Sanghoon Cho, Mark Ferguson, Pelin Pekgün, Andrew Vakhutinsky
Problem definition: Estimating customer demand for revenue management solutions faces two main hurdles: unobservable no-purchases and nonhomogenous customer populations with varying preferences. We propose a novel and practical estimation and segmentation methodology that overcomes both challenges simultaneously. Academic/practical relevance: We combine the estimation of discrete choice modeling under unobservable no-purchases with a data-driven identification of customer segments. In collaboration with our industry partner, Oracle Hospitality Global Business Unit, we demonstrate our methodology in the hotel industry setting where increased competition has driven hoteliers to look for more innovative revenue management practices, such as personalized offers for their guests. Methodology: Our methodology predicts demand for multiple types of hotel rooms based on guest characteristics, travel attributes, and room features. Our framework combines clustering techniques with choice modeling to develop a mixture of multinomial logit discrete choice models and uses Bayesian inference to estimate model parameters. In addition to predicting the probability of an individual guest’s room type choice, our model delivers additional insights on segmentation with its capability to classify each guest into segments (or a mixture of segments) based on their characteristics. Results: We first show using Monte Carlo simulations that our method outperforms several benchmark methods in prediction accuracy, with nearly unbiased estimates of the choice model parameters and the size of the no-purchase incidents. We then demonstrate our method on a real hotel data set and illustrate how the model results can be used to drive insights for personalized offers and pricing. Managerial implications: Our proposed framework provides a practical approach for a complicated demand estimation problem and can help hoteliers segment their guests based on their preferences, which can serve as a valuable input for personalized offer selection and pricing decisions. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Oracle Labs, part of Oracle America, Inc. [Gift 2380]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1094 .
问题定义:估计客户对收益管理解决方案的需求面临两个主要障碍:不可观察的不购买和具有不同偏好的非同质客户群体。我们提出了一种新颖实用的估计和分割方法，同时克服了这两个挑战。学术/实践相关性:我们将不可观察的无购买下的离散选择建模估计与数据驱动的客户细分识别相结合。通过与我们的行业合作伙伴Oracle Hospitality Global Business Unit的合作，我们在酒店业中展示了我们的方法。日益激烈的竞争促使酒店经营者寻求更创新的收益管理实践，例如为客人提供个性化服务。方法:我们的方法根据客人特征、旅行属性和房间特征来预测对多种酒店客房的需求。我们的框架将聚类技术与选择建模相结合，开发了多项logit离散选择模型的混合物，并使用贝叶斯推理来估计模型参数。除了预测单个客人选择房间类型的概率之外，我们的模型还提供了关于细分的额外见解，它能够根据客人的特征将每个客人划分为不同的细分(或混合细分)。结果:我们首先使用蒙特卡罗模拟表明，我们的方法在预测精度上优于几种基准方法，对选择模型参数和未购买事件的大小进行了近乎无偏的估计。然后，我们在一个真实的酒店数据集上展示了我们的方法，并说明了如何使用模型结果来驱动个性化优惠和定价的见解。管理意义:我们提出的框架为复杂的需求估计问题提供了一种实用的方法，可以帮助酒店经营者根据他们的偏好对客人进行细分，这可以作为个性化优惠选择和定价决策的宝贵输入。历史:本文已被接受为2021年制造业&服务营运管理实务研究比赛。资金:本研究由Oracle实验室(Oracle America, Inc.的一部分)提供支持[Gift 2380]。补充材料:在线附录可在https://doi.org/10.1287/msom.2022.1094上获得。
{"title":"Estimating Personalized Demand with Unobserved No-Purchases Using a Mixture Model: An Application in the Hotel Industry","authors":"Sanghoon Cho, Mark Ferguson, Pelin Pekgün, Andrew Vakhutinsky","doi":"10.1287/msom.2022.1094","DOIUrl":"https://doi.org/10.1287/msom.2022.1094","url":null,"abstract":"Problem definition: Estimating customer demand for revenue management solutions faces two main hurdles: unobservable no-purchases and nonhomogenous customer populations with varying preferences. We propose a novel and practical estimation and segmentation methodology that overcomes both challenges simultaneously. Academic/practical relevance: We combine the estimation of discrete choice modeling under unobservable no-purchases with a data-driven identification of customer segments. In collaboration with our industry partner, Oracle Hospitality Global Business Unit, we demonstrate our methodology in the hotel industry setting where increased competition has driven hoteliers to look for more innovative revenue management practices, such as personalized offers for their guests. Methodology: Our methodology predicts demand for multiple types of hotel rooms based on guest characteristics, travel attributes, and room features. Our framework combines clustering techniques with choice modeling to develop a mixture of multinomial logit discrete choice models and uses Bayesian inference to estimate model parameters. In addition to predicting the probability of an individual guest’s room type choice, our model delivers additional insights on segmentation with its capability to classify each guest into segments (or a mixture of segments) based on their characteristics. Results: We first show using Monte Carlo simulations that our method outperforms several benchmark methods in prediction accuracy, with nearly unbiased estimates of the choice model parameters and the size of the no-purchase incidents. We then demonstrate our method on a real hotel data set and illustrate how the model results can be used to drive insights for personalized offers and pricing. Managerial implications: Our proposed framework provides a practical approach for a complicated demand estimation problem and can help hoteliers segment their guests based on their preferences, which can serve as a valuable input for personalized offer selection and pricing decisions. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Oracle Labs, part of Oracle America, Inc. [Gift 2380]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1094 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maxime C. Cohen, Michael-David Fiszer, Avia Ratzon, Roy Sasson
Problem definition: Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. Academic/practical relevance: In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. Methodology: Our field experiment involves more than half a million users across four U.S. states between June 10 and July 3, 2019. We identify users who can save a significant commute time by carpooling through the use of a high-occupancy vehicle (HOV) lane, users who can still use an HOV lane but have a low time saving, and users who do not have access to an HOV lane on their commute. We send them in-app notifications with different framings: mentioning the HOV lane, highlighting the time saving, emphasizing the monetary welcome bonus (for users who do not have access to an HOV lane), and a generic carpool invitation. Results: We find a strong relationship between the affinity to carpool and the potential time saving through an HOV lane. Managerial implications: Specifically, we estimate that mentioning the HOV lane increases the click-through rate (i.e., proportion of users who clicked on the button inviting them to try the carpool service) and the onboarding rate (i.e., proportion of users who signed up and created an account with the carpool service) by 133%–185% and 64%–141%, respectively, relative to a generic invitation. We conclude by discussing the implications of our findings for carpool platforms and public policy. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1033 .
{"title":"Incentivizing Commuters to Carpool: A Large Field Experiment with Waze","authors":"Maxime C. Cohen, Michael-David Fiszer, Avia Ratzon, Roy Sasson","doi":"10.1287/msom.2021.1033","DOIUrl":"https://doi.org/10.1287/msom.2021.1033","url":null,"abstract":"Problem definition: Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. Academic/practical relevance: In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. Methodology: Our field experiment involves more than half a million users across four U.S. states between June 10 and July 3, 2019. We identify users who can save a significant commute time by carpooling through the use of a high-occupancy vehicle (HOV) lane, users who can still use an HOV lane but have a low time saving, and users who do not have access to an HOV lane on their commute. We send them in-app notifications with different framings: mentioning the HOV lane, highlighting the time saving, emphasizing the monetary welcome bonus (for users who do not have access to an HOV lane), and a generic carpool invitation. Results: We find a strong relationship between the affinity to carpool and the potential time saving through an HOV lane. Managerial implications: Specifically, we estimate that mentioning the HOV lane increases the click-through rate (i.e., proportion of users who clicked on the button inviting them to try the carpool service) and the onboarding rate (i.e., proportion of users who signed up and created an account with the carpool service) by 133%–185% and 64%–141%, respectively, relative to a generic invitation. We conclude by discussing the implications of our findings for carpool platforms and public policy. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1033 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients’ overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.1210 .
{"title":"Probabilistic Forecasting of Patient Waiting Times in an Emergency Department","authors":"Siddharth Arora, James W. Taylor, Ho-Yin Mak","doi":"10.1287/msom.2023.1210","DOIUrl":"https://doi.org/10.1287/msom.2023.1210","url":null,"abstract":"Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients’ overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.1210 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136376069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring. Funding: X. Hu thanks Faculty of Business and Economics of the University of Hong Kong and R. Caldentey thanks the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1203 .
{"title":"Trust and Reciprocity in Firms’ Capacity Sharing","authors":"Xing Hu, René Caldentey","doi":"10.1287/msom.2023.1203","DOIUrl":"https://doi.org/10.1287/msom.2023.1203","url":null,"abstract":"Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring. Funding: X. Hu thanks Faculty of Business and Economics of the University of Hong Kong and R. Caldentey thanks the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1203 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Academic/practical relevance: Increasing match rates is a prevalent objective of online platforms. We provide insights into how to leverage users’ preferences and behavior toward this end. Our proposed algorithm was piloted by our collaborator, a major online dating company in the United States. Methodology: Our work combines several methodologies. We model the platform’s problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company’s algorithm in order to estimate the users’ preferences and the causal effect of previous matches on the like behavior of users, as well as other parameters of interest. Leveraging our data findings, we propose a family of heuristics to solve the platform’s problem and use simulations and field experiments to assess their benefits. Results: We find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. We propose a family of heuristics to decide the profiles to show to each user on each day that accounts for this finding. Two field experiments show that our algorithm yields at least 27% more matches relative to our industry partner’s algorithm. Managerial implications: Our results highlight the importance of correctly accounting for the preferences, behavior, and activity metrics of users on both ends of a transaction to improve the operational efficiency of matching platforms. In addition, we propose a novel identification strategy to measure the effect of previous matches on the users’ preferences in a two-sided matching market, the result of which is leveraged by our algorithm. Our methodology may also be applied to online matching platforms in other domains. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1107 .
{"title":"Improving Match Rates in Dating Markets Through Assortment Optimization","authors":"Ignacio Rios, Daniela Saban, Fanyin Zheng","doi":"10.1287/msom.2022.1107","DOIUrl":"https://doi.org/10.1287/msom.2022.1107","url":null,"abstract":"Problem definition: Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Academic/practical relevance: Increasing match rates is a prevalent objective of online platforms. We provide insights into how to leverage users’ preferences and behavior toward this end. Our proposed algorithm was piloted by our collaborator, a major online dating company in the United States. Methodology: Our work combines several methodologies. We model the platform’s problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company’s algorithm in order to estimate the users’ preferences and the causal effect of previous matches on the like behavior of users, as well as other parameters of interest. Leveraging our data findings, we propose a family of heuristics to solve the platform’s problem and use simulations and field experiments to assess their benefits. Results: We find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. We propose a family of heuristics to decide the profiles to show to each user on each day that accounts for this finding. Two field experiments show that our algorithm yields at least 27% more matches relative to our industry partner’s algorithm. Managerial implications: Our results highlight the importance of correctly accounting for the preferences, behavior, and activity metrics of users on both ends of a transaction to improve the operational efficiency of matching platforms. In addition, we propose a novel identification strategy to measure the effect of previous matches on the users’ preferences in a two-sided matching market, the result of which is leveraged by our algorithm. Our methodology may also be applied to online matching platforms in other domains. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1107 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gad Allon, Maxime C. Cohen, Wichinpong Park Sinchaisri
Problem definition: Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers’ flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, whether to work and work duration. Our model revisits competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of flexible workers and understanding how to design better incentives. Methodology/results: Using a large comprehensive data set, we develop an econometric model to analyze workers’ labor decisions and responses to incentives while accounting for sample selection and endogeneity. We find that financial incentives have a significant positive influence on the decision to work and on the work duration—confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). Managerial implications: We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10%–17% below the optimal capacity level. Lastly, our insights inform the design of platform strategy to manage flexible workers amidst an intensified competition among gig platforms. Funding: This study was supported by The Jay H. Baker Retailing Center, The William and Phyllis Mack Institute for Innovation Management, The Wharton Risk Management and Decision Processes Center, and The Fishman-Davidson Center for Service and Operations Management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1191 .
{"title":"The Impact of Behavioral and Economic Drivers on Gig Economy Workers","authors":"Gad Allon, Maxime C. Cohen, Wichinpong Park Sinchaisri","doi":"10.1287/msom.2023.1191","DOIUrl":"https://doi.org/10.1287/msom.2023.1191","url":null,"abstract":"Problem definition: Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers’ flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, whether to work and work duration. Our model revisits competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of flexible workers and understanding how to design better incentives. Methodology/results: Using a large comprehensive data set, we develop an econometric model to analyze workers’ labor decisions and responses to incentives while accounting for sample selection and endogeneity. We find that financial incentives have a significant positive influence on the decision to work and on the work duration—confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). Managerial implications: We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10%–17% below the optimal capacity level. Lastly, our insights inform the design of platform strategy to manage flexible workers amidst an intensified competition among gig platforms. Funding: This study was supported by The Jay H. Baker Retailing Center, The William and Phyllis Mack Institute for Innovation Management, The Wharton Risk Management and Decision Processes Center, and The Fishman-Davidson Center for Service and Operations Management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1191 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shi Chen, Kamran Moinzadeh, Jing-Sheng Song, Yuan Zhong
Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .
{"title":"Cloud Computing Value Chains: Research from the Operations Management Perspective","authors":"Shi Chen, Kamran Moinzadeh, Jing-Sheng Song, Yuan Zhong","doi":"10.1287/msom.2022.1178","DOIUrl":"https://doi.org/10.1287/msom.2022.1178","url":null,"abstract":"Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Taozeng Zhu, Nicholas Teck Boon Yeo, Sarah Yini Gao, Gar Goei Loke
Problem definition: In the blood-donor-management problem, the blood bank incentivizes donors to donate, given blood inventory levels. We propose a model to optimize such incentivization schemes under the context of random demand, blood perishability, observation period between donations, and variability in donor arrivals and dropouts. Methodology/results: We propose an optimization model that simultaneously accounts for the dynamics in the blood inventory and the donor’s donation process, as a coupled queueing network. We adopt the Pipeline Queue paradigm, which leads us to a tractable convex reformulation. The coupled setting requires new methodologies to be developed upon the existing Pipeline Queue framework. Numerical results demonstrate the advantages of the optimal policy by comparing it with the commonly adopted and studied threshold policy. Our optimal policy can effectively reduce both shortages and wastage. Managerial implications: Our model is the first to operationalize a dynamic donor-incentivization scheme, by determining the optimal number of donors of different donation responsiveness to receive each type of incentive. It can serve as a decision-support tool that incorporates practical features of blood supply-chain management not addressed thus far, to the best of our knowledge. Simulations on existing policies indicate the dangers of myopic approaches and justify the need for smoother and forward-looking donor-incentivization schedules that can hedge against future demand variation. Our model also has potential wider applications in supply chains with perishable inventory. Funding: This study was funded by the Singapore Management University through a research [Grant 20-C207-SMU-015] from the Ministry of Education Academic Research Fund Tier 1. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1228 .
{"title":"Inventory-Responsive Donor-Management Policy: A Tandem Queueing Network Model","authors":"Taozeng Zhu, Nicholas Teck Boon Yeo, Sarah Yini Gao, Gar Goei Loke","doi":"10.1287/msom.2023.1228","DOIUrl":"https://doi.org/10.1287/msom.2023.1228","url":null,"abstract":"Problem definition: In the blood-donor-management problem, the blood bank incentivizes donors to donate, given blood inventory levels. We propose a model to optimize such incentivization schemes under the context of random demand, blood perishability, observation period between donations, and variability in donor arrivals and dropouts. Methodology/results: We propose an optimization model that simultaneously accounts for the dynamics in the blood inventory and the donor’s donation process, as a coupled queueing network. We adopt the Pipeline Queue paradigm, which leads us to a tractable convex reformulation. The coupled setting requires new methodologies to be developed upon the existing Pipeline Queue framework. Numerical results demonstrate the advantages of the optimal policy by comparing it with the commonly adopted and studied threshold policy. Our optimal policy can effectively reduce both shortages and wastage. Managerial implications: Our model is the first to operationalize a dynamic donor-incentivization scheme, by determining the optimal number of donors of different donation responsiveness to receive each type of incentive. It can serve as a decision-support tool that incorporates practical features of blood supply-chain management not addressed thus far, to the best of our knowledge. Simulations on existing policies indicate the dangers of myopic approaches and justify the need for smoother and forward-looking donor-incentivization schedules that can hedge against future demand variation. Our model also has potential wider applications in supply chains with perishable inventory. Funding: This study was funded by the Singapore Management University through a research [Grant 20-C207-SMU-015] from the Ministry of Education Academic Research Fund Tier 1. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1228 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135210481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alison Borenstein, Ankit Mangal, Georgia Perakis, Stefan Poninghaus, Divya Singhvi, Omar Skali Lami, Jiong Wei Lua
Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the net present value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy—that is, scalable and efficient—to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%–3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0491 .
{"title":"Ancillary Services in Targeted Advertising: From Prediction to Prescription","authors":"Alison Borenstein, Ankit Mangal, Georgia Perakis, Stefan Poninghaus, Divya Singhvi, Omar Skali Lami, Jiong Wei Lua","doi":"10.1287/msom.2020.0491","DOIUrl":"https://doi.org/10.1287/msom.2020.0491","url":null,"abstract":"Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the net present value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy—that is, scalable and efficient—to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%–3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0491 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135210579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Whereas firms are increasingly relying on sourcing globally as a key constituent of their supply chain strategy, there is no empirical evidence on whether investors of these firms adequately reflect firms’ global sourcing strategy (GSS) in their stock-valuation process. In this paper, we empirically test whether stock market participants are efficient in doing so. Methodology/results: Using the empirical asset-pricing framework, we find that information concerning firms’ GSS strongly predicts their future stock returns. We compile a transaction-level imports database for U.S.-listed firms and construct measures for five widely studied GSS aspects in the operations management literature: the extent of global sourcing, supplier relationship strength, supplier concentration, sourcing lead time, and sourcing countries’ logistical efficiency. For each measure, we examine returns of a zero-cost investment strategy of buying from the highest and selling from the lowest quintile of that measure. Collectively, these investment strategies yield an average annual four-factor alpha of 6%–9.6% (6%–13.9%) with value (equal)-weighted portfolios. Their return predictability is incremental over other operations- and cost arbitrage–motivated predictors, such as inventory turnover, cash conversion cycle, and gross profitability; is persistent across different supply chain positions; and is robust to alternate risk models, subsamples, and empirical specifications. Together, our results indicate that the GSS measures embody independent information about firms’ future profitability, and this information is mispriced by market participants, leading to predictable returns. In accordance with this mechanism, we find that the GSS measures strongly predict both firms’ future earnings and the surprise in market reactions around the earnings announcement days. Managerial implications: The robust return predictability of our GSS measures suggests that investors are not fully incorporating GSS-related information in their stock valuation frameworks. Therefore, our results call for greater investor education on global sourcing and better dissemination of global-sourcing information so as to mitigate valuation inefficiency. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1189 .
{"title":"Can Global Sourcing Strategy Predict Stock Returns?","authors":"Nitish Jain, Di (Andrew) Wu","doi":"10.1287/msom.2023.1189","DOIUrl":"https://doi.org/10.1287/msom.2023.1189","url":null,"abstract":"Problem definition: Whereas firms are increasingly relying on sourcing globally as a key constituent of their supply chain strategy, there is no empirical evidence on whether investors of these firms adequately reflect firms’ global sourcing strategy (GSS) in their stock-valuation process. In this paper, we empirically test whether stock market participants are efficient in doing so. Methodology/results: Using the empirical asset-pricing framework, we find that information concerning firms’ GSS strongly predicts their future stock returns. We compile a transaction-level imports database for U.S.-listed firms and construct measures for five widely studied GSS aspects in the operations management literature: the extent of global sourcing, supplier relationship strength, supplier concentration, sourcing lead time, and sourcing countries’ logistical efficiency. For each measure, we examine returns of a zero-cost investment strategy of buying from the highest and selling from the lowest quintile of that measure. Collectively, these investment strategies yield an average annual four-factor alpha of 6%–9.6% (6%–13.9%) with value (equal)-weighted portfolios. Their return predictability is incremental over other operations- and cost arbitrage–motivated predictors, such as inventory turnover, cash conversion cycle, and gross profitability; is persistent across different supply chain positions; and is robust to alternate risk models, subsamples, and empirical specifications. Together, our results indicate that the GSS measures embody independent information about firms’ future profitability, and this information is mispriced by market participants, leading to predictable returns. In accordance with this mechanism, we find that the GSS measures strongly predict both firms’ future earnings and the surprise in market reactions around the earnings announcement days. Managerial implications: The robust return predictability of our GSS measures suggests that investors are not fully incorporating GSS-related information in their stock valuation frameworks. Therefore, our results call for greater investor education on global sourcing and better dissemination of global-sourcing information so as to mitigate valuation inefficiency. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1189 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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