Knowledge that Transforms

Problem definition: We provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Methodology/results: Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. Managerial implications: We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0229 .

Problem definition: We investigate how the characteristics of consumers and a service firm influence the firm’s optimal pricing and promised delivery-time decisions as well as the optimal investment in the quality of delivery reliability information available to consumers. Methodology/results: We use utility, queuing, and choice modeling theories to model consumers’ behavior and to find solutions to the firm’s profit maximization problem. Managerial implications: The optimal strategy is to disclose either error-free delivery reliability information or no information at all. We also delineate conditions for each of the two strategies to dominate. Funding: This research was supported by the General Research Fund (GRF) of the Hong Kong Research Grants Council under Research Project LU13500822. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0223 .

Problem definition: The electric vehicle (EV) manufacturer NIO adopts a swappable-battery design and a battery-leasing business model known as battery as a service (BaaS). It recently introduced flexible battery leasing, which allows customers to temporarily up-/downgrade their primary leased batteries based on the needs for range. We investigate whether this business model innovation is viable, namely whether introducing flexible battery leasing in BaaS could benefit the manufacturer, the customers, and the environment compared with simple battery leasing. Methodology/results: Adopting a game-theoretical model, we find that introducing flexible battery leasing in BaaS can simultaneously improve the manufacturer profit as well as reduce the total customer cost and the total battery capacity. Such win-win-win outcomes generally occur for large high-capacity battery ranges and moderate high-capacity battery costs—both consistent with the ongoing trend in the EV industry and a model-calibration exercise. We further show that this key finding is robust for correlated regular and peak needs for range and when launching BaaS with flexible battery leasing and that if the manufacturer was to choose a high-capacity battery range for flexible battery leasing, it would choose one such that battery reallocation alone can meet all battery up-/downgrade demand without acquiring additional batteries. Managerial implications: Our findings confirm that flexible battery leasing can be a viable BaaS business model innovation and offer insights into when this may be the case. This insight strengthens the strategic support for EV manufacturers’ potential adoption of the swappable-battery design and the BaaS model, and it may inform their operating policies to implement flexible battery leasing. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0587 .

Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers’ purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers’ delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com , an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174 .

Problem definition: In a multistage model of a bilateral supply chain, we study two postponement strategies that the downstream retailer may adopt to mitigate the supply yield risk originating from the upstream production process. The retailer could either postpone the procurement payment until after the yield is realized and pay only for the delivered amount; postpone the pricing decision to better utilize the available supply; or do both. Although both strategies have been separately studied in literature, there is little research on their combined effect and system-wide implications in a decentralized setting. Methodology/results: Taking a game-theoretic approach, we formulate a Stackelberg game and solve for the equilibrium in four scenarios, respectively, in which the retailer uses different combinations of the postponement strategies. There are three main findings. First, when the production cost is low and the yield loss is highly likely, the retailer never strictly benefits from either postponement strategy; with relatively high production cost, the retailer is more likely to adopt payment, rather than pricing, postponement. Second, we uncover a situation where postponing payment and postponing pricing are strategic complements for the retailer. That is, the use of one strategy may increase the benefit of using the other. Third, we identify conditions under which the postponement strategies can be Pareto optimal to the entire supply chain, making the firms’ profits and the consumer surplus simultaneously higher. Managerial implications: These results can be applied in many practical settings to provide guidance for firms to better design the procurement contract and properly use marketing instrument (pricing) to effectively mitigate supply risk and increase profit. Funding: G. Xiao acknowledges financial support from the Research Grants Council of Hong Kong [General Research Fund Grant PolyU 15503920]. X. Guo acknowledges the support from the National Natural Science Foundation of China [Grant 72293564/72293560]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0198 .

Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms’ assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer’s operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm’s extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408 .

Problem definition: Uncrewed aerial vehicles (UAVs) are transforming emergency service logistics applications across sectors, offering easy deployment and rapid response. In the context of emergency medical services (EMS), UAVs have the potential to augment ambulances by leveraging bystander assistance, thereby reducing response times for delivering urgent medical interventions and improving EMS outcomes. Notably, the use of UAVs for opioid overdose cases is particularly promising as it addresses the challenges faced by ambulances in delivering timely medication. This study aims to optimize the integration of UAVs and bystanders into EMS in order to minimize average response times for overdose interventions. Methodology/results: We formulate the joint operation of UAVs with ambulances through a Markov decision process that captures random emergency vehicle travel times and bystander availability. We apply an approximate dynamic programming approach to mitigate the solution challenges from high-dimensional state variables and complex decisions through a neural network-based approximation of the value functions (NN-API). To design the approximation, we construct a set of basis functions based on queueing and geographic properties of the UAV-augmented EMS system. Managerial implications: The simulation results suggest that our NN-API policy tends to outperform several noteworthy rule- and optimization-based benchmark policies in terms of accumulated rewards, particularly for situations that are primarily characterized by high request arrival rates and a limited number of available ambulances and UAVs. The results also demonstrate the benefits of incorporating UAVs into the EMS system and the effectiveness of an intelligent real-time operations strategy in addressing capacity shortages, which are often a problem in rural areas of the United States. Additionally, the results provide insights into specific contributions of each dispatching or redeployment strategy to overall performance improvement. Funding: This work was supported by the National Science [Grant 1761022]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0166

Problem definition: Drone delivery has recently garnered significant attention due to its potential for faster delivery at a lower cost than other delivery options. When scheduling drones from a depot for delivery to various destinations, the dispatcher must take into account the uncertain wind conditions, which affect the delivery times of drones to their destinations, leading to late deliveries. Methodology/results: To mitigate the risk of delivery delays caused by wind uncertainty, we propose a two-period drone scheduling model to robustly optimize the delivery schedule. In this framework, the scheduling decisions are made in the morning, with the provision for different delivery schedules in the afternoon that adapt to updated weather information available by midday. Our approach minimizes the essential riskiness index, which can simultaneously account for the probability of tardy delivery and the magnitude of lateness. Using wind observation data, we characterize the uncertain flight times via a cluster-wise ambiguity set, which has the benefit of tractability while avoiding overfitting the empirical distribution. A branch-and-cut (B&C) algorithm is developed for this adaptive distributionally framework to improve its scalability. Our adaptive distributionally robust model can effectively reduce lateness in out-of-sample tests compared with other classical models. The proposed B&C algorithm can solve instances to optimality within a shorter time frame than a general modeling toolbox. Managerial implications: Decision makers can use the adaptive robust model together with the cluster-wise ambiguity set to effectively reduce service lateness at customers for drone delivery systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101049 and 72232001], the Natural Science Foundation of Liaoning Province [Grant 2023-BS-091], the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045], and the Major Project of the National Social Science Foundation [Grant 22&ZD151]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0339 .

Problem definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the U.S. National Science Foundation [Grant CMMI-156334]. Funding from the Oracle Corporation through an ERO grant is also gratefully acknowledged. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0893 .

Problem definition: Early research has documented significant growth in ride-hailing services worldwide and allied benefits. However, growing evidence of their negative externalities is leading to significant policy scrutiny. Despite demonstrated socioeconomic benefits and consumer surplus worth billions of dollars, cities are choosing to curb these services in a bid to mitigate first order urban mobility problems. Existing studies on the congestion effects of ride-hailing are limited, report mixed evidence, and exclusively focus on the United States, where the supply consists primarily of part-time drivers. Methodology/results: We study how the absence of ride-hailing services affects congestion levels in three major cities in India, a market where most ride-hailing drivers participate full time. Using rich real-time traffic and route trajectory data from Google Maps, we show that in, all three cities, periods of ride-hailing unavailability due to driver strikes see a discernible drop in travel time. The effects are largest for the most congested regions during the busiest hours, which see 10.1%–14.8% reduction in travel times. Additionally, we provide suggestive evidence for some of the mechanisms behind the observed effects, including deadheading elimination, substitution with public transit, and opening up of shorter alternative routes. Managerial implications: These results suggest that despite their paltry modal share, ride-hailing vehicles are substituting more sustainable means of transport and are contributing significantly to congestion in the cities studied. The reported effect sizes quantify the maximum travel time gains that can be expected on curbing them. Funding: This work was supported by the Srini Raju Center for Information Technology and the Networked Economy at Indian School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1158 .