Knowledge that Transforms

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers’ inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging.

Problem definition: This paper investigates the issue of sharing the private demand information of a manufacturer that sells a product to retailers competing on prices and service efforts. Academic/practical relevance: In the existing literature, which ignores service effort competition, it is known that demand signaling induces an informed manufacturer to distort the wholesale price downward, which benefits the retailers, and so, they do not have any incentive to receive the manufacturer’s private information. In practice, many manufacturers share demand information with their retailers that compete on prices and service efforts (e.g., demand-enhancing retail activities), a setting that has not received much attention from the literature. Methodology: We develop a game-theoretic model with one manufacturer selling to two competing retailers and solve for the equilibrium of the game. Results: We show how an informed manufacturer may distort the wholesale price upward or downward to signal demand information to the retailers, depending on the cost of service effort, the intensity of effort competition, and the number of uninformed retailers. We fully characterize the impact of such wholesale price distortion on the firms’ incentive to share information and derive the conditions under which the manufacturer shares information with none, one, or both of the retailers. We derive conditions under which a higher cost of service effort makes the retailers or the manufacturer better off. Managerial implications: Our results provide novel insights about how service effort competition impacts the incentives for firms in a supply chain to share a manufacturer’s private demand information. For instance, when the cost of effort is high or service effort competition is intense, a manufacturer should share information with none or some, but not all, of the retailers.

Problem definition: This paper considers a drone delivery system that delivers packages to multiple locations using a model that captures three distinctive features of interest: first, a battery-operated drone that can fly a limited number of distance units on a full battery; second, a set of demand locations with different flight distances and service-level requirements; and third, an adjustable flight speed to trade off energy efficiency for faster deliveries. By leveraging drone speed, job sequencing, and admission control, the system operator strives to achieve two managerial objectives: (i) to minimize energy- and congestion-related costs and (ii) to maximize the use of the battery energy between consecutive battery replacements. Academic/practical relevance: E-commerce and shipping companies worldwide are testing and launching commercial drone delivery services to residences. According to a recent study, such services are expected to grow to $100 billion market by the end of the next decade. Our work focuses on improving drone delivery operations, which can reduce costs and improve user satisfaction. Methodology: Focusing on the first objective, we approximate system dynamics using diffusion processes and derive an optimal control policy for the approximating system. Results: Interpreting that policy in the context of the physical system while taking into account the second objective, we devise an implementable set of speed control, job sequencing, and admission strategies. The sequencing strategy, which selects a portfolio of jobs (termed as an activity) to be accomplished within a full battery cycle, results in a win-win scenario—it allows both objectives to be approximately optimized at the same time. Managerial implications: Using the activity-based prioritization scheme provides opportunities to improve the overall performance of the system. Additionally, to achieve the intended job prioritization, the system operator can rely on a small set of activities that are of equal size to the number of job classes.

Problem definition: In high-product-variety businesses like fashion, mass production (MP) systems create environmental waste in the form of overproduction on a colossal scale. Mass customization (MC) has been proposed—without solid evidence—as a solution. In this paper, we analyze whether MC can indeed offer a win-win solution that helps both the bottom line and the environment. We also study the impact of three real policy options: promoting MC, charging a disposal fee for overproduction, and recycling. Academic/practical relevance: There is increasing interest in mass-customizing fashion goods, not only because consumers value customization, but also because MC is perceived to be environmentally friendly. Our paper puts this advocacy for MC to the test. We contribute to the literature, which has been largely silent on the issue, by uncovering when MC offers a win-win and relating such market outcomes to policy ideas. Methodology: We develop an analytical model of an MP firm adopting MC (going hybrid). The firm’s profit-maximizing variety, price, and inventory decisions then form the basis of our understanding the environmental impact of adopting MC and assessing various policy options. Results: Adopting MC can be a win-win, but it can also increase overproduction and hurt the environment. Our policy analyses reveal two kinds of insights. The first kind is about whether a policy expands win-win outcomes—encouraging sustainable adoption of MC. Among the policy ideas we explore, only promoting MC so as to increase consumers’ tolerance for waiting for mass-customized products can do that unambiguously. The second kind of insight is about whether a policy reduces the hybrid firm’s environmental impact. Only a disposal fee and costly recycling programs can do that unambiguously. Managerial implications: For MC adoption to be a win-win, policy makers must (1) work on convincing consumers to wait for bespoke fashion; (2) target MP firms with low cost of variety (high product-mix flexibility) with disposal fees or costly recycling programs; and (3) encourage those with relatively higher cost of variety to develop/acquire technology that would make recycling profitable. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1088 .

Problem definition: The beer game is widely used in supply chain management classes to demonstrate the bullwhip effect and the importance of supply chain coordination. The game is a decentralized, multiagent, cooperative problem that can be modeled as a serial supply chain network in which agents choose order quantities while cooperatively attempting to minimize the network’s total cost, although each agent only observes local information. Academic/practical relevance: Under some conditions, a base-stock replenishment policy is optimal. However, in a decentralized supply chain in which some agents act irrationally, there is no known optimal policy for an agent wishing to act optimally. Methodology: We propose a deep reinforcement learning (RL) algorithm to play the beer game. Our algorithm makes no assumptions about costs or other settings. As with any deep RL algorithm, training is computationally intensive, but once trained, the algorithm executes in real time. We propose a transfer-learning approach so that training performed for one agent can be adapted quickly for other agents and settings. Results: When playing with teammates who follow a base-stock policy, our algorithm obtains near-optimal order quantities. More important, it performs significantly better than a base-stock policy when other agents use a more realistic model of human ordering behavior. We observe similar results using a real-world data set. Sensitivity analysis shows that a trained model is robust to changes in the cost coefficients. Finally, applying transfer learning reduces the training time by one order of magnitude. Managerial implications: This paper shows how artificial intelligence can be applied to inventory optimization. Our approach can be extended to other supply chain optimization problems, especially those in which supply chain partners act in irrational or unpredictable ways. Our RL agent has been integrated into a new online beer game, which has been played more than 17,000 times by more than 4,000 people.

Problem definition: This paper examines the economic effects of the fulfillment services offered by Amazon (FBA) to the third-party sellers on its retail platform. Academic/practical relevance: Logistics is critical for e-commerce. It is intriguing that the third-party sellers that use FBA may directly compete with Amazon. By FBA, their service levels are substantially improved and so is their competitiveness. Such a phenomenon has been little investigated in prior literature. Methodology: We develop a strategic competition model where Amazon and a representative third-party seller engage in both price and service competition. Results: Interestingly, we find that although FBA may intensify the service competition between Amazon and the third-party seller, it can mitigate their price competition besides the direct revenue Amazon collects from the service. The latter effects dominate the former when the cross-service sensitivity of the consumers is either sufficiently low or sufficiently high. Therefore, not only the third-party sellers but also Amazon may benefit from FBA. Moreover, because the sales of Amazon’s products may increase because of FBA, its OEM (original equipment manufacturer) supplier may gain from FBA also. These findings extend to the settings where the third-party seller is Amazon’s OEM supplier, cross-price and cross-service sensitivities are correlated, or the parties set their retail prices sequentially. Managerial implications: Our analysis provides insights about when FBA is more likely to be beneficial and when it can be harmful. They are useful for understanding the impacts of FBA on both Amazon and the third-party sellers.

Problem definition: This paper examines whether and, if so, how much an online–off-line return partnership between online and third-party retailers with physical stores (or “location partners”) generates additional value to location partners. Academic/practical relevance: Online shoppers often prefer to return products to stores rather than mailing them back. Many online retailers have recently started to collaborate with location partners to offer the store return option to their customers, and we quantify its economic benefit to a location partner. Methodology: We analyze proprietary data sets from Happy Returns (which provides return services for more than 30 online retailers) and one of its location partners, using a panel difference-in-differences model. In our study, a treatment is the initiation of the return service at each of the location partner’s stores, and an outcome is the store and online channel performance of the location partner. We then explore the mechanisms of underlying customer behavior that drive these outcomes. Results: We find that the partnership increases the number of unique customers, items sold, and net revenue in both store and online channels. We identify two drivers for this improved performance: (1) the location partner acquires new customers in both store and online channels, and (2) existing customers change their shopping patterns only in the store channel after using the return service; in particular, they visit stores more often, purchase more items, and generate higher revenue after their first return service. Managerial implications: To our knowledge, we provide the first direct empirical evidence of value to location partners from a return partnership, and as these partnerships become more prevalent, our findings have important managerial implications for location partners and online retailers alike.

Problem definition: This paper investigates the channel choice problem of an online platform that exerts service effort to enhance the demand in its sales channels. Academic/practical relevance: In the existing literature on the channel structure of an online retail platform, it is usually assumed that a manufacturer sells through either the platform’s agency or reselling channel but not both. In practice, many manufacturers sell the same products through both channels of the same online retail platform, a phenomenon that cannot be explained by the existing theory. Moreover, online retail platforms routinely invest in retail services that enhance the demand in their sales channels. Methodology: We develop a game-theoretic model to investigate the equilibrium channel choice, wholesale price, and retail quantity decisions. We also conduct sensitivity analysis to evaluate the impact of some parameters on the equilibrium. Results: We derive conditions under which each of the three channel structures (agency channel, reselling channel, and dual channel) emerges in equilibrium. We show that the wholesale price in the reselling channel is reduced because of the addition of the agency channel even when both channels are equally efficient, which extends the wholesale price effect because of the addition of a less efficient direct channel in the supplier encroachment literature. Our analysis highlights the flexibility of a dual channel for firms to shift sales between the two channels, which could increase the retail platform’s incentive to exert service effort. Managerial implications: Our study provides useful insights to managers to understand and make channel choice decisions in supply chains with manufacturers selling through online retail platforms.

Problem definition: This paper investigates the threshold effects on the consideration set formation for consumers and the associated pricing decisions for firms. Academic/practical relevance: In the class of random utility maximization choice models, consumers choose the alternative with the largest utility after resolving utility uncertainties for all options. However, in practice, consumers may not inspect all available alternatives due to bounded rationality or search cost. Methodology: This paper incorporates bounded rationality into consumer choice behavior: in the first stage, a representative consumer forms her consideration set by removing alternatives whose nominal utility is lower than the largest by a threshold; in the second stage, she examines all alternatives in her consideration set and chooses the one with the highest realized utility. Results: For the Gumbel-distributed random utilities, we derive the two-stage threshold multinomial logit model with a consideration set. For the pricing problem, we show the quasi-same-markup/same-utility policy is optimal: high-cost products charge the prices such that their profit markups are the same, and low-cost products charge prices such that their nominal utilities are the same. For the price competition, there may exist zero, one, two, or infinite Nash equilibria, depending on the magnitude of the threshold effects. Managerial implications: Our analysis shows that the consideration set and bounded rationality play an important role in consumer choice behavior, so they should be taken into account in firms’ decision making.

We reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment.