Seungrae Lee, Michael K. Lim, Seung Jae Park, Sridhar Seshadri
Problem definition: As related party transactions (RPTs) increase in global supply chains, understanding the impact of corporate governance on such transactions becomes crucial for businesses. RPTs often lead to operational diversion due to power disparities between parent and its subsidiaries. In this study, we explore how operational diversion in RPTs within multinational firms is affected by the roles of foreign subsidiaries and corporate governance mechanisms. Methodology/results: Using a unique data set on RPTs of Korean multinational firms from 2006 to 2013, we compare the performance of multinational firms engaging in RPTs with two types of foreign subsidiaries: vertical and horizontal. We conduct our empirical analysis based on the adoption of International Financial Reporting Standards (IFRS) in Korea in 2011, which acts as a policy shock affecting corporate governance and deterring operational diversion. Our results show that the improvement in operational performance of a multinational firm following the IFRS adoption is more significant when the parent firm engages in transactions with vertical subsidiaries compared with horizontal ones. We further show that strong corporate governance mechanisms, such as internal governance, institutional ownership, and large shareholders, play a crucial role in restraining operational diversion in RPTs involving vertical subsidiaries. Managerial implications: The implications of our study extend to shareholders and auditors, highlighting the importance of prioritizing monitoring efforts concerning a parent firm’s RPTs with vertical subsidiaries, especially when corporate governance mechanisms are weak. In contrast, RPTs with horizontal subsidiaries are relatively robust against operational diversion, making them a natural deterrent to such malpractices.Funding: This work was supported by the Institute of Management Research at Seoul National University, the Hankuk University of Foreign Studies Research Fund [2023], and the Research Fellowship Fund of the Sangnam Institute of Management, Yonsei University [2020-22-0007].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0372 .
{"title":"Corporate Governance and Related Party Transactions in Global Supply Chains","authors":"Seungrae Lee, Michael K. Lim, Seung Jae Park, Sridhar Seshadri","doi":"10.1287/msom.2022.0372","DOIUrl":"https://doi.org/10.1287/msom.2022.0372","url":null,"abstract":"Problem definition: As related party transactions (RPTs) increase in global supply chains, understanding the impact of corporate governance on such transactions becomes crucial for businesses. RPTs often lead to operational diversion due to power disparities between parent and its subsidiaries. In this study, we explore how operational diversion in RPTs within multinational firms is affected by the roles of foreign subsidiaries and corporate governance mechanisms. Methodology/results: Using a unique data set on RPTs of Korean multinational firms from 2006 to 2013, we compare the performance of multinational firms engaging in RPTs with two types of foreign subsidiaries: vertical and horizontal. We conduct our empirical analysis based on the adoption of International Financial Reporting Standards (IFRS) in Korea in 2011, which acts as a policy shock affecting corporate governance and deterring operational diversion. Our results show that the improvement in operational performance of a multinational firm following the IFRS adoption is more significant when the parent firm engages in transactions with vertical subsidiaries compared with horizontal ones. We further show that strong corporate governance mechanisms, such as internal governance, institutional ownership, and large shareholders, play a crucial role in restraining operational diversion in RPTs involving vertical subsidiaries. Managerial implications: The implications of our study extend to shareholders and auditors, highlighting the importance of prioritizing monitoring efforts concerning a parent firm’s RPTs with vertical subsidiaries, especially when corporate governance mechanisms are weak. In contrast, RPTs with horizontal subsidiaries are relatively robust against operational diversion, making them a natural deterrent to such malpractices.Funding: This work was supported by the Institute of Management Research at Seoul National University, the Hankuk University of Foreign Studies Research Fund [2023], and the Research Fellowship Fund of the Sangnam Institute of Management, Yonsei University [2020-22-0007].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0372 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201854","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: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks.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.2022.0641 .
问题的定义：近来，商品价格大幅波动，给商品加工和商品贸易公司带来了外生风险因素。准确的商品价格预测可以帮助企业利用数据驱动的采购政策，将潜在的价格波动纳入财务和运营对冲决策。然而，由于波动剧烈，仅凭历史价格不足以获得合理的预测。方法/结果：基于商品价格受现实世界事件驱动的假设，我们提出了一种方法，它能自动从新闻报道中提取事件，并利用基于神经网络的预测模型将其与价格数据相结合，从而预测价格。我们提出的模型不仅预测准确率高，超过了多个基准（高达 13%），而且还具有可解释性，使我们能够识别驱动价格波动的有意义事件。我们发现，经常与价格大幅波动相关的事件包括 "自然"、"加息"、"政策 "和 "选举"，所有这些都是已知的价格变化驱动因素。我们使用了一个语料库，该语料库包含印度一家主要报纸 15 年来的约 160 万篇新闻报道，以及印度四种农作物（洋葱、马铃薯、大米和小麦）的每日价格。我们提出的方法非常灵活，可用于预测其他时间序列数据，如同样受现实世界事件影响的疾病发病率水平或宏观经济指标。管理意义：面对不确定的商品价格，企业可以利用我们系统的价格预测来设计库存和采购政策。当价格波动时，商品商家也可以利用预测结果来设计商品实物交易的最佳存储政策。我们的研究成果还能对政策制定者产生重大影响，他们可以利用即将发生的价格变化和相关事件的信息来减轻价格冲击的负面影响：本文已被《制造业与市场》（Manufacturing & Service Operations Management）杂志的《运营管理前沿》（Frontiers in Operations Initiative）收录：在线附录见 https://doi.org/10.1287/msom.2022.0641 。
{"title":"Frontiers: News Event-Driven Forecasting of Commodity Prices","authors":"Sunandan Chakraborty, Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman","doi":"10.1287/msom.2022.0641","DOIUrl":"https://doi.org/10.1287/msom.2022.0641","url":null,"abstract":"Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks.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.2022.0641 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201789","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}
Amir Karimi, Anant Mishra, Karthik V. Natarajan, Kingshuk K. Sinha
Problem definition: Improving access to contraceptives is one of the most effective interventions to prevent unintended pregnancies and save the lives of women in least developed countries (LDCs), where the overwhelming majority of maternal deaths occur. However, access to reproductive health commodities is often limited in LDCs because of frequent stock-outs at last-mile health facilities. In this study, we evaluate and compare the effect of two distribution models on last-mile contraceptive availability and key public health outcomes (e.g., unintended pregnancies, maternal and newborn deaths). These distribution models are (i) the commonly used pull distribution model, in which health facilities are fully responsible for managing inventory, and (ii) an alternative model known as the informed push distribution model, which delegates inventory management tasks to external logistics providers. Methodology/results: We leverage the staggered transition from pull distribution to informed push distribution in Senegal, a country that redesigned its contraceptive distribution system. We conduct empirical analyses, including a triple differences estimation, on novel field data compiled from multiple sources to evaluate the effect of the transition. We find that the transition significantly reduces contraceptive stock-outs, frontline health worker workload, unintended pregnancies, and maternal and newborn mortalities and also improves client satisfaction, especially in health facilities with less mature inventory management practices and less developed road infrastructure. A comprehensive cost–benefit analysis shows that the aforementioned benefits are achieved in a cost-efficient manner at these facilities, making them prime candidates for the transition. However, for facilities with less mature inventory management practices but more developed road infrastructure, upgrading the inventory management system is a substantially more cost-efficient alternative than transitioning to a new distribution model without compromising the benefits. Managerial implications: Given the resource constraints faced by the public health sector in LDCs, it is imperative to understand how the operational and public health benefits of the transition to the informed push model vary based on facility characteristics. Our findings offer actionable insights for resource allocation by identifying health facilities that benefit the most from the transition.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0488 .
{"title":"Toward Advancing Women’s Health in Least Developed Countries: Evaluating Contraceptive Distribution Models in Senegal","authors":"Amir Karimi, Anant Mishra, Karthik V. Natarajan, Kingshuk K. Sinha","doi":"10.1287/msom.2021.0488","DOIUrl":"https://doi.org/10.1287/msom.2021.0488","url":null,"abstract":"Problem definition: Improving access to contraceptives is one of the most effective interventions to prevent unintended pregnancies and save the lives of women in least developed countries (LDCs), where the overwhelming majority of maternal deaths occur. However, access to reproductive health commodities is often limited in LDCs because of frequent stock-outs at last-mile health facilities. In this study, we evaluate and compare the effect of two distribution models on last-mile contraceptive availability and key public health outcomes (e.g., unintended pregnancies, maternal and newborn deaths). These distribution models are (i) the commonly used pull distribution model, in which health facilities are fully responsible for managing inventory, and (ii) an alternative model known as the informed push distribution model, which delegates inventory management tasks to external logistics providers. Methodology/results: We leverage the staggered transition from pull distribution to informed push distribution in Senegal, a country that redesigned its contraceptive distribution system. We conduct empirical analyses, including a triple differences estimation, on novel field data compiled from multiple sources to evaluate the effect of the transition. We find that the transition significantly reduces contraceptive stock-outs, frontline health worker workload, unintended pregnancies, and maternal and newborn mortalities and also improves client satisfaction, especially in health facilities with less mature inventory management practices and less developed road infrastructure. A comprehensive cost–benefit analysis shows that the aforementioned benefits are achieved in a cost-efficient manner at these facilities, making them prime candidates for the transition. However, for facilities with less mature inventory management practices but more developed road infrastructure, upgrading the inventory management system is a substantially more cost-efficient alternative than transitioning to a new distribution model without compromising the benefits. Managerial implications: Given the resource constraints faced by the public health sector in LDCs, it is imperative to understand how the operational and public health benefits of the transition to the informed push model vary based on facility characteristics. Our findings offer actionable insights for resource allocation by identifying health facilities that benefit the most from the transition.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0488 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"279 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201922","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: Inaccurate records of inventory occur frequently and, by some measures, cost retailers approximately 4% in annual sales. Detecting inventory inaccuracies manually is cost-prohibitive, and existing algorithmic solutions rely almost exclusively on learning from longitudinal data, which is insufficient in the dynamic environment induced by modern retail operations. Instead, we propose a solution based on cross-sectional data over stores and stock-keeping units (SKUs), viewing inventory inaccuracies as a problem of identifying anomalies in a (low-rank) Poisson matrix. State-of-the-art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that nonanomalous entries be observed with vanishingly small noise (which is not the case in our problem and, indeed, in many applications). Methodology/results: So motivated, we propose a conceptually simple entrywise approach to anomaly detection in low-rank Poisson matrices. Our approach accommodates a general class of probabilistic anomaly models. We show that the cost incurred by our algorithm approaches that of an optimal algorithm at a min-max optimal rate. Using synthetic data and real data from a consumer goods retailer, we show that our approach provides up to a 10× cost reduction over incumbent approaches to anomaly detection. Along the way, we build on recent work that seeks entrywise error guarantees for matrix completion, establishing such guarantees for subexponential matrices, a result of independent interest. Managerial implications: By utilizing cross-sectional data at scale, our novel approach provides a practical solution to the issue of inventory inaccuracies in retail operations. Our method is cost-effective and can help managers detect inventory inaccuracies quickly, leading to increased sales and improved customer satisfaction. In addition, the entrywise error guarantees that we establish are of interest to academics working on matrix-completion problems.History: This paper was selected for Fast Track in M&SOM from the 2022 MSOM Supply Chain Management SIG Conference.Funding: Financial support from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI 1727239] is gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0146 .
{"title":"Fixing Inventory Inaccuracies at Scale","authors":"Vivek F. Farias, Andrew A. Li, Tianyi Peng","doi":"10.1287/msom.2023.0146","DOIUrl":"https://doi.org/10.1287/msom.2023.0146","url":null,"abstract":"Problem definition: Inaccurate records of inventory occur frequently and, by some measures, cost retailers approximately 4% in annual sales. Detecting inventory inaccuracies manually is cost-prohibitive, and existing algorithmic solutions rely almost exclusively on learning from longitudinal data, which is insufficient in the dynamic environment induced by modern retail operations. Instead, we propose a solution based on cross-sectional data over stores and stock-keeping units (SKUs), viewing inventory inaccuracies as a problem of identifying anomalies in a (low-rank) Poisson matrix. State-of-the-art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that nonanomalous entries be observed with vanishingly small noise (which is not the case in our problem and, indeed, in many applications). Methodology/results: So motivated, we propose a conceptually simple entrywise approach to anomaly detection in low-rank Poisson matrices. Our approach accommodates a general class of probabilistic anomaly models. We show that the cost incurred by our algorithm approaches that of an optimal algorithm at a min-max optimal rate. Using synthetic data and real data from a consumer goods retailer, we show that our approach provides up to a 10× cost reduction over incumbent approaches to anomaly detection. Along the way, we build on recent work that seeks entrywise error guarantees for matrix completion, establishing such guarantees for subexponential matrices, a result of independent interest. Managerial implications: By utilizing cross-sectional data at scale, our novel approach provides a practical solution to the issue of inventory inaccuracies in retail operations. Our method is cost-effective and can help managers detect inventory inaccuracies quickly, leading to increased sales and improved customer satisfaction. In addition, the entrywise error guarantees that we establish are of interest to academics working on matrix-completion problems.History: This paper was selected for Fast Track in M&SOM from the 2022 MSOM Supply Chain Management SIG Conference.Funding: Financial support from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI 1727239] is gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0146 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150191","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}
F. Miedaner, L. Kuntz, K. Eilermann, B. Roth, S. Scholtes
Problem definition: We examine the effects of prolonged consecutive working days without breaks on care quality and explore its association with daily staffing levels in neonatal intensive care nursing teams. Academic/practical relevance: Healthcare organizations typically base staffing guidelines on safe daily metrics like nurse-to-patient ratios. However, in response to unforeseen demand spikes or staff shortages, managers often depend on staff working additional consecutive days to bridge staffing gaps. This approach, although addressing immediate staffing needs, can inadvertently impact care quality and safety, potentially undermining the benefits of higher staffing levels. Methodology: Using longitudinal data from 62 German neonatal units, we analyze the effect of nursing teams’ consecutive working days on the time from admission to full enteral feeding for 847 low-birth-weight babies, considering nurse-to-patient ratios and patient complexity. Results: Longer consecutive working periods harmfully affect care quality, especially during staffing shortages. The detrimental impact on days with low staffing is particularly pronounced in patients with less complex medical needs. Limiting the team-average number of consecutive working days to two days would have reduced the time to full enteral feeding in our study by 6.4%. Shifting from half a day less to half a day more than the average number of consecutive working days has an impact equal to 20% of the difference in time taken to reach full enteral feeding between low- and high-birth-weight babies. Managerial implications: Limiting consecutive working days could significantly improve intensive care outcomes. Management should monitor consecutive working days alongside daily staffing levels. Policy makers should consider introducing limits on the number of consecutive working days for intensive care nurses.Funding: This work was supported by the Federal Ministry of Education and Research in Germany [Grant 01GY1152].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0021 .
{"title":"Service Quality Implications of Long Periods of Consecutive Working Days: An Empirical Study of Neonatal Intensive Care Nursing Teams","authors":"F. Miedaner, L. Kuntz, K. Eilermann, B. Roth, S. Scholtes","doi":"10.1287/msom.2022.0021","DOIUrl":"https://doi.org/10.1287/msom.2022.0021","url":null,"abstract":"Problem definition: We examine the effects of prolonged consecutive working days without breaks on care quality and explore its association with daily staffing levels in neonatal intensive care nursing teams. Academic/practical relevance: Healthcare organizations typically base staffing guidelines on safe daily metrics like nurse-to-patient ratios. However, in response to unforeseen demand spikes or staff shortages, managers often depend on staff working additional consecutive days to bridge staffing gaps. This approach, although addressing immediate staffing needs, can inadvertently impact care quality and safety, potentially undermining the benefits of higher staffing levels. Methodology: Using longitudinal data from 62 German neonatal units, we analyze the effect of nursing teams’ consecutive working days on the time from admission to full enteral feeding for 847 low-birth-weight babies, considering nurse-to-patient ratios and patient complexity. Results: Longer consecutive working periods harmfully affect care quality, especially during staffing shortages. The detrimental impact on days with low staffing is particularly pronounced in patients with less complex medical needs. Limiting the team-average number of consecutive working days to two days would have reduced the time to full enteral feeding in our study by 6.4%. Shifting from half a day less to half a day more than the average number of consecutive working days has an impact equal to 20% of the difference in time taken to reach full enteral feeding between low- and high-birth-weight babies. Managerial implications: Limiting consecutive working days could significantly improve intensive care outcomes. Management should monitor consecutive working days alongside daily staffing levels. Policy makers should consider introducing limits on the number of consecutive working days for intensive care nurses.Funding: This work was supported by the Federal Ministry of Education and Research in Germany [Grant 01GY1152].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0021 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149803","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: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .
{"title":"Optimal Consolidation of Polling Locations","authors":"Adam P. Schmidt, Duncan Buell, Laura A. Albert","doi":"10.1287/msom.2022.0497","DOIUrl":"https://doi.org/10.1287/msom.2022.0497","url":null,"abstract":"Problem definition: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076681","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: To improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched online agri-platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and, more generally, how they can be leveraged to enable more efficient agricultural supply chains and markets. This paper describes work conducted in close collaboration with the state government of Karnataka, India, to design, implement, and assess the impact of a new two-stage auction on the state’s online agri-platform, the United Market Platform (UMP). Methodology/results: To ensure implementability and protect farmers’ revenue, the auction design is guided by practical operational considerations, as well as semistructured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By the end of May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%–94% profit gain) for over 10,000 farmers who traded in the treatment market. Managerial implications: The results from this paper offer tangible lessons on how innovative price discovery mechanisms could be enabled by online agri-platforms in resource-constrained environments. Importantly, the success of these designs critically depends on careful considerations of systemic operational and behavioral factors that affect trades in the physical markets.History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition.Funding: Financial support from the Tata Center for Technology and Design at MIT and the National Science Foundation [Grant 1452875] is gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0437 .
问题定义：为了改善小农的福利，多个国家（如埃塞俄比亚和印度）推出了在线农业平台，以改变传统市场。然而，关于这些平台的影响，以及更广泛地讲，如何利用这些平台实现更高效的农业供应链和市场，目前仍证据不一。本文介绍了与印度卡纳塔克邦政府密切合作，在该邦的在线农业平台--联合市场平台（UMP）上设计、实施和评估新的两阶段拍卖的工作。方法/结果：为确保可实施性并保护农民收入，拍卖设计以实际操作考虑因素以及与现场大多数交易商的半结构式访谈为指导。根据实地考察结果建立了一个新的行为拍卖模型，以确定何时拟议的两阶段拍卖能为农民带来比传统的单阶段、第一价格、密封出价拍卖更高的收益。新的拍卖机制于 2019 年 2 月在小扁豆主要市场的 UMP 上实施。截至 2019 年 5 月底，在新拍卖机制下交易的商品价值超过 600 万美元。差异分析表明，对于在处理市场进行交易的 10,000 多名农民来说，新机制的实施使价格大幅上涨了 3.6%（相当于 55%-94% 的利润收益）。管理意义：本文的研究结果为在线农业平台如何在资源有限的环境中启用创新价格发现机制提供了切实可行的经验。重要的是，这些设计的成功关键取决于对影响实物市场交易的系统性操作和行为因素的仔细考虑：本论文已作为 2023 年制造与amp; 服务运营管理实践研究竞赛的一部分被接受：感谢麻省理工学院塔塔技术与设计中心（Tata Center for Technology and Design at MIT）和美国国家科学基金会（National Science Foundation [Grant 1452875]）的资助：在线附录见 https://doi.org/10.1287/msom.2022.0437 。
{"title":"Improving Farmers’ Income on Online Agri-Platforms: Evidence from the Field","authors":"Retsef Levi, Manoj Rajan, Somya Singhvi, Yanchong Zheng","doi":"10.1287/msom.2022.0437","DOIUrl":"https://doi.org/10.1287/msom.2022.0437","url":null,"abstract":"Problem definition: To improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched online agri-platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and, more generally, how they can be leveraged to enable more efficient agricultural supply chains and markets. This paper describes work conducted in close collaboration with the state government of Karnataka, India, to design, implement, and assess the impact of a new two-stage auction on the state’s online agri-platform, the United Market Platform (UMP). Methodology/results: To ensure implementability and protect farmers’ revenue, the auction design is guided by practical operational considerations, as well as semistructured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By the end of May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%–94% profit gain) for over 10,000 farmers who traded in the treatment market. Managerial implications: The results from this paper offer tangible lessons on how innovative price discovery mechanisms could be enabled by online agri-platforms in resource-constrained environments. Importantly, the success of these designs critically depends on careful considerations of systemic operational and behavioral factors that affect trades in the physical markets.History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition.Funding: Financial support from the Tata Center for Technology and Design at MIT and the National Science Foundation [Grant 1452875] is gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0437 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054902","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: Slotting fees are lump-sum payments retailers demand from manufacturers to include manufacturers’ products in their assortments. Although retailers regard slotting fees as part of doing business, some manufacturers claim that slotting fees limit their ability to compete on a level playing field with other manufacturers. Considering these conflicting views, we study the role of manufacturer competition in the emergence of slotting fees and how slotting fees affect retailers’ category management (i.e., assortment and pricing) decisions. Methodology/results: We consider a game-theoretic model with a single retailer and two competing manufacturers, each offering a single product. The retailer makes slotting fee, assortment, and pricing decisions in the presence of an operational cost term that increases in the assortment size. The manufacturers that can afford the slotting fee set the wholesale prices for their products. This study leads to three key findings. First, slotting fees can be suboptimal when their absence would trigger intense wholesale price competition. Second, depending on the retailer’s operational cost and the intensity of manufacturer competition, slotting fees create three distinct effects -category expansion, rent extraction, and competitive exclusion under which product variety (i.e., the retailer’s assortment size) increases, remains unchanged, and decreases, respectively. Third, slotting fees are most (least) beneficial for the retailer when they lead to a decrease (increase) in product variety. Managerial implications: This study not only illustrates that retailers can use slotting fees as a strategic tool to control the intensity of manufacturer competition but also reveals how slotting fees impact retailers’ assortment and pricing decisions, with implications for manufacturers and policy makers.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0143 .
{"title":"Retail Category Management with Slotting Fees","authors":"Yasin Alan, Mümin Kurtuluş, Alper Nakkas","doi":"10.1287/msom.2022.0143","DOIUrl":"https://doi.org/10.1287/msom.2022.0143","url":null,"abstract":"Problem definition: Slotting fees are lump-sum payments retailers demand from manufacturers to include manufacturers’ products in their assortments. Although retailers regard slotting fees as part of doing business, some manufacturers claim that slotting fees limit their ability to compete on a level playing field with other manufacturers. Considering these conflicting views, we study the role of manufacturer competition in the emergence of slotting fees and how slotting fees affect retailers’ category management (i.e., assortment and pricing) decisions. Methodology/results: We consider a game-theoretic model with a single retailer and two competing manufacturers, each offering a single product. The retailer makes slotting fee, assortment, and pricing decisions in the presence of an operational cost term that increases in the assortment size. The manufacturers that can afford the slotting fee set the wholesale prices for their products. This study leads to three key findings. First, slotting fees can be suboptimal when their absence would trigger intense wholesale price competition. Second, depending on the retailer’s operational cost and the intensity of manufacturer competition, slotting fees create three distinct effects -category expansion, rent extraction, and competitive exclusion under which product variety (i.e., the retailer’s assortment size) increases, remains unchanged, and decreases, respectively. Third, slotting fees are most (least) beneficial for the retailer when they lead to a decrease (increase) in product variety. Managerial implications: This study not only illustrates that retailers can use slotting fees as a strategic tool to control the intensity of manufacturer competition but also reveals how slotting fees impact retailers’ assortment and pricing decisions, with implications for manufacturers and policy makers.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0143 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054922","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: Reducing the likelihood of man-made disasters that cause harm to life, property, and the environment is a key societal goal. To that end, regulatory agencies are responsible for ensuring man-made disasters do not occur. In practice, to accomplish this task, regulators have to rely on and cooperate with operators. However, how much cooperation is optimal? In this study, we explore the prudent level of cooperation between operators and regulators to avoid man-made disasters and the role whistleblowers and other policy levers can play in maintaining it within a Goldilocks range. Methodology/results: We synthesize various theories and accounts of man-made disasters to construct a system dynamics model of the mechanisms that lead up to these unwanted outcomes. We employ simulations to uncover how changes in crucial parameters related to cooperation lead to different outcomes and influence the likelihood of the occurrence of man-made disasters. Managerial implications: We resist explanations for man-made disasters rooted in regulatory capture and offer instead a nuanced account rooted in excess cooperation between operators and regulators emerging as a result of everyday operational imperatives and constraints. Our findings indicate that absence of disasters leads operators and regulators to fall into a “confidence trap” that perpetuates limited regulatory oversight and excess cooperation and eventually leads to a disaster. To mitigate this tendency, we investigate, in particular, the role that timely whistleblowing and other policy levers can play in mitigating man-made disasters. We provide managerial and policy implications such as incentivizing and safeguarding whistleblowers, limits on the revolving door between operators and regulators, and more stringent operating and safety scientific standards. Overall, we offer a new frame and potentially fruitful frontier for the operations management community to explore.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.0034 .
问题定义：减少人为灾害对生命、财产和环境造成危害的可能性是一个重要的社会目标。为此，监管机构有责任确保人为灾难不会发生。实际上，要完成这项任务，监管机构必须依靠运营商并与运营商合作。然而，怎样的合作才是最佳的？在本研究中，我们探讨了运营商与监管机构之间为避免人为灾难而进行合作的谨慎程度，以及举报人和其他政策杠杆在将合作维持在黄金分割范围内所能发挥的作用。方法/结果：我们综合了有关人为灾难的各种理论和说法，构建了一个导致这些意外结果的机制的系统动力学模型。我们通过模拟来揭示与合作相关的关键参数的变化如何导致不同的结果，以及如何影响人为灾难发生的可能性。管理意义：我们抵制以监管俘获为根基的人为灾难解释，而是提供了一种细致入微的解释，即运营商和监管机构之间的过度合作是日常运营需要和限制的结果。我们的研究结果表明，缺乏灾难会导致运营商和监管机构陷入 "信任陷阱"，从而使有限的监管监督和过度合作长期存在，最终导致灾难的发生。为了缓解这种趋势，我们特别研究了及时举报和其他政策杠杆在减轻人为灾难方面可以发挥的作用。我们提供了管理和政策方面的启示，如激励和保护举报人、限制运营商和监管机构之间的 "旋转门"，以及更严格的运营和安全科学标准。总之，我们为运营管理界提供了一个新的框架和潜在的富有成效的探索领域：本文已被《制造与amp; 服务运营管理》（Manufacturing & Service Operations Management Frontiers in Operations Initiative）接受：在线补充材料可在 https://doi.org/10.1287/msom.2023.0034 上获取。
{"title":"Frontiers in Operations: The Confidence Trap in Operations Management Practices: Anatomy of Man-Made Disasters","authors":"Akhil Bhardwaj, Henk Akkermans","doi":"10.1287/msom.2023.0034","DOIUrl":"https://doi.org/10.1287/msom.2023.0034","url":null,"abstract":"Problem definition: Reducing the likelihood of man-made disasters that cause harm to life, property, and the environment is a key societal goal. To that end, regulatory agencies are responsible for ensuring man-made disasters do not occur. In practice, to accomplish this task, regulators have to rely on and cooperate with operators. However, how much cooperation is optimal? In this study, we explore the prudent level of cooperation between operators and regulators to avoid man-made disasters and the role whistleblowers and other policy levers can play in maintaining it within a Goldilocks range. Methodology/results: We synthesize various theories and accounts of man-made disasters to construct a system dynamics model of the mechanisms that lead up to these unwanted outcomes. We employ simulations to uncover how changes in crucial parameters related to cooperation lead to different outcomes and influence the likelihood of the occurrence of man-made disasters. Managerial implications: We resist explanations for man-made disasters rooted in regulatory capture and offer instead a nuanced account rooted in excess cooperation between operators and regulators emerging as a result of everyday operational imperatives and constraints. Our findings indicate that absence of disasters leads operators and regulators to fall into a “confidence trap” that perpetuates limited regulatory oversight and excess cooperation and eventually leads to a disaster. To mitigate this tendency, we investigate, in particular, the role that timely whistleblowing and other policy levers can play in mitigating man-made disasters. We provide managerial and policy implications such as incentivizing and safeguarding whistleblowers, limits on the revolving door between operators and regulators, and more stringent operating and safety scientific standards. Overall, we offer a new frame and potentially fruitful frontier for the operations management community to explore.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.0034 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"258 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016695","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 rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, electric storage, thermal storage, and combined heat and power plants. Because of the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper the question of how to integrate these DER supplies into the electricity market, with the objective of achieving full market efficiency. Methodology/results: We study four aggregation models, where there is an aggregator who, with the knowledge of DERs’ utility functions and generations, procures electricity from DERs, and sells them in the wholesale market. In the first aggregation model, a profit-maximizing aggregator announces a differential two-part pricing policy to the DER owners. We show that this model preserves full market efficiency, that is, the social welfare achieved by this model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, the profit-seeking aggregator is forced to impose a uniform two-part pricing policy to prosumers from the same location, and we numerically show that there can be large efficiency loss. In the third (fourth) aggregation model, a uniform (semiuniform) two-part pricing policy is applied to DER owners, whereas the aggregator becomes fully regulated but is guaranteed nonnegative (positive) profit. It is shown that these models again achieve full market efficiency. Furthermore, we show that DER aggregation also leads to a reduction in the market power of conventional generators. Managerial implications: DER aggregation via profit-seeking and/or regulated aggregators has been investigated by California Independent System Operator and New York Independent System Operator, among others, and the recent Federal Energy Regulatory Commission Order No. 2222 paved the way for aggregators to bid in the wholesale market. Our four aggregation models may shed light on how DERs should be included in the wholesale electricity market.Funding: This work was supported by the King Fahd University of Petroleum and Minerals [Grant INML2106] and the National Science Foundation [Grant 1832230].Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0539 .
问题定义：分布式能源资源（DER）的迅速发展是全球电力系统最重大的变化之一。DER 的例子包括太阳能电池板、电力存储、热存储和热电联产电厂。由于这些 DER 的供应能力较小，让它们直接参与电力批发市场并不现实。我们在本文中研究了如何将这些 DER 供应纳入电力市场的问题，目的是实现充分的市场效率。方法/结果：我们研究了四种聚合模型，其中有一个聚合器，在了解 DER 的效用函数和发电量后，从 DER 处采购电力，并在批发市场上出售。在第一种聚合模型中，利润最大化的聚合器向 DER 所有者公布了一种由两部分组成的差别定价政策。我们的研究表明，该模型保留了完全的市场效率，也就是说，该模型实现的社会福利与 DER 直接参与批发市场时的社会福利相同。在第二种聚合模型中，追求利润的聚合者被迫对来自同一地点的用户实行统一的两部分定价政策，我们通过数值计算表明，这种情况下可能会出现较大的效率损失。在第三种（第四种）聚合模型中，对 DER 所有者采用统一（半统一）的两部分定价政策，而聚合器则完全受管制，但保证非负（正）利润。结果表明，这些模型再次实现了完全的市场效率。此外，我们还表明，DER 聚合还能降低传统发电商的市场支配力。管理意义：加利福尼亚州独立系统运营商和纽约州独立系统运营商等已对通过追求利润和/或受监管的聚合器进行 DER 聚合进行了调查，最近联邦能源管理委员会第 2222 号命令为聚合器在批发市场竞标铺平了道路。我们的四种聚合模式可能会对 DERs 应如何纳入电力批发市场有所启示：本研究得到了法赫德国王石油和矿业大学（King Fahd University of Petroleum and Minerals）[INML2106]和美国国家科学基金会（National Science Foundation）[1832230]的资助：在线附录见 https://doi.org/10.1287/msom.2021.0539 。
{"title":"Aggregating Distributed Energy Resources: Efficiency and Market Power","authors":"Zuguang Gao, Khaled Alshehri, John R. Birge","doi":"10.1287/msom.2021.0539","DOIUrl":"https://doi.org/10.1287/msom.2021.0539","url":null,"abstract":"Problem definition: The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, electric storage, thermal storage, and combined heat and power plants. Because of the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper the question of how to integrate these DER supplies into the electricity market, with the objective of achieving full market efficiency. Methodology/results: We study four aggregation models, where there is an aggregator who, with the knowledge of DERs’ utility functions and generations, procures electricity from DERs, and sells them in the wholesale market. In the first aggregation model, a profit-maximizing aggregator announces a differential two-part pricing policy to the DER owners. We show that this model preserves full market efficiency, that is, the social welfare achieved by this model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, the profit-seeking aggregator is forced to impose a uniform two-part pricing policy to prosumers from the same location, and we numerically show that there can be large efficiency loss. In the third (fourth) aggregation model, a uniform (semiuniform) two-part pricing policy is applied to DER owners, whereas the aggregator becomes fully regulated but is guaranteed nonnegative (positive) profit. It is shown that these models again achieve full market efficiency. Furthermore, we show that DER aggregation also leads to a reduction in the market power of conventional generators. Managerial implications: DER aggregation via profit-seeking and/or regulated aggregators has been investigated by California Independent System Operator and New York Independent System Operator, among others, and the recent Federal Energy Regulatory Commission Order No. 2222 paved the way for aggregators to bid in the wholesale market. Our four aggregation models may shed light on how DERs should be included in the wholesale electricity market.Funding: This work was supported by the King Fahd University of Petroleum and Minerals [Grant INML2106] and the National Science Foundation [Grant 1832230].Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0539 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016840","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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