Pricing and Matching with Forward-Looking Buyers and Sellers

2020 ◽  
Vol 22 (4) ◽  
pp. 717-734 ◽  
Author(s):  
Yiwei Chen ◽  
Ming Hu
Keyword(s):  
Operations Management ◽  
Sharing Economy ◽  
Problem Definition ◽  
Fixed Base ◽  
Managerial Implications ◽  
Dynamic Stochastic ◽  
Bid Prices ◽  
Supply Costs ◽  
Heuristic Policy ◽  
Forward Looking

Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.

Dynamic Type Matching

2021 ◽  
Author(s):  
Ming Hu ◽  
Yun Zhou
Keyword(s):  
Sufficient Conditions ◽  
Sharing Economy ◽  
Problem Definition ◽  
Periodic Review ◽  
Optimal Matching ◽  
Demand And Supply ◽  
Holding Costs ◽  
Current Period ◽  
Dynamic Type ◽  
Managerial Implications

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.

Incentivized Actions in Freemium Games

2020 ◽  
Author(s):  
Lifei Sheng ◽  
Christopher Thomas Ryan ◽  
Mahesh Nagarajan ◽  
Yuan Cheng ◽  
Chunyang Tong
Keyword(s):  
Operations Management ◽  
Sufficient Conditions ◽  
Problem Definition ◽  
Data Set ◽  
New Class ◽  
Recent Innovation ◽  
Markov Decision ◽  
Managerial Implications ◽  
Operational Issues ◽  
Data Driven Decisions

Problem definition: Games are the fastest-growing sector of the entertainment industry. Freemium games are the fastest-growing segment within games. The concept behind freemium is to attract large pools of players, many of whom will never spend money on the game. When game publishers cannot earn directly from the pockets of consumers, they employ other revenue-generating content, such as advertising. Players can become irritated by revenue-generating content. A recent innovation is to offer incentives for players to interact with such content, such as clicking an ad or watching a video. These are termed incentivized (incented) actions. We study the optimal deployment of incented actions. Academic/practical relevance: Removing or adding incented actions can essentially be done in real-time. Accordingly, the deployment of incented actions is a tactical, operational question for game designers. Methodology: We model the deployment problem as a Markov decision process (MDP). We study the performance of simple policies, as well as the structure of optimal policies. We use a proprietary data set to calibrate our MDP and derive insights. Results: Cannibalization—the degree to which incented actions distract players from making in-app purchases—is the key parameter for determining how to deploy incented actions. If cannibalization is sufficiently high, it is never optimal to offer incented actions. If cannibalization is sufficiently low, it is always optimal to offer. We find sufficient conditions for the optimality of threshold strategies that offer incented actions to low-engagement users and later remove them once a player is sufficiently engaged. Managerial implications: This research introduces operations management academics to a new class of operational issues in the games industry. Managers in the games industry can gain insights into when incentivized actions can be more or less effective. Game designers can use our MDP model to make data-driven decisions for deploying incented actions.

The Incentive Game Under Target Effects in Ridesharing: A Structural Econometric Analysis

2021 ◽  
Author(s):  
Xirong Chen ◽  
Zheng Li ◽  
Liu Ming ◽  
Weiming Zhu
Keyword(s):  
Stackelberg Game ◽  
Profit Maximization ◽  
Working Hours ◽  
Sharing Economy ◽  
Problem Definition ◽  
Reduced Form ◽  
Counterfactual Analysis ◽  
Monetary Rewards ◽  
Behavioral Preferences ◽  
Managerial Implications

Problem definition: We study a ridesharing platform’s optimal bonus-setting decisions for capacity and profit maximization problems in which drivers set daily income targets. Academic and Practical Relevance: Sharing-economy companies have been providing monetary rewards to incentivize self-scheduled drivers to work longer. We study the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. Methodology: We model a driver’s decision-making processes and the platform’s optimization problem as a Stackelberg game. Then, utilizing comprehensive datasets obtained from a leading ridesharing platform, we develop a novel empirical strategy to provide evidence on the existence of drivers’ income-targeting behavior through a reduced-form and structural analysis. Furthermore, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios by using the characteristics of heterogeneous drivers derived from the estimation outcomes. Results: Our theoretical model suggests that the drivers’ working hours do not increase monotonically with the bonus rate under the target effect and that the platform may not use all its budget on bonuses to maximize capacity or profit. We empirically demonstrate that the drivers engage in income-targeting behavior, and furthermore, we estimate the income targets for heterogeneous drivers. Through counterfactual analysis, we illustrate how the optimal bonus scheme varies when the platform faces different driver compositions and market conditions. We also find that, compared with the platform’s previous bonus setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 26%, boosting the total profit by $4.3 million per month. Managerial implications: It is challenging to develop a flexible self-scheduled supply of drivers that can match the ever-changing demand and maintain the market share of the ridesharing platform. When offering monetary bonuses to incentivize drivers to work longer, the drivers’ income-targeting behavior can undermine the effectiveness of such bonus schemes. The platform needs to understand the heterogeneity of drivers’ behavioral preferences regarding monetary rewards to design an effective bonus strategy.

The Effects of Multisiting on Productivity and Quality

2020 ◽  
Author(s):  
Diwas KC ◽  
Sokol Tushe
Keyword(s):  
Operations Management ◽  
Low Complexity ◽  
Cognitive Effort ◽  
Problem Definition ◽  
Negative Effects ◽  
Data Set ◽  
Managerial Implications ◽  
Potential Benefits ◽  
Frequent Site

Problem definition: In the modern workplace, it is increasingly common for workers to concurrently attend to tasks across multiple physical locations. However, frequent site switching can lead to increased setup and overhead costs. Specifically, workers expend significant time and cognitive effort getting reoriented with personnel, operating processes, tools, and resources whenever they switch sites. In this paper, we look at the productivity and quality implications of multisite work. Academic/practical relevance: Although multisite workplace deployment is increasingly common, its impact on people operations has not been examined in the operations management literature. We contribute to the literature by studying the effect of multisiting on individual worker productivity and quality of output. Methodology: To estimate the effect of multisite operations on performance, we turn to a setting where multisite worker assignment is common—that of physicians who have admitting privileges at multiple hospitals. We collected detailed data on individual physicians practicing in 83 hospitals between 1999 and 2010. Our extensive data set includes detailed operational and clinical factors associated with more than 950,000 patient encounters. Our empirical analysis takes the form of a panel, where we follow a given physician over time and link short-term multisiting to patient-level outcomes. Results: We find that multisiting negatively impacts productivity. Specifically, for each additional site at which a physician works, we observe a 2% increase in patient length of stay. For each site served, the likelihood of a patient developing a complication increases by 3%. Greater travel distance between sites and lack of focus at a given site explain the performance declines due to multisiting. In addition, we find that the performance declines resulting from multisite operation are reduced among low-complexity patients and among highly experienced physicians. Managerial implications: Multisite performance losses need to be traded off against the potential benefits. The negative effects of multisiting can be mitigated by limiting multisite deployment to simpler tasks and among highly experienced physicians. Managers can decrease switching costs of multisite work by standardizing workflows, processes, and tools across sites. In addition, the practice of multisite work can be limited to sites that are physically proximate to avoid the overhead costs associated with excessive travel.

scholarly journals Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

2021 ◽  
Author(s):  
Xiaojia Guo ◽  
Yael Grushka-Cockayne ◽  
Bert De Reyck
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Operations Management ◽  
Regression Tree ◽  
Problem Definition ◽  
Second Phase ◽  
Time Data ◽  
Managerial Implications ◽  
Passenger Flows ◽  
Data Driven Decisions

Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted.

Competition Between Two-Sided Platforms Under Demand and Supply Congestion Effects

2020 ◽  
Author(s):  
Fernando Bernstein ◽  
Gregory A. DeCroix ◽  
N. Bora Keskin
Keyword(s):  
Service Providers ◽  
Supply And Demand ◽  
Relative Number ◽  
Sharing Economy ◽  
Problem Definition ◽  
Ride Sharing ◽  
Managerial Implications ◽  
The Impact ◽  
Number Of Customers ◽  
Congestion Effects

Problem definition: This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining the service provided by a driver. A portion of that price is paid to the driver who delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multihoming” (or “multiapping,” as it is known in practice), drivers deliver their service through both platforms. Academic/practical relevance: This is one of the first papers to study competition and multihoming in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multihoming). Methodology: We formulate our problem as a pricing game between two platforms and employ the concept of a Nash equilibrium to analyze equilibrium outcomes in various settings. Results: In both the single-homing and multihoming settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multihoming. Managerial implications: We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or multihome. We find that although individual drivers may have an incentive to multihome, all players are worse off when all drivers multihome. We conclude by proposing an incentive mechanism to discourage multihoming.

scholarly journals Is Diversity (Un-)Biased? Project Selection Decisions in Executive Committees

2020 ◽  
Vol 22 (5) ◽  
pp. 906-924 ◽  
Author(s):  
Nektarios Oraiopoulos ◽  
Stylianos Kavadias
Keyword(s):  
Operations Management ◽  
Project Selection ◽  
Problem Definition ◽  
Decision Quality ◽  
Theoretic Model ◽  
Type I ◽  
Wrong Decision ◽  
Business Units ◽  
Selection Decisions ◽  
Managerial Implications

Problem definition: Is a committee composed of more or less cognitively diverse members better at approving the “good” projects and rejecting the “bad” ones? Academic/practical relevance: We contribute to the operations management literature by accounting for the fact that critical selection decisions are often made by a committee rather than a single decision maker. Understanding how the magnitude of diversity affects the decision quality of such a committee is an important consideration for practitioners. Methodology: We utilize a game-theoretic model to show that diverse perspectives are rarely “averaged out.” Results: Diversity leads to systematic biases in project selection. To mitigate the effect of diverse perspectives, managers need to uncover the sources of diversity: do they originate from different individual valuations and preferences, or do they express different assimilations of the information that arises during the project execution? We show that this distinction is crucial. Higher preference diversity always leads to higher likelihood of making the wrong decision. Higher interpretive diversity may be beneficial for the organization. Managerial implications: A clear managerial action is the need to identify and reduce such preference diversity. Senior management can achieve this by highlighting the need for more transparency in the pipeline of the business units. Moreover, our analysis shows that interpretive diversity can be a powerful managerial lever to influence the propensity for Type I and II errors. The latter might be easier to manage than the organizational structure.

Reveal the Supplier List? A Trade-off in Capacity vs. Responsibility

2020 ◽  
Vol 22 (6) ◽  
pp. 1251-1267 ◽  
Author(s):  
Basak Kalkanci ◽  
Erica L. Plambeck
Keyword(s):  
Operations Management ◽  
Cost Reduction ◽  
Problem Definition ◽  
Expected Profit ◽  
Environmental Responsibility ◽  
Management Literature ◽  
New Business ◽  
Managerial Implications ◽  
Electrical Wiring ◽  
Do So

Problem definition: Under what conditions and how can a buying firm, by committing to publish a list of its suppliers and/or the identities and violations of terminated suppliers, increase its expected profit and supplier responsibility? Academic/practical relevance: This paper contributes to a recent thrust in the operations-management literature on how various sorts of transparency influence social and environmental responsibility in a supply chain. In practice, companies are under pressure to publish their supplier lists and suppliers’ violations, and some are beginning to do so. This paper could help guide their decisions. Methodology: The methodology is game theory. Results: This paper shows how a buying firm can use transparency to reward a supplier for responsibility effort to eliminate social or environmental violations. By publishing its supplier list, the buying firm can signal that a supplier is responsible and generate profitable new business for the supplier. However, the resulting competition for the supplier’s scarce capacity could cause the buying firm to obtain fewer units or pay a higher price. We identify the conditions under which a buying firm should commit to publish its supplier list and conditions under which the buying firm should also help a supplier with cost reduction or capacity expansion. In addition, the paper shows how a buying firm can use transparency to punish a supplier for a responsibility violation—by warning other buying firms not to source from that supplier. Commitment to do so increases the supplier’s responsibility effort and can screen out a supplier with a known responsibility violation, thereby increasing a buying firm’s expected profit. If the supplier is uncertain whether it has a violation (e.g., faulty electrical wiring likely to cause a fire), then the two forms of transparency can be complementary. Managerial implications: Buying firms should consider transparency as a potentially profitable approach to mitigating social and environmental violations in their supply chains.

scholarly journals Budgeting in International Humanitarian Organizations

2021 ◽  
Author(s):  
Milad Keshvari Fard ◽  
Ivana Ljubić ◽  
Felix Papier
Keyword(s):  
Utility Function ◽  
Operations Management ◽  
Benders Decomposition ◽  
International Committee ◽  
Problem Definition ◽  
Red Cross ◽  
Solution Approach ◽  
Humanitarian Organizations ◽  
Managerial Implications ◽  
Using Data

Problem definition: International humanitarian organizations (IHOs) prepare a detailed annual allocation plan for operations that are conducted in the countries they serve. The annual plan is strongly affected by the available financial budget. The budget of IHOs is derived from donations, which are typically limited, uncertain, and to a large extent earmarked for specific countries or programs. These factors, together with the specific utility function of IHOs, render budgeting for IHOs a challenging managerial problem. In this paper, we develop an approach to optimize budget allocation plans for each country of operations. Academic/practical relevance: The current research provides a better understanding of the budgeting problem in IHOs given the increasing interest of the operations management community for nonprofit operations. Methodology: We model the problem as a two-stage stochastic optimization model with a concave utility function and identify a number of analytical properties for the problem. We develop an efficient generalized Benders decomposition algorithm as well as a fast heuristic. Results: Using data from the International Committee of the Red Cross, our results indicate 21.3% improvement in the IHO’s utility by adopting stochastic programming instead of the expected value solution. Moreover, our solution approach is computationally more efficient than other approaches. Managerial implications: Our analysis highlights the importance of nonearmarked donations for the overall performance of IHOs. We also find that putting pressure on IHOs to fulfill the targeted missions (e.g., by donors or media) results in lower beneficiaries’ welfare. Moreover, the IHOs benefit from negative correlation among donations. Finally, our findings indicate that, if donors allow the IHO to allocate unused earmarked donations to other delegations, the performance of the IHO improves significantly.

Operational Transparency: Showing When Work Gets Done

2020 ◽  
Author(s):  
Robert L. Bray
Keyword(s):  
Operations Management ◽  
Causal Effect ◽  
Service Operations ◽  
Problem Definition ◽  
Delivery Service ◽  
Management Literature ◽  
Managerial Implications ◽  
Average Action ◽  
Psychology Literature ◽  
Activity Times

Problem definition: Do the benefits of operational transparency depend on when the work is done? Academic/practical relevance: This work connects the operations management literature on operational transparency with the psychology literature on the peak-end effect. Methodology: This study examines how customers respond to operational transparency with parcel delivery data from the Cainiao Network, the logistics arm of Alibaba. The sample comprises 4.68 million deliveries. Each delivery has between 4 and 10 track-package activities, which customers can check in real time, and a delivery service score, which customers leave after receiving the package. Instrumental-variable regressions quantify the causal effect of track-package-activity times on delivery scores. Results: The regressions suggest that customers punish early idleness less than late idleness, leaving higher delivery service scores when track-package activities cluster toward the end of the shipping horizon. For example, if a shipment takes 100 hours, then delaying the time of the average action from hour 20 to hour 80 increases the expected delivery score by approximately the same amount as expediting the arrival time from hour 100 to hour 73. Managerial implications: Memory limitations make customers especially sensitive to how service operations end.

Sign in / Sign up

Export Citation Format

Share Document