synthetic and AB InBev's data sets of three different markets (each corresponds to a country) from March 2020 to March 2021. The AB InBev's numerical experiments show that our method is capable of reducing the forecast error in terms of weighted mean absolute percentage error (WMAPE) and mean squared error (MSE) by more than 37% for the company. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was partially supported by the Massachusetts Institute of Technology Data Science Lab and AB-InBev Corporation. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4969. [ABSTRACT FROM AUTHOR]

Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers' renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings' web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers' booking sequence data to consistently estimate the impact of photo layout on customers' renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing's unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100. This paper was accepted by Swaminathan, Jayashankar, operations management. Funding: This research was partially sponsored by the MIT Data Science Lab and also benefited from generous support provided by Zalando. Supplemental Material: The online companion and data are available at https://doi.org/10.1287/mnsc.2022.4616. [ABSTRACT FROM AUTHOR]

The presence of strategic customers may force an already financially distressed firm into a death spiral: sensing the firm's financial difficulty, customers may wait strategically for deep discounts in liquidation sales. In turn, such waiting lowers the firm's profitability and increases the firm's bankruptcy risk. Using a two-period model to capture these dynamics, this paper identifies customers' strategic waiting behavior as a source of a firm's cost of financial distress. We also find that customers' anticipation of bankruptcy can be self-fulfilling: when customers anticipate a high bankruptcy probability, they prefer to delay their purchases, making the firmmore likely to go bankrupt than when customers anticipate a low probability of bankruptcy. Such behavior has important operational and financial implications. First, the firm acts more conservatively when facing either more severe financial distress or a large share of strategic customers. As its financial situation deteriorates, the firmlowers inventory alone when financial distress is mild or only a small share of customers are strategic and lowers both inventory and price in the presence of severe financial distress and a large fraction of strategic customers. Under optimal price and inventory decisions, strategic waiting accounts for a large part of the firm's total cost of financial distress, although a larger proportion of strategic customers may result in a lower probability of bankruptcy. In addition to inventory reduction and (immediate) price discount, we find that a deferred discount, in the form of rebates and/or store credits for future purchases, can act as an effective mechanism to mitigate strategic waiting. As a contingent price reduction, deferred discounts align the interests of customers and the firm and are most effective when the fraction of strategic customers is high and the firm's financial distress is at a medium level. [ABSTRACT FROM AUTHOR]