Problem definition: Online retailers often receive customer orders comprising several products of differing origins. To fulfill these orders, retailers must ship multiple parcels from different locations and—unless they are grouped somewhere along the supply chain—these may reach the customer’s doorstep one by one. Academic/practical relevance: We conjecture here that receiving products sequentially instead of all together affects a consumer’s reaction to her purchases, possibly influencing—for good or ill—her decision to return products, as well as her overall service satisfaction. We use two-year granular data from an online fashion marketplace to test this hypothesis and characterize consumer behavioral responses to delivery consolidation and examine how it impacts supply chain stakeholders. Methodology: To achieve causal inference, we exploit the fact that the couriers used by the focal marketplace gather together certain parcels for reasons related more to the timing of their arrival than their actual customers, thereby exogenously consolidating the delivery of some orders. We construct a balanced sample of matched twin multiproduct orders that are alike in all respects except their delivery: consolidated (all parcels delivered jointly) versus otherwise (split). Results: We find that delivery consolidation benefits the marketplace and all its suppliers. By eliminating the stress associated with split deliveries, delivery consolidation pleases consumers as it leads to fewer returns and higher overall satisfaction. Managerial implications: Delivering all products in an order together, even if later, reduces the probability of a return, which improves the financial performance of the marketplace and its suppliers and reduces reverse logistics. Our results suggest that in our context, delivery speed matters less than the convenience of receiving all ordered goods in a single delivery, and we provide directions for adapting logistics strategies accordingly. Our empirical findings also imply that the return decisions of multiple products purchased at once should not be considered to be independent. Finding tractable ways of modeling this feature will be necessary in further driving retail practice through theoretical research that accounts for the behavioral implications of delivery consolidation when optimizing fulfillment decisions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1200 .
{"title":"Better Together! The Consumer Implications of Delivery Consolidation","authors":"Laura Wagner, E. Calvo, P. Amorim","doi":"10.1287/msom.2023.1200","DOIUrl":"https://doi.org/10.1287/msom.2023.1200","url":null,"abstract":"Problem definition: Online retailers often receive customer orders comprising several products of differing origins. To fulfill these orders, retailers must ship multiple parcels from different locations and—unless they are grouped somewhere along the supply chain—these may reach the customer’s doorstep one by one. Academic/practical relevance: We conjecture here that receiving products sequentially instead of all together affects a consumer’s reaction to her purchases, possibly influencing—for good or ill—her decision to return products, as well as her overall service satisfaction. We use two-year granular data from an online fashion marketplace to test this hypothesis and characterize consumer behavioral responses to delivery consolidation and examine how it impacts supply chain stakeholders. Methodology: To achieve causal inference, we exploit the fact that the couriers used by the focal marketplace gather together certain parcels for reasons related more to the timing of their arrival than their actual customers, thereby exogenously consolidating the delivery of some orders. We construct a balanced sample of matched twin multiproduct orders that are alike in all respects except their delivery: consolidated (all parcels delivered jointly) versus otherwise (split). Results: We find that delivery consolidation benefits the marketplace and all its suppliers. By eliminating the stress associated with split deliveries, delivery consolidation pleases consumers as it leads to fewer returns and higher overall satisfaction. Managerial implications: Delivering all products in an order together, even if later, reduces the probability of a return, which improves the financial performance of the marketplace and its suppliers and reduces reverse logistics. Our results suggest that in our context, delivery speed matters less than the convenience of receiving all ordered goods in a single delivery, and we provide directions for adapting logistics strategies accordingly. Our empirical findings also imply that the return decisions of multiple products purchased at once should not be considered to be independent. Finding tractable ways of modeling this feature will be necessary in further driving retail practice through theoretical research that accounts for the behavioral implications of delivery consolidation when optimizing fulfillment decisions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1200 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131248529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruoting Li, Margaret Tobey, M. Mayorga, Sherrie Caltagirone, Osman Y. Özaltın
Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 .
{"title":"Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses","authors":"Ruoting Li, Margaret Tobey, M. Mayorga, Sherrie Caltagirone, Osman Y. Özaltın","doi":"10.1287/msom.2023.1196","DOIUrl":"https://doi.org/10.1287/msom.2023.1196","url":null,"abstract":"Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karthik Balasubramanian, D. Drake, Jason Acimovic, Douglas Fearing
Problem definition: Mobile money systems—platforms built and managed by mobile network platform operators (MMPOs) to allow money to be stored as digital currency—connect millions of poor and “unbanked” people to the formal financial system. Unfortunately, low service levels because of the suboptimal management of cash and digital currency (e-float) inventory impede the development of these ecosystems. Accordingly, we seek to answer the question of how agents should manage inventories of cash and e-float. Academic/practical relevance: This paper extends inventory theory to the mobile money context, unique in that sales of cash generate inventory of e-float and vice versa. In doing so, we address a key pain point for an emerging sector that improves lives at the base of the pyramid. Methodology: We develop an analytical heuristic to determine initial stocking levels for cash and e-float and analyze its performance on simulated and actual data. Results: By partnering with an MMPO, we tested the performance of the heuristic inventory policy with data from more than 35 million transactions. The heuristic captured 99.9998% of the optimal profit on simulated data and, on actual data, we found that following the recommendations could increase agents’ profits by an average of 15.4%. Managerial implications: We develop a pragmatic inventory policy that performs nearly optimally. We also analyze under which conditions the performance deteriorates and examine heterogeneity among agents with respect to the heuristic’s impact on their performance. Thus, we equip MMPOs with guidance as to whom to target and how. By contributing to service level and profit improvements, this work can make mobile money a more effective financial inclusion tool in the developing world as well as improve the livelihoods of agents. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1175 .
{"title":"Mobile Money Operations: Policies for Managing Cash and Digital Currency Inventories in the Developing World","authors":"Karthik Balasubramanian, D. Drake, Jason Acimovic, Douglas Fearing","doi":"10.1287/msom.2022.1175","DOIUrl":"https://doi.org/10.1287/msom.2022.1175","url":null,"abstract":"Problem definition: Mobile money systems—platforms built and managed by mobile network platform operators (MMPOs) to allow money to be stored as digital currency—connect millions of poor and “unbanked” people to the formal financial system. Unfortunately, low service levels because of the suboptimal management of cash and digital currency (e-float) inventory impede the development of these ecosystems. Accordingly, we seek to answer the question of how agents should manage inventories of cash and e-float. Academic/practical relevance: This paper extends inventory theory to the mobile money context, unique in that sales of cash generate inventory of e-float and vice versa. In doing so, we address a key pain point for an emerging sector that improves lives at the base of the pyramid. Methodology: We develop an analytical heuristic to determine initial stocking levels for cash and e-float and analyze its performance on simulated and actual data. Results: By partnering with an MMPO, we tested the performance of the heuristic inventory policy with data from more than 35 million transactions. The heuristic captured 99.9998% of the optimal profit on simulated data and, on actual data, we found that following the recommendations could increase agents’ profits by an average of 15.4%. Managerial implications: We develop a pragmatic inventory policy that performs nearly optimally. We also analyze under which conditions the performance deteriorates and examine heterogeneity among agents with respect to the heuristic’s impact on their performance. Thus, we equip MMPOs with guidance as to whom to target and how. By contributing to service level and profit improvements, this work can make mobile money a more effective financial inclusion tool in the developing world as well as improve the livelihoods of agents. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1175 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133457250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kaitlin D. Wowak, John P. Lalor, S. Somanchi, Corey M. Angst
Problem definition: Business analytics (BA) in healthcare research offers numerous valuable insights that can enhance patient care and hospital performance. Consequently, there has been a rapid surge of research in this area. Academic/practical relevance: The objective of this study is to provide a data-driven summary of the extant BA in healthcare literature and a guide for future research. Methodology: Leveraging a topic modeling technique and network analysis, we provide insight into how BA topics change over time. Results: We provide an in-depth analysis of 320 articles from the University of Texas at Dallas journal list and a basic topic model and network analyses for an additional 6,515 relevant articles from PubMed published in top-tier journals across 69 medical subcategories. Our study bridges research in operations management, information systems, healthcare, and analytics by providing a definition of BA in healthcare and a road map for future research. Managerial implications: Our study provides a single source of information into operations- and analytics-related issues, such as wait times, admissions, hospital performance, etc., that scholars and administrators might use to rethink how specific processes are handled in hospitals. In addition, this work highlights how operations management research has addressed clinically important issues such as patient satisfaction, doctor ratings, readmission rates, mortality, efficiency, cost of care, and compliance with protocols of care, as all are represented in our sample. Another key contribution of our study is that we provide an interactive article analysis tool as a web application for scholars in hopes of facilitating research in this area. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1192 .
{"title":"Business Analytics in Healthcare: Past, Present, and Future Trends","authors":"Kaitlin D. Wowak, John P. Lalor, S. Somanchi, Corey M. Angst","doi":"10.1287/msom.2023.1192","DOIUrl":"https://doi.org/10.1287/msom.2023.1192","url":null,"abstract":"Problem definition: Business analytics (BA) in healthcare research offers numerous valuable insights that can enhance patient care and hospital performance. Consequently, there has been a rapid surge of research in this area. Academic/practical relevance: The objective of this study is to provide a data-driven summary of the extant BA in healthcare literature and a guide for future research. Methodology: Leveraging a topic modeling technique and network analysis, we provide insight into how BA topics change over time. Results: We provide an in-depth analysis of 320 articles from the University of Texas at Dallas journal list and a basic topic model and network analyses for an additional 6,515 relevant articles from PubMed published in top-tier journals across 69 medical subcategories. Our study bridges research in operations management, information systems, healthcare, and analytics by providing a definition of BA in healthcare and a road map for future research. Managerial implications: Our study provides a single source of information into operations- and analytics-related issues, such as wait times, admissions, hospital performance, etc., that scholars and administrators might use to rethink how specific processes are handled in hospitals. In addition, this work highlights how operations management research has addressed clinically important issues such as patient satisfaction, doctor ratings, readmission rates, mortality, efficiency, cost of care, and compliance with protocols of care, as all are represented in our sample. Another key contribution of our study is that we provide an interactive article analysis tool as a web application for scholars in hopes of facilitating research in this area. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1192 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121159004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: This paper provides a theoretical investigation into the value and design of a traceability-driven blockchain under different supply chain structures. Methodology/results: We use game theory to study the quality contracting equilibrium between one buyer and two suppliers and identify two fundamental functionalities of a traceability-driven blockchain. In serial supply chains, the ability to trace the sequential production process creates value by mitigating double moral hazard. In this case, traceability always improves product quality and all firms’ profits and naturally creates a win-win. In parallel supply chains, the ability to trace the product origin enables flexible product recall, which can reduce product quality. In this case, traceability can benefit the buyer while hurting the suppliers, creating an incentive conflict. Managerial implications: Firms operating in different kinds of supply chains could face unique challenges when they adopt and design a traceability-driven blockchain. First, in serial supply chains, any firm can be the initiator of the blockchain, whereas in parallel supply chains, it may be critical for the buyer to take the lead in initiating the blockchain and properly compensate the suppliers. Second, in serial supply chains, a restricted data permission policy where each supplier shares their own traceability data with the buyer but not with each other can improve the supply chain profit, whereas in parallel supply chains, it is never optimal to restrict a firm’s access to the traceability data. Third, the suppliers’ incentive to enhance the governance of data quality is more aligned with the supply chain optimum in serial supply chains compared with parallel supply chains. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. J. Liu was supported by the National Natural Science Foundation of China [Grant 72101110] and The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Grant 20YJC630084]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1161 .
{"title":"Value and Design of Traceability-Driven Blockchains","authors":"Yao Cui, Ming Hu, Jingchen Liu","doi":"10.1287/msom.2022.1161","DOIUrl":"https://doi.org/10.1287/msom.2022.1161","url":null,"abstract":"Problem definition: This paper provides a theoretical investigation into the value and design of a traceability-driven blockchain under different supply chain structures. Methodology/results: We use game theory to study the quality contracting equilibrium between one buyer and two suppliers and identify two fundamental functionalities of a traceability-driven blockchain. In serial supply chains, the ability to trace the sequential production process creates value by mitigating double moral hazard. In this case, traceability always improves product quality and all firms’ profits and naturally creates a win-win. In parallel supply chains, the ability to trace the product origin enables flexible product recall, which can reduce product quality. In this case, traceability can benefit the buyer while hurting the suppliers, creating an incentive conflict. Managerial implications: Firms operating in different kinds of supply chains could face unique challenges when they adopt and design a traceability-driven blockchain. First, in serial supply chains, any firm can be the initiator of the blockchain, whereas in parallel supply chains, it may be critical for the buyer to take the lead in initiating the blockchain and properly compensate the suppliers. Second, in serial supply chains, a restricted data permission policy where each supplier shares their own traceability data with the buyer but not with each other can improve the supply chain profit, whereas in parallel supply chains, it is never optimal to restrict a firm’s access to the traceability data. Third, the suppliers’ incentive to enhance the governance of data quality is more aligned with the supply chain optimum in serial supply chains compared with parallel supply chains. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. J. Liu was supported by the National Natural Science Foundation of China [Grant 72101110] and The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Grant 20YJC630084]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1161 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128717339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: In this work, we examine how ridesharing platforms affect changes in short-term vehicle purchasing. On the one hand, if the introduction of such platforms motivates owners to use the idle capacity of their existing vehicles to accrue rents, vehicle sales might fall. On the other hand, if the platform induces would-be drivers to purchase new vehicles in order to participate, vehicle sales might rise. Academic/practical relevance: Whereas operations management researchers have begun to broach this subject analytically, this work provides empirical evidence of the impact of ridesharing platforms on new vehicle ownership. Further, we assess heterogeneity in the effect across vehicle type and location. Methodology: We examine this tension using a unique data set of new vehicle registrations in China. In doing so, we exploit the variation in the timing of Uber entry using a difference-in-difference approach. Results: Findings suggest Uber entry is associated with a significant short-term increase in private new vehicle ownership, indicating that consumers actively change their stock of held resources to capture excess rents offered by the platform. These effects exclusively manifest among vehicle brands that qualify for the platform. Further, inasmuch as sales of vehicles with smaller displacement increase more than large-displacement vehicles, results indicate that the effect of Uber entry varies considerably across vehicle types. Finally, results indicate that the effects are stronger in locations where established public transportation options are weaker. Managerial implications: Results provide initial evidence that manufacturers can benefit from the emergence of the sharing economy, especially manufacturers whose products align with the needs of platform participants. For policy makers, our findings further undercut claims made by platforms that the individuals working on them are exploiting already existing resources, suggesting some form of nascent professionalism on the part of platform workers. Funding: The research was supported by the Key Program of National Natural Science of China [Grant 71832010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1183 .
{"title":"An Empirical Investigation of Ridesharing and New Vehicle Purchase","authors":"Jing Gong, Brad N. Greenwood, Yiping Song","doi":"10.1287/msom.2022.1183","DOIUrl":"https://doi.org/10.1287/msom.2022.1183","url":null,"abstract":"Problem definition: In this work, we examine how ridesharing platforms affect changes in short-term vehicle purchasing. On the one hand, if the introduction of such platforms motivates owners to use the idle capacity of their existing vehicles to accrue rents, vehicle sales might fall. On the other hand, if the platform induces would-be drivers to purchase new vehicles in order to participate, vehicle sales might rise. Academic/practical relevance: Whereas operations management researchers have begun to broach this subject analytically, this work provides empirical evidence of the impact of ridesharing platforms on new vehicle ownership. Further, we assess heterogeneity in the effect across vehicle type and location. Methodology: We examine this tension using a unique data set of new vehicle registrations in China. In doing so, we exploit the variation in the timing of Uber entry using a difference-in-difference approach. Results: Findings suggest Uber entry is associated with a significant short-term increase in private new vehicle ownership, indicating that consumers actively change their stock of held resources to capture excess rents offered by the platform. These effects exclusively manifest among vehicle brands that qualify for the platform. Further, inasmuch as sales of vehicles with smaller displacement increase more than large-displacement vehicles, results indicate that the effect of Uber entry varies considerably across vehicle types. Finally, results indicate that the effects are stronger in locations where established public transportation options are weaker. Managerial implications: Results provide initial evidence that manufacturers can benefit from the emergence of the sharing economy, especially manufacturers whose products align with the needs of platform participants. For policy makers, our findings further undercut claims made by platforms that the individuals working on them are exploiting already existing resources, suggesting some form of nascent professionalism on the part of platform workers. Funding: The research was supported by the Key Program of National Natural Science of China [Grant 71832010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1183 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125039253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers’ movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort. Funding: This work was supported by the Dutch Research Council (NWO) Gravitation Programme NETWORKS [Grant 024.002.003], the Israel Ministry of Science and Technology [Grant 880011], and the Israel Science Foundation [Grant 1955/15]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1167 .
{"title":"State-Dependent Estimation of Delay Distributions in Fork-Join Networks","authors":"Nitzan Carmeli, G. Yom-Tov, O. Boxma","doi":"10.1287/msom.2022.1167","DOIUrl":"https://doi.org/10.1287/msom.2022.1167","url":null,"abstract":"Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers’ movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort. Funding: This work was supported by the Dutch Research Council (NWO) Gravitation Programme NETWORKS [Grant 024.002.003], the Israel Ministry of Science and Technology [Grant 880011], and the Israel Science Foundation [Grant 1955/15]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1167 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amine Bennouna, Joshua Joseph, David Nze-Ndong, G. Perakis, Divya Singhvi, O. S. Lami, Yannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas
Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the methods we developed for predicting cases and deaths using a novel machine-learning-based aggregation method to create a single prediction that we call MIT-Cassandra. We further incorporate COVID-19 case prediction to determine true prevalence and incorporate this prevalence into an optimization model for efficiently and fairly managing the operations of vaccine allocation. We study the trade-offs of vaccine allocation between different regions and age groups, as well as first- and second-dose distribution of different vaccines. This also allows us to provide insights into how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fair way. Managerial implications: MIT-Cassandra is currently being used by the Centers for Disease Control and Prevention and is consistently among the best-performing methods in terms of accuracy, often ranking at the top. In addition, our work has been helping decision makers by predicting how cases and true prevalence of COVID-19 will progress over the next few months in different regions and utilizing the knowledge for vaccine distribution under various operational constraints. Finally, and very importantly, our work has specifically been used as part of a collaboration with the Massachusetts Institute of Technology's (MIT’s) Quest for Intelligence and as part of MIT’s process to reopen the institute. Funding: Financial support from MIT Quest for Intelligence is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1160 .
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Problem definition: Smart contract improves the supply chain efficiency by enabling the supplier’s commitment to postshipment financing decisions, which mitigates the bank’s lending risk exposure and thereby reduces the financing cost. This paper investigates how smart contract adoption could facilitate trade finance activities and create value for supply chain firms. Academic/practical relevance: As the emerging blockchain technology could potentially reshape the trade financing landscape, understanding the impact of smart contract adoption and its interaction with trade finance activities is practically relevant and of great importance. Methodology: We develop a two-stage game-theoretic model and adopt supply chain finance theory to characterize the strategic interactions between supply chain firms in the presence of both operational risk (demand uncertainty) and financial risks (credit and liquidity risks). Results: We find that the value of smart contract depends critically on the trade finance structures, including both preshipment and postshipment financing schemes. Under the baseline trade finance model (with purchase order financing as preshipment financing and factoring as postshipment financing), smart contract alleviates the supplier’s overpricing behavior caused by commitment frictions and helps restore the supply chain efficiency. When buyer direct financing serves as an alternative preshipment financing, smart contract might discourage the retailer from offering buyer direct financing, which significantly hurts the supplier and thus reduces the supply chain profit. When invoice trading serves as the alternative postshipment financing, the supplier always chooses invoice trading over factoring because of its trading flexibility, which in turn, makes the commitment frictions ubiquitous and unresolvable (namely, commitment trap). As a result, invoice trading could unexpectedly lead to a lower supplier’s profit. Luckily, such an adoption dilemma can be resolved by smart contract adoption in conjunction with factoring. Managerial implications: Our findings provide guidelines for and insights into when smart contract should be adopted and its interactions with different trade finance schemes. In particular, smart contract adoption does not always benefit the supply chain.
{"title":"The Value of Smart Contract in Trade Finance","authors":"Xiaoyu Wang, Fasheng Xu","doi":"10.1287/msom.2022.1126","DOIUrl":"https://doi.org/10.1287/msom.2022.1126","url":null,"abstract":"Problem definition: Smart contract improves the supply chain efficiency by enabling the supplier’s commitment to postshipment financing decisions, which mitigates the bank’s lending risk exposure and thereby reduces the financing cost. This paper investigates how smart contract adoption could facilitate trade finance activities and create value for supply chain firms. Academic/practical relevance: As the emerging blockchain technology could potentially reshape the trade financing landscape, understanding the impact of smart contract adoption and its interaction with trade finance activities is practically relevant and of great importance. Methodology: We develop a two-stage game-theoretic model and adopt supply chain finance theory to characterize the strategic interactions between supply chain firms in the presence of both operational risk (demand uncertainty) and financial risks (credit and liquidity risks). Results: We find that the value of smart contract depends critically on the trade finance structures, including both preshipment and postshipment financing schemes. Under the baseline trade finance model (with purchase order financing as preshipment financing and factoring as postshipment financing), smart contract alleviates the supplier’s overpricing behavior caused by commitment frictions and helps restore the supply chain efficiency. When buyer direct financing serves as an alternative preshipment financing, smart contract might discourage the retailer from offering buyer direct financing, which significantly hurts the supplier and thus reduces the supply chain profit. When invoice trading serves as the alternative postshipment financing, the supplier always chooses invoice trading over factoring because of its trading flexibility, which in turn, makes the commitment frictions ubiquitous and unresolvable (namely, commitment trap). As a result, invoice trading could unexpectedly lead to a lower supplier’s profit. Luckily, such an adoption dilemma can be resolved by smart contract adoption in conjunction with factoring. Managerial implications: Our findings provide guidelines for and insights into when smart contract should be adopted and its interactions with different trade finance schemes. In particular, smart contract adoption does not always benefit the supply chain.","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114601343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Personalized medicine (PM) seeks the best treatment for each patient among a set of available treatment methods. Because a specific treatment does not work well on all patients, traditionally, the best treatment was selected based on the doctor’s personal experience and expertise, which is subject to human errors. In the meantime, stochastic models have been well developed in the literature for a lot of major diseases. This gives rise to a simulation-based solution for PM, which uses the simulation tool to evaluate the performance for pairs of treatment and patient biometric characteristics and, based on that, selects the best treatment for each patient characteristic. Methodology/results: In this research, we extend the ranking and selection (R&S) model in simulation-based decision making to solving PM. The biometric characteristics of a patient are treated as a context for R&S, and we call it contextual ranking and selection (CR&S). We consider two formulations of CR&S with small and large context spaces, respectively, and develop new techniques for solving them and identifying the rate-optimal budget allocation rules. Based on them, two selection algorithms are proposed, which can be shown to be numerically superior via a set of tests on abstract and real-world examples. Managerial implications: This research provides a systematic way of conducting simulation-based decision-making for PM. To improve the overall decision quality for the possible contexts, more simulation efforts should be devoted to contexts in which it is difficult to distinguish between the best treatment and non-best treatments, and our results quantify the optimal trade-off of the simulation efforts between the pairs of contexts and treatments. Funding: J. Du is partially supported by the National Natural Science Foundation of China [Grant 72091211]. C.-H. Chen is partially supported by the National Science Foundation under Awards FAIN212368. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0232 .
{"title":"A Contextual Ranking and Selection Method for Personalized Medicine","authors":"Jianzhong Du, Siyang Gao, C.-H. Chen","doi":"10.1287/msom.2022.0232","DOIUrl":"https://doi.org/10.1287/msom.2022.0232","url":null,"abstract":"Problem definition: Personalized medicine (PM) seeks the best treatment for each patient among a set of available treatment methods. Because a specific treatment does not work well on all patients, traditionally, the best treatment was selected based on the doctor’s personal experience and expertise, which is subject to human errors. In the meantime, stochastic models have been well developed in the literature for a lot of major diseases. This gives rise to a simulation-based solution for PM, which uses the simulation tool to evaluate the performance for pairs of treatment and patient biometric characteristics and, based on that, selects the best treatment for each patient characteristic. Methodology/results: In this research, we extend the ranking and selection (R&S) model in simulation-based decision making to solving PM. The biometric characteristics of a patient are treated as a context for R&S, and we call it contextual ranking and selection (CR&S). We consider two formulations of CR&S with small and large context spaces, respectively, and develop new techniques for solving them and identifying the rate-optimal budget allocation rules. Based on them, two selection algorithms are proposed, which can be shown to be numerically superior via a set of tests on abstract and real-world examples. Managerial implications: This research provides a systematic way of conducting simulation-based decision-making for PM. To improve the overall decision quality for the possible contexts, more simulation efforts should be devoted to contexts in which it is difficult to distinguish between the best treatment and non-best treatments, and our results quantify the optimal trade-off of the simulation efforts between the pairs of contexts and treatments. Funding: J. Du is partially supported by the National Natural Science Foundation of China [Grant 72091211]. C.-H. Chen is partially supported by the National Science Foundation under Awards FAIN212368. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0232 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130352887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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