Knowledge that Transforms

Problem definition: An emerging financial innovation enabled by technological advancements in agricultural supply chains is the ability to “tip the farmers.” This mechanism empowers socially conscious consumers to identify and support the individual farmers of their products by sending them direct payments, or tips. We shed light on the implications of tipping on different market participants. Methodology/results: We construct a game-theoretic model that captures the interactions between a mass of infinitesimal farmers, a population of socially conscious consumers, and an agricultural firm that plays the intermediary role between farmers and consumers. We characterize the equilibrium of the game with and without tipping and identify conditions under which each group of key stakeholders (farmers, consumers, and the agricultural firm) may be better or worse off with tipping. If implemented under the right conditions, tipping can create a triple win for all supply chain members, including every individual farmer. This happens when farmers’ outside option takes moderate values and the consumers’ social consciousness is sufficiently high. In contrast, when these conditions are violated, farmers and/or consumers could be worse off after the implementation of tipping. Furthermore, even in situations where farmers benefit from tipping in expectation, tipping may exacerbate inequity in the farmer population, which is undesirable from a social responsibility standpoint. Adverse outcomes such as these occur in parameter regions wherein the firm exploits the consumers’ tips to lower its sourcing costs by reducing its wholesale price commitment to farmers. Managerial implications: Caution must be exercised in implementing a tipping program, as it may lead to a reduction in farmers’ expected and/or realized incomes as well as consumer welfare. Even when the firm commits to not adjusting the wholesale price a priori, tipping can be detrimental to the firm while helping consumers and farmers.

Problem definition: Ever-increasing coupling of energy and mobility sectors is underway in our cities. However, whether and how to use such coupling to optimize the portfolio and operations of urban energy assets have rarely been studied. We fill this gap by studying “omni-sharing,” which is a novel business model (beyond “peer-to-peer” energy sharing) that allows a community of energy consumers with storage devices to also crowdsource electricity from electric vehicles (EVs). Methodology/results: We analytically model two salient features of omni-sharing operations: the optimal payment to crowdsourced EV drivers and the cost allocation among energy consumers. In doing so, we enrich the newsvendor cooperative game theory by generalizing the newsvendor model with the underage cost being endogenous to the storage investment decision. We prove (and quantify with data on residential energy consumption and ride-sharing markets) that omni-sharing can reduce the total storage capacity needed and the total energy cost for the community. Managerial implications: Our analysis reveals that omni-sharing can bring storage and cost savings for all consumers (whereas peer-to-peer sharing cannot) by efficiently matching local energy supply and demand. Moreover, omni-sharing remains operationally stable and financially robust against the variations in ride-sharing driver incomes. These findings strengthen our understanding of urban energy-mobility orchestration.

Problem definition: Zoonotic viruses that jump from animals to humans, like avian influenza or severe acute respiratory syndrome, have caused major pandemics in recent decades. Many of these pandemics originated in China, a major supplier and consumer in the global food supply chain, and in other low- and middle-income countries. Interestingly, these outbreaks have been linked to live animal markets, even when the surrounding farms supplying markets had very low infection prevalence. This suggests that these markets potentially amplify and propagate the spread of zoonotic viruses. Yet, traditional epidemiological models cannot explain the observed outbreaks in these markets. Methodology/results: This paper introduces an epidemiological model that integrates supply chain and in-market operational dynamics to explain the role of markets in zoonotic virus spread, focusing on avian influenza. The transmission, interaction, and persistence model incorporates stochastic supply chain and in-market dynamics into traditional epidemiological frameworks. The analytical results demonstrate how major outbreaks could occur in markets, even when the infection prevalence among animals sourced from the surrounding farms is very low, and animals remain in the market for less than a day. This is the result of two important dynamics. The first is concerned with the supply chain structure in which markets serve as a consolidation point of agricultural inputs from many small farms. The other is driven by in-market operations that affect environmental in-market infection evolution, particularly persistent infection through two-sided interactions between animals and wastewater, surfaces, and feed that exist in the environment within the market. Managerial implications: This paper highlights the important role of supply chain structure and market operational dynamics in influencing environmental infections and facilitating the spread of zoonotic viruses. Additionally, the TIP model enables the evaluation of the effectiveness of critical in-market and supply chain interventions to prevent infection outbreaks. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This paper was part of the 2024 MSOM Supply Chain Management SIG Conference.

Problem definition: Service quality is assessed with objective and subjective measures. Objective measures include queue length/wait times, service times, and service failures, whereas subjective measures include service evaluations and social media commentary. Subjective measures are often easier to collect, requiring no sophisticated tracking technology, and can capture multiple dimensions of the service experience in a single response. However, subjective data are prone to bias. We focus on bridging the gap between objective and subjective measures of service quality by reducing bias in service evaluations. The structure of service evaluations is consistent across organizations: star ratings are collected before comments. We propose a simple intervention—reversing this order—to mitigate bias in star ratings because the process of writing provides time and space for the evaluator to reflect on their experience. Methodology/results: We conducted an experiment where participants received a sequence-based service, with the same overall level of service, from servers who varied in their demographic characteristics. Following the service, participants evaluated the performance of the servers with the order of star ratings and comments randomized. We find no evidence of demographic bias toward the servers but find that the sequence of good and bad experiences in the service leads to biased star ratings. Most importantly, we find that collecting comments prior to star ratings mitigates the sequential bias in star ratings because of participants reflecting on the service experience. Managerial implications: We show that star ratings can be biased if collected in the traditional manner and that this bias can be reduced if comments are collected first. Implementing this change to service evaluations could help to ensure that the job performance of human servers is more accurately and fairly assessed. For artificial intelligence service systems (i.e., AI servers), this change can provide a simple way to debias training data. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.

Problem definition: Emergency medical services (EMS) are vital for ensuring timely and effective healthcare delivery. Ambulance dispatching in EMS directly influences patient outcomes. These systems face the challenge of managing limited resources to respond promptly to emergency calls while maintaining the capacity to address potential future incidents. Methodology/results: We model the problem as a continuous-time stochastic system and determine the dispatch decision for each sequentially arriving call to minimize the system-wide average cost. To address this problem, we develop an easy-to-implement and near-optimal policy based on Lagrangian relaxation of the original problem. Using a novel proof technique, we show that our policy achieves performance within [Formula: see text] of the optimal, where [Formula: see text] represents the scaling factor for the number of ambulances and arrival rates. Additionally, in the low-traffic regime, where arrival and service rates are scaled such that traffic intensity approaches zero, our policy remains asymptotically optimal. Managerial implications: Numerical experiments show that the proposed policy performs well compared with optimal solutions. The results also demonstrate the benefits of proactively deploying flexible units, while requiring only infrequent use of this resource. This also supports a key insight: a small amount of flexibility, when used strategically, can enhance system efficiency. Our case study, based on real data from New York City, further shows that the proposed policy effectively reduces system costs associated with patient response times. Empirically, it consistently outperforms widely used benchmark policies in both small and large system settings. Funding: C. Hua was partly supported by the National Natural Science Foundation of China [Grants 72301172, 72394370:72394375, and 72495130:72495132] and Shanghai Jiao Tong University Office of Liberal Arts [Grant ZHWK2502]. T. Wang was partly supported by the National Natural Science Foundation of China [Grants 72221001, 72192833/72192830, and 72131010] and the Research Grants Council of Hong Kong [Grant GRF 11502225]. J. Zhang was partly supported by the National Natural Science Foundation of China [Grant 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Z. Zhou was partly supported by the National Natural Science Foundation of China [Grants 72588101 and 72571231].

Problem definition: Vehicle routing problems (VRPs) with deadlines have received significant attention around the world. Motivated by a real-world food delivery problem, we assume that the travel time depends on the routing decisions, and we study a data-driven stochastic VRP with deadlines and endogenous uncertainty. Methodology/results: We use the nonparametric approaches, including k-nearest neighbor (kNN) and kernel density estimation (KDE), to estimate the decision-dependent probability distribution of travel time. To solve the resulting problem efficiently, we employ a logic-based Benders decomposition (LBBD) algorithm with several algorithmic enhancements. In particular, we propose a novel family of optimality cuts that includes the expected delay for all the subroutes. Moreover, we solve a total travel cost minimization problem to warm start the algorithm. We also use a local search procedure to improve the current routing decision and propose a machine learning–based lower bound heuristic to efficiently solve problems of realistic size. A practical case study for a food delivery routing problem using real-world data is conducted to show the efficiency of the proposed techniques and the advantage of the data-driven stochastic VRP in reducing the expected delay. Managerial implications: In our case study, we show that incorporating routing decisions into a nonparametric model outperforms a state-of-the-art data-driven parametric model by 23% on average in terms of the expected delay and the order-assignment decisions obtained from a robust model with travel-time predictors by 26% on average. Moreover, compared with the drivers’ actual routes and arc-based VRP models that ignore the endogenous uncertainty, our suggested routes can significantly improve the on-time performance of delivery services. We also quantify the value of the proposed routes with different service deadlines.

Problem definition: A grocery retailer incurs expiration waste (EW) at its store when a perishable product crosses its expiration date without being sold. One frequent scenario accounting for EW occurs when units of a given product with multiple expiration dates are simultaneously available on store shelves. In such situations, a consumer is likely to purchase a later-to-expire unit, which in turn increases the likelihood of EW of a sooner-to-expire unit. To mitigate the occurrence of such multiple-dates-led expiration waste (MDEW), retailers undertake a variety of interventions, including a price markdown of sooner-to-expire units and in-store inventory rotation. Most retailers, however, are often unaware of the extent of MDEW in their stores and thus are constrained in mitigating its occurrence. Methodology/results: We provide the first large-scale evidence of the MDEW share of EW. We collaborate with a grocery retailer to compile a multicategory-multistore data set ([Formula: see text]15.3 million sales transactions) on grocery products with 3–14 days of shelf life. Across these products, at the product-store-week level, EW as a percentage of sales is 23% on average. To quantify MDEW’s share, we propose a novel and easy to implement methodology for computing its lower and upper bounds. In our retailer’s context, the MDEW’s lower and upper bounds equal 25% and 52% on average of the generated EW, respectively. Managerial implications: Our study highlights MDEW’s material share in generating EW; thus, it provides a solid premise for future in-depth academic investigation on MDEW management. Furthermore, for practitioners, it provides an immediately actionable methodology to measure MDEW in their store operations. Empowered with such a measurement ability, retailers can better plan their EW waste management interventions.

Problem definition: Bikeshare systems are expanding worldwide, but empirical research is hindered by the lack of high-frequency data on system status. Public bikeshare data sets typically include only trip start and end records, with no information on real-time station conditions (e.g., how many bikes or docks are available). This gap in data limits the analysis of operational performance and user service levels in bikeshare systems. Methodology/results: We address this gap by collecting nearly minute-by-minute status data from Washington, DC’s Capital Bikeshare system over a three-year period (April 2019 to September 2022). For each station and minute in this data set, we record the number of available bikes and open docks along with station location details. We also include data on dock-less bikes introduced during the collection period. The raw data were cleaned to correct errors and can be connected to official trip records data. This high-resolution data set enables direct measurement of system performance. For example, analysis of August 2022 shows that bikes were completely unavailable (bike stockout) for approximately 1.94% of station-minutes, whereas all docks were occupied (dock stockout) for about 0.78% of station-minutes. We also propose a method for using the data to identify potential rebalance events. Managerial implications: The availability of this granular status data opens new avenues for empirical research and decision making in operations management. Researchers and practitioners can leverage the data to improve demand forecasting models, design more effective rebalancing strategies for bike distribution, and rigorously measure system performance (e.g., availability and stockouts) over time. By providing previously unavailable insights into full system status, this data set supports more informed operational planning and real-time management of bikeshare services.

Problem definition: Omnichannel retail systems consist of online sales channels and physical stores. We consider a decentralized omnichannel retail supply chain (ORSC), where the online channel is the manufacturer’s sales platform and the physical store is an independent retailer selling the manufacturer’s products. The assortment showcased by the retailer in its store affects sales and potential product returns for both sales channels because customers can over- or undervalue the products that are available only online. This interaction causes inefficiencies for both parties if they rely on this decentralized business relationship. Methodology/results: Using a Stackelberg game, we characterize the optimal decisions on wholesale prices and assortment in the decentralized setting. Then, we characterize the optimal assortment in a centralized setting and show that it can be substantially different from that of the decentralized setting, which is evidence for inefficiency. To eliminate this inefficiency, we propose a scope contract designed by the manufacturer that offers wholesale price discounts on selected products that appear in the optimal assortment of the centralized setting. Managerial implications: The proposed contract is instrumental in coordinating the ORSC so that both parties are more profitable than in the decentralized setting. In some cases, coordination requires a generous contract that offers some products for free along with a lump-sum payment. The profit allocation between the parties can be adjusted through the discount rate specified in an equivalent single-parameter version of the contract. However, channel coordination may lead to reduced customer welfare. In such cases, we propose a welfare-constrained framework that preserves welfare while improving profitability.

Problem definition: Innovations in digital and mobile health (mHealth) services are transforming chronic care delivery by improving health outcomes and efficiency. However, despite the proven benefits of mHealth, usage remains hindered by a critical challenge: patient engagement after adoption. Without timely and frequent patient data, mHealth apps become ineffective, preventing providers from making optimal interventions. This lack of patient engagement leads to health complications that are expensive, especially for payers. Timely provider interventions are essential to mitigate these costs. Our study explores how payers can leverage targeted incentives to enhance patient usage of mHealth technology. Methodology/results: Using a game-theoretic model, we analyze how payers can design contracts to increase patient usage of mHealth. Our study examines key factors such as disease progression, rewards, premium, copay, and the effectiveness and efficiency of mHealth apps. From our analysis, two optimal strategies emerge: (i) a reward-based strategy in which direct incentives are provided to patients to encourage mHealth usage and (ii) a reminder-based strategy in which contracts are designed to incentivize providers to remind patients to use mHealth apps. The effectiveness of these strategies depends on the ability to facilitate rapid and impactful mobile interventions, which we define as mHealth technology productivity (mHTP). Our findings indicate that, when mHTP is low, payers benefit more from a reminder-based strategy, supported by higher capitation payments to providers. When mHTP is high, a reward-based strategy is more effective with patient rewards increasing as mHTP improves. Managerial implications: Our study highlights how payers can foster a mutually beneficial relationship with patients by involving providers, leading to improved health outcomes and increased payer profitability. By designing optimal incentive structures, payers can drive sustained mHealth engagement, ultimately improving healthcare efficiency and reducing costs.