{"title":"Optimal inventory control with cyclic fixed order costs","authors":"Florian Taube, S. Minner","doi":"10.1111/poms.14035","DOIUrl":"https://doi.org/10.1111/poms.14035","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48638358","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}
{"title":"Dynamic pricing and production control in a two‐item make‐to‐stock system with flexible dual sourcing and lost sales","authors":"Ruobing Li, Li Xiao, Dacheng Yao","doi":"10.1111/poms.14026","DOIUrl":"https://doi.org/10.1111/poms.14026","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41910332","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}
Streaming media companies have changed how contents are consumed, produced, and delivered. This research develops a theoretical model on optimal content policies for streaming media companies in order to maximize customer engagement. We have the following interesting findings. First, in contrast to the results in prior literature that firms produce just enough programs and coverage intervals of the programs do not overlap, we show that placing programs closer can be a better policy for engagement-based firms. Second, more contents are produced under engagement-based model when the customer value of the content is high. Third, on learning the distribution of the customers, the media firm will always place programs closer when the distribution density is higher such that neighboring programs always have overlapped target audience. Additionally, in facing the tradeoffs of content quality and quantity, the firm should use a high-quality and low-variety policy for crowded clusters but a low-quality and high-variety policy for niche clusters. Furthermore, when customers consume multiple shows in a period, a good policy is to produce TV shows or series with multiple episodes, whereas individual movies are more suitable for an infrequent watcher market. Our research contributes to the literature on digital media, and the results provide interesting and insightful implications for streaming companies.
{"title":"Content proliferation and narrowcasting in the age of streaming media","authors":"Zhen Fang, Ming Fan, Apurva Jain","doi":"10.1111/poms.14036","DOIUrl":"https://doi.org/10.1111/poms.14036","url":null,"abstract":"Streaming media companies have changed how contents are consumed, produced, and delivered. This research develops a theoretical model on optimal content policies for streaming media companies in order to maximize customer engagement. We have the following interesting findings. First, in contrast to the results in prior literature that firms produce just enough programs and coverage intervals of the programs do not overlap, we show that placing programs closer can be a better policy for engagement-based firms. Second, more contents are produced under engagement-based model when the customer value of the content is high. Third, on learning the distribution of the customers, the media firm will always place programs closer when the distribution density is higher such that neighboring programs always have overlapped target audience. Additionally, in facing the tradeoffs of content quality and quantity, the firm should use a high-quality and low-variety policy for crowded clusters but a low-quality and high-variety policy for niche clusters. Furthermore, when customers consume multiple shows in a period, a good policy is to produce TV shows or series with multiple episodes, whereas individual movies are more suitable for an infrequent watcher market. Our research contributes to the literature on digital media, and the results provide interesting and insightful implications for streaming companies.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"70 10","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518552","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}
{"title":"Implementing trade‐in programs in the presence of resale platforms: Mode selection and pricing1","authors":"X. Bai, T. Choi, Yongjian Li, Xiaochen Sun","doi":"10.1111/poms.14030","DOIUrl":"https://doi.org/10.1111/poms.14030","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46127417","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}
Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic, Torbjørn H. Netland, Stefan Feuerriegel
To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data—so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision making under distributional shifts.
{"title":"Addressing distributional shifts in operations management: The case of order fulfillment in customized production","authors":"Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic, Torbjørn H. Netland, Stefan Feuerriegel","doi":"10.1111/poms.14021","DOIUrl":"https://doi.org/10.1111/poms.14021","url":null,"abstract":"To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data—so-called <i>distributional shifts</i>. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision making under distributional shifts.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"60 10","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518544","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}
Abstract This paper provides evidence that the formation of global supply chain partnerships leads to an increased usage of cross‐border financing. The findings are detected in all three major financing markets—equities, syndicated loans, and public debt. Difference‐in‐differences tests allow us to draw a causal interpretation of our main findings, which also holds in several robustness tests. Our findings suggest that increased cross‐border financing reflects greater ability to access global financial markets, due to enhanced firm visibility and investor attention, as well as operational forces that arise when establishing new global supply chain partnerships. Specifically, we provide evidence that firms that are small, held less by institutional investors, and followed by fewer analysts have more benefits in cross‐border financing from the formation of the global supply chain. Our findings have important implications for firms attempting to integrate into the global supply chain network. More broadly, our findings suggest that the information generated by operational activities can have significant effects on subsequent financing activities.
{"title":"Global supply chains and cross‐border financing","authors":"Michael Hertzel, Jie Peng, Jing Wu, Yu Zhang","doi":"10.1111/poms.14014","DOIUrl":"https://doi.org/10.1111/poms.14014","url":null,"abstract":"Abstract This paper provides evidence that the formation of global supply chain partnerships leads to an increased usage of cross‐border financing. The findings are detected in all three major financing markets—equities, syndicated loans, and public debt. Difference‐in‐differences tests allow us to draw a causal interpretation of our main findings, which also holds in several robustness tests. Our findings suggest that increased cross‐border financing reflects greater ability to access global financial markets, due to enhanced firm visibility and investor attention, as well as operational forces that arise when establishing new global supply chain partnerships. Specifically, we provide evidence that firms that are small, held less by institutional investors, and followed by fewer analysts have more benefits in cross‐border financing from the formation of the global supply chain. Our findings have important implications for firms attempting to integrate into the global supply chain network. More broadly, our findings suggest that the information generated by operational activities can have significant effects on subsequent financing activities.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135239413","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}
Abstract Motivated by the booming online grocery market and the extensive use of contingent free‐shipping (CFS) policies in the e‐grocery industry, we investigate the optimal CFS and pricing decisions for online grocers. Under a CFS policy, consumers enjoy free shipping for orders exceeding a certain threshold value; otherwise, they are charged a flat fee for orders below this threshold. We adopt a utility‐based model to capture consumers' behavior of purchasing additional items to qualify for free shipping under a CFS policy and analyze its impact on policy structure and consumer surplus. We characterize the e‐grocer's optimal pricing and CFS policy and find that consumer heterogeneity and demand distribution lead to different forms of the optimal shipping policy. When consumer heterogeneity is large enough, the optimal policy induces some consumers to top up and may allow some others to ship for free. In this case, the e‐grocer can charge a high‐profit margin. Otherwise, a top‐up option is unnecessary, and a flat‐rate shipping fee policy is optimal. Moreover, while the optimal policy never induces all consumers to top up when they are rational, it is possible to do so when consumers associate some psychological disutility with the shipping fee. Surprisingly, the total consumer surplus under the optimal policy may increase in the latter case. We further model a Stackelberg game between an e‐grocer and an offline channel and find that the difference between the e‐grocer's internal shipping cost and consumers' inconvenience cost of shopping offline is a main driver for market segmentation. Lastly, we show that a subscription‐based free‐shipping program, in addition to the jointly optimized CFS and pricing policy, cannot improve profits when consumers' order size and frequency are independent. Our findings help online grocers make operational and marketing decisions under the impact of consumers' top‐up behavior.
{"title":"Designing shipping policies with top‐up options to qualify for free delivery","authors":"Guang Li, Lifei Sheng, Dongyuan Zhan","doi":"10.1111/poms.14002","DOIUrl":"https://doi.org/10.1111/poms.14002","url":null,"abstract":"Abstract Motivated by the booming online grocery market and the extensive use of contingent free‐shipping (CFS) policies in the e‐grocery industry, we investigate the optimal CFS and pricing decisions for online grocers. Under a CFS policy, consumers enjoy free shipping for orders exceeding a certain threshold value; otherwise, they are charged a flat fee for orders below this threshold. We adopt a utility‐based model to capture consumers' behavior of purchasing additional items to qualify for free shipping under a CFS policy and analyze its impact on policy structure and consumer surplus. We characterize the e‐grocer's optimal pricing and CFS policy and find that consumer heterogeneity and demand distribution lead to different forms of the optimal shipping policy. When consumer heterogeneity is large enough, the optimal policy induces some consumers to top up and may allow some others to ship for free. In this case, the e‐grocer can charge a high‐profit margin. Otherwise, a top‐up option is unnecessary, and a flat‐rate shipping fee policy is optimal. Moreover, while the optimal policy never induces all consumers to top up when they are rational, it is possible to do so when consumers associate some psychological disutility with the shipping fee. Surprisingly, the total consumer surplus under the optimal policy may increase in the latter case. We further model a Stackelberg game between an e‐grocer and an offline channel and find that the difference between the e‐grocer's internal shipping cost and consumers' inconvenience cost of shopping offline is a main driver for market segmentation. Lastly, we show that a subscription‐based free‐shipping program, in addition to the jointly optimized CFS and pricing policy, cannot improve profits when consumers' order size and frequency are independent. Our findings help online grocers make operational and marketing decisions under the impact of consumers' top‐up behavior.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136265505","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}
Abstract Ensuring continuity of care for patients after an intensive hospitalization episode is a complex dilemma that plagues the US health‐care system. Despite its influence on health outcomes such as mortality and readmissions, it is difficult to construct policy instruments such as report cards or penalties for improving continuity of care due to the fragmented nature of post‐intensive hospitalization care. However, policy instruments that target other related health outcomes can also benefit continuity of care. We examine whether a quality regulation that penalized hospitals for excess readmissions has implications for an unregulated aspect, that is, continuity of care through intra‐ailment and spillover effects. Intra ‐ailment effects occur from the effects of penalty regulation on the continuity of care of ailments targeted for regulation. Spillovers occur from the effects of penalty regulation on continuity of care for ailments that were not targeted by the policy but share complementarities with targeted ailments. We conduct difference‐in‐differences analyses using patient‐level data for 2004–2014 from the state of California. Our empirical strategy utilizes the nature of the hospital production function, which is organized by medically related specialties. We construct three cohorts of patients, all of whom belong to specialties that house the ailments targeted for readmission penalties. These include (1) ailments targeted by the penalty policy for readmissions, (2) closely related, non‐targeted ailments, and (3) unrelated ailments. Results reveal evidence of intra‐ailment effects, which manifest as increases in continuity of care of targeted ailments, and spillovers, which manifest as increases in continuity of care of non‐targeted but related ailments. We find that processual mechanisms, such as the source of patient admissions and length of stay, and structural mechanisms, such as system size, accentuate the intra‐ailment effects. Our study provides novel insights into how quality regulation can have intra‐ailment and spillover effects and bespeaks the importance of incorporating these effects in the regulatory benefit‐cost calculus.
{"title":"Virtuous spillover effects of quality penalties on the continuity of health care","authors":"Aishwarrya Deore, Ranjani Krishnan, Anand Nair","doi":"10.1111/poms.14012","DOIUrl":"https://doi.org/10.1111/poms.14012","url":null,"abstract":"Abstract Ensuring continuity of care for patients after an intensive hospitalization episode is a complex dilemma that plagues the US health‐care system. Despite its influence on health outcomes such as mortality and readmissions, it is difficult to construct policy instruments such as report cards or penalties for improving continuity of care due to the fragmented nature of post‐intensive hospitalization care. However, policy instruments that target other related health outcomes can also benefit continuity of care. We examine whether a quality regulation that penalized hospitals for excess readmissions has implications for an unregulated aspect, that is, continuity of care through intra‐ailment and spillover effects. Intra ‐ailment effects occur from the effects of penalty regulation on the continuity of care of ailments targeted for regulation. Spillovers occur from the effects of penalty regulation on continuity of care for ailments that were not targeted by the policy but share complementarities with targeted ailments. We conduct difference‐in‐differences analyses using patient‐level data for 2004–2014 from the state of California. Our empirical strategy utilizes the nature of the hospital production function, which is organized by medically related specialties. We construct three cohorts of patients, all of whom belong to specialties that house the ailments targeted for readmission penalties. These include (1) ailments targeted by the penalty policy for readmissions, (2) closely related, non‐targeted ailments, and (3) unrelated ailments. Results reveal evidence of intra‐ailment effects, which manifest as increases in continuity of care of targeted ailments, and spillovers, which manifest as increases in continuity of care of non‐targeted but related ailments. We find that processual mechanisms, such as the source of patient admissions and length of stay, and structural mechanisms, such as system size, accentuate the intra‐ailment effects. Our study provides novel insights into how quality regulation can have intra‐ailment and spillover effects and bespeaks the importance of incorporating these effects in the regulatory benefit‐cost calculus.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134922124","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}
Abstract This study analyzes a monopolistic seller's optimal differential pricing problem with strategic consumers connected in social networks. The consumers who purchase in the later period can get positive externalities from their friends who purchased in the early period but have to bear a utility discount for the delayed consumption. We first characterize consumers' strategic purchase decisions under general network structures. We then derive the optimal differential pricing strategies and demonstrate that different network structures lead to substantially different strategies. We find that when the intensity of the network externality effect is lower than a threshold and the influence matrix is symmetric, it is always optimal for the seller to conduct an increasing‐pricing strategy. However, when the network externality effect is strong, a decreasing‐pricing strategy may also be optimal. We further examine how the imbalance of influence, degree heterogeneity, and network topology impact the optimal pricing policy and profit. We find that when the intensity of network externality is relatively low, it is more profitable to sell products through many interconnected low‐influencer networks; however, when the network externality intensity is high, it is better to sell through a few high‐influencer networks. Finally, we show that the profit loss caused by uniform pricing strategies or by ignoring consumer network structures can be significant under certain conditions, thereby revealing the substantial value of differential pricing in social networks.
{"title":"Differential pricing in social networks with strategic consumers","authors":"Rui Zheng, Biying Shou, Yingju Chen","doi":"10.1111/poms.13999","DOIUrl":"https://doi.org/10.1111/poms.13999","url":null,"abstract":"Abstract This study analyzes a monopolistic seller's optimal differential pricing problem with strategic consumers connected in social networks. The consumers who purchase in the later period can get positive externalities from their friends who purchased in the early period but have to bear a utility discount for the delayed consumption. We first characterize consumers' strategic purchase decisions under general network structures. We then derive the optimal differential pricing strategies and demonstrate that different network structures lead to substantially different strategies. We find that when the intensity of the network externality effect is lower than a threshold and the influence matrix is symmetric, it is always optimal for the seller to conduct an increasing‐pricing strategy. However, when the network externality effect is strong, a decreasing‐pricing strategy may also be optimal. We further examine how the imbalance of influence, degree heterogeneity, and network topology impact the optimal pricing policy and profit. We find that when the intensity of network externality is relatively low, it is more profitable to sell products through many interconnected low‐influencer networks; however, when the network externality intensity is high, it is better to sell through a few high‐influencer networks. Finally, we show that the profit loss caused by uniform pricing strategies or by ignoring consumer network structures can be significant under certain conditions, thereby revealing the substantial value of differential pricing in social networks.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135160895","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}
Enayon Sunday Taiwo, Sergei Savin, Yuohua (Frank) Chen, Kwai‐Sang Chin
Abstract The aging population and increasing chronic disease load are rapidly changing the face of primary care delivery, with mid‐level (e.g., nurse) practitioners providing growing proportion of patient care. Potential differences in the quality of care offered by physicians and nurse practitioners may affect patient preferences, thus leading to patient choice behavior. This paper focuses on the problem of appointment scheduling for physician–nurse teams in the presence of patient choice and no‐shows. We propose a novel model that accounts for patient choices in a system with two provider types. Despite the increased structural complexity of the model, we derive sufficient conditions under which the problem is efficiently solvable. To counter the computational challenges arising in the general setting, we propose an easy‐to‐implement heuristic, which is proven to be optimal in the absence of patient no‐shows. Our numerical study shows how the ratio of qualities of care delivered by nurses and physicians affect the profitability of the medical practice, enabling the analysis of the trade‐offs involved in hiring a nurse practitioner. This paper introduces a patient‐controlled approach to incorporating nonphysician providers into physician‐led outpatient care delivery systems and compares it to widely used “ice breaker” and “standalone” modes of using nonphysician providers. Our findings reveal that clinical practices that employ mixed (physicians and nonphysicians) provider pools can significantly improve their financial and operational performance by moving away from the “ice breaker” and “standalone” use of nonphysician providers by delaying the selection of an appropriate care provider till the actual day of care delivery.
{"title":"Patient‐controlled use of nonphysician providers: Appointment scheduling in mixed‐provider settings","authors":"Enayon Sunday Taiwo, Sergei Savin, Yuohua (Frank) Chen, Kwai‐Sang Chin","doi":"10.1111/poms.14000","DOIUrl":"https://doi.org/10.1111/poms.14000","url":null,"abstract":"Abstract The aging population and increasing chronic disease load are rapidly changing the face of primary care delivery, with mid‐level (e.g., nurse) practitioners providing growing proportion of patient care. Potential differences in the quality of care offered by physicians and nurse practitioners may affect patient preferences, thus leading to patient choice behavior. This paper focuses on the problem of appointment scheduling for physician–nurse teams in the presence of patient choice and no‐shows. We propose a novel model that accounts for patient choices in a system with two provider types. Despite the increased structural complexity of the model, we derive sufficient conditions under which the problem is efficiently solvable. To counter the computational challenges arising in the general setting, we propose an easy‐to‐implement heuristic, which is proven to be optimal in the absence of patient no‐shows. Our numerical study shows how the ratio of qualities of care delivered by nurses and physicians affect the profitability of the medical practice, enabling the analysis of the trade‐offs involved in hiring a nurse practitioner. This paper introduces a patient‐controlled approach to incorporating nonphysician providers into physician‐led outpatient care delivery systems and compares it to widely used “ice breaker” and “standalone” modes of using nonphysician providers. Our findings reveal that clinical practices that employ mixed (physicians and nonphysicians) provider pools can significantly improve their financial and operational performance by moving away from the “ice breaker” and “standalone” use of nonphysician providers by delaying the selection of an appropriate care provider till the actual day of care delivery.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136319225","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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