Umit Celik, Sandeep Rath, Saravanan Kesavan, Bradley R. Staats
Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians’ workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028 .
问题定义：医生每天花在电子病历 (EHR) 系统上的时间超过 5 小时，下班后花在电子病历工作上的时间超过 1 小时。许多研究都指出了过度使用电子病历和下班后工作的有害影响，包括医生倦怠、医生流失和预约延迟。然而，电子病历时间并不纯粹是一个外生因素，因为它取决于医生的使用行为，而医生的使用行为可能会产生重要的操作后果。有趣的是，之前的文献并没有严格考虑过这个问题。在本文中，我们研究了医生关于何时执行电子病历任务的工作流程决策如何影响：（1）使用电子病历的总时间和（2）下班后花费的时间。方法/结果：我们的数据包括一个大型学术医学中心家庭医学科 74 名医生的约 15 万个预约。我们的数据集包含预约进展和电子病历使用的详细过程级时间戳。我们发现，电子病历系统工作的效果取决于工作是在预约前还是预约后进行。预约前的电子病历工作会减少电子病历的总工作量和下班后花在电子病历上的时间。预约后的电子健康记录工作减少了电子健康记录的下班后时间，但增加了电子健康记录的总时间。我们发现，增加预约之间的空闲时间可以鼓励预约前和预约后的电子病历工作。管理意义：我们的研究结果不仅有助于我们更全面地了解次要任务的工作时间和结构，还有助于医疗保健管理者创建电子病历工作流程和预约时间表，以减少医生因过度使用电子病历而产生的倦怠感：本文已被《制造与印记》（Manufacturing & Service Operations Management）杂志的《运营管理前沿》（Frontiers in Operations Initiative）收录：本文的研究得到了北卡罗来纳大学教堂山分校凯南-弗拉格勒商学院健康商业中心的部分资助：在线附录见 https://doi.org/10.1287/msom.2023.0028 。
{"title":"Frontiers in Operations: Does Physician’s Choice of When to Perform EHR Tasks Influence Total EHR Workload?","authors":"Umit Celik, Sandeep Rath, Saravanan Kesavan, Bradley R. Staats","doi":"10.1287/msom.2023.0028","DOIUrl":"https://doi.org/10.1287/msom.2023.0028","url":null,"abstract":"Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians’ workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016729","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: Medical operations require a large volume and variety of consumable supplies that are kept in hospital inventory and replenished on a regular basis. Stringent requirements on the availability of these supplies, together with high variability in their daily usage, contribute to the high inventory costs of the surgical departments in hospitals. We investigate the value of utilizing Advance Booking Information (ABI) on elective surgeries—which are often booked up to months in advance—in reducing inventory costs. Methodology/results: We study a single-item, periodic-review, stochastic inventory control problem, where the item demand in each period is driven by the number and type of surgeries requiring the item, and with the available information on elective surgeries integrated into the ordering decisions. Given that item usage from each case is uncertain and only realized after the surgery, ABI provides imperfect information on future demand. Through exact analysis of a simplified version of the problem, as well as extensive numerical experiments using synthetic and real data, enabled using a state aggregation technique, we provide insights on and quantify the value of using ABI as a function of the number of periods of ABI integrated into the ordering decisions. We identify a relevant parameter regime—namely, high backlog (relative to holding) costs and when surgeries are booked sufficiently in advance—where the value of using ABI could be significant and the majority of the benefits can be gained through incorporating only one period of ABI beyond the order lead time. In a case study conducted using real data, we observe up to 26% reduction in average inventory levels, without violating the service levels. Managerial implications: By incorporating readily available elective surgery schedules into replenishment decisions of surgical supplies, hospitals could significantly reduce inventory costs without compromising the availability of the supplies.Funding: This work was partially funded by The Ontario Ministry of Government and Consumer Services (MGCS). The views expressed in the paper are the views of the authors and do not necessarily reflect those of the Province.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0063 .
问题的定义：医疗操作需要大量和各种消耗品，这些消耗品需要保存在医院库存中并定期补充。对这些耗材可用性的严格要求，加上其日常使用的高变化性，导致医院外科部门的库存成本居高不下。我们研究了利用选择性手术的提前预订信息（ABI）来降低库存成本的价值--这些手术通常要提前数月预订。方法/结果：我们研究的是一个单品、定期回顾、随机库存控制问题，其中每期的单品需求量由需要该单品的手术数量和类型驱动，并将有关择期手术的可用信息整合到订购决策中。鉴于每个病例的物品使用量都是不确定的，而且只有在手术后才会实现，因此 ABI 提供了关于未来需求的不完全信息。通过对简化版问题的精确分析，以及使用状态聚合技术对合成数据和真实数据进行的大量数值实验，我们深入了解了使用 ABI 的价值，并将其量化为 ABI 纳入订货决策的周期数的函数。我们确定了一个相关的参数机制--即高积压（相对于持有）成本和手术提前足够时间预订--在该机制下，使用自动调整指数的价值可能会非常显著，而且只需在订货前置时间之外加入一个自动调整指数期，就能获得大部分收益。在使用真实数据进行的案例研究中，我们观察到平均库存水平最多可降低 26%，且不会违反服务水平。管理意义：通过将随时可用的择期手术时间表纳入手术用品的补货决策，医院可以在不影响用品可用性的前提下大幅降低库存成本：本研究部分经费由安大略省政府和消费者服务部（MGCS）提供。文中观点仅代表作者本人，不代表安大略省的观点：电子附录可在 https://doi.org/10.1287/msom.2021.0063 上查阅。
{"title":"Inventory Management with Advance Booking Information: The Case of Surgical Supplies and Elective Surgeries","authors":"Jacky Chan, Berk Görgülü, Vahid Sarhangian","doi":"10.1287/msom.2021.0063","DOIUrl":"https://doi.org/10.1287/msom.2021.0063","url":null,"abstract":"Problem definition: Medical operations require a large volume and variety of consumable supplies that are kept in hospital inventory and replenished on a regular basis. Stringent requirements on the availability of these supplies, together with high variability in their daily usage, contribute to the high inventory costs of the surgical departments in hospitals. We investigate the value of utilizing Advance Booking Information (ABI) on elective surgeries—which are often booked up to months in advance—in reducing inventory costs. Methodology/results: We study a single-item, periodic-review, stochastic inventory control problem, where the item demand in each period is driven by the number and type of surgeries requiring the item, and with the available information on elective surgeries integrated into the ordering decisions. Given that item usage from each case is uncertain and only realized after the surgery, ABI provides imperfect information on future demand. Through exact analysis of a simplified version of the problem, as well as extensive numerical experiments using synthetic and real data, enabled using a state aggregation technique, we provide insights on and quantify the value of using ABI as a function of the number of periods of ABI integrated into the ordering decisions. We identify a relevant parameter regime—namely, high backlog (relative to holding) costs and when surgeries are booked sufficiently in advance—where the value of using ABI could be significant and the majority of the benefits can be gained through incorporating only one period of ABI beyond the order lead time. In a case study conducted using real data, we observe up to 26% reduction in average inventory levels, without violating the service levels. Managerial implications: By incorporating readily available elective surgery schedules into replenishment decisions of surgical supplies, hospitals could significantly reduce inventory costs without compromising the availability of the supplies.Funding: This work was partially funded by The Ontario Ministry of Government and Consumer Services (MGCS). The views expressed in the paper are the views of the authors and do not necessarily reflect those of the Province.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0063 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016779","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: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system’s production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system’s production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally.Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.17.708].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0473 .
{"title":"Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and Learning","authors":"Collin Drent, Melvin Drent, Joachim Arts","doi":"10.1287/msom.2022.0473","DOIUrl":"https://doi.org/10.1287/msom.2022.0473","url":null,"abstract":"Problem definition: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system’s production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system’s production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally.Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.17.708].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0473 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016904","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: The electric vehicle (EV) manufacturer NIO adopts a swappable-battery design and a battery-leasing business model known as battery as a service (BaaS). It recently introduced flexible battery leasing, which allows customers to temporarily up-/downgrade their primary leased batteries based on the needs for range. We investigate whether this business model innovation is viable, namely whether introducing flexible battery leasing in BaaS could benefit the manufacturer, the customers, and the environment compared with simple battery leasing. Methodology/results: Adopting a game-theoretical model, we find that introducing flexible battery leasing in BaaS can simultaneously improve the manufacturer profit as well as reduce the total customer cost and the total battery capacity. Such win-win-win outcomes generally occur for large high-capacity battery ranges and moderate high-capacity battery costs—both consistent with the ongoing trend in the EV industry and a model-calibration exercise. We further show that this key finding is robust for correlated regular and peak needs for range and when launching BaaS with flexible battery leasing and that if the manufacturer was to choose a high-capacity battery range for flexible battery leasing, it would choose one such that battery reallocation alone can meet all battery up-/downgrade demand without acquiring additional batteries. Managerial implications: Our findings confirm that flexible battery leasing can be a viable BaaS business model innovation and offer insights into when this may be the case. This insight strengthens the strategic support for EV manufacturers’ potential adoption of the swappable-battery design and the BaaS model, and it may inform their operating policies to implement flexible battery leasing.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0587 .
{"title":"Frontiers in Operations: Battery as a Service: Flexible Electric Vehicle Battery Leasing","authors":"Lingling Shi, Bin Hu","doi":"10.1287/msom.2022.0587","DOIUrl":"https://doi.org/10.1287/msom.2022.0587","url":null,"abstract":"Problem definition: The electric vehicle (EV) manufacturer NIO adopts a swappable-battery design and a battery-leasing business model known as battery as a service (BaaS). It recently introduced flexible battery leasing, which allows customers to temporarily up-/downgrade their primary leased batteries based on the needs for range. We investigate whether this business model innovation is viable, namely whether introducing flexible battery leasing in BaaS could benefit the manufacturer, the customers, and the environment compared with simple battery leasing. Methodology/results: Adopting a game-theoretical model, we find that introducing flexible battery leasing in BaaS can simultaneously improve the manufacturer profit as well as reduce the total customer cost and the total battery capacity. Such win-win-win outcomes generally occur for large high-capacity battery ranges and moderate high-capacity battery costs—both consistent with the ongoing trend in the EV industry and a model-calibration exercise. We further show that this key finding is robust for correlated regular and peak needs for range and when launching BaaS with flexible battery leasing and that if the manufacturer was to choose a high-capacity battery range for flexible battery leasing, it would choose one such that battery reallocation alone can meet all battery up-/downgrade demand without acquiring additional batteries. Managerial implications: Our findings confirm that flexible battery leasing can be a viable BaaS business model innovation and offer insights into when this may be the case. This insight strengthens the strategic support for EV manufacturers’ potential adoption of the swappable-battery design and the BaaS model, and it may inform their operating policies to implement flexible battery leasing.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0587 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016621","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}
Juan Camilo David Gomez, Amy L. Cochran, Brian W. Patterson, Gabriel Zayas-Cabán
Problem definition: Split flow models, in which a physician rather than a nurse performs triage, are increasingly being used in hospital emergency departments (EDs) to improve patient flow. Before deciding whether such interventions should be adopted, it is important to understand how split flows causally impact patient flow and outcomes. Methodology/results: We employ causal inference methodology to estimate average causal effects of a split flow model on time to be roomed, time to disposition after being roomed, admission decisions, and ED revisits at a large tertiary teaching hospital that uses a split flow model during certain hours each day. We propose a regression discontinuity design to identify average causal effects, which we formalize with causal diagrams. Using electronic health records data (n = 21,570), we estimate that split flow increases average time to be roomed by about 4.6 minutes (95% confidence interval (95% CI): 2.9, 6.2 minutes) but decreases average time to disposition by 14.4 minutes (95% CI: 4.1, 24.7 minutes), leading to an overall reduction in length of stay. Split flow is also found to decrease admission rates by 5.9% (95% CI: 2.3%, 9.4%) but not at the expense of a significant change in revisit rates. Lastly, we find that the split flow model is especially effective at reducing length of stay during low congestion levels, which mediation analysis partly attributes to early task initiation by the physician assigned to triage. Managerial implications: A split flow model can improve flow and may have downstream effects on admissions but not revisits.Funding: This work was supported by the National Institutes of Health [Grants KL2TR002374 and UL1TR002373].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0003
{"title":"Evaluation of a Split Flow Model for the Emergency Department","authors":"Juan Camilo David Gomez, Amy L. Cochran, Brian W. Patterson, Gabriel Zayas-Cabán","doi":"10.1287/msom.2022.0003","DOIUrl":"https://doi.org/10.1287/msom.2022.0003","url":null,"abstract":"Problem definition: Split flow models, in which a physician rather than a nurse performs triage, are increasingly being used in hospital emergency departments (EDs) to improve patient flow. Before deciding whether such interventions should be adopted, it is important to understand how split flows causally impact patient flow and outcomes. Methodology/results: We employ causal inference methodology to estimate average causal effects of a split flow model on time to be roomed, time to disposition after being roomed, admission decisions, and ED revisits at a large tertiary teaching hospital that uses a split flow model during certain hours each day. We propose a regression discontinuity design to identify average causal effects, which we formalize with causal diagrams. Using electronic health records data (n = 21,570), we estimate that split flow increases average time to be roomed by about 4.6 minutes (95% confidence interval (95% CI): 2.9, 6.2 minutes) but decreases average time to disposition by 14.4 minutes (95% CI: 4.1, 24.7 minutes), leading to an overall reduction in length of stay. Split flow is also found to decrease admission rates by 5.9% (95% CI: 2.3%, 9.4%) but not at the expense of a significant change in revisit rates. Lastly, we find that the split flow model is especially effective at reducing length of stay during low congestion levels, which mediation analysis partly attributes to early task initiation by the physician assigned to triage. Managerial implications: A split flow model can improve flow and may have downstream effects on admissions but not revisits.Funding: This work was supported by the National Institutes of Health [Grants KL2TR002374 and UL1TR002373].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0003","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016625","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: Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver-order matching process. Methodology/results: We formulate real-time matching algorithms that take into account uncertain food processing times to strategically “delay” the assignment of drivers to orders. This intentional delay is designed to create a “thicker” marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k-level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi-convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. Managerial implications: Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications.Funding: This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Award ref: MOE-2019-T3-1-010].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0354 .
问题定义：在线食品配送（OFD）平台在全球范围内迅速扩张，部分原因是 COVID-19 大流行期间消费者行为的转变。这些平台使顾客能够通过手机方便地从各种餐馆订餐。这些平台的一个核心功能是通过算法将司机与订餐进行匹配，这也是我们研究的重点，因为我们的目标是优化司机与订餐的匹配过程。方法/结果：我们制定了实时匹配算法，将不确定的食品加工时间考虑在内，战略性地 "延迟 "司机与订单的分配。这种有意的延迟旨在创造一个 "更厚 "的市场，增加司机和订单的可用性。我们的算法使用机器学习技术来预测食品加工时间，随后通过平衡司机空闲等待和延迟交货的成本来决定司机的调度。在单个订单孤立存在的情况下，我们发现最优策略采用了阈值结构。在此基础上，我们针对多订单的一般情况，提出了一种具有驾驶时间限制的 k 级加厚新策略。这种策略会推迟司机的分配，直到有最多 k 个合适的匹配选项。我们使用简化模型对政策进行了评估，并确定了一些分析特性，包括总成本与市场厚度的准凸性，这表明市场厚度的中间水平是最优的。利用美团网真实数据进行的数值实验表明，与现有政策相比，我们的政策能使总成本降低 54%。管理意义：我们的研究表明，将食品加工时间纳入调度算法可显著提高司机分配的效率。我们的策略使平台能够控制实时匹配决策的两个重要市场参数：可及时取送订单的司机数量及其与餐厅的距离。基于这两个参数，我们的算法可以实时匹配司机和订单，从而提供重要的管理意义：本研究得到了新加坡教育部 2019 年学术研究基金第 3 层资助[获奖编号：MOE-2019-T3-1-010]：在线附录见 https://doi.org/10.1287/msom.2021.0354 。
{"title":"Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times","authors":"Yanlu Zhao, Felix Papier, Chung-Piaw Teo","doi":"10.1287/msom.2021.0354","DOIUrl":"https://doi.org/10.1287/msom.2021.0354","url":null,"abstract":"Problem definition: Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver-order matching process. Methodology/results: We formulate real-time matching algorithms that take into account uncertain food processing times to strategically “delay” the assignment of drivers to orders. This intentional delay is designed to create a “thicker” marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k-level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi-convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. Managerial implications: Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications.Funding: This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Award ref: MOE-2019-T3-1-010].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0354 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"213 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016769","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: We investigate the effects of waiting time, mainly due to production in a make-to-batch-order (MTBO) system, on consumer choice behavior, pricing, assortment, and model estimation. In an MTBO system, the seller/manufacturer first collects orders placed within a certain period of time into a batch and then starts the production process. After the production of all orders in a batch are complete, the products are then shipped and delivered to individual consumers. Because of batch production and shipping, the disutility of the waiting time exhibits negative externality. Methodology/results: We adopt the widely used multinomial logit (MNL) model as a starting point and incorporate the anticipated wait into consumers’ decision making. The derived model, referred to as the MNL with wait model, is a solution of the rational expectation equilibrium and is capable of capturing the effects of negative externality induced by anticipated wait that may change the substitution patterns dramatically. We characterize the multiproduct price optimization problem under the MNL with wait model by establishing a one-to-one mapping between the price vector and the choice probability vector. We find that firms tend to charge higher prices for time-consuming items and charge lower prices for time-saving items compared with the optimal prices under the standard MNL model. In addition to price competition, we also study the Cournot-type competition, in which the decision is the choice probability for each firm and establish the existence of a Nash equilibrium. For assortment optimization, we identify mild conditions under which the optimality of revenue-ordered assortments still holds. However, the assortment problem under the MNL with wait model is generally NP-hard, so we develop approximation algorithms with performance guarantees and provide an easy-to-compute tight upper bound. Moreover, we develop an efficient maximum likelihood-based algorithm for model calibration and further conduct numerical studies to showcase the importance of incorporating disutility due to wait in estimation, pricing, and assortment planning problems. Managerial implications: The MNL with wait model can increase prediction accuracy for consumers’ choice behavior especially when they are aware of the potential wait. Failure to take into account the effects of anticipated wait in firms’ decision making may lead to substantial losses.Funding: The research of C. Ke is supported in part by the National Natural Science Foundation of China [Grant 72101113].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0346 .
{"title":"Anticipated Wait and Its Effects on Consumer Choice, Pricing, and Assortment Management","authors":"Ruxian Wang, Chenxu Ke, Zifeng Zhao","doi":"10.1287/msom.2020.0346","DOIUrl":"https://doi.org/10.1287/msom.2020.0346","url":null,"abstract":"Problem definition: We investigate the effects of waiting time, mainly due to production in a make-to-batch-order (MTBO) system, on consumer choice behavior, pricing, assortment, and model estimation. In an MTBO system, the seller/manufacturer first collects orders placed within a certain period of time into a batch and then starts the production process. After the production of all orders in a batch are complete, the products are then shipped and delivered to individual consumers. Because of batch production and shipping, the disutility of the waiting time exhibits negative externality. Methodology/results: We adopt the widely used multinomial logit (MNL) model as a starting point and incorporate the anticipated wait into consumers’ decision making. The derived model, referred to as the MNL with wait model, is a solution of the rational expectation equilibrium and is capable of capturing the effects of negative externality induced by anticipated wait that may change the substitution patterns dramatically. We characterize the multiproduct price optimization problem under the MNL with wait model by establishing a one-to-one mapping between the price vector and the choice probability vector. We find that firms tend to charge higher prices for time-consuming items and charge lower prices for time-saving items compared with the optimal prices under the standard MNL model. In addition to price competition, we also study the Cournot-type competition, in which the decision is the choice probability for each firm and establish the existence of a Nash equilibrium. For assortment optimization, we identify mild conditions under which the optimality of revenue-ordered assortments still holds. However, the assortment problem under the MNL with wait model is generally NP-hard, so we develop approximation algorithms with performance guarantees and provide an easy-to-compute tight upper bound. Moreover, we develop an efficient maximum likelihood-based algorithm for model calibration and further conduct numerical studies to showcase the importance of incorporating disutility due to wait in estimation, pricing, and assortment planning problems. Managerial implications: The MNL with wait model can increase prediction accuracy for consumers’ choice behavior especially when they are aware of the potential wait. Failure to take into account the effects of anticipated wait in firms’ decision making may lead to substantial losses.Funding: The research of C. Ke is supported in part by the National Natural Science Foundation of China [Grant 72101113].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0346 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139979901","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: We study the procurement decisions of a trade agent: The agent chooses a bid (unit price to pay) to procure the goods available from seller(s). If the agent wins the bid, the supply is used to meet the buyer’s demand. Methodology/results: The trade agent’s single-period, single-product problem is a new type of newsvendor problem. We analyze the agent’s optimal bid for a seller with yield uncertainty. We show that the bid outcome distribution needs to satisfy an easy-to-check condition but no conditions on the yield distribution are needed for a unique optimal bid to exist. We also show that the expected sales-to-supply ratio that measures scarcity affects the optimal bid. We investigate the monotonicity of the optimal bid with respect to economic parameters, demand, and distributions of bid outcome and yield. The agent’s problem with multiple sellers is also a novel newsvendor network problem. For the two-seller case, we show when diversification is optimal for the agent. We show that working with both sellers may not always be optimal despite the opportunity for risk pooling and bidding only at the unreliable seller may be optimal even when the other seller is reliable. Managerial implications: We make the following recommendations for the agent: (i) bid at a seller only when the expected sales-to-supply ratio for a seller is higher than the critical ratio, considering the agent’s cost of underage and overage, (ii) increase the bid if the bid outcome distribution increases in the reversed hazard rate order, and (iii) increase or decrease the bid depending on the demand-to-supply ratio when a seller’s expected yield increases. Inclusion of additional sellers lowers the optimal bids across the seller network, but it may not be optimal to bid at all sellers. For the two-seller problem, whether to diversify is a decision easily made by computing the expected benefit of bidding at each seller.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0190 .
{"title":"When Yield Is Not the Only Supply Uncertainty: Newsvendor Model of a Trade Agent","authors":"Özden Engin Çakıcı, Itir Karaesmen","doi":"10.1287/msom.2023.0190","DOIUrl":"https://doi.org/10.1287/msom.2023.0190","url":null,"abstract":"Problem definition: We study the procurement decisions of a trade agent: The agent chooses a bid (unit price to pay) to procure the goods available from seller(s). If the agent wins the bid, the supply is used to meet the buyer’s demand. Methodology/results: The trade agent’s single-period, single-product problem is a new type of newsvendor problem. We analyze the agent’s optimal bid for a seller with yield uncertainty. We show that the bid outcome distribution needs to satisfy an easy-to-check condition but no conditions on the yield distribution are needed for a unique optimal bid to exist. We also show that the expected sales-to-supply ratio that measures scarcity affects the optimal bid. We investigate the monotonicity of the optimal bid with respect to economic parameters, demand, and distributions of bid outcome and yield. The agent’s problem with multiple sellers is also a novel newsvendor network problem. For the two-seller case, we show when diversification is optimal for the agent. We show that working with both sellers may not always be optimal despite the opportunity for risk pooling and bidding only at the unreliable seller may be optimal even when the other seller is reliable. Managerial implications: We make the following recommendations for the agent: (i) bid at a seller only when the expected sales-to-supply ratio for a seller is higher than the critical ratio, considering the agent’s cost of underage and overage, (ii) increase the bid if the bid outcome distribution increases in the reversed hazard rate order, and (iii) increase or decrease the bid depending on the demand-to-supply ratio when a seller’s expected yield increases. Inclusion of additional sellers lowers the optimal bids across the seller network, but it may not be optimal to bid at all sellers. For the two-seller problem, whether to diversify is a decision easily made by computing the expected benefit of bidding at each seller.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0190 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953421","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: For many information goods, longer publication cycles (or batches of information) are more economical, but often result in less timely—and, therefore, less valuable—information. Whereas the digitalization of publication processes has reduced fixed publication costs, making shorter publication cycles more economically viable, competing firms have adapted their publication cycles differently: some of them publish more frequently, whereas others publish less frequently. In the face of growing competition and digitalization, how should information providers change their publication frequency strategies? Methodology/results: In this paper, we build a game-theoretic model to determine how information providers should set their publication cycles and prices in a duopoly. We find that, compared with a monopolistic environment, competition gives rise to differentiation by cycles and expands product variety. Specifically, competing firms should seek to differentiate on their publication frequency when the fixed publication is high and their contents share a high degree of commonality, but not otherwise. Whereas a reduction in the fixed cost of publication tends to yield shorter publication cycles, it could also intensify the competitive dynamics, leading firms to further differentiate their publication cycles, hurting consumer surplus. However, this could be temporary, as firms may ultimately converge in their choices of publication cycles. Managerial implications: The digitalization of publication processes is disrupting many information provision industries (e.g., news, weather, financial). We show that competing firms should anticipate nonmonotone or abrupt changes in their publication strategy as their publication processes get digitalized and may actually be hurt—as well as consumers—in the process of digitalization.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.0024 .
{"title":"Fast or Slow? Competing on Publication Frequency","authors":"Lin Chen, Guillaume Roels","doi":"10.1287/msom.2023.0024","DOIUrl":"https://doi.org/10.1287/msom.2023.0024","url":null,"abstract":"Problem definition: For many information goods, longer publication cycles (or batches of information) are more economical, but often result in less timely—and, therefore, less valuable—information. Whereas the digitalization of publication processes has reduced fixed publication costs, making shorter publication cycles more economically viable, competing firms have adapted their publication cycles differently: some of them publish more frequently, whereas others publish less frequently. In the face of growing competition and digitalization, how should information providers change their publication frequency strategies? Methodology/results: In this paper, we build a game-theoretic model to determine how information providers should set their publication cycles and prices in a duopoly. We find that, compared with a monopolistic environment, competition gives rise to differentiation by cycles and expands product variety. Specifically, competing firms should seek to differentiate on their publication frequency when the fixed publication is high and their contents share a high degree of commonality, but not otherwise. Whereas a reduction in the fixed cost of publication tends to yield shorter publication cycles, it could also intensify the competitive dynamics, leading firms to further differentiate their publication cycles, hurting consumer surplus. However, this could be temporary, as firms may ultimately converge in their choices of publication cycles. Managerial implications: The digitalization of publication processes is disrupting many information provision industries (e.g., news, weather, financial). We show that competing firms should anticipate nonmonotone or abrupt changes in their publication strategy as their publication processes get digitalized and may actually be hurt—as well as consumers—in the process of digitalization.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.0024 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"174 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927921","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: We study supply chain contract design under uncertainty. In this problem, the retailer has full information about the demand distribution, whereas the supplier only has partial information drawn from historical demand realizations and contract terms. The supplier wants to optimize the contract terms, but she only has limited data on the true demand distribution. Methodology/results: We show that the classical approach for contract design is fragile in the small data regime by identifying cases where it incurs a large loss. We then show how to combine the historical demand and retailer data to improve the supplier’s contract design. On top of this, we propose a robust contract design model where the uncertainty set requires little prior knowledge from the supplier. We show how to optimize the supplier’s worst-case profit based on this uncertainty set. In the single-product case, the worst-case profit can be found with bisection search. In the multiproduct case, the worst-case profit can be found with a cutting plane algorithm. Managerial implications: Our framework demonstrates the importance of combining the demand and retailer information into the supplier’s contract design problem. We also demonstrate the advantage of our robust model by comparing it against classical data-driven approaches. This comparison sheds light on the value of information from interactions between agents in a game-theoretic setting and suggests that such information should be utilized in data-driven decision making.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0325 .
{"title":"Supply Chain Contracts in the Small Data Regime","authors":"Xuejun Zhao, William B. Haskell, Guodong Yu","doi":"10.1287/msom.2022.0325","DOIUrl":"https://doi.org/10.1287/msom.2022.0325","url":null,"abstract":"Problem definition: We study supply chain contract design under uncertainty. In this problem, the retailer has full information about the demand distribution, whereas the supplier only has partial information drawn from historical demand realizations and contract terms. The supplier wants to optimize the contract terms, but she only has limited data on the true demand distribution. Methodology/results: We show that the classical approach for contract design is fragile in the small data regime by identifying cases where it incurs a large loss. We then show how to combine the historical demand and retailer data to improve the supplier’s contract design. On top of this, we propose a robust contract design model where the uncertainty set requires little prior knowledge from the supplier. We show how to optimize the supplier’s worst-case profit based on this uncertainty set. In the single-product case, the worst-case profit can be found with bisection search. In the multiproduct case, the worst-case profit can be found with a cutting plane algorithm. Managerial implications: Our framework demonstrates the importance of combining the demand and retailer information into the supplier’s contract design problem. We also demonstrate the advantage of our robust model by comparing it against classical data-driven approaches. This comparison sheds light on the value of information from interactions between agents in a game-theoretic setting and suggests that such information should be utilized in data-driven decision making.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0325 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754967","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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