Knowledge that Transforms

This article addresses a periodic repair problem for free-floating shared bikes that incorporates uncertain failure rates and covariate information. We conceptualize a physical landscape resembling black holes in cosmology to represent locations with exceptionally high failure rates. To mitigate this “black hole” effect, we introduce two special strategies: dedicated repair periods and preventive maintenance. The effectiveness of these two strategies is first theoretically validated within a two-region system. We develop the operational data analytics (ODA) framework to generate enhanced data-integrated solutions for the periodic repair problem, improving decision quality under limited data. Within this framework, baseline solutions from existing models, including a scenario-wise distributionally robust optimization (DRO) model with an exact linear decision rule, are evaluated and refined to guide the ODA solution. A real-world case study validates the effectiveness of our approach and offers valuable managerial insights. The ODA framework guides the selection of ambiguity sets in DRO models and enhances solution quality, even when the oracle data-integrated solution underperforms. Notably, the two strategies reduce regional disparities in penalty costs, helping to mitigate the black hole effect, as evidenced by the Gini coefficient in a generalized multi-region system.

Transportation has been one of the obstacles preventing people from obtaining timely and appropriate care. Emergency departments (EDs) are uniquely positioned in the healthcare system, providing around-the-clock care for illnesses and injuries that span a wide spectrum of clinical severity. While transportation is an important factor patients consider when making decisions regarding ED visits, it is unclear a priori whether ridesharing platforms—enabled by new disruptive technology and serving as an alternative transportation option to personal vehicles, public transit, taxis, and ambulances—can alleviate transportation barriers and significantly influence emergency care utilization beyond merely substituting existing transportation methods. This article empirically examines how the entry of ridesharing platforms influences patients’ emergency care utilization patterns, with a particular focus on how the effects vary across visits of different severity levels. We leverage the sequential entries of ridesharing services in different counties in California as a natural experiment setting and use a staggered difference-in-differences model to estimate this impact. We find that ridesharing's entry, on average, significantly increases the number of high-severity ED visits while simultaneously reducing the number of low-severity ED visits. Analyses using additional data provide suggestive evidence that the contrasting effects may be related to improved primary care access. We also observe heterogeneous effects of ridesharing services across hospitals with different ED patient compositions and location characteristics. Additionally, we find a significant increase in ED wait times after ridesharing's entry. Our findings have important managerial and policy implications and contribute to the growing stream of research on ridesharing's societal impact, healthcare access disparities, and ED overcrowding.

The number of recruitment postings on digital recruitment hiring platforms has increased since the COVID-19 pandemic. However, the weak surveillance and operations of these platforms, combined with the fact that most job seekers have relatively low vigilance and strong desire for recruitment offers, enable scammers to easily deceive job seekers for their money and confidential information. In this work, we combine prevailing text mining techniques (i.e., ChatGPT with prompting engineering and supervised machine learning) with interpersonal deception theory (IDT) from social science to design an interpretable IT system to predict fraudulent recruitment posting on digital recruitment-hiring platforms. We compare our designed framework with the state-of-the-art general-purpose algorithms to demonstrate the efficacy of our system using two testbeds. To further confer intuitive and human understandable operational guidance of IDT-driven design for the fraudulent recruitment phenomenon, we perform instance-agnostic and instance-specific explanation analysis based on the aggregated marginal contributions of IDT-driven contextualized features. A between-subjects user experiment empirically shows that IDT-driven explanations enhance users’ trust, understanding, and perceived usefulness. Using an illustrative example, we further quantify the economic value of IDT-driven design via a cost-revenue analysis. We conclude the academic contributions and practical implications of our work to job seekers, recruiters, and third-party recruitment-hiring platforms.

A major concern that customers face when considering buying an electric vehicle is the uncertainty about its ability to cover their mobility needs. While an electric vehicle’s rated range is publicly known, the range it realistically achieves for a given customer is not, as it depends on idiosyncratic driving factors that customers can fully understand only through hands-on experience. Dealer services like extended test drives can alleviate customers’ concerns and have the potential to increase electric vehicle adoption. However, not all dealers adopt such demonstration practices and their environmental implications are not straightforward. In this paper, we develop a game-theoretic model to investigate when a dealer should offer demonstration services and whether doing so can increase electric vehicle adoption and reduce the environmental impact. We consider a car dealer who procures conventional and/or electric vehicles from a manufacturer and sells them to customers with heterogeneous mobility needs. Customers are a priori uncertain about the electric vehicle’s ability to meet their mobility needs; the dealer can provide demonstration services to mitigate the range uncertainty. We find that the dealer should offer demonstration as the electric vehicle range increases, but may refrain from offering demonstration when the production cost of electric vehicle decreases. In addition, offering demonstration leads to greater electric vehicle adoption only when the electric vehicle technology is less developed. Interestingly, even when offering demonstration leads to greater electric vehicle adoption, it may also lead to greater total usage emissions. We further examine the manufacturer’s Corporate Average Fuel Economy (CAFE) compliance and show that dealer demonstration can compromise the manufacturer’s ability to comply with the CAFE regulation while promoting electric vehicle adoption. Perhaps unexpectedly, to comply with a more stringent CAFE standard, the manufacturer may raise the wholesale price of the electric vehicle and, in the presence of demonstration, the electric vehicle adoption may be lower.

Agrochemical companies operate multi-echelon, long-lead-time supply chains to serve seasonal and uncertain demand for crop protection products from farmers around the globe. To match supply and demand in this challenging setting, alignment of the sales and supply chain functions is crucial. We investigate this alignment task, building on a case study. At the case company, cross-functional coordination is achieved through annual sales and operations planning (S&OP) budget meetings. In the budgeting process, sales and supply chain organizations agree on a set of supply volume guarantees to align commercial and production plans. The guarantees must respect a maximum allowable inventory investment imposed by the business unit head. Our work focuses on the choice of these volume guarantees. Specifically, we support the S&OP negotiation process by developing an optimization-based budget planning model. Importantly, the model reflects differences in the decision-making scope and power of the relevant actors. Moreover, it captures the available flexibility of future plan adjustments by means of an affinely adjustable robust optimization approach. We evaluate the performance of our model against relevant benchmarks in two numerical studies, based on synthetic and real-world data. We also study the structure of the obtained solution. We find that optimizing the volume guarantees can substantially reduce lost margins, relative to uniform guarantees. For our real-world data set, our approach saves up to 32% relative to a current practice benchmark.

Firms routinely outsource their business requirements to external agents for many reasons; for example, to focus on their core competencies or to save costs. However, overbilling by agents has been well-acknowledged as a notorious problem across major industries, including healthcare, information technology, legal services, and engineering. Mitigating overbilling is challenging in practice; typical options for firms include auditing agents or suspending relationships with them. While firms would like to combat overbilling by utilizing these options, they also want to minimize their total cost, including agents’ payments and auditing costs. We consider a repeated principal–agent setting in which, in each period, the principal makes two decisions: (i) Whether to allocate the task to the agent or an outside option. (ii) If the agent executes the task, whether to audit the agent’s cost. We propose a class of Dynamic Auditing mechanisms under which it is near-optimal for the agent to report his costs truthfully and the principal’s cost is also near-optimal, for sufficiently large discount factors. We study the role of auditing in our dynamic mechanism design framework. We show that auditing is an effective tool for the principal if either (i) the unit cost of auditing is sufficiently small, or (ii) the principal lacks knowledge of the agent’s cost distribution. We also show that our results are robust when there is competition among multiple agents for the principal’s business, and when the agent can deliberately inflate his costs by performing additional (redundant) work.

This article examines supply-base configuration, supplier selection, and order allocation under supply risk and downstream rivalry. We develop a Cournot duopoly model in which two firms sell partially substitutable products and procure a critical input from two unreliable suppliers with asymmetric cost-risk profiles and correlated yield risks. We formulate a two-stage game: firms first choose their supply bases either endogenously or under a one-sided sourcing-flexibility structure and then allocate orders across selected suppliers. We derive closed-form, supplier-specific sourcing thresholds that quantify the marginal revenue gain from adding either supplier to an otherwise sole-sourced supply base. These thresholds vary systematically with supplier heterogeneity, yield correlation, the rival’s sourcing strategy, and competition intensity. In the endogenous sourcing game, supply-base choices are structurally independent of strategic rivalry—which affects only equilibrium order allocations—and the unique equilibrium takes the form of either mutual dual-sourcing or mutual sole-sourcing from the same supplier. Yet mutual dual-sourcing can be Pareto-dominated by distinct-supplier sole-sourcing. This Prisoner’s Dilemma arises only when yield correlation exceeds an explicit cutoff that declines with competition intensity. We also identify conditions under which the classical cost-based supplier-selection rule fails: above a critical correlation threshold, firms may optimally sole-source from the supplier with the higher effective cost because it is more reliable, and that equilibrium is Pareto-optimal. Under one-sided sourcing flexibility, when the rival is committed to sole-sourcing, both the sourcing threshold for the locked-in supplier and the correlation cutoff for supplier-selection reversal decline, strengthening the flexible firm’s incentive to sole-source from the other supplier. Finally, we show how the extent of firms’ supply-base overlap shapes strategic interaction and equilibrium order allocation under correlated yield risks.

There are more than 7,000 known rare diseases, yet around <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>95</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> of them lack effective treatments. This paper explores how Operations Management (OM) research can help improve patient access to rare disease treatments. We first examine key challenges and opportunities from the perspectives of governments, industry, insurers, and patients. We then outline future research directions, focusing on regulatory frameworks, subsidy and incentive schemes, pricing and coverage decisions, (bio)pharmaceutical manufacturing, and challenges in developing economies. The application of OM methodologies to the rare disease landscape is still limited, but offers significant potential to advance new drug development, increase patient access to correct diagnosis and treatment, and reduce healthcare inequalities for patients with rare diseases.

Should a retailer disclose its variable costs to consumers? While disclosing variable costs (e.g., material cost, production cost, and shipping cost) can build consumer trust through transparency, it may also lead to resentment if the actual gross profit margin, deduced from the disclosed costs, is perceived as excessively high. By incorporating key findings from behavioral experiments in the literature, we present an exploratory model to examine if and when a firm should disclose its variable costs in both monopoly and duopoly markets. Assuming that consumers use the concept of rational expectation equilibrium to infer the firm’s actual variable costs when they are not disclosed, our equilibrium analysis yields the following results that provide insights when a retailer should adopt cost disclosure. Specifically, in a monopoly model, we find that consumers tend to overestimate the firm’s true variable costs when the firm does not disclose them. This occurs because consumers use the observed price as a “signal” about the undisclosed true cost, and believe that the firm would set its price closer to the true cost. Also, a monopoly should not disclose its true variable costs to prevent pressure to lower its gross profit margin, especially when consumers are highly concerned about the firm’s gross profit margin. This behavior persists in a duopoly model. In a duopoly model, we find two additional results: as competition intensifies, both firms are more likely to disclose their true variable costs in equilibrium to gain additional trust from consumers. However, when competition is moderate but the cost differential between firms is sufficiently high, only one firm would disclose its true costs in equilibrium.

This study is focused on periodic Fisher markets where items with time-dependent and stochastic values are regularly replenished, and buyers aim to maximize their utilities by spending budgets on these items. Traditional approaches of finding a market equilibrium in the single-period Fisher market rely on complete information about buyers’ utility functions and budgets. However, it is impractical to consistently enforce buyers to disclose this private information in a periodic setting. We introduce a distributed bidding algorithm, online proportional response , wherein buyers update bids solely based on the randomly fluctuating values of items in each period. The market then allocates items based on the bids provided by the buyers. We show connections between the online proportional response and the online mirror descent algorithm. Utilizing the known Shmyrev convex program, a variant of the Eisenberg–Gale convex program that establishes market equilibrium of a Fisher market, two performance metrics are proposed: the fairness regret is the cumulative difference in the objective value of a stochastic Shmyrev convex program between an online algorithm and an offline optimum, and the individual buyer’s regret gauges the deviation in terms of utility for each buyer between the online algorithm and the offline optimum. Our algorithm attains a problem-dependent upper bound in fairness regret under stationary inputs. This bound is contingent on the number of items and buyers. Additionally, we conduct analysis of regret under various nonstationary stochastic input models to demonstrate the algorithm’s efficiency across diverse scenarios. The online proportional response algorithm addresses privacy concerns by allowing buyers to update bids without revealing sensitive information and ensures decentralized decision-making, fostering autonomy and potential improvements in buyer satisfaction. Furthermore, our algorithm is universally applicable to many worlds and shows the robustness of performance guarantees.