Drug-drug interactions (DDI) are common medical errors due to the negative interaction of medicines taken by a patient. up to 96% instances, doctors ignore the even the mandatory DDI alerts, leading to adverse reactions and poor health outcomes. To solve this issue, we collaborated with a large health-tech firm in India, which conducted a field experiment to identify the impact of real-time information nudges on DDIs. We find that nudges not only reduce DDIs by 8.7% but also promotes learning.

Due to social distancing requirements during the Covid-19 pandemic, the capacity of elevators in high-rise buildings has been reduced by 50-70%. This reduction in capacity has led to queue build-ups, making it difficult to maintain social distancing in the lobby and increasing the risk of disease transmission.

The objective of this work is to minimize elevator wait times in a multi-clinic facility by optimizing the clinic scheduling.

Research suggests that the regulatory inspection outcomes are influenced by factors unrelated to non-conformance. One of the reasons is shift in the U.S. political geography every two years which exposes firms to differential policy risks. We use political alignment to empirically uncover the impact of time-varying policy risks on quality performance (recalls) of food operations via regulatory inspection outcomes. We identify firm-level strategies to mitigate it and make policy recommendations.

In a contest, participants exert effort to win the a prize. The cost of their effort is irreversible. Most of the research on contests has treated a contest as an isolated event. However, in many real-life situations, we choose among several contests that are going on in parallel. We combine theoretical and empirical question to address the following questions: How does an individual choose among contests and how does the existence of multiple alternative contests affect her behavior?

New apparel brands have emerged that offer sustainable fashion at affordable prices. Designing an effective CSR disclosure strategy for affordable apparel presents new challenges, as the literature has shown that consumers often equate low prices with low quality. We use an experiment in an online purchase context to examine the mechanisms behind consumers' valuations of such CSR disclosures and how price-related factors (retail price, historical price paid) moderate these indirect effects.

We study a new business practice of “crowdsourcing buying” wherein a retailer first seeks input from customers on the desirability of a product and then bases their purchasing decision on their votes. We collaborate with an apparel rental platform and analyze a dataset comprised of the platform’s products and users. We find that the initiation of the service improves both conversion rate and the number of turns. We also investigate several potential mechanisms that drive this improvement.

We show that WCs can improve their resilience to digital competition by making strategic retail location decisions. We find that ecommerce intensity in a geographic market negatively impacts Costco foot traffic in the same market. However, the location matters. Specifically, locating WCs in a “low agglomeration” area, distant from competing WCs and grocery stores, not only contributes to increased foot traffic but also has the potential to offset the negative impact from ecommerce competition.

This study explores multi-agent data-driven decision-making in a supply chain with vertical competition. We find that the sample average approximation and kernel optimization approaches can tacitly collude, reduce double marginalization, improve profits for all participants while protecting consumer welfare. Besides, non-coordinating contracts can even reduce more double marginalization than coordinating ones in the data-driven setting, especially when the feature distribution exhibits skewness.

We propose a novel bimodal inventory optimization (BIO) model and algorithm to position inventory across a retail chain to meet time-varying omnichannel demand. While prior Robust optimization (RO) models emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of potential positive outcomes. Experiments project a profitability gain of 15% for BIO over RO on real-life data from a major retail chain.

The automotive sales has focused on selling an experience or a lifestyle via features. Features are optional add-ons that increase product attractiveness (from stereo audio to cruise control) and basis for purchasing decision. While previous studies conducted car-level analysis on sales, the effect of features has remained unexplored. We examine features' collective impact on sales time along new and used-car segments, propose a two-stage estimation framework, and suggest policy recommendations.

Freight-sharing platforms, where shippers can offer excess transportation capacity at a discounted cost, enable the effective sharing of shipping capacity. This occasional offer of excess transportation capacity introduces new opportunities in inventory management. We study inventory decisions for a company that interacts with a freight-sharing platform. We derive optimal ordering policy characteristics and propose replenishment heuristics.

In this paper, we formulate an optimal information gathering strategy (IGS) to identify which uncertainties in the supply chain network drive our decisions. Existing approaches consider uncertainties, but do not consider the benefit of resolving them. Based on stylized, numerical, and case study results, we show a significant value of optimal IGS. As a non-monotone non-submodular minimization problem, we solve the problem with an algorithm which achieves a constant approximation guarantee.

Leading thinkers in supply chain management have proposed the long-term effects of supply chain disruptions on market share as customers shift their purchases to competitors. Motivated by this insight, we empirically analyze the lasting effects of supply chain disruptions on firms' market shares. Focusing on pharmaceutical supply chain disruptions, we find that products do not fully recover from the market share loss even after they recover from supply chain disruptions.

This paper investigates the impact of advance commitment on supply chain network design under demand uncertainty. The study develops a robust cooperative model and solves it using a column-and-constraint generation algorithm, finding that this approach significantly reduces conservatism and improves performance compared to a non-cooperative model. The research highlights the potential benefits of contractual commitment for companies seeking to enhance their supply chain operations.

This study examines the relationship between the digital divide and online retail sales, encompassing internet infrastructure and socioeconomic inequalities. We demonstrate how retailers can better bridge the divide by leveraging omnichannel and online fulfillment options. Our research emphasizes the significance of incorporating the digital divide into ecommerce fulfillment strategies. We make a contribution to the literature on diversity, equity, and inclusion in operations management.

We propose a data-driven platform that provides traceability to the first mile of agricultural supply chains by coordinating the transactions of farmers and intermediaries. We model unique aspects of the supply chain, including pre-existing informal relationships between farmers and intermediaries, and we develop algorithms to solve real-world instances. We test the results on data from the palm oil supply chain and show the platform’s potential to reduce costs and increase farmers’ welfare.

We examine whether and how renewable energy (RE) adoption can increase or decrease energy efficiency (EE) improvement. Using site-level data, we estimate the impact of changes in RE usage and in the acquisition approach on the EE. We find that using RE to meet 10% more of a site's energy demand led to an additional 2.0% improvement in EE. While purchasing RE credits or entering into power purchase agreements led to gains in EE, installing on-site RE generators had no effect.

Clean energy technologies require large amounts of critical metals and minerals, the demand for which is skyrocketing as the global energy sector rapidly shifts towards renewable energy. The scarcity of critical materials may prevent governments from reaching their ambitious clean energy targets and jeopardize the profitability of the renewable energy sector. Inspired by this challenge, we investigate mechanisms that can address the impact of scarcity on the renewable energy transition momentum.

The ability to produce electricity when renewables allow and store it for later demand is crucial for emissions reduction. Electric Vehicles (EVs) could provide distributed energy storage to the electric grid through optimal charging and discharging, however, highly complex, non-linear driver behavior must be accounted for. In this work we collaborate with an American EV manufacturer to combine machine learning with optimization to model driver behavior to size the capacity of EV energy storage.

Quantifying the operating cost of a battery has been considered a key challenge for economic profitability in the battery literature/industry. One key modeling difficulty is that the cost is realized through different types of performance degradation. This work takes an analytical approach to compare the optimal dynamic policy under different degradation models (capacity-degradation-only, efficiency-degradation-only, and correlated degradation).

We investigate the effect of upstream supply risk correlation of substitutable items. Suppliers offer menu of price-quantity or wholesale price contracts. Using common agency methodology we characterize the equilibrium contracts. We find that the buyer's profits are increasing in yield correlation under wholesale price contracts but are increasing under price quantity contracts only when product substitutability is low and in fact decrease for high product substitutability.

We consider the problem faced by spare parts inventory that demand intensity for components is unclear at the beginning of a product life cycle. We present a robust optimization (RO) approach in spare parts inventory to against demand uncertainties and design two more time-efficient algorithms capable of finding solutions in case of a large number of items in the model. Our experiments show that the RO model exhibits remarkable efficacy in case of limited information on the demand distribution.

We examine the impact of business disruptions in explaining the gender gap in small firm performance in developing countries. We find that business disruptions significantly increase gender gap in firm performance – on average, business disruptions decrease sales and sales growth of a small women-led firm by 11.6% and 15.2 percentage points, respectively, compared to their male counterpart. Importantly, building resilience helps small women-led firms to close this performance gap.

Strategic inventory refers to the inventory held by firms purely out of strategic considerations other than operational reasons (e.g., economies of scale). In this paper, we investigate the roles of strategic inventory in a system with two parallel supply chains under both full bargaining and partial bargaining, which differ in whether inventory is included in the bargaining terms.

This study proposes a markdown strategy to optimize joint replenishment and pricing decisions in a dynamically changing fresh/non-fresh inventory assortment with stochastic lifetimes, lead times, and demands. We model the problem as a generally modified (r, Q) policy and reformulate that as a MIP model to solve the model exactly. We propose an EOQ approximation and provide some bounds on the optimality gap with respect to market demand, maximum WTP, and lifetime that vanishes asymptotically.

In global retail marketplaces, returns may result in long shipping distances and loss of welfare through time-induced price deterioration. We propose to minimize these negative impacts by exchanging returns within the marketplace’s supplier network. We show that an auction-based system where suppliers bid on the returned items reduces returns’ shipping distance by up to 88% and increases resell value on average by 5% mitigating the drawbacks of benevolent return policies of retail marketplaces.

Motivated by our interaction with a labor market, we focus of matching jobs to workers with heterogenous preferences. In each period, jobs arrive sequentially. Each worker has three parameters: her work quality, capacity, and minimum number of jobs she desires. The platform wishes to maximize the matches quality via its online matching policy. We use our model to understand limitations of simple policies and propose an optimal index-based policy. We supplement our theory with extensive numerics.

Motivated by the rapid growth of the cloud computing industry, we study an infinite-horizon, stochastic optimization problem from the viewpoint of a firm that employs cloud resources to process incoming orders (or jobs) over time. Orders and resources are heterogeneous. The firm's goal is to minimize the long-run average expected cost per period, considering reserved-capacity costs, on-demand capacity costs, and order-delay costs. We show that our proposed policy is asymptotically optimal.

This paper uses a newsvendor setting to analyze the effect of payment methods and parking prices on payment amounts, occupancy, and wait times. Empirical results show that cash or mobile app payments are less than credit card payments, and lower prices increase payments for all methods. Simulation shows that lowering prices has a greater effect on occupancy and wait times than increasing them. The study also indicates surroundings of parking spaces significantly affect performance measures.

With the emergence of on-demand platforms, it has become viable for drivers to participate in package delivery and passenger rides operations at the same time. The integration and coordination of the two services, known as co-modality, is considered a promising solution for improving the efficiency of urban logistics and mobility systems. In this work, we propose a simple zoning-based coordination policy and analyze its performance against pure delivery and ride-hailing policies.

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This paper studies how service-age information influences customers’ strategic joining decisions in an observable M/G/1 queue. We ask how such information is reflected in customers' strategies at equilibrium, and what would be the corresponding system throughput and social welfare. The managerial insights indicate that a revenue-maximizing provider should disclose the service-age information when congestion is high, while a social planner should disclose the information when congestion is low.

We study a problem where a fixed number of servers must be split between a shared and a dedicated track. High-priority customers can be served on the dedicated track or the shared track with non-preemptive priority over low-priority customers. Low-priority customers are strategic and choose to either join the shared track or balk from the system based on wait time information. We use a queueing game to study the capacity allocation decision under different information revealing policies.

Strategic servers may discreetly disclose or hide queue length information to manage customers' joining behavior. For customer-intensive services, the choice of service rate further complicates this queue observability decision by affecting the service quality and consequently customers’ service rewards. In this work, we provide an integral analysis of managing speed-quality trade-off with the joint use of pricing and queue disclosure strategies.

Online labor markets are growing rapidly, where the numerical rating to workers provides a critical metric of service quality. In this study, we propose a two-stage machine learning framework to predict such quality. We apply our method on a unique from a leading online labor platform. We provide an understanding of how various features impact service quality. Then, we discuss the value of prediction in decision-making and provide actionable insights to improve service quality in OLMs.

Service providers often offer multiple variants of a service to their customers simultaneously through different servers. These servers could differ in their service value and expected service times, making one server preferable to customers over the other. However, even when the preferred server is busy and the less preferred server is free, customers may choose to wait for the preferred server. This behavior is called "stalling". Our goal is to price the servers in order to maximize revenue.

Ride-hailing platforms are highly popular so their decisions can affect social satisfaction levels significantly. The price determined by the platform is so critical decision factor in managing their demand and pool of available drivers. However, consumers are affected by pricing in different ways due to their heterogeneity of price or waiting time sensitivity.

We analyze a waiting time differentiation pricing strategy using a game-theoretic model in both monopoly and duopoly environments.

Prior discussions of predictive model development highlight advances in methods, but the value of tools that implement those methods has been understudied. In a field experiment, we study the importance of tools by restricting access to machine learning libraries in a prediction competition. We find that teams with unrestricted access perform 30% better, and teams with high general data-science skills are less affected by the intervention than teams with high tool-specific skills.

We investigate the remanufacturing strategy for the original equipment manufacturer (OEM) and independent remanufacturer (IR) in an innovative industry where the consumer valuation of the products increases with the innovation level and we consider the investments of an OEM to enhance innovation, in the face of a potential entry onto the market by an IR, together with two remanufacturing strategies: whether to remanufacture and whether to include innovative features in remanufactured products.

Optimization in sustainability is inherently multi-criteria and the underlying value function is usually unknown and difficult to elicit. Firms seeking to be more sustainable face difficult choices during material and supplier selection with no clear guidelines on how to make trade-offs between conflicting environmental impact categories. We propose an interactive optimization method to help decision makers, using an illustrative example of sustainable sourcing in the apparel industry.

This paper considers an industrial symbiosis system composed of a supplier and a manufacturer. The supplier produces product A with the output of by-product, which can be reused by the manufacturer as input to produce product B. Competition and cooperation are often juxtaposed in the same system. This paper examines the mode choice of the supplier, i.e., continue to cooperate to supply by-products to the manufacturer or generate direct competition by encroaching on the manufacturer's market.

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We propose a multi-stage choice model in which customers can choose multiple products and multiple units of each product in a single shopping trip. We characterize the optimal assortment under the cardinality, space, and basket size constraints, respectively. We prove the NP-hardness of the problem under the latter two constraints and develop approximation algorithms to find near optimal assortments. We solve the price optimization problem efficiently and provide a calibration method.

This paper studies assortment optimization and product line design problems under the mixed multinomial logit model and discrete pricing. Both literature streams are connected by improving exact, extending approximate, and novel heuristic methods. We show that an FPTAS algorithm exists even if prices are taken into account. To improve the state-of-the-art conic formulation, valid inequalities are introduced to a branch and cut method. Our results show an average time reduction of 35 % - 66 %.

Many product recalls are caused by quality-related product failures. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms’ chosen quality investments, and either firm can experience a recall.

Customers are concerned about personalized pricing (PP) tactics. Using a Bayesian persuasion framework, we study whether and under what conditions price can signal such PP implementation to customers. We also investigate whether disclosing inventory availability information can alleviate customer concerns and benefit the firm and customers. We show that price alone may not signal PP, and firms can create transparency over the pricing strategies by disclosing inventory availability information.