Manufacturing & Service Operations Management最新文献

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Can a Supplier’s Yield Risk Be Truthfully Communicated via Cheap Talk? 供应商的收益风险能否通过廉价言论如实传达?
Manufacturing & Service Operations Management Pub Date : 2024-08-05 DOI: 10.1287/msom.2023.0089
Tao Lu
{"title":"Can a Supplier’s Yield Risk Be Truthfully Communicated via Cheap Talk?","authors":"Tao Lu","doi":"10.1287/msom.2023.0089","DOIUrl":"https://doi.org/10.1287/msom.2023.0089","url":null,"abstract":"Problem definition: When a firm (buyer) outsources the production of a new product/component to a supplier subject to random yield, a major challenge is that the supplier’s yield is usually private information. In practice, yield information is often shared via nonbinding communication—for example, a supplier self-assessment report. We examine whether such communication can be truthful and credible. Methodology/results: We analyze a cheap-talk game in which, given a simple contract that specifies the prices for each unit ordered and for each effective unit delivered, the supplier first communicates its yield level, and then the buyer determines an order quantity. We prove that truthful communication can emerge in equilibrium. To do so, we first show that if knowing the supplier’s type, the buyer will either inflate or reduce the order quantity to cope with a lower yield, depending on the product’s market potential. Under asymmetric information, the supplier will truthfully communicate its type if (i) the buyer with a high market potential intends to inflate the order quantity for a lower yield, but the buyer with a low market potential prefers to do the reverse; and (ii) the supplier is uncertain about the product’s market potential, which is the buyer’s private information, and anticipates that a hard-to-make product is more likely to have a higher market potential. Managerial implications: Truthful cheap-talk communication can emerge in equilibrium when the product’s market size and yield are negatively correlated. Truthful communication always benefits the buyer and consumers and may benefit the supplier if the product has sufficient market potential and the supplier’s production cost is not too high. Moreover, the buyer can be better off paying more for the input quantity (although part of the output is defective) or paying a higher wholesale rate if the adjustment in payment terms enhances communication credibility.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0089 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"194 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934116","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}
引用次数: 0
Workforce Scheduling with Heterogeneous Time Preferences: Effective Wages and Workers’ Supply 具有异质时间偏好的劳动力调度:有效工资与工人供给
Manufacturing & Service Operations Management Pub Date : 2024-08-01 DOI: 10.1287/msom.2022.0414
Omar Besbes, Vineet Goyal, Garud Iyengar, Raghav Singal
{"title":"Workforce Scheduling with Heterogeneous Time Preferences: Effective Wages and Workers’ Supply","authors":"Omar Besbes, Vineet Goyal, Garud Iyengar, Raghav Singal","doi":"10.1287/msom.2022.0414","DOIUrl":"https://doi.org/10.1287/msom.2022.0414","url":null,"abstract":"Problem definition: Motivated by the debate around workers’ welfare in the gig economy, we propose a framework to evaluate current practices and possible alternatives. We study a setting in which customers seek service from workers and a platform facilitates such matches over the course of the day. The platform allocates time slots to workers using an allocation policy, and the workers are strategic agents (with respect to “when to work”) who maximize their expected utility that depends on their preferred times to work, the allocated slots, and the total availability time. The platform seeks to ensure that a sufficient number of workers is available to satisfy demand, whereas the workers aim to maximize their wage-driven utility. Methodology/results: We evaluate policies on two dimensions critical to any firm: the supply of workers across the day, and the effective wages of workers. We illustrate that several families of currently deployed policies have serious limitations. We find these limitations exist because the policies do not let workers fully express their preferences and/or cannot account for heterogeneity in such preferences. We propose a new allocation policy and establish strong performance guarantees with respect to both the workers’ supply and effective wages. The policy is simple and fully leverages the market information to reach better market outcomes. We supplement our theory with numerical experiments in the context of ride-hailing calibrated on various New York City data sets that illustrate performance across a range of markets. Managerial implications: We highlight a fundamental inefficiency of policies currently deployed that limit workers’ ability to express their preferences. By allowing workers to express their temporal preferences, and by judiciously prioritizing “full-time” workers over “part-time” workers, we can obtain a potentially significant Pareto improvement, maintaining (or even increasing) workers’ supply while increasing their effective wages.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0414 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881790","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}
引用次数: 0
Optimal Policies and Heuristics to Match Supply with Demand for Online Retailing 网络零售供需匹配的最优政策和启发式方法
Manufacturing & Service Operations Management Pub Date : 2024-07-26 DOI: 10.1287/msom.2021.0394
Qiyuan Deng, Xiaobo Li, Yun Fong Lim, Fang Liu
{"title":"Optimal Policies and Heuristics to Match Supply with Demand for Online Retailing","authors":"Qiyuan Deng, Xiaobo Li, Yun Fong Lim, Fang Liu","doi":"10.1287/msom.2021.0394","DOIUrl":"https://doi.org/10.1287/msom.2021.0394","url":null,"abstract":"Problem definition: We consider an online retailer selling multiple products to different zones over a finite horizon with multiple periods. At the start of the horizon, the retailer orders the products from a single supplier and stores them at multiple warehouses. The retailer determines the products’ order quantities and their storage quantities at each warehouse subject to its capacity constraint. At the end of each period, after random demands in the period are realized, the retailer chooses the retrieval quantities from each warehouse to fulfill the demands of each zone. The objective is to maximize the retailer’s expected profit over the finite horizon. Methodology/results: For the single-zone case, we show that the multiperiod problem is equivalent to a single-period problem and the optimal retrieval decisions follow a greedy policy that retrieves products from the lowest-cost warehouse. We design a nongreedy algorithm to find the optimal storage policy, which preserves a nested property: Among all nonempty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. We also analytically characterize the optimal ordering policy. The multizone case is unfortunately intractable analytically, and we propose an efficient heuristic to solve it, which involves a nontrivial hybrid of three approximations. This hybrid heuristic outperforms two conventional benchmarks by up to 22.5% and 3.5% in our numerical experiments with various horizon lengths, fulfillment frequencies, warehouse capacities, demand variations, and demand correlations. Managerial implications: A case study based on data from a major fashion online retailer in Asia confirms the superiority of the hybrid heuristic. With delicate optimization, the heuristic improves the average profit by up to 16% compared with a dedicated policy adopted by the retailer. The hybrid heuristic continues to outperform the benchmarks for larger networks with various structures.Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001]. Y. F. Lim is grateful for the support from the Singapore Management University under the Maritime and Port Authority Research Fellowship and the Singapore Ministry of Education [Tier 1 Grant MSS23B001].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0394 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780631","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}
引用次数: 0
The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management 两全其美:运营管理中的机器学习和行为科学
Manufacturing & Service Operations Management Pub Date : 2024-07-25 DOI: 10.1287/msom.2022.0553
Andrew M. Davis, Shawn Mankad, Charles J. Corbett, Elena Katok
{"title":"The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management","authors":"Andrew M. Davis, Shawn Mankad, Charles J. Corbett, Elena Katok","doi":"10.1287/msom.2022.0553","DOIUrl":"https://doi.org/10.1287/msom.2022.0553","url":null,"abstract":"Problem definition: Two disciplines increasingly applied in operations management (OM) are machine learning (ML) and behavioral science (BSci). Rather than treating these as mutually exclusive fields, we discuss how they can work as complements to solve important OM problems. Methodology/results: We illustrate how ML and BSci enhance one another in non-OM domains before detailing how each step of their respective research processes can benefit the other in OM settings. We then conclude by proposing a framework to help identify how ML and BSci can jointly contribute to OM problems. Managerial implications: Overall, we aim to explore how the integration of ML and BSci can enable researchers to solve a wide range of problems within OM, allowing future research to generate valuable insights for managers, companies, and society.","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780630","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}
引用次数: 0
Renewable, Flexible, and Storage Capacities: Friends or Foes? 可再生、灵活和存储能力:是敌是友?
Manufacturing & Service Operations Management Pub Date : 2024-07-03 DOI: 10.1287/msom.2023.0068
Xiaoshan Peng, Owen Q. Wu, Gilvan C. Souza
{"title":"Renewable, Flexible, and Storage Capacities: Friends or Foes?","authors":"Xiaoshan Peng, Owen Q. Wu, Gilvan C. Souza","doi":"10.1287/msom.2023.0068","DOIUrl":"https://doi.org/10.1287/msom.2023.0068","url":null,"abstract":"Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential.Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547889","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}
引用次数: 0
Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach 零售业需求预测的集合和提升:迁移学习法
Manufacturing & Service Operations Management Pub Date : 2024-05-30 DOI: 10.1287/msom.2022.0453
Dazhou Lei, Yongzhi Qi, Sheng Liu, Dongyang Geng, Jianshen Zhang, Hao Hu, Zuo-Jun Max Shen
{"title":"Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach","authors":"Dazhou Lei, Yongzhi Qi, Sheng Liu, Dongyang Geng, Jianshen Zhang, Hao Hu, Zuo-Jun Max Shen","doi":"10.1287/msom.2022.0453","DOIUrl":"https://doi.org/10.1287/msom.2022.0453","url":null,"abstract":"Problem definition: How should retailers leverage aggregate (category) sales information for individual product demand prediction? Motivated by inventory risk pooling, we develop a new prediction framework that integrates category-product sales information to exploit the benefit of pooling. Methodology/results: We propose to combine data from different aggregation levels in a transfer learning framework. Our approach treats the top-level sales information as a regularization for fitting the bottom-level prediction model. We characterize the error performance of our model in linear cases and demonstrate the benefit of pooling. Moreover, our approach exploits a natural connection to regularized gradient boosting trees that enable a scalable implementation for large-scale applications. Based on an internal study with JD.com on more than 6,000 weekly observations between 2020 and 2021, we evaluate the out-of-sample forecasting performance of our approach against state-of-the-art benchmarks. The result shows that our approach delivers superior forecasting performance consistently with more than 9% improvement over the benchmark method of JD.com . We further validate its generalizability on a Walmart retail data set and through alternative pooling and prediction methods. Managerial implications: Using aggregate sales information directly may not help with product demand prediction. Our result highlights the value of transfer learning to demand prediction in retail with both theoretical and empirical support. Based on a conservative estimate of JD.com , the improved forecasts can reduce the operating cost by 0.01–0.29 renminbi (RMB) per sold unit on the retail platform, which implies significant cost savings for the low-margin e-retail business.History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition.Funding: This work was supported by the National Natural Science Foundation of China [Grant 71991462].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0453 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189611","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}
引用次数: 0
Should Only Popular Products Be Stocked? Warehouse Assortment Selection for E-Commerce Companies 是否只应储存热门产品?电子商务公司的仓库分类选择
Manufacturing & Service Operations Management Pub Date : 2024-05-30 DOI: 10.1287/msom.2022.0428
Xiaobo Li, Hongyuan Lin, Fang Liu
{"title":"Should Only Popular Products Be Stocked? Warehouse Assortment Selection for E-Commerce Companies","authors":"Xiaobo Li, Hongyuan Lin, Fang Liu","doi":"10.1287/msom.2022.0428","DOIUrl":"https://doi.org/10.1287/msom.2022.0428","url":null,"abstract":"Problem definition: This paper studies the single-warehouse assortment selection problem that aims to minimize the order fulfillment cost under the cardinality constraint. We propose two fulfillment-related cost functions corresponding to spillover fulfillment and order splitting. This problem includes the fill rate maximization problem as a special case. We show that although the objective function is submodular for a broad class of cost functions, the fill rate maximization problem with the largest order size being two is NP-hard. Methodology/results: To make the problem tractable to solve, we formulate the general warehouse assortment problem under the two types of cost functions as mixed integer linear programs (MILPs). We also provide a dynamic programming algorithm to solve the problem in polynomial time if orders are nonoverlapping. Furthermore, we propose a simple heuristic called the marginal choice indexing (MCI) policy that allows the warehouse to store the most popular products. This policy is easy to compute, and hence, it is scalable to large-size problems. Although the performance of MCI can be arbitrarily bad in some extreme scenarios, we find a general condition under which it is optimal. This condition is satisfied by many multi-purchase choice models. Managerial implications: Through extensive numerical experiments on a real-world data set from RiRiShun Logistics, we find that the MCI policy is surprisingly near optimal in all the settings we tested. Simply applying the MCI policy, the fill rate is estimated to improve by 9.18% on average compared with the current practice for the local transfer centers on the training data set. More surprisingly, the MCI policy outperforms the MILP optimal solution in 14 of 25 cases on the test data set, illustrating its robustness against demand fluctuations.History: This paper has been accepted as part of the 2021 MSOM Data-Driven Research Challenge.Funding: This work was supported by the Singapore Ministry of Education (MoE) Tier 1 [Grant 23-0619-P0001].Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0428 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189481","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}
引用次数: 0
MSOM Society Student Paper Competition: Abstracts of 2023 Winners MSOM 学会学生论文竞赛:2023 年获奖者摘要
Manufacturing & Service Operations Management Pub Date : 2024-05-13 DOI: 10.1287/msom.2024.studentabs.v26.n3
{"title":"MSOM Society Student Paper Competition: Abstracts of 2023 Winners","authors":"","doi":"10.1287/msom.2024.studentabs.v26.n3","DOIUrl":"https://doi.org/10.1287/msom.2024.studentabs.v26.n3","url":null,"abstract":"The journal is pleased to publish the abstracts of the six finalists of the 2023 Manufacturing and Service Operations Management Society’s student paper competition. The 2023 prize committee was chaired by Ersin Korpeoglu (UCL), Simone Marinesi (Wharton), and Nur Sunar (UNC). The judges were Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alper Nakkas, Amrita Kundu, Antoine Desir, Antoine Feylessoufi, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bin Hu, Bob Batt, Bora Keskin, Brent Moritz, Can Zhang, Chloe Glaeser, Cuihong Li, Daniel Freund, Daniel Lin, David Drake, Divya Singhvi, Dongyuan Zhan, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Enis Kayis, Fabian Sting, Fanyin Zheng, Fei Gao, Florin Ciocan, Francisco Castro, George Chen, Georgina Hall, Gloria Urrea, Gonzalo Romero, Guihua Wang, Guoming Lai, Heikki Peura, Hessam Bavafa, Hummy Song, Huseyin Gurkan, Ioannis Stamatopoulos, Iris Wang, Jiankun Sun, Jiayi Joey Yu, Jing Wu, Joel Wooten, John Silberholz, Jonas Oddur Jonasson, Jonathan Helm, Jose Guajardo, Junyu Cao, Kaitlin Daniels, Karen Zheng, Ken Moon, Kostas Bimpikis, Lennart Baardman, Lesley Meng, Lina Song, Luyi Yang, Mazhar Arikan, Mehmet Ayvaci, Meng Li, Mengzhenyu Zhang, Miao Bai, Michael Freeman, Mika Sumida, Ming Hu, Morvarid Rahmani, Mostafa Rezaei, Mumin Kurtulus, Nan Yang, Nazli Sonmez, Negin Golrezaei, Nektarios Oraiopoulos, Nikhil Garg, Nikos Trichakis, Nil Karacaoglu, Olga Perdikaki, Onesun Steve Yoo, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Pengyi Shi, Philipp Cornelius, Qiuping Yu, Renyu Zhang, Robert Bray, Ruth Beer, Ruxian Wang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Seyed Emadi, Sheng Liu, Shouqiang Wang, Siddharth Singh, Sidika Candogan, Sina Khorasani, So Yeon Chun, Somya Singhvi, Soo-Haeng Cho, Sriram Dasu, Stefanus Jasin, Stephen Leider, Suresh Muthulingam, Sytske Wijnsma, Taghi Khaniyev, Tian Chan, Tim Kraft, Tom Tan, Tugce Martagan, Vasiliki Kostamj, Velibor Misic, Vishal Agrawal, Xiaojia Guo, Xiaoshuai Fan, Xiaoyang Long, Yannis Bellos, Yao Cui, Yehua Wei, Yiangos Papanastasiou, Yi-Chun Chen, Yinghao Zhang, Ying-Ju Chen, Yinghao Zhang, Yuan-Mao Kao, Yuexing Li, Zhaohui (Zoey) Jiang, Zhaowei She, and Zumbul Atan.","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140927628","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}
引用次数: 0
2023 M&SOM Meritorious Service Award 2023 M&SOM 杰出服务奖
Manufacturing & Service Operations Management Pub Date : 2024-05-13 DOI: 10.1287/msom.2024.meritsa.v26.n3
{"title":"2023 M&SOM Meritorious Service Award","authors":"","doi":"10.1287/msom.2024.meritsa.v26.n3","DOIUrl":"https://doi.org/10.1287/msom.2024.meritsa.v26.n3","url":null,"abstract":"The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, editor-in-chief Georgia Perakis expresses her deepest gratitude to all those who served as reviewers for the journal in 2023. Among all reviewers, some individuals have distinguished themselves by reviewing several manuscripts and, with each manuscript, by writing a fair, critical, and constructive review in a timely fashion. In recognition of their outstanding service provided to support the journal’s scholarly mission, M&SOM grants the 2023 Meritorious Service Award to…","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140927625","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}
引用次数: 0
Frontiers in Operations: Valuing Nursing Productivity in Emergency Departments 业务前沿:评估急诊科护理工作效率
Manufacturing & Service Operations Management Pub Date : 2024-05-08 DOI: 10.1287/msom.2023.0039
Hao Ding, Sokol Tushe, Diwas Singh KC, Donald K. K. Lee
{"title":"Frontiers in Operations: Valuing Nursing Productivity in Emergency Departments","authors":"Hao Ding, Sokol Tushe, Diwas Singh KC, Donald K. K. Lee","doi":"10.1287/msom.2023.0039","DOIUrl":"https://doi.org/10.1287/msom.2023.0039","url":null,"abstract":"Problem definition: We quantify the increase in productivity in emergency departments (EDs) from increasing nurse staff. We then estimate the associated revenue gains for the hospital and the associated welfare gains for society. The United States is over a decade into the worst nursing shortage crisis in history fueled by chronic underinvestment. To demonstrate to hospital managers and policymakers the benefits of investing in nursing, we clarify the positive downstream effects of doing so in the ED setting. Methodology/results: We use a high-resolution data set of patient visits to the ED of a major U.S. academic hospital. Time-dependent hazard estimation methods (nonparametric and parametric) are used to study how the real-time service speed of a patient varies with the state of the ED, including the time-varying workloads of the assigned nurse. A counterfactual simulation is used to estimate the gains from increasing nursing staff in the ED. We find that lightening a nurse’s workload by one patient is associated with a 14% service speedup for every patient under the nurse’s care. Simulation studies suggest that adding one more nurse to the busiest 12-hour shift of each day can shorten stays and avert $160,000 in lost patient wages per 10,000 visits. The reduction in service times also frees up capacity for treating more patients and generates $470,000 in additional net revenues for the hospital per 10,000 visits. Extensive sensitivity analyses suggest that our key message—that investing in nursing will more than pay for itself—is likely to hold across a wide range of EDs. Managerial implications: In determining whether to invest in more nursing resources, hospital managers need to look beyond whether payer reimbursements alone are sufficient to cover the up-front costs to also account for the resulting downstream benefits.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Funding: D. K. K. Lee was supported by the National Heart, Lung, and Blood Institute [Grant R01-HL164405].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0039 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140927545","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}
引用次数: 0
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