{"title":"Erratum: Correction of Affiliation","authors":"","doi":"10.1287/msom.2022.1121","DOIUrl":"https://doi.org/10.1287/msom.2022.1121","url":null,"abstract":"","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128374893","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}
{"title":"Acknowledgment to Editors and Reviewers (2021)","authors":"","doi":"10.1287/msom.2017.0632","DOIUrl":"https://doi.org/10.1287/msom.2017.0632","url":null,"abstract":"The continued success of Manufacturing & Service Operations Management (M&SOM) relies on the support of authors and reviewers. On behalf of M&SOM, I would like to express my sincere appreciation to the department editors, guest editors, associate editors, guest associate editors, and reviewers who provided expert counsel and guidance on a voluntary basis. The following list acknowledges those who served from January to December 2021. Through their efforts, the journal was able to provide submitting authors with timely, thoughtful, and constructive reviews. I hereby acknowledge their service for the journal and gratefully appreciate their contributions for our profession. Georgia Perakis, M&SOM editor","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953870","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}
{"title":"Demand Learning and Pricing for Varying Assortments","authors":"K. Ferreira, Emily Mower","doi":"10.1287/msom.2022.1080","DOIUrl":"https://doi.org/10.1287/msom.2022.1080","url":null,"abstract":"Problem definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, for example, due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to frequently offer new styles. Academic/practical relevance: We are one of the first to consider the demand learning and pricing problem for retailers who offer product assortments that change frequently, and we propose and implement a learn-then-earn algorithm for use in this setting. Our algorithm prioritizes a short learning phase, an important practical characteristic that is only considered by few other algorithms. Methodology: We develop a novel demand learning and pricing algorithm that learns quickly in an environment with varying assortments and limited price changes by adapting the commonly used marketing technique of conjoint analysis to our setting. We partner with Zenrez, an e-commerce company that partners with fitness studios to sell excess capacity of fitness classes, to implement our algorithm in a controlled field experiment to evaluate its effectiveness in practice using the synthetic control method. Results: Relative to a control group, our algorithm led to an expected initial dip in revenue during the learning phase, followed by a sustained and significant increase in average daily revenue of 14%–18% throughout the earning phase, illustrating that our algorithmic contributions can make a significant impact in practice. Managerial implications: The theoretical benefit of demand learning and pricing algorithms is well understood—they allow retailers to optimally match supply and demand in the face of uncertain preseason demand. However, most existing demand learning and pricing algorithms require substantial sales volume and the ability to change prices frequently for each product. Our work provides retailers who do not have this luxury a powerful demand learning and pricing algorithm that has been proven in practice.","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115033843","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}
{"title":"Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items","authors":"J. Pauphilet","doi":"10.1287/msom.2022.1118","DOIUrl":"https://doi.org/10.1287/msom.2022.1118","url":null,"abstract":"Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance, to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In causal inference terms, we estimate heterogeneous treatment effects when (a) the response to treatment (here, whether a customer buys a product) is binary, and (b) treatment assignments are partially observed (here, full information is only available for purchased items). Methodology/Results: We propose a recursive partitioning procedure to estimate heterogeneous odds ratio, a widely used measure of treatment effect in medicine and social sciences. We integrate an adversarial imputation step to allow for robust estimation even in presence of partially observed treatment assignments. We validate our methodology on synthetic data and apply it to three case studies from political science, medicine, and revenue management. Managerial implications: Our robust heterogeneous odds ratio estimation method is a simple and intuitive tool to quantify heterogeneity in patients or customers and personalize interventions, while lifting a central limitation in many revenue management data.","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655151","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}
{"title":"Multi-purchase Behavior: Modeling, Estimation, and Optimization","authors":"Theja Tulabandhula, Deeksha Sinha, Saketh Reddy Karra, Prasoon Patidar","doi":"10.1287/msom.2020.0238","DOIUrl":"https://doi.org/10.1287/msom.2020.0238","url":null,"abstract":"Problem definition: We study the problem of modeling purchase of multiple products and using it to display optimized recommendations for online retailers and e-commerce platforms. Rich modeling of users and fast computation of optimal products to display given these models can lead to significantly higher revenues and simultaneously enhance the user experience. Methodology/results: We present a parsimonious multi-purchase family of choice models called the BundleMVL-K family and develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model. We establish the hardness of computing optimal recommendation sets and derive several structural properties of the optimal solution that aid in speeding up computation. This is one of the first attempts at operationalizing multi-purchase class of choice models. We show one of the first quantitative links between modeling multiple purchase behavior and revenue gains. The efficacy of our modeling and optimization techniques compared with competing solutions is shown using several real-world data sets on multiple metrics such as model fitness, expected revenue gains, and run-time reductions. For example, the expected revenue benefit of taking multiple purchases into account is observed to be [Formula: see text] in relative terms for the Ta Feng and UCI shopping data sets compared with the multinomial choice model for instances with ∼1,500 products. Additionally, across six real-world data sets, the test log-likelihood fits of our models are on average 17% better in relative terms. Managerial implications: Our work contributes to the study of multi-purchase decisions, analyzing consumer demand, and the retailers optimization problem. The simplicity of our models and the iterative nature of our optimization technique allows practitioners meet stringent computational constraints while increasing their revenues in practical recommendation applications at scale, especially in e-commerce platforms and other marketplaces. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0238 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132611411","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}
{"title":"How Advance Sales Can Reduce Profits: When to Buy, When to Sell, and What Price to Charge","authors":"A. Glazer, Refael Hassin, Irit Nowik","doi":"10.1287/msom.2023.1218","DOIUrl":"https://doi.org/10.1287/msom.2023.1218","url":null,"abstract":"Problem definition: Consider consumers who prefer to consume a good later rather than earlier. If the price is constant, then we would expect consumers to wait to buy the good. That does not hold if consumers are concerned that others will buy the good early, so that a shortage will later occur. When will consumers arrive when they fear a shortage? What is the profit-maximizing policy of a monopolist? Might the firm lose profits by offering advance sales? The timing of consumer arrivals is much studied. Little consideration, however, has addressed how anticipated shortages affect arrival times. The application is important: managers want to know when consumers will arrive, when they should make the product available, and what price to charge to maximize profits. Methodology/results: We use game theory. We analyze analytically outcomes when a single item is for sale: we give closed solutions for the equilibrium customer behavior and profit-maximizing firm strategy and conduct sensitivity analysis. For generalization concerning more than one unit, we give some analytical results and provide many numerical solutions. When the price is constant over time, then even with no operating cost of doing so, offering advance sales reduces profits. If, however, the firm must offer both advance sales and later sales, then the profit-maximizing price induces all arrivals at the same time (either early or late, depending on the parameters). An increase in the number of units offered for sale increases the profit-maximizing price and increases the firm’s expected profit. The equilibrium strategy of consumers can generate some unexpected behavior. The arrival rate may increase with the price of the good. For a given price, an increase in the number of units for sale increases the number of consumers who arrive early. Managerial implications: The firm should offer the good only at the time consumers most desire it, and not earlier. Additionally, the profit-maximizing price can be derived from our analysis. This price is not the price which maximizes the expected number of arrivals. Funding: This work was supported by the Israel Science Foundation [Grant 852/22]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1218 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994190","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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