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 .
{"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}
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 .
{"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}
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 .
问题定义：我们考虑一个在线零售商在有限的时间跨度内向不同地区销售多种产品。在时间跨度开始时，零售商从单一供应商处订购产品，并将其存储在多个仓库中。零售商根据其产能约束确定产品的订单量和每个仓库的存储量。在每个周期结束时，当该周期的随机需求实现后，零售商从每个仓库中选择检索数量，以满足每个区域的需求。目标是在有限的时间跨度内使零售商的预期利润最大化。方法/结果：对于单区情况，我们证明多期问题等同于单期问题，最优检索决策遵循贪婪策略，即从成本最低的仓库检索产品。我们设计了一种非贪婪算法来找到最优存储策略，它保留了嵌套属性：在所有非空仓库中，小索引仓库包含了大索引仓库中存储的所有产品。我们还分析了最优排序策略的特征。不幸的是，多区情况在分析上是难以解决的，因此我们提出了一种高效的启发式方法来解决这一问题，其中涉及三种近似方法的非难混合。在我们的数值实验中，这种混合启发式的性能分别比两个传统基准高出 22.5% 和 3.5%，实验条件包括各种期限长度、履行频率、仓库容量、需求变化和需求相关性。管理意义：基于亚洲一家大型时尚在线零售商数据的案例研究证实了混合启发式的优越性。通过精细优化，启发式方法比零售商采用的专用策略提高了 16% 的平均利润。对于具有各种结构的大型网络，混合启发式的表现继续优于基准：X. Li 由新加坡教育部 [Tier 1 Grant 23-0619-P0001] 资助。Y. F. Lim 感谢新加坡管理大学海事与港务局研究奖学金和新加坡教育部 [Tier 1 Grant MSS23B001] 的支持：在线附录见 https://doi.org/10.1287/msom.2021.0394 。
{"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}
Andrew M. Davis, Shawn Mankad, Charles J. Corbett, Elena Katok
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.
问题定义：机器学习（ML）和行为科学（BSci）这两门学科越来越多地应用于运营管理（OM）领域。我们并没有将这两个学科视为相互排斥的领域，而是讨论了它们如何互为补充，共同解决重要的运营管理问题。方法/结果：我们首先说明了智能语言和智能科学如何在非 OM 领域相互促进，然后详细介绍了它们各自研究过程中的每一步如何在 OM 环境中为对方带来益处。最后，我们提出了一个框架，以帮助确定 ML 和 BSci 如何共同解决 OM 问题。管理意义：总之，我们旨在探索如何将 ML 和 BSci 结合起来，使研究人员能够解决 OM 中的各种问题，从而使未来的研究能够为管理者、公司和社会提供有价值的见解。
{"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}
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 .
{"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}
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 .
{"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}
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 .
{"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}
Pub Date : 2024-05-13DOI: 10.1287/msom.2024.meritsa.v26.n3
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…
{"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}
Hao Ding, Sokol Tushe, Diwas Singh KC, Donald K. K. Lee
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 .
问题定义：我们量化了急诊科（ED）因增加护士人数而提高的生产率。然后，我们估算医院的相关收入收益和社会的相关福利收益。在长期投资不足的助推下，美国十多年来经历了历史上最严重的护士短缺危机。为了向医院管理者和政策制定者展示护理投资的益处，我们阐明了在急诊室进行护理投资的积极下游效应。方法/结果：我们使用了美国一家大型学术医院急诊室病人就诊的高分辨率数据集。使用随时间变化的危险估计方法（非参数和参数）来研究病人的实时服务速度如何随急诊室的状态（包括指派护士随时间变化的工作量）而变化。通过反事实模拟来估算增加急诊室护理人员的收益。我们发现，护士的工作量减少一名病人，其护理的每名病人的服务速度就会提高 14%。模拟研究表明，在每天最繁忙的 12 小时轮班中增加一名护士，可缩短住院时间，并避免每 10,000 次就诊损失 160,000 美元的病人工资。服务时间的缩短还能腾出空间治疗更多病人，每 10,000 人次可为医院带来 47 万美元的额外净收入。广泛的敏感性分析表明，我们的关键信息--对护理的投资将得不偿失--很可能在各种急诊室都适用。管理意义：在决定是否投资更多护理资源时，医院管理者需要考虑的不仅仅是支付方的补偿是否足以支付前期成本，还要考虑由此带来的下游效益：本文已被《制造与amp; 服务运营管理》（Manufacturing & Service Operations Management Frontiers in Operations Initiative）收录：D. K. K. Lee 得到了美国国家心肺血液研究所 [Grant R01-HL164405] 的资助：在线附录见 https://doi.org/10.1287/msom.2023.0039 。
{"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}
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