Problem definition: We estimate the impact of electric vehicle (EV) charging stations on volumes of consumer foot traffic received by nearby retail establishments. We also explore the conditions under which any effects manifest. Methodology/results: We use a differences-in-differences design, exploiting the staggered introduction of Tesla Supercharger stations across the United States. We combine data on Supercharger installations with mobile phone–based estimates of retailer foot traffic. We explore heterogeneity in the treatment effect, in terms of EV charger characteristics, visitor characteristics, establishment type, and local physical context. We estimate that establishments experience an average 4% increase in monthly visits following the installation of a Tesla Supercharger. These effects arise primarily for retailers that offer relatively quick services (e.g., fast food) and for those located very near to the charger (within 150 meters). The effects are also more pronounced when the Supercharger is one of the first EV chargers introduced into the local area. Managerial implications: We document evidence of the positive retail demand spillovers arising from EV charging station infrastructure. We also document the conditions under which the benefits manifest. Insights for EV network operators, retailers, and policymakers are included.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0519 .
{"title":"Recharging Retail: Estimating Consumer Demand Spillovers from Electric Vehicle Charging Stations","authors":"Yash Babar, Gordon Burtch","doi":"10.1287/msom.2022.0519","DOIUrl":"https://doi.org/10.1287/msom.2022.0519","url":null,"abstract":"Problem definition: We estimate the impact of electric vehicle (EV) charging stations on volumes of consumer foot traffic received by nearby retail establishments. We also explore the conditions under which any effects manifest. Methodology/results: We use a differences-in-differences design, exploiting the staggered introduction of Tesla Supercharger stations across the United States. We combine data on Supercharger installations with mobile phone–based estimates of retailer foot traffic. We explore heterogeneity in the treatment effect, in terms of EV charger characteristics, visitor characteristics, establishment type, and local physical context. We estimate that establishments experience an average 4% increase in monthly visits following the installation of a Tesla Supercharger. These effects arise primarily for retailers that offer relatively quick services (e.g., fast food) and for those located very near to the charger (within 150 meters). The effects are also more pronounced when the Supercharger is one of the first EV chargers introduced into the local area. Managerial implications: We document evidence of the positive retail demand spillovers arising from EV charging station infrastructure. We also document the conditions under which the benefits manifest. Insights for EV network operators, retailers, and policymakers are included.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0519 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"140 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773108","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: Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a robust optimization problem that maximizes a vehicle owner’s expected profit from selling primary frequency regulation to the grid and guarantees that market commitments are met at all times for all frequency deviation trajectories in a functional uncertainty set that encodes applicable legislation. Faithfully modeling the energy conversion losses during battery charging and discharging renders this optimization problem nonconvex. Methodology/results: By exploiting a total unimodularity property of the uncertainty set and an exact linear decision rule reformulation, we prove that this nonconvex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. Managerial implications: We find that the prevailing penalties for nondelivery of promised regulation power are too low to incentivize vehicle owners to honor the delivery guarantees given to grid operators.Funding: This work was supported by the Institut Vedecom.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0154 .
{"title":"Reliable Frequency Regulation Through Vehicle-to-Grid: Encoding Legislation with Robust Constraints","authors":"Dirk Lauinger, François Vuille, Daniel Kuhn","doi":"10.1287/msom.2022.0154","DOIUrl":"https://doi.org/10.1287/msom.2022.0154","url":null,"abstract":"Problem definition: Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a robust optimization problem that maximizes a vehicle owner’s expected profit from selling primary frequency regulation to the grid and guarantees that market commitments are met at all times for all frequency deviation trajectories in a functional uncertainty set that encodes applicable legislation. Faithfully modeling the energy conversion losses during battery charging and discharging renders this optimization problem nonconvex. Methodology/results: By exploiting a total unimodularity property of the uncertainty set and an exact linear decision rule reformulation, we prove that this nonconvex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. Managerial implications: We find that the prevailing penalties for nondelivery of promised regulation power are too low to incentivize vehicle owners to honor the delivery guarantees given to grid operators.Funding: This work was supported by the Institut Vedecom.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0154 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754965","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 prevalence of paid priority programs in practice, we study a service provider operating a system in which customers have random waiting costs and choose between two queues: regular (no cost) or priority (for a fee). We also consider a mechanism by which the provider redistributes a portion of priority revenue to compensate regular-queue customers for their longer waits. Methodology/results: To determine the waiting-cost-dependent equilibrium priority purchasing strategies, we establish structural results at a sample-path level and prove that they generalize. In models both with and without compensation, the equilibrium exhibits a cost-dependent, increasing-threshold structure. We also prove that compensation entails fewer priority purchases because compensating regular-queue customers makes priority less attractive. We then analyze system-wide performance. Despite the fewer priority purchases, for a fixed (low) priority fee, compensation can actually reduce equilibrium aggregate waiting cost by filtering low-waiting-cost customers out of the priority queue; however, this finding does not hold when comparing at the optimal fees. We then test our models in the laboratory. Key behavioral regularities are that low-cost subjects are overrepresented (underrepresented) in the priority (regular) queue compared with equilibrium, and subjects with low and high waiting costs tend to overbuy priority at high fees. Managerial implications: Our theoretical and behavioral results guide service providers in managing priority service systems. First, we find that compensation does not provide short-term performance benefits. Second, our experiments reveal that suboptimal customer decisions partially prevent efficient reordering of customers by waiting cost, leading to higher aggregate waiting cost than the equilibrium predicts, but still lower than under first-come, first-serve service. Finally, because customers tolerate higher fees than they should, a revenue-maximizing provider can set a higher priority fee and extract more revenue than it could if customers acted rationally.Funding: This work was supported by the Center and Laboratory for Behavioral Operations and Economics at the Naveen Jindal School of Management at The University of Texas at Dallas.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0387 .
{"title":"Paid Priority in Service Systems: Theory and Experiments","authors":"Andrew E. Frazelle, Elena Katok","doi":"10.1287/msom.2021.0387","DOIUrl":"https://doi.org/10.1287/msom.2021.0387","url":null,"abstract":"Problem definition: Motivated by the prevalence of paid priority programs in practice, we study a service provider operating a system in which customers have random waiting costs and choose between two queues: regular (no cost) or priority (for a fee). We also consider a mechanism by which the provider redistributes a portion of priority revenue to compensate regular-queue customers for their longer waits. Methodology/results: To determine the waiting-cost-dependent equilibrium priority purchasing strategies, we establish structural results at a sample-path level and prove that they generalize. In models both with and without compensation, the equilibrium exhibits a cost-dependent, increasing-threshold structure. We also prove that compensation entails fewer priority purchases because compensating regular-queue customers makes priority less attractive. We then analyze system-wide performance. Despite the fewer priority purchases, for a fixed (low) priority fee, compensation can actually reduce equilibrium aggregate waiting cost by filtering low-waiting-cost customers out of the priority queue; however, this finding does not hold when comparing at the optimal fees. We then test our models in the laboratory. Key behavioral regularities are that low-cost subjects are overrepresented (underrepresented) in the priority (regular) queue compared with equilibrium, and subjects with low and high waiting costs tend to overbuy priority at high fees. Managerial implications: Our theoretical and behavioral results guide service providers in managing priority service systems. First, we find that compensation does not provide short-term performance benefits. Second, our experiments reveal that suboptimal customer decisions partially prevent efficient reordering of customers by waiting cost, leading to higher aggregate waiting cost than the equilibrium predicts, but still lower than under first-come, first-serve service. Finally, because customers tolerate higher fees than they should, a revenue-maximizing provider can set a higher priority fee and extract more revenue than it could if customers acted rationally.Funding: This work was supported by the Center and Laboratory for Behavioral Operations and Economics at the Naveen Jindal School of Management at The University of Texas at Dallas.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0387 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515431","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}
Sıdıka Tunç Candoğan, Philipp B. Cornelius, Bilal Gokpinar, Ersin Körpeoğlu, Christopher S. Tang
Problem definition: Crowdfunding goes beyond raising funds. Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products and may therefore prefer to launch their crowdfunding campaigns using basic versions of their products with fewer features. However, customers may not be persuaded by a campaign if the product appears to be underdeveloped. In view of this tradeoff, a key question for entrepreneurs is how much to develop a product before launching a crowdfunding campaign. Methodology/results: Analyzing a game-theoretical model and testing its predictions empirically, we study (1) how the development level of a product at campaign launch, measured by the initial number of product features, influences whether customers will make comments that help entrepreneurs improve the product; (2) whether entrepreneurs continue to improve the product during the campaign; and (3) whether the campaign is successful. We show that, as the number of product features at campaign launch increases, the likelihood that customers will make comments and that the product will be improved during the campaign first increases but then decreases. Furthermore, the likelihood of campaign success first increases but then decreases with the number of product features at campaign launch. Finally, by analyzing the interactions between customer feedback, product improvement, and campaign success, we show that customer feedback motivates entrepreneurs to improve the product during the campaign. Moreover, entrepreneurs should take account of the initial number of features and customer feedback when improving the product, because otherwise product improvements can harm campaign success. Managerial implications: Our study provides practical insights on how entrepreneurs can use crowdfunding to aid product development and improvement. Specifically, entrepreneurs should avoid overdeveloping their products before crowdfunding campaigns because, as well as decreasing the chance of campaign success, this could hinder their ability to save development costs (e.g., market research costs) through involving customers in product development.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0344 .
{"title":"Product Development in Crowdfunding: Theoretical and Empirical Analysis","authors":"Sıdıka Tunç Candoğan, Philipp B. Cornelius, Bilal Gokpinar, Ersin Körpeoğlu, Christopher S. Tang","doi":"10.1287/msom.2022.0344","DOIUrl":"https://doi.org/10.1287/msom.2022.0344","url":null,"abstract":"Problem definition: Crowdfunding goes beyond raising funds. Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products and may therefore prefer to launch their crowdfunding campaigns using basic versions of their products with fewer features. However, customers may not be persuaded by a campaign if the product appears to be underdeveloped. In view of this tradeoff, a key question for entrepreneurs is how much to develop a product before launching a crowdfunding campaign. Methodology/results: Analyzing a game-theoretical model and testing its predictions empirically, we study (1) how the development level of a product at campaign launch, measured by the initial number of product features, influences whether customers will make comments that help entrepreneurs improve the product; (2) whether entrepreneurs continue to improve the product during the campaign; and (3) whether the campaign is successful. We show that, as the number of product features at campaign launch increases, the likelihood that customers will make comments and that the product will be improved during the campaign first increases but then decreases. Furthermore, the likelihood of campaign success first increases but then decreases with the number of product features at campaign launch. Finally, by analyzing the interactions between customer feedback, product improvement, and campaign success, we show that customer feedback motivates entrepreneurs to improve the product during the campaign. Moreover, entrepreneurs should take account of the initial number of features and customer feedback when improving the product, because otherwise product improvements can harm campaign success. Managerial implications: Our study provides practical insights on how entrepreneurs can use crowdfunding to aid product development and improvement. Specifically, entrepreneurs should avoid overdeveloping their products before crowdfunding campaigns because, as well as decreasing the chance of campaign success, this could hinder their ability to save development costs (e.g., market research costs) through involving customers in product development.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0344 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515831","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: Delays in admission to rehabilitation care can adversely impact patient outcomes. In addition, delayed patients keep occupying their acute care beds, making them unavailable for incoming patients. Admission delays are mainly caused by a lack of rehabilitation bed capacity and the time required to plan for rehabilitation activities, which we refer to as processing times. Because of non-standard bed allocation decisions and data limitations in practice, quantifying the magnitude of the two sources of delays can be technically challenging yet critical to the design of evidence-based interventions to reduce delays. We propose an empirical approach to understanding the contributions of the two sources of delays when only a single (combined) measure of admission delay is available. Methodology/results: We propose a hidden Markov model (HMM) to estimate the unobserved processing times and the status-quo bed allocation policy. Our estimation results quantify the magnitude of processing times versus capacity-driven delays and provide insights into factors impacting the bed allocation decision. We validate our estimated policy using a queueing model of patient flow and find that ignoring processing times or using simple bed allocation policies can lead to highly inaccurate delay estimates. In contrast, our estimated policy allows for accurate evaluation of different operational interventions. We find that reducing processing times can be highly effective in reducing admission delays and bed-blocking costs. In addition, allowing early transfer—whereby patients can complete some of their processing requirements in the rehabilitation unit—can significantly reduce admission delays, with only a small increase in rehab LOS. Managerial implications: Our study demonstrates the importance of quantifying different sources of delays in the design of effective operational interventions for reducing delays in admission to rehabilitation care. The proposed estimation framework can be applied in other transition-of-care settings with personalized capacity allocation decisions and hidden processing delays.History: This paper was selected for Fast Track in the M&SOM journal from the 2022 MSOM Healthcare SIG Conference.Funding: J. Dong was supported in part by the National Science Foundation [Grant CMMI-1762544]. V. Sarhangian was supported in part by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-04518] and the Connaught Fund.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0377 .
问题的定义：延迟康复护理入院会对患者的治疗效果产生不利影响。此外，延迟入院的病人会一直占用急症护理床位，导致无法为新入院的病人提供床位。入院延误的主要原因是康复病床容量不足，以及康复活动计划所需的时间（我们称之为处理时间）。由于床位分配决策的非标准性和实践中数据的局限性，量化这两种延误来源的严重程度在技术上具有挑战性，但对于设计循证干预措施以减少延误至关重要。我们提出了一种实证方法，以了解在只有单一（综合）入院延迟测量指标的情况下，两种延迟来源的贡献。方法/结果：我们提出了一个隐马尔可夫模型（HMM）来估计未观察到的处理时间和现状床位分配政策。我们的估算结果量化了处理时间与容量驱动延迟的大小，并提供了对影响床位分配决策的因素的见解。我们使用病人流排队模型验证了我们的估计政策，发现忽略处理时间或使用简单的床位分配政策会导致非常不准确的延迟估计。相比之下，我们估算的政策可以对不同的运营干预措施进行准确评估。我们发现，缩短处理时间可以非常有效地减少入院延迟和床位阻塞成本。此外，允许提前转院--即患者可以在康复科完成部分处理要求--可以显著减少入院延迟，而康复科的 LOS 只需少量增加。管理意义：我们的研究表明，在设计有效的操作干预措施以减少康复护理入院延误的过程中，量化不同的延误来源非常重要。提出的估算框架可应用于其他具有个性化容量分配决策和隐性处理延迟的护理过渡环境：本文入选 2022 年 MSOM 医疗保健 SIG 会议的 M&SOM 期刊快速通道：J. Dong部分获得了美国国家科学基金会[Grant CMMI-1762544]的资助。V. Sarhangian得到了加拿大自然科学与工程研究委员会[RGPIN-2018-04518号基金]和康诺基金的部分资助：电子版可在 https://doi.org/10.1287/msom.2022.0377 上查阅。
{"title":"What Causes Delays in Admission to Rehabilitation Care? A Structural Estimation Approach","authors":"Jing Dong, Berk Görgülü, Vahid Sarhangian","doi":"10.1287/msom.2022.0377","DOIUrl":"https://doi.org/10.1287/msom.2022.0377","url":null,"abstract":"Problem definition: Delays in admission to rehabilitation care can adversely impact patient outcomes. In addition, delayed patients keep occupying their acute care beds, making them unavailable for incoming patients. Admission delays are mainly caused by a lack of rehabilitation bed capacity and the time required to plan for rehabilitation activities, which we refer to as processing times. Because of non-standard bed allocation decisions and data limitations in practice, quantifying the magnitude of the two sources of delays can be technically challenging yet critical to the design of evidence-based interventions to reduce delays. We propose an empirical approach to understanding the contributions of the two sources of delays when only a single (combined) measure of admission delay is available. Methodology/results: We propose a hidden Markov model (HMM) to estimate the unobserved processing times and the status-quo bed allocation policy. Our estimation results quantify the magnitude of processing times versus capacity-driven delays and provide insights into factors impacting the bed allocation decision. We validate our estimated policy using a queueing model of patient flow and find that ignoring processing times or using simple bed allocation policies can lead to highly inaccurate delay estimates. In contrast, our estimated policy allows for accurate evaluation of different operational interventions. We find that reducing processing times can be highly effective in reducing admission delays and bed-blocking costs. In addition, allowing early transfer—whereby patients can complete some of their processing requirements in the rehabilitation unit—can significantly reduce admission delays, with only a small increase in rehab LOS. Managerial implications: Our study demonstrates the importance of quantifying different sources of delays in the design of effective operational interventions for reducing delays in admission to rehabilitation care. The proposed estimation framework can be applied in other transition-of-care settings with personalized capacity allocation decisions and hidden processing delays.History: This paper was selected for Fast Track in the M&SOM journal from the 2022 MSOM Healthcare SIG Conference.Funding: J. Dong was supported in part by the National Science Foundation [Grant CMMI-1762544]. V. Sarhangian was supported in part by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-04518] and the Connaught Fund.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0377 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139413218","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: Charities face tension when deciding whether to earmark donations, that is, allow donors to restrict the use of their donations for a specific purpose. Research shows that earmarking decreases operational performance because it limits charities’ flexibility to use donations. However, there is also a common belief that earmarking increases donations. Earmarking is assumed to increase donations through three mechanisms: by (i) giving donors control over their donations, (ii) increasing operational transparency of donations, and (iii) changing donors’ levels of altruism and warm-glow. To resolve this tension, we study how, when, and why earmarking affects donors’ decisions. We consider three important decisions donors make that impact the fundraising outcome: (i) preference between earmarking and nonearmarking, (ii) decision to donate or not (i.e., donor activation), and (iii) the donation amount. Methodology/results: We design three online experiments that allow us to quantify the effect of earmarking on donors’ decisions and to investigate the role of the three aforementioned mechanisms in fundraising. Our results reveal, for example, that earmarking activates more donors but it does not always increase donation amounts. In addition, we determine the conditions under which the three mechanisms affect the outcome of fundraising campaigns. Managerial implications: Our findings provide actionable insights for how charities can design fundraising campaigns more effectively and suggest when to leverage earmarking and the three mechanisms depending on the charities’ fundraising goals.Funding: The authors gratefully acknowledge financial support provided by the Leeds School of Business at the University of Colorado Boulder.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0096 .
{"title":"To Earmark or to Nonearmark? The Role of Control, Transparency, and Warm-Glow","authors":"Özalp Özer, Gloria Urrea, Sebastián Villa","doi":"10.1287/msom.2022.0096","DOIUrl":"https://doi.org/10.1287/msom.2022.0096","url":null,"abstract":"Problem definition: Charities face tension when deciding whether to earmark donations, that is, allow donors to restrict the use of their donations for a specific purpose. Research shows that earmarking decreases operational performance because it limits charities’ flexibility to use donations. However, there is also a common belief that earmarking increases donations. Earmarking is assumed to increase donations through three mechanisms: by (i) giving donors control over their donations, (ii) increasing operational transparency of donations, and (iii) changing donors’ levels of altruism and warm-glow. To resolve this tension, we study how, when, and why earmarking affects donors’ decisions. We consider three important decisions donors make that impact the fundraising outcome: (i) preference between earmarking and nonearmarking, (ii) decision to donate or not (i.e., donor activation), and (iii) the donation amount. Methodology/results: We design three online experiments that allow us to quantify the effect of earmarking on donors’ decisions and to investigate the role of the three aforementioned mechanisms in fundraising. Our results reveal, for example, that earmarking activates more donors but it does not always increase donation amounts. In addition, we determine the conditions under which the three mechanisms affect the outcome of fundraising campaigns. Managerial implications: Our findings provide actionable insights for how charities can design fundraising campaigns more effectively and suggest when to leverage earmarking and the three mechanisms depending on the charities’ fundraising goals.Funding: The authors gratefully acknowledge financial support provided by the Leeds School of Business at the University of Colorado Boulder.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0096 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139375438","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}
Dimitris Bertsimas, Ryan Cory-Wright, Vassilis Digalakis
Problem definition: We present our collaboration with the OCP Group, one of the world’s largest producers of phosphate and phosphate-based products, in support of a green initiative designed to reduce OCP’s carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its time-discounted investment cost, plus the cost of satisfying its remaining demand via the Moroccan national grid. OCP is currently designing its renewable investment strategy, using insights gleaned from our optimization model, and has pledged to invest 130 billion Moroccan dirham (MAD) (approximately 13 billion U.S. dollars (USD)) in a green initiative by 2027, a subset of which involves decarbonization. Methodology/results: We immunize our model against deviations between forecast and realized solar generation output via a combination of robust and distributionally robust optimization. To account for variability in daily solar generation, we propose a data-driven robust optimization approach that prevents excessive conservatism by averaging across uncertainty sets. To protect against variability in seasonal weather patterns induced by climate change, we invoke distributionally robust optimization techniques. Under a 10 billion MAD (approximately 1 billion USD) investment by OCP, the proposed methodology reduces the carbon emissions that arise from OCP’s energy needs by more than 70%, while generating a net present value (NPV) of 5 billion MAD over a 20-year planning horizon. Moreover, a 20 billion MAD investment induces a 95% reduction in carbon emissions and generates an NPV of around 2 billion MAD. Managerial implications: To fulfill the Paris climate agreement, rapidly decarbonizing the global economy in a financially sustainable fashion is imperative. Accordingly, this work develops a robust optimization methodology that enables OCP to decarbonize at a profit by purchasing solar panels and batteries. Moreover, the methodology could be applied to decarbonize other industrial consumers. Indeed, our approach suggests that decarbonization’s profitability depends on solar capacity factors, energy prices, and borrowing costs.History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0467 .
{"title":"Decarbonizing OCP","authors":"Dimitris Bertsimas, Ryan Cory-Wright, Vassilis Digalakis","doi":"10.1287/msom.2022.0467","DOIUrl":"https://doi.org/10.1287/msom.2022.0467","url":null,"abstract":"Problem definition: We present our collaboration with the OCP Group, one of the world’s largest producers of phosphate and phosphate-based products, in support of a green initiative designed to reduce OCP’s carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its time-discounted investment cost, plus the cost of satisfying its remaining demand via the Moroccan national grid. OCP is currently designing its renewable investment strategy, using insights gleaned from our optimization model, and has pledged to invest 130 billion Moroccan dirham (MAD) (approximately 13 billion U.S. dollars (USD)) in a green initiative by 2027, a subset of which involves decarbonization. Methodology/results: We immunize our model against deviations between forecast and realized solar generation output via a combination of robust and distributionally robust optimization. To account for variability in daily solar generation, we propose a data-driven robust optimization approach that prevents excessive conservatism by averaging across uncertainty sets. To protect against variability in seasonal weather patterns induced by climate change, we invoke distributionally robust optimization techniques. Under a 10 billion MAD (approximately 1 billion USD) investment by OCP, the proposed methodology reduces the carbon emissions that arise from OCP’s energy needs by more than 70%, while generating a net present value (NPV) of 5 billion MAD over a 20-year planning horizon. Moreover, a 20 billion MAD investment induces a 95% reduction in carbon emissions and generates an NPV of around 2 billion MAD. Managerial implications: To fulfill the Paris climate agreement, rapidly decarbonizing the global economy in a financially sustainable fashion is imperative. Accordingly, this work develops a robust optimization methodology that enables OCP to decarbonize at a profit by purchasing solar panels and batteries. Moreover, the methodology could be applied to decarbonize other industrial consumers. Indeed, our approach suggests that decarbonization’s profitability depends on solar capacity factors, energy prices, and borrowing costs.History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0467 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063184","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 provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Methodology/results: Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. Managerial implications: We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0229 .
{"title":"Inventory Productivity and Stock Returns in Manufacturing Networks","authors":"Deepak Agrawal, Nikolay Osadchiy","doi":"10.1287/msom.2022.0229","DOIUrl":"https://doi.org/10.1287/msom.2022.0229","url":null,"abstract":"Problem definition: We provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Methodology/results: Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. Managerial implications: We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0229 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139030648","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: Overproduction is often cited as the fashion industry’s biggest environmental issue, as textile production is notoriously resource intensive and pollutive, and much of the textile produced may end up as “deadstock” fabric or finished goods that do not sell. In this paper, we study two major approaches to address this issue: quick response, whereby finished goods inventory is replenished on demand, and upcycling, whereby deadstock fabric is reused to make new clothes. Proponents of these strategies typically focus on their positive environmental impact in downstream supply chain stages (e.g., finished goods production and waste disposal). Less is known, however, about their impact on upstream activities such as raw material acquisition, which we investigate in this work. Methodology/results: We analyze the effect of quick response and upcycling options on firms’ fabric acquisition and production decisions, as well as firms’ incentives to adopt these strategies. We then assess these strategies’ environmental impact in a life cycle framework. Our results show that quick response—when implemented in isolation—reduces deadstock of finished goods, but could increase the amount of fabric acquired. This not only results in more total deadstock (in both finished goods and fabric form), but also aggravates the environmental burden associated with fabric production in the upstream of the fashion supply chain, and could lead to a worse overall environmental impact for the industry. Upcycling together with quick response could alleviate total deadstock generation, but further increases the firm’s demand for fabric. We analyze the effectiveness of two types of policies—subsidizing quick response/upcycling and banning deadstock destruction—in reducing deadstock and curbing firms’ need for fabric. Managerial implications: Our work highlights a tradeoff between downstream deadstock reduction and upstream fabric acquisition, and suggests that regional policies that aim to reduce local deadstock could often have adverse global impacts.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0040 .
{"title":"Waste Not Want Not? The Environmental Implications of Quick Response and Upcycling","authors":"Xiaoyang Long, Luyi Gui","doi":"10.1287/msom.2022.0040","DOIUrl":"https://doi.org/10.1287/msom.2022.0040","url":null,"abstract":"Problem definition: Overproduction is often cited as the fashion industry’s biggest environmental issue, as textile production is notoriously resource intensive and pollutive, and much of the textile produced may end up as “deadstock” fabric or finished goods that do not sell. In this paper, we study two major approaches to address this issue: quick response, whereby finished goods inventory is replenished on demand, and upcycling, whereby deadstock fabric is reused to make new clothes. Proponents of these strategies typically focus on their positive environmental impact in downstream supply chain stages (e.g., finished goods production and waste disposal). Less is known, however, about their impact on upstream activities such as raw material acquisition, which we investigate in this work. Methodology/results: We analyze the effect of quick response and upcycling options on firms’ fabric acquisition and production decisions, as well as firms’ incentives to adopt these strategies. We then assess these strategies’ environmental impact in a life cycle framework. Our results show that quick response—when implemented in isolation—reduces deadstock of finished goods, but could increase the amount of fabric acquired. This not only results in more total deadstock (in both finished goods and fabric form), but also aggravates the environmental burden associated with fabric production in the upstream of the fashion supply chain, and could lead to a worse overall environmental impact for the industry. Upcycling together with quick response could alleviate total deadstock generation, but further increases the firm’s demand for fabric. We analyze the effectiveness of two types of policies—subsidizing quick response/upcycling and banning deadstock destruction—in reducing deadstock and curbing firms’ need for fabric. Managerial implications: Our work highlights a tradeoff between downstream deadstock reduction and upstream fabric acquisition, and suggests that regional policies that aim to reduce local deadstock could often have adverse global impacts.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0040 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139030836","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 a periodic-review dual-sourcing inventory system with a regular source (lower unit cost but longer lead time) and an expedited source (shorter lead time but higher unit cost) under carried-over supply and backlogged demand. Unlike existing literature, we assume that the firm does not have access to the demand distribution a priori and relies solely on past demand realizations. Even with complete information on the demand distribution, it is well known in the literature that the optimal inventory replenishment policy is complex and state dependent. Therefore, we focus our attention on a class of popular, easy-to-implement, and near-optimal heuristic policies called the dual-index policy. Methodology/results: The performance measure is the regret, defined as the cost difference of any feasible learning algorithm against the full-information optimal dual-index policy. We develop a nonparametric online learning algorithm that admits a regret upper bound of [Formula: see text], which matches the regret lower bound for any feasible learning algorithms up to a logarithmic factor. Our algorithm integrates stochastic bandits and sample average approximation techniques in an innovative way. As part of our regret analysis, we explicitly prove that the underlying Markov chain is ergodic and converges to its steady state exponentially fast via coupling arguments, which could be of independent interest. Managerial implications: Our work provides practitioners with an easy-to-implement, robust, and provably good online decision support system for managing a dual-sourcing inventory system.Funding: This work was supported by the Amazon Research Award.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0323 .
{"title":"Online Learning for Dual-Index Policies in Dual-Sourcing Systems","authors":"Jingwen Tang, Boxiao Chen, Cong Shi","doi":"10.1287/msom.2022.0323","DOIUrl":"https://doi.org/10.1287/msom.2022.0323","url":null,"abstract":"Problem definition: We consider a periodic-review dual-sourcing inventory system with a regular source (lower unit cost but longer lead time) and an expedited source (shorter lead time but higher unit cost) under carried-over supply and backlogged demand. Unlike existing literature, we assume that the firm does not have access to the demand distribution a priori and relies solely on past demand realizations. Even with complete information on the demand distribution, it is well known in the literature that the optimal inventory replenishment policy is complex and state dependent. Therefore, we focus our attention on a class of popular, easy-to-implement, and near-optimal heuristic policies called the dual-index policy. Methodology/results: The performance measure is the regret, defined as the cost difference of any feasible learning algorithm against the full-information optimal dual-index policy. We develop a nonparametric online learning algorithm that admits a regret upper bound of [Formula: see text], which matches the regret lower bound for any feasible learning algorithms up to a logarithmic factor. Our algorithm integrates stochastic bandits and sample average approximation techniques in an innovative way. As part of our regret analysis, we explicitly prove that the underlying Markov chain is ergodic and converges to its steady state exponentially fast via coupling arguments, which could be of independent interest. Managerial implications: Our work provides practitioners with an easy-to-implement, robust, and provably good online decision support system for managing a dual-sourcing inventory system.Funding: This work was supported by the Amazon Research Award.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0323 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138580473","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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