Knowledge that Transforms

Forced displacement crises have become a pressing humanitarian concern. Refugee movements expose individuals to dire living conditions with severe inaccessibility to essential resources. Humanitarian organizations play a vital role in alleviating these hardships through relief aid interventions. This study aims to optimize the fulfillment of recurring needs for geographically dispersed refugee groups en route to safe destinations. Here, capacitated mobile facilities are tasked with delivering relief aid to refugee groups periodically to ensure equitable service frequency. We formulate the problem as a Markov decision process with multinomial state-transition distributions, shaped by external migration pull factors such as safety conditions, road accessibility, and spatial proximity. The objective is to minimize the relocation and replenishment costs of mobile facilities, along with the deprivation costs faced by underserved refugees. We develop an approximate dynamic programming algorithm featuring a novel policy replication routine. To complement this offline method, we introduce a state-dependent variable threshold policy that enables high-quality, real-time relief provision. Using instances inspired by the Syrian refugee crisis, our results demonstrate the substantial value of stochastic modeling, yielding a 25% reduction in expected total costs compared to deterministic baselines and up to 12% savings through coordinated planning among humanitarian actors. The proposed methods remain effective under dispersed and cohesive refugee group dynamics and multi-destination migration scenarios. Furthermore, we uncover high-frequency traversal and service hotspots along migration paths to provide tactical insights for parameter calibration and resource prepositioning. Collectively, our findings offer practical insights for managing ongoing and future refugee migration crises.

Online consumer reviews (OCRs) publicly disclose consumer uncertainty about product valuations, enabling firms to acquire more accurate demand information. This paper develops a game-theoretic model in which a manufacturer sells products through both physical stores and a third-party online platform, which may operate in reselling or agency business modes. We investigate the interplay between OCR disclosure and business mode choices. We find that the manufacturer and the platform generally share aligned interests in OCR disclosure across business modes, with the information value of OCRs shaped by their accuracy and the uncertainty of consumers regarding product valuations. Specifically, OCR disclosure benefits both parties, yet it leads to higher gains for the manufacturer than the platform in the reselling mode, whereas their profit gains depend on the commission rate in the agency mode. When either consumer uncertainty about product valuations or the accuracy of OCRs is relatively low, both parties prefer the agency mode if the commission rate is moderate or the reselling mode if it is sufficiently high. When both consumer uncertainty about product valuations and the accuracy of OCRs are sufficiently high, both parties prefer the agency mode only if the commission rate is sufficiently high. Notably, the effects of OCR disclosure on the equilibrium business mode selection are non-monotonic: More accurate OCR disclosure facilitates the realization of an equilibrium business mode when the initial accuracy of OCRs is low and inhibits it when their accuracy is high. Last, we find that limited disclosure of OCRs and an endogenous commission rate can be employed as strategic tools to align business mode preferences. Our findings caution against the assumption that full OCR disclosure uniformly benefits platforms and manufacturers, and we offer practical insights for the design and operation of review systems.

Getting downstream firms in a supply chain to share private information about consumer demand with their suppliers is generally considered a prerequisite to dampening the bullwhip effect, that is, the amplification of demand shocks as they pass upstream in a supply chain. If downstream firms are reluctant to share their demand information, how can they best be incentivized to do so, and what does the optimal incentive scheme imply for the bullwhip effect and supply chain efficiency? We examine these questions by developing a supply chain model with asymmetric information, and show that the supplier should optimally follow a two-pronged strategy. One prong consists of the optimal incentive contract to induce information revelation. Information revelation requires a distortion in the downstream firm’s orders that makes them more responsive to demand shocks, which tends to strengthen the bullwhip effect. The other prong helps to reduce this distortion and dampen the bullwhip effect. It consists of getting the downstream firm to increase its initial order and stock up on inventory before it learns about demand. A larger initial order is sufficient to eliminate the bullwhip effect if demand shocks are not too persistent. Our results hold if the marginal production cost does not increase too quickly with output and the inventory holding cost is not too high.

We analyze product reusability, executed through refurbishment, amid supply disruptions. Consumers trade in used units, which can later be refurbished and sold. Using a three-period model, we determine the optimal reusability level, trade-in and refurbishment policies, trade-in fee, and the prices of new and refurbished units. Our analysis provides a useful framework to understand the interaction between a firm's choice of product reusability and the possibility of supply disruptions. First, we establish a threshold refurbishment policy: the firm refurbishes more as reusability increases but avoids refurbishment at low reusability. When both consumer valuation of used units and supply disruption probability are high, the firm builds a safety-stock of traded-in units, which it refrains from refurbishing when there is no supply disruption, unless the product reusability level is sufficiently high. Second, we find that it benefits to increase product reusability as the supply disruption risk increases until a certain threshold. Beyond this threshold, it is advantageous for the firm to reduce reusability and save on design costs to be profitable, contrary to popular belief. Our numerical examples reveal that the firm reduces reusability when production cost is high due to narrow margins. Finally, we demonstrate that the firm shares the benefits of higher product reusability with its consumers through higher trade-in fees and lower refurbished unit prices. This results in a “Pareto-efficient” win for the firm, its trade-in customers, and purchasers of refurbished units. Thus, our analysis offers insights for product designers on how supply disruption influences reusability choices in products.

How will peer pressure among sellers affect their operations in an online marketplace? Motivated by online platforms’ marketplace designs that prompt sellers to compare their performances, in this paper, we develop and study a price competition model in which sellers account for both profits and peer comparison outcomes. In our model, two sellers offer substitutable products, and each of them sets a price ex ante to maximize their expected total utility, which is the sum of one’s profit and the payoff from peer comparison. In particular, peer comparison takes place ex post based on sellers’ realized sales. It results in a penalty for one’s underperformance (i.e., sellers are behind-averse) or a reward for outperformance (i.e., sellers are ahead-seeking) relative to the other seller. Contrary to what extant research on social comparison would predict, we find that peer comparison is not always pro-competitive. Indeed, while the behind-aversion aspect of peer comparison fosters competition, the ahead-seeking aspect can be anti-competitive when the market uncertainty is sufficiently large. This is because market uncertainty causes a greater variation in sellers’ performance disparity ex post ( uncertainty effect ), which can have a more salient impact on sellers than the expectation of their performance gap ( comparison effect ); While sellers’ behind-aversion further aggravates the uncertainty effect and encourages them to take more aggressive actions, their ahead-seeking counterbalances the tension by absorbing part of it into the comparison effect and moderating the marginal disutility of lagging behind. Overall, we find that peer comparison can intensify sellers’ price competition, which lowers the expected profits and utilities for both sellers, benefits the consumers, and reduces the hosting platform’s profit. Our main insights are robust in a number of extensions, including general demand specifications, seller asymmetry, sellers’ misperceptions of market uncertainties, and consumers’ reference-dependent decision-making. They highlight the importance of sellers’ behavioral regularities in online platforms’ daily operations and shed light on marketplace designs regarding algorithmic transparency, information sharing, and so forth.

Customer service has evolved beyond in-person visits and phone calls to include live chat, AI chatbots and social media, among other contact options. Service providers typically refer to these contact modalities as “channels.” Within each channel, customer service agents are tasked with managing and resolving a stream of inbound service requests. Each request involves milestones where the agent must decide whether to keep assisting the customer or to transfer them to a more skilled—and often costlier—provider. To understand how this request resolution process should be managed, we develop a model in which each channel is represented as a gatekeeper system and characterize the structure of the optimal request resolution policy. We then turn to the broader question of the firm’s customer service design, which includes the strategic problem of which channels to deploy, the tactical questions of at what level to staff the live-agent channel and to what extent to train an AI chatbot, and the operational question of how to control the live-agent channel. Examining the interplay between strategic, tactical, and operational decisions through numerical methods, we show, among other insights, that service quality can be improved, rather than diminished, by chatbot implementation.

Counterfeits cause tremendous damages to brand companies by eroding their market shares and reducing customers’ buying motives. In response to such damages, many companies choose to develop and deploy anti-counterfeiting technologies, thus preventing counterfeiters from imitating their products. The companies can also rely on law enforcement exerted by local regulatory authorities to outlaw counterfeits. In this article, we study how brand companies may leverage the two approaches effectively. Specifically, we consider an authentic company that sells its products to a market that is administered by its regulatory authority. A counterfeiter exists in the market and may imitate the product offered by the authentic company. To combat counterfeiting, the authentic company may invest in anti-counterfeiting technology with uncertain outcomes, aiming to help customers effectively distinguish between genuine and fake products. We show that an increase in the authentic company’s anti-counterfeiting capability does not necessarily yield a higher profit. We also find that, in equilibrium, the anti-counterfeiting effort exerted by the authentic company does not necessarily decrease with the penalty levied by the authority, even though they serve the same purpose. Our analysis further highlights the importance of coupling law enforcement type with anti-counterfeiting capability in the cooperation with the regularity authority. Specifically, when having a high (low, respectively) anti-counterfeiting capability, the authentic company should promote a cooperation that focuses on preventive (corrective, respectively) law enforcement.

Many firms adopt behavioral-based pricing (BBP) to customize prices for both existing and new consumers whose valuations of products are influenced by the variety-seeking behavior, that is, the tendency to pursue diversity in brand or product choices. While this behavior is common in practice, it remains underexplored in the literature. To investigate BBP in markets with variety-seeking consumers, we develop a two-period model involving two competing firms. Our findings reveal that when firms adopt BBP, a greater degree of variety-seeking among consumers mitigates first-period price competition, resulting in higher profits for the firms. The adoption of BBP increases profits when consumers exhibit a high variety-seeking degree, enhances consumer welfare when this degree is low, and can lead to mutually beneficial outcomes for both the firms and consumers. If a firm unilaterally adopts BBP, its competitor benefits when the consumer's variety-seeking degree is high. In scenarios where the firms can strategically decide whether to adopt BBP, asymmetric equilibria may arise, with only one firm choosing to implement it. The firms may strategically adopt BBP without necessarily price-discriminating consumers, as the potential for price discrimination can reduce first-period competition. When BBP is exogenously feasible, the firms experience higher profits in relation to the consumer's variety-seeking degree compared with when it is not feasible, with gains from BBP feasibility increasing as the degree of variety-seeking rises. In addition, with behavioral-based personalized pricing, that is, the firms can charge tailored prices to existing consumers, profits also increase with the consumer's variety-seeking degree. However, with behavioral-based services, that is, firms can customize prices for both new and past consumers and provide additional services to previous customers, as the consumers variety-seeking degree increases, service levels decline while profits initially decrease before they rise again. These findings highlight the distinct characteristics of BBP and elucidate various real-world operational strategies.

The advancement of Internet of Things (IoT) technology has enabled IoT device manufacturers to collect consumer usage data (IoT data) whenever their devices are in use. Our paper examines the following setting: A manufacturer collects IoT data and decides whether to share it with a retailer, while consumers remain concerned about their privacy; the retailer, in turn, can leverage the shared data for cross-selling by investing in data-mining efforts that transform raw data into actionable insights. This aspect of data mining differentiates our study from traditional research on information sharing. Beyond selling through the existing retail channel, the manufacturer also has the option to establish a direct channel, thereby encroaching on the retailer’s market. Our analysis reveals several key insights. First, when the manufacturer both encroaches and shares IoT data, we observe a counterintuitive positive effect of the channel substitution rate: As the substitution rate increases, both the manufacturer and the retailer may see higher profits. Second, while the manufacturer always chooses to encroach when data sharing is absent, its motivation to do so weakens when it shares IoT data. Finally, we find that an increase in the value of IoT data can unexpectedly lead to a decline in the retailer’s profit.

In many service systems, an individual server’s workload can have a substantial impact on service time and quality. Such effects are particularly important in healthcare systems which often operate under resource and time constraints. In much of the literature, this effect of workload has been primarily considered at the system and instantaneous level rather than the individual and cumulative level. In this study, we investigate this relationship in the context of cardiac surgery, that is, how surgery duration and patient outcomes are affected by the individual surgeon’s daily workload. Using a detailed data set of more than 5,600 cardiac operations in a large hospital, we quantify how individual surgeon daily workload (the number of operations performed by the focal surgeon) affects surgery duration and patient outcomes. To handle the endogeneity of surgeon daily workload, we construct instrumental variables using operational factors of the cardiac surgery department, including the regular surgery schedule of surgeons. We find that high daily workload for the focal surgeon is associated with longer surgery duration as well as post-surgery length-of-stay in the intensive care unit and hospital. These results highlight the potential negative impact of high individual surgeon workload. We develop a surgical scheduling model that incorporates the estimated impact of surgeon daily workload. We solve the model by mixed-integer quadratic programing and show that our proposed schedule can substantially reduce total operating room (OR) time and post-surgery length-of-stay. Our results suggest that hospitals should take into account the effects of individual surgeon daily workload when managing their ORs. Specifically, they can substantially improve patient flow and patient outcomes by smoothing individual surgeon’s workload across days.