Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in price sensitivity for different tickets, binary outcomes, and nonlinear interactions between ticket features. Our secondary goal is to highlight how this estimation can be integrated into a prescriptive trading strategy for buying and selling tickets in an active marketplace. Academic/practical relevance: We present a novel method for demand estimation with heterogeneous treatment effect in the presence of confounding. In practice, we embed this method within an optimization framework for ticket reselling, providing the ticket reselling platform with a new framework for pricing tickets on its platform. Methodology: We introduce a general double/orthogonalized machine learning method for classification problems. This method allows us to isolate the causal effects of price on the outcome by removing the conditional effects of the ticket and market features. Furthermore, we introduce a novel loss function that can be easily incorporated into powerful, off-the-shelf machine learning algorithms, including gradient boosted trees. We show how, in the presence of hidden confounding variables, instrumental variables can be incorporated. Results: Using a wide range of synthetic data sets, we show this approach beats state-of-the-art machine learning and causal inference approaches for estimating treatment effects in the classification setting. Furthermore, using National Basketball Association ticket listings from the 2014–2015 season, we show that probit models with instrumental variables, previously used for price estimation of tickets in the resale market, are significantly less accurate and potentially misspecified relative to our proposed approach. Through pricing simulations, we show our proposed method can achieve an 11% return on investment by buying and selling tickets, whereas existing techniques are not profitable. Managerial implications: The knowledge of how to price tickets on its platform offers a range of potential opportunities for our collaborator, both in terms of understanding sellers on their platform and in developing new products to offer them. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the National Science Foundation [Grant CMMI-1563343]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.1065 .
{"title":"Pricing for Heterogeneous Products: Analytics for Ticket Reselling","authors":"Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, Georgia Perakis","doi":"10.1287/msom.2021.1065","DOIUrl":"https://doi.org/10.1287/msom.2021.1065","url":null,"abstract":"Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in price sensitivity for different tickets, binary outcomes, and nonlinear interactions between ticket features. Our secondary goal is to highlight how this estimation can be integrated into a prescriptive trading strategy for buying and selling tickets in an active marketplace. Academic/practical relevance: We present a novel method for demand estimation with heterogeneous treatment effect in the presence of confounding. In practice, we embed this method within an optimization framework for ticket reselling, providing the ticket reselling platform with a new framework for pricing tickets on its platform. Methodology: We introduce a general double/orthogonalized machine learning method for classification problems. This method allows us to isolate the causal effects of price on the outcome by removing the conditional effects of the ticket and market features. Furthermore, we introduce a novel loss function that can be easily incorporated into powerful, off-the-shelf machine learning algorithms, including gradient boosted trees. We show how, in the presence of hidden confounding variables, instrumental variables can be incorporated. Results: Using a wide range of synthetic data sets, we show this approach beats state-of-the-art machine learning and causal inference approaches for estimating treatment effects in the classification setting. Furthermore, using National Basketball Association ticket listings from the 2014–2015 season, we show that probit models with instrumental variables, previously used for price estimation of tickets in the resale market, are significantly less accurate and potentially misspecified relative to our proposed approach. Through pricing simulations, we show our proposed method can achieve an 11% return on investment by buying and selling tickets, whereas existing techniques are not profitable. Managerial implications: The knowledge of how to price tickets on its platform offers a range of potential opportunities for our collaborator, both in terms of understanding sellers on their platform and in developing new products to offer them. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the National Science Foundation [Grant CMMI-1563343]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.1065 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers to balance the tradeoff between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. Academic/practical relevance: Modern buying channels, including e-commerce and public procurement, often consist of a platform that mediates transactions. Frequently, these platforms implement simple and transparent mechanisms to induce suppliers’ direct participation, which typically results in pay-as-bid (or first-price) mechanisms where suppliers set their prices. Methodology: We introduce a novel class of assortment mechanisms that we call k-soft reserves (k-SRs): If at least k suppliers choose a price below the soft-reserve price, then only those suppliers are added to the assortment; otherwise, all the suppliers are added. Results: We show the optimality of k-SRs for a class of stylized symmetric models to derive the intuition behind these mechanisms. Then, through extensive numerical simulations, we provide evidence of the robustness of k-SRs in more general and realistic settings. Managerial implications: Our results give intuitive and simple-to-use prescriptions on how to optimize pay-as-bid assortment mechanisms in practice, with an emphasis on public procurement settings. Funding: J. Choi thanks the Samsung Scholarship and Stanford Graduate School of Business for financial support. G. Weintraub thanks Joseph and Laurie Lacob for the support during the 2018–2019 academic year as a Joseph and Laurie Lacob Faculty Scholar at Stanford Graduate School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1180 .
{"title":"The Design of Optimal Pay-as-Bid Procurement Mechanisms","authors":"Je-ok Choi, Daniela Saban, Gabriel Weintraub","doi":"10.1287/msom.2022.1180","DOIUrl":"https://doi.org/10.1287/msom.2022.1180","url":null,"abstract":"Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers to balance the tradeoff between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. Academic/practical relevance: Modern buying channels, including e-commerce and public procurement, often consist of a platform that mediates transactions. Frequently, these platforms implement simple and transparent mechanisms to induce suppliers’ direct participation, which typically results in pay-as-bid (or first-price) mechanisms where suppliers set their prices. Methodology: We introduce a novel class of assortment mechanisms that we call k-soft reserves (k-SRs): If at least k suppliers choose a price below the soft-reserve price, then only those suppliers are added to the assortment; otherwise, all the suppliers are added. Results: We show the optimality of k-SRs for a class of stylized symmetric models to derive the intuition behind these mechanisms. Then, through extensive numerical simulations, we provide evidence of the robustness of k-SRs in more general and realistic settings. Managerial implications: Our results give intuitive and simple-to-use prescriptions on how to optimize pay-as-bid assortment mechanisms in practice, with an emphasis on public procurement settings. Funding: J. Choi thanks the Samsung Scholarship and Stanford Graduate School of Business for financial support. G. Weintraub thanks Joseph and Laurie Lacob for the support during the 2018–2019 academic year as a Joseph and Laurie Lacob Faculty Scholar at Stanford Graduate School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1180 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136173687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: This paper studies an entrepreneur’s pricing strategy in a reward-based crowdfunding campaign under asymmetric product quality information. We propose two signaling mechanisms and investigate how entrepreneurs can leverage their pricing strategy to signal a high-quality project. Academic/practical relevance: This problem is relevant to practice, as asymmetric quality information is a significant concern in reward-based crowdfunding. High-quality entrepreneurs seek credible mechanisms to signal the quality of projects to customers. Methodology: We develop a stylized game-theoretic signaling model with funding and regular selling periods that captures asymmetric quality information between an entrepreneur and customers. Results: We propose a new theory on quality signaling in crowdfunding. We show that contingent access to the regular selling market after running a successful crowdfunding campaign allows high-quality entrepreneurs to signal their quality through low funding prices (one-price signaling). A high-quality entrepreneur can increase his funding price and still signal his high-quality level if he commits to the future regular selling price (two-price signaling). We show that the distinct feature of crowdfunding, that is, the probabilistic nature of crowdfunding, plays different roles in one- and two-price signaling. It is the driving force for the separating equilibrium in one-price signaling, and in two-price signaling, it affects how the entrepreneur should manipulate his funding and regular selling prices to reduce signaling cost. Managerial implications: Entrepreneurs should be mindful of pricing in funding and regular selling periods because it could play an essential role in signaling quality information. Our findings suggest practical tools for quality signaling in crowdfunding. We also investigate when price commitment is the most beneficial for a high-quality entrepreneur, looking for potential signaling mechanisms. Funding: W. Zhou acknowledges financial support from the National Natural Science Foundation of China [Grant 72192823 and Grant 71821002]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1177 .
问题定义:研究在产品质量信息不对称的情况下，企业家在基于奖励的众筹活动中的定价策略。我们提出了两种信号机制，并研究了企业家如何利用他们的定价策略来发出高质量项目的信号。学术/实践相关性:这个问题与实践相关，因为不对称的质量信息是基于奖励的众筹的一个重要问题。高质量的企业家寻求可靠的机制，向客户表明项目的质量。方法:我们开发了一个风格化的博弈论信号模型，该模型包含资金和定期销售周期，可以捕获企业家和客户之间不对称的质量信息。结果:提出了一种新的众筹质量信号理论。我们表明，在成功的众筹活动后，偶然进入常规销售市场，使高质量的企业家能够通过低融资价格(单一价格信号)来表明他们的质量。一个高质量的企业家可以提高他的融资价格，但如果他承诺未来的正常销售价格(双价格信号)，他仍然可以表明他的高质量水平。我们证明了众筹的显著特征，即众筹的概率性质，在一价和二价信号中起着不同的作用。它是单价格信号中分离均衡的驱动力，在双价格信号中，它影响企业家应该如何操纵其融资和正常销售价格以降低信号成本。管理方面的影响:企业家应注意融资和定期销售期间的定价，因为这可能在传递质量信息方面发挥重要作用。我们的研究结果为众筹中的质量信号提供了实用的工具。我们还研究了价格承诺何时对高质量企业家最有利，寻找潜在的信号机制。基金资助:W. Zhou感谢国家自然科学基金资助[Grant 72192823 and Grant 71821002]。补充材料:在线附录可在https://doi.org/10.1287/msom.2022.1177上获得。
{"title":"Quality Signaling Through Crowdfunding Pricing","authors":"Ehsan Bolandifar, Zhong Chen, Panos Kouvelis, Weihua Zhou","doi":"10.1287/msom.2022.1177","DOIUrl":"https://doi.org/10.1287/msom.2022.1177","url":null,"abstract":"Problem definition: This paper studies an entrepreneur’s pricing strategy in a reward-based crowdfunding campaign under asymmetric product quality information. We propose two signaling mechanisms and investigate how entrepreneurs can leverage their pricing strategy to signal a high-quality project. Academic/practical relevance: This problem is relevant to practice, as asymmetric quality information is a significant concern in reward-based crowdfunding. High-quality entrepreneurs seek credible mechanisms to signal the quality of projects to customers. Methodology: We develop a stylized game-theoretic signaling model with funding and regular selling periods that captures asymmetric quality information between an entrepreneur and customers. Results: We propose a new theory on quality signaling in crowdfunding. We show that contingent access to the regular selling market after running a successful crowdfunding campaign allows high-quality entrepreneurs to signal their quality through low funding prices (one-price signaling). A high-quality entrepreneur can increase his funding price and still signal his high-quality level if he commits to the future regular selling price (two-price signaling). We show that the distinct feature of crowdfunding, that is, the probabilistic nature of crowdfunding, plays different roles in one- and two-price signaling. It is the driving force for the separating equilibrium in one-price signaling, and in two-price signaling, it affects how the entrepreneur should manipulate his funding and regular selling prices to reduce signaling cost. Managerial implications: Entrepreneurs should be mindful of pricing in funding and regular selling periods because it could play an essential role in signaling quality information. Our findings suggest practical tools for quality signaling in crowdfunding. We also investigate when price commitment is the most beneficial for a high-quality entrepreneur, looking for potential signaling mechanisms. Funding: W. Zhou acknowledges financial support from the National Natural Science Foundation of China [Grant 72192823 and Grant 71821002]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1177 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135796884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maxime C. Cohen, Alexandre Jacquillat, Haotian Song
Problem definition: It is common practice for firms to deploy strategies based on customer segmentation (by clustering customers into different segments) and price discrimination (by offering different prices to different customer segments). Price discrimination, although seemingly beneficial, can hurt firms in competitive environments. Academic/practical relevance: It is thus critical for firms to understand when to engage in price discrimination and how to support discriminatory pricing practices with appropriate inventory management strategies. This paper tackles this overarching question through operational lenses by studying the joint impact of price discrimination and the allocation of limited inventory across customer segments. Methodology: We develop a Bertrand competition game featuring capacity restrictions, quality differentiation, and customer heterogeneity. Results: We characterize (pure- or mixed-strategy) Nash equilibria for a single-stage game reflecting uniform pricing and for a two-stage inventory-price game reflecting discriminatory pricing along with endogenous inventory allocation. Managerial implications: We identify three sources of market friction in price competition enabling firms to earn higher profits: capacity limitations, quality differentiation, and customer heterogeneity. Price discrimination eliminates the market frictions from customer heterogeneity, but strategic inventory allocation restores (or strengthens) the market frictions from capacity limitations. As such, price discrimination is only beneficial when combined with optimal inventory allocation across segments. We discuss relevant real-world examples featuring regional price discrimination along with strategic inventory allocation, including fast fashion and vaccines. Otherwise, uniform pricing may outperform discriminatory pricing. Our results thus underscore the critical role of inventory allocation in the design of competitive pricing strategies. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1146 .
{"title":"Price Discrimination and Inventory Allocation in Bertrand Competition","authors":"Maxime C. Cohen, Alexandre Jacquillat, Haotian Song","doi":"10.1287/msom.2022.1146","DOIUrl":"https://doi.org/10.1287/msom.2022.1146","url":null,"abstract":"Problem definition: It is common practice for firms to deploy strategies based on customer segmentation (by clustering customers into different segments) and price discrimination (by offering different prices to different customer segments). Price discrimination, although seemingly beneficial, can hurt firms in competitive environments. Academic/practical relevance: It is thus critical for firms to understand when to engage in price discrimination and how to support discriminatory pricing practices with appropriate inventory management strategies. This paper tackles this overarching question through operational lenses by studying the joint impact of price discrimination and the allocation of limited inventory across customer segments. Methodology: We develop a Bertrand competition game featuring capacity restrictions, quality differentiation, and customer heterogeneity. Results: We characterize (pure- or mixed-strategy) Nash equilibria for a single-stage game reflecting uniform pricing and for a two-stage inventory-price game reflecting discriminatory pricing along with endogenous inventory allocation. Managerial implications: We identify three sources of market friction in price competition enabling firms to earn higher profits: capacity limitations, quality differentiation, and customer heterogeneity. Price discrimination eliminates the market frictions from customer heterogeneity, but strategic inventory allocation restores (or strengthens) the market frictions from capacity limitations. As such, price discrimination is only beneficial when combined with optimal inventory allocation across segments. We discuss relevant real-world examples featuring regional price discrimination along with strategic inventory allocation, including fast fashion and vaccines. Otherwise, uniform pricing may outperform discriminatory pricing. Our results thus underscore the critical role of inventory allocation in the design of competitive pricing strategies. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1146 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135235754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study the optimal design of crowdfunding campaigns and develop a model that maximizes revenue for a given crowdfunding campaign by optimizing both the pledge level sought from donors and the duration of the campaign. Academic/practical relevance: Our model explains the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. This model can be used to calibrate the revenue impact from using prespecified pledge levels or campaign durations. Methodology: We develop a theoretical model of the dynamics of the pledging process within the campaign duration, which employs a continuous-time, finite-horizon framework with two types of backer populations. Our model follows a diffusion-based approach and incorporates the structure of empirical observations. Results: We show that when campaign creators must follow an external standard for either the pledge level or the campaign duration, they should match low pledge levels with long campaign durations and high pledge levels with short campaign durations. We show that the optimal duration of a campaign, when not fixed by external constraints, depends on the composition of the backer population. Shorter campaigns are attuned to independent backers, and longer campaigns cater to herding backers. Managerial implications: Our analysis provides creators of crowdfunding campaigns with straightforward prescriptions for how to set the optimal pledge level and campaign duration. Platform managers could use the analysis to calibrate the arrival process of backers and creators’ campaign parameters. Funding: This work was supported by the Fishman-Davidson Center. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1147 .
{"title":"Revenue Management in Crowdfunding","authors":"Jiding Zhang, Sergei Savin, Senthil Veeraraghavan","doi":"10.1287/msom.2022.1147","DOIUrl":"https://doi.org/10.1287/msom.2022.1147","url":null,"abstract":"Problem definition: We study the optimal design of crowdfunding campaigns and develop a model that maximizes revenue for a given crowdfunding campaign by optimizing both the pledge level sought from donors and the duration of the campaign. Academic/practical relevance: Our model explains the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. This model can be used to calibrate the revenue impact from using prespecified pledge levels or campaign durations. Methodology: We develop a theoretical model of the dynamics of the pledging process within the campaign duration, which employs a continuous-time, finite-horizon framework with two types of backer populations. Our model follows a diffusion-based approach and incorporates the structure of empirical observations. Results: We show that when campaign creators must follow an external standard for either the pledge level or the campaign duration, they should match low pledge levels with long campaign durations and high pledge levels with short campaign durations. We show that the optimal duration of a campaign, when not fixed by external constraints, depends on the composition of the backer population. Shorter campaigns are attuned to independent backers, and longer campaigns cater to herding backers. Managerial implications: Our analysis provides creators of crowdfunding campaigns with straightforward prescriptions for how to set the optimal pledge level and campaign duration. Platform managers could use the analysis to calibrate the arrival process of backers and creators’ campaign parameters. Funding: This work was supported by the Fishman-Davidson Center. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1147 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136229586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm’s profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers’ limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1032 .
{"title":"Queueing Systems with Rationally Inattentive Customers","authors":"Caner Canyakmaz, Tamer Boyacı","doi":"10.1287/msom.2021.1032","DOIUrl":"https://doi.org/10.1287/msom.2021.1032","url":null,"abstract":"Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm’s profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers’ limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1032 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136297321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicole DeHoratius, Andreas Holzapfel, Heinrich Kuhn, Adam J. Mersereau, Michael Sternbeck
Problem definition: We compare several approaches for generating a prioritized list of items to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out of stocks. Academic/practical relevance: We consider both “rule-based” approaches, which sort items based on heuristic indices, and “model-based” approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Methodology: Our study evaluates these approaches on multiple metrics using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. Results: Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives but that rule-based approaches are also effective as long as they are matched to the retailer’s goal. We find that “high-activity” rule-based policies, which favor items with high sales volumes, inventory levels, and past errors, are more effective at detecting inventory discrepancies. The best policies uncover over twice the discrepancies detected by random selection. A “low-activity” rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out of stocks. The best policy detects over eight times the unknown out of stocks found by random selection. Managerial implications: Our findings provide immediate guidance to our retail partner on appropriate methods for detecting inventory record inaccuracy and unknown out of stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs. Funding: This work was supported by the Bavarian Ministry for Science and Arts [Grant BayIntAn_KUEI_2018_43] and the EHI Foundation and GS1 Germany [Prize for Best Collaboration Between Science and Practice in Retail Research (2019)]. A. J. Mersereau thanks the Sarah Graham Kenan Foundation for support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1119 .
问题定义:为了检测库存记录的不准确性和未知的缺货情况，我们比较了几种用于生成零售商店中要计数的物品的优先级列表的方法。学术/实践相关性:我们考虑了“基于规则”的方法，它基于启发式指数对物品进行排序，以及“基于模型”的方法，它维护基于销售和补充观察更新的真实库存水平的概率分布。方法:我们的研究评估了这些方法在多个指标使用数据的库存审计，我们进行了在欧洲家庭和个人护理零售商零售用品市场。结果:我们的结果支持基于规则和基于模型的方法。我们发现，基于模型的方法提供了对库存状态的通用可见性，并且对广泛的目标有用，但基于规则的方法也有效，只要它们与零售商的目标相匹配。我们发现“高活跃度”的基于规则的策略在检测库存差异方面更有效，这些策略倾向于高销量、高库存水平和过去错误的项目。最好的策略发现的差异是随机选择发现的两倍多。另一方面，基于低记录库存水平的“低活动”规则策略在检测未知库存方面更有效。最好的策略可以检测到8倍于随机选择的未知库存。管理意义:我们的研究结果为我们的零售合作伙伴提供了关于检测库存记录不准确和未知缺货的适当方法的直接指导。我们的方法可以复制到其他零售商感兴趣的定制优化他们的计数程序。资助:这项工作得到了巴伐利亚科学和艺术部[Grant BayIntAn_KUEI_2018_43]、EHI基金会和德国GS1[零售研究中科学与实践最佳协作奖(2019)]的支持。A. J. Mersereau感谢Sarah Graham Kenan基金会的支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2022.1119上获得。
{"title":"Evaluating Count Prioritization Procedures for Improving Inventory Accuracy in Retail Stores","authors":"Nicole DeHoratius, Andreas Holzapfel, Heinrich Kuhn, Adam J. Mersereau, Michael Sternbeck","doi":"10.1287/msom.2022.1119","DOIUrl":"https://doi.org/10.1287/msom.2022.1119","url":null,"abstract":"Problem definition: We compare several approaches for generating a prioritized list of items to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out of stocks. Academic/practical relevance: We consider both “rule-based” approaches, which sort items based on heuristic indices, and “model-based” approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Methodology: Our study evaluates these approaches on multiple metrics using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. Results: Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives but that rule-based approaches are also effective as long as they are matched to the retailer’s goal. We find that “high-activity” rule-based policies, which favor items with high sales volumes, inventory levels, and past errors, are more effective at detecting inventory discrepancies. The best policies uncover over twice the discrepancies detected by random selection. A “low-activity” rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out of stocks. The best policy detects over eight times the unknown out of stocks found by random selection. Managerial implications: Our findings provide immediate guidance to our retail partner on appropriate methods for detecting inventory record inaccuracy and unknown out of stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs. Funding: This work was supported by the Bavarian Ministry for Science and Arts [Grant BayIntAn_KUEI_2018_43] and the EHI Foundation and GS1 Germany [Prize for Best Collaboration Between Science and Practice in Retail Research (2019)]. A. J. Mersereau thanks the Sarah Graham Kenan Foundation for support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1119 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gonçalo Figueira, Willem van Jaarsveld, Pedro Amorim, Jan C. Fransoo
Problem definition: Online retailers are on a consistent drive to increase on-time delivery and reduce customer lead time. However, in reality, an increasing share of consumers places orders early. Academic/practical relevance: Such advance demand information can be deployed strategically to reduce costs and improve the customer service experience. This requires inventory and allocation policies that make optimal use of this information and that induce consumers to place their orders early. An increasing number of online retailers not only offer customers a choice of lead time but also, actively back-order missing items from a consumer basket. Methodology: We develop new allocation policies that commit to a customer order upon arrival of the order rather than at the moment the order is due. We provide analytical results for the performance of these allocation policies and evaluate their behavior with real data from a large food retailer. Results: Our policy leads to a higher fill rate at the expense of a slight increase in average delay. The analysis based on real-life data suggests a sizeable impact that should impact current best practices in online retail. Managerial implications: With the changing landscape in online retail, customers increasingly place baskets of orders that they would like to receive at a planned and confirmed moment in time. Especially in grocery, this has grown fast. This fundamentally changes the strategic management of inventory. We demonstrate that online retailers should commit early to customer orders to enhance the customer service experience and eventually, to also create opportunities for reducing the cost of operations. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1124 .
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Yesim Koca, Tugce Martagan, Ivo Adan, Lisa Maillart, Bram van Ravenstein
Problem definition: Bleed–feed is a novel technology that allows biomanufacturers to skip intermediary bioreactor setups. However, the specific time at which bleed–feed is performed is critical for success. The process is stringently regulated, and its implementation involves unique tradeoffs in operational decision making. Our analysis formalizes the operational challenges related to bleed–feed decisions, and our results inform biomanufacturers and policymakers on the potential impact of this technology on current practice. Academic/practical relevance: Operations management (OM) methodologies have not yet been widely adopted in the biomanufacturing industry. This research presents one of the first attempts to demonstrate how OM can complement biomanufacturing to improve operational decisions. We present a practically relevant problem and a rigorous solution approach that is relevant to both OM and biomanufacturing. Methodology: We develop a finite-horizon, discrete-time Markov decision processes model and analyze the structural characteristics of optimal bleed–feed policies. Moreover, we characterize the behavior of the value function as a function of regulatory restrictions. As a salient feature, the MDP model captures both the biological dynamics of fermentation and the operational tradeoffs in biomanufacturing. Results: We show that optimal bleed–feed policies have a three-way control-limit structure under mild conditions that were validated with industry data. Moreover, our analysis reveals that the marginal benefits of bleed–feed diminish as additional bleed–feeds are performed. As a practically relevant benchmark, we consider a risk-averse heuristic and identify sufficient conditions for its optimality. Managerial implications: Our analysis (supported with an industry case study) shows that bleed–feed implementation can provide benefits. Real-world implementation at MSD resulted in an 82.5% improvement in the batch yield per setup (using one bleed–feed). We find that low-risk fermentation systems benefit the most from bleed–feed implementation. We also find that the performance gap between optimal policies and the risk-averse heuristic is higher when the failure risks or the critical biomass levels are lower. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO-VENI scheme. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1163 .
{"title":"Increasing Biomanufacturing Yield with Bleed–Feed: Optimal Policies and Insights","authors":"Yesim Koca, Tugce Martagan, Ivo Adan, Lisa Maillart, Bram van Ravenstein","doi":"10.1287/msom.2022.1163","DOIUrl":"https://doi.org/10.1287/msom.2022.1163","url":null,"abstract":"Problem definition: Bleed–feed is a novel technology that allows biomanufacturers to skip intermediary bioreactor setups. However, the specific time at which bleed–feed is performed is critical for success. The process is stringently regulated, and its implementation involves unique tradeoffs in operational decision making. Our analysis formalizes the operational challenges related to bleed–feed decisions, and our results inform biomanufacturers and policymakers on the potential impact of this technology on current practice. Academic/practical relevance: Operations management (OM) methodologies have not yet been widely adopted in the biomanufacturing industry. This research presents one of the first attempts to demonstrate how OM can complement biomanufacturing to improve operational decisions. We present a practically relevant problem and a rigorous solution approach that is relevant to both OM and biomanufacturing. Methodology: We develop a finite-horizon, discrete-time Markov decision processes model and analyze the structural characteristics of optimal bleed–feed policies. Moreover, we characterize the behavior of the value function as a function of regulatory restrictions. As a salient feature, the MDP model captures both the biological dynamics of fermentation and the operational tradeoffs in biomanufacturing. Results: We show that optimal bleed–feed policies have a three-way control-limit structure under mild conditions that were validated with industry data. Moreover, our analysis reveals that the marginal benefits of bleed–feed diminish as additional bleed–feeds are performed. As a practically relevant benchmark, we consider a risk-averse heuristic and identify sufficient conditions for its optimality. Managerial implications: Our analysis (supported with an industry case study) shows that bleed–feed implementation can provide benefits. Real-world implementation at MSD resulted in an 82.5% improvement in the batch yield per setup (using one bleed–feed). We find that low-risk fermentation systems benefit the most from bleed–feed implementation. We also find that the performance gap between optimal policies and the risk-averse heuristic is higher when the failure risks or the critical biomass levels are lower. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO-VENI scheme. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1163 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134955161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: In what kinds of business method innovation do firms in the manufacturing and trade sectors engage? Does engaging in business method innovation create value for these firms? The present paper answers these questions using empirical evidence. Methodology/results: Using text analysis of business method patents, we show that business method innovation in the U.S. manufacturing and trade sectors is aimed primarily at improving the business operations that support the sales of tangible products—that is, how the firm targets customers, manages product delivery, or enhances the product through service offerings. We then evaluate the effect of having business method innovation, as evidenced by patents, on a firm’s value. Leveraging the exogenous shock of the State Street ruling, which first recognized business methods as a patentable category of innovation, we identify a set of firms that possess business method patents and a matched set of comparable firms without such patents. Then, using a difference-in-differences with firm fixed effects model on the matched sample, we show that the valuation of the former set of firms increased by 9% after State Street, as measured by Tobin’s q. We further show that (1) business method innovators in the manufacturing sector gained a 7% increase, whereas business method innovators in the trade sectors gained a 25% increase; and (2) only firms with broader innovation scope—that is, business method innovations covering the range of customer targeting, product delivery, and service support of products—experienced a significant (18%) value bump. Managerial implications: This research provides evidence that business method innovation in the manufacturing and trade sectors primarily involves innovating in business operations that support product sales. Our work also provides empirical support for the proposition that engaging in business method innovation drives manufacturing and trade firms’ market performance. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1129 .
{"title":"Business Method Innovation in U.S. Manufacturing and Trade","authors":"Tian Heong Chan, Anandhi Bharadwaj, Deepa Varadarajan","doi":"10.1287/msom.2022.1129","DOIUrl":"https://doi.org/10.1287/msom.2022.1129","url":null,"abstract":"Problem definition: In what kinds of business method innovation do firms in the manufacturing and trade sectors engage? Does engaging in business method innovation create value for these firms? The present paper answers these questions using empirical evidence. Methodology/results: Using text analysis of business method patents, we show that business method innovation in the U.S. manufacturing and trade sectors is aimed primarily at improving the business operations that support the sales of tangible products—that is, how the firm targets customers, manages product delivery, or enhances the product through service offerings. We then evaluate the effect of having business method innovation, as evidenced by patents, on a firm’s value. Leveraging the exogenous shock of the State Street ruling, which first recognized business methods as a patentable category of innovation, we identify a set of firms that possess business method patents and a matched set of comparable firms without such patents. Then, using a difference-in-differences with firm fixed effects model on the matched sample, we show that the valuation of the former set of firms increased by 9% after State Street, as measured by Tobin’s q. We further show that (1) business method innovators in the manufacturing sector gained a 7% increase, whereas business method innovators in the trade sectors gained a 25% increase; and (2) only firms with broader innovation scope—that is, business method innovations covering the range of customer targeting, product delivery, and service support of products—experienced a significant (18%) value bump. Managerial implications: This research provides evidence that business method innovation in the manufacturing and trade sectors primarily involves innovating in business operations that support product sales. Our work also provides empirical support for the proposition that engaging in business method innovation drives manufacturing and trade firms’ market performance. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1129 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135235910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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