Songtao Li, Lauren Xiaoyuan Lu, S. F. Lu, Simin Huang
Problem definition: In brick-and-mortar fashion retail stores, inventory stockouts are frequent. When a specific size of a fashion product is out of stock, the unmet demand might not be completely lost because of spillovers to adjacent sizes of the same style or to other styles. Little research has been done to study consumer response to stockouts of fashion products because researchers had limited access to proprietary data of fashion retailers and because it is challenging to estimate stockout-based demand spillover patterns using existing approaches due to the enormous number of stockkeeping units (SKUs) and frequent stockouts in fashion retail stores. To fill this void in the literature, we empirically estimate the stockout-based demand spillover effect in a fashion retail setting. Methodology/results: We obtain a large-scale data set from a fashion retail chain selling world-renowned sportswear brands. The retail stores in the sample are dedicated to products of a single brand. Using around 1.5 million granular and real-time sales and inventory records of 217 stores, 503 men’s footwear products, and 4,024 SKUs over a two-year period, we develop a difference-in-differences framework to estimate the stockout-based cross-size demand spillover effect. We demonstrate the validity of this framework by conducting a pretrend test and a placebo test. We find that roughly 51.7% of the unmet demand of an out-of-stock SKU spills over to adjacent sizes of the same style when they are in stock: 25.1% to the adjacent-larger size and 26.6% to the adjacent-smaller size. The cross-size demand spillover effect is larger in regular stores than in flagship stores, larger for casual sports shoes than for specialized sports shoes, and larger for low-price products than for high-price products. Adapting an existing attribute-based demand model to our setting, we estimate that roughly 20.2% of the unmet demand of an out-of-stock SKU spills over to different styles when they are in stock. Taken together, these estimations suggest that about 28.1% of the unmet demand of an out-of-stock SKU becomes lost sales. We further find that when stockouts are widespread among SKUs, stockout-based demand spillovers are significantly reduced, resulting in much increased lost sales. Managerial implications: First, we empirically quantify the stockout-based cross-size demand spillover effect and its impact on lost sales in a brick-and-mortar fashion retail setting. Second, our simulation analysis shows that incorporating the cross-size demand spillover effect into the sportswear retail chain’s proactive transshipment decision can substantially reduce its transshipment cost and improve its profitability.
{"title":"Estimating the Stockout-Based Demand Spillover Effect in a Fashion Retail Setting","authors":"Songtao Li, Lauren Xiaoyuan Lu, S. F. Lu, Simin Huang","doi":"10.1287/msom.2022.1135","DOIUrl":"https://doi.org/10.1287/msom.2022.1135","url":null,"abstract":"Problem definition: In brick-and-mortar fashion retail stores, inventory stockouts are frequent. When a specific size of a fashion product is out of stock, the unmet demand might not be completely lost because of spillovers to adjacent sizes of the same style or to other styles. Little research has been done to study consumer response to stockouts of fashion products because researchers had limited access to proprietary data of fashion retailers and because it is challenging to estimate stockout-based demand spillover patterns using existing approaches due to the enormous number of stockkeeping units (SKUs) and frequent stockouts in fashion retail stores. To fill this void in the literature, we empirically estimate the stockout-based demand spillover effect in a fashion retail setting. Methodology/results: We obtain a large-scale data set from a fashion retail chain selling world-renowned sportswear brands. The retail stores in the sample are dedicated to products of a single brand. Using around 1.5 million granular and real-time sales and inventory records of 217 stores, 503 men’s footwear products, and 4,024 SKUs over a two-year period, we develop a difference-in-differences framework to estimate the stockout-based cross-size demand spillover effect. We demonstrate the validity of this framework by conducting a pretrend test and a placebo test. We find that roughly 51.7% of the unmet demand of an out-of-stock SKU spills over to adjacent sizes of the same style when they are in stock: 25.1% to the adjacent-larger size and 26.6% to the adjacent-smaller size. The cross-size demand spillover effect is larger in regular stores than in flagship stores, larger for casual sports shoes than for specialized sports shoes, and larger for low-price products than for high-price products. Adapting an existing attribute-based demand model to our setting, we estimate that roughly 20.2% of the unmet demand of an out-of-stock SKU spills over to different styles when they are in stock. Taken together, these estimations suggest that about 28.1% of the unmet demand of an out-of-stock SKU becomes lost sales. We further find that when stockouts are widespread among SKUs, stockout-based demand spillovers are significantly reduced, resulting in much increased lost sales. Managerial implications: First, we empirically quantify the stockout-based cross-size demand spillover effect and its impact on lost sales in a brick-and-mortar fashion retail setting. Second, our simulation analysis shows that incorporating the cross-size demand spillover effect into the sportswear retail chain’s proactive transshipment decision can substantially reduce its transshipment cost and improve its profitability.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"20 1","pages":"468-488"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78311030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The uncertainty around trade and foreign economic policy contributes to supply chain risk. Academic/practical relevance: Whether such policy uncertainty will bring some production back to the United States or only redistribute the global supply chains among foreign sources is theoretically ambiguous and warrants an empirical analysis. In this paper, we study the relationship between trade and foreign economic policy uncertainty and the supply chain networks of American firms. Methodology: We use firm-level global supply chain data, transaction-level shipping container data, and policy uncertainty indexes constructed from leading media outlets to study how policy uncertainty correlates with changes in supply chain networks. Results: When U.S. trade policy uncertainty rises, firms with majority domestic sales decrease their supplier base abroad, whereas firms with majority foreign sales increase the number of foreign suppliers. Firms also substitute among foreign countries in response to their respective economic policy uncertainty—shifting suppliers from countries with higher uncertainty to ones with lower uncertainty. Firms requiring more specific inputs, producing more differentiated products, having higher market shares, and more central to the production network are more sensitive to policy uncertainty. Managerial implications: Supply chain restructuring following higher policy uncertainty puts the market value at risk. Managers should consider customers’ locations when making global supply chain restructuring decisions.
{"title":"Trade and Foreign Economic Policy Uncertainty in Supply Chain Networks: Who Comes Home?","authors":"Ben Charoenwong, Miaozhe Han, Jing Wu","doi":"10.1287/msom.2022.1136","DOIUrl":"https://doi.org/10.1287/msom.2022.1136","url":null,"abstract":"Problem definition: The uncertainty around trade and foreign economic policy contributes to supply chain risk. Academic/practical relevance: Whether such policy uncertainty will bring some production back to the United States or only redistribute the global supply chains among foreign sources is theoretically ambiguous and warrants an empirical analysis. In this paper, we study the relationship between trade and foreign economic policy uncertainty and the supply chain networks of American firms. Methodology: We use firm-level global supply chain data, transaction-level shipping container data, and policy uncertainty indexes constructed from leading media outlets to study how policy uncertainty correlates with changes in supply chain networks. Results: When U.S. trade policy uncertainty rises, firms with majority domestic sales decrease their supplier base abroad, whereas firms with majority foreign sales increase the number of foreign suppliers. Firms also substitute among foreign countries in response to their respective economic policy uncertainty—shifting suppliers from countries with higher uncertainty to ones with lower uncertainty. Firms requiring more specific inputs, producing more differentiated products, having higher market shares, and more central to the production network are more sensitive to policy uncertainty. Managerial implications: Supply chain restructuring following higher policy uncertainty puts the market value at risk. Managers should consider customers’ locations when making global supply chain restructuring decisions.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"295 1","pages":"126-147"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83440433","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 focus on modeling and evaluating an emergency service system in which cross-trained fire-medics are pooled and respond to both fire calls and medical emergencies. Academic/practical relevance: Fire demand in the United States has decreased dramatically in the last nearly four decades, whereas emergency medical calls have surged. With this changing landscape, cities are under pressure to reduce their budgets by closing fire stations. We evaluate the alternative of implementing a fire-medic system in terms of cost savings and response time performance. Methodology: A cross-trained fire-medic unit may be in one of three states: available, busy at an emergency medical incident, or busy at a fire call. An exact model for the fire-medic system has exponential complexity. We develop a fast approximation algorithm that has linear complexity and can be used to solve three-state problems of any size. Results: Our approximation algorithm yields accurate predictions of response times and unit workloads and provides rapid solution times. We apply our model to the fire-medic system in St. Paul, MN, and find close agreement between predicted and actual average response times. A traditional system would require 33% more personnel to achieve about the same average response times. In sensitivity analyses, we show that the fire-medic system outperforms a traditional system over a wide range of values for call rates and number of joint units. Managerial implications: The fire-medic system in St. Paul, MN, saves more than three million dollars annually. The greatest benefit of the fire-medic system is in reducing response times to medical emergencies. We also show that in a fire-medic system that includes separate engine units, it is advantageous to convert the separate engines to fire-medic units. Our fast approximation algorithm can be applied even in the largest cities that implement fire-medic systems to improve resource deployment and reduce costs.
{"title":"Cross-Trained Fire-Medics Respond to Medical Calls and Fire Incidents: A Fast Algorithm for a Three-State Spatial Queuing Problem","authors":"Cheng-hao Hua, Arthur J. Swersey","doi":"10.2139/ssrn.4158330","DOIUrl":"https://doi.org/10.2139/ssrn.4158330","url":null,"abstract":"Problem definition: We focus on modeling and evaluating an emergency service system in which cross-trained fire-medics are pooled and respond to both fire calls and medical emergencies. Academic/practical relevance: Fire demand in the United States has decreased dramatically in the last nearly four decades, whereas emergency medical calls have surged. With this changing landscape, cities are under pressure to reduce their budgets by closing fire stations. We evaluate the alternative of implementing a fire-medic system in terms of cost savings and response time performance. Methodology: A cross-trained fire-medic unit may be in one of three states: available, busy at an emergency medical incident, or busy at a fire call. An exact model for the fire-medic system has exponential complexity. We develop a fast approximation algorithm that has linear complexity and can be used to solve three-state problems of any size. Results: Our approximation algorithm yields accurate predictions of response times and unit workloads and provides rapid solution times. We apply our model to the fire-medic system in St. Paul, MN, and find close agreement between predicted and actual average response times. A traditional system would require 33% more personnel to achieve about the same average response times. In sensitivity analyses, we show that the fire-medic system outperforms a traditional system over a wide range of values for call rates and number of joint units. Managerial implications: The fire-medic system in St. Paul, MN, saves more than three million dollars annually. The greatest benefit of the fire-medic system is in reducing response times to medical emergencies. We also show that in a fire-medic system that includes separate engine units, it is advantageous to convert the separate engines to fire-medic units. Our fast approximation algorithm can be applied even in the largest cities that implement fire-medic systems to improve resource deployment and reduce costs.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"2 1","pages":"3177-3192"},"PeriodicalIF":0.0,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82888441","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}
Mehran Navabi-Shirazi, Mohamed El Tonbari, N. Boland, D. Nazzal, L. Steimle
Problem definition: Although physical (or “social”) distancing is an important public health intervention during airborne pandemics, physical distancing dramatically reduces the effective capacity of classrooms. Academic/practical relevance: During the COVID-19 pandemic, this presented a unique problem to campus planners who hoped to deliver a meaningful amount of in-person instruction in a way that respected physical distancing. This process involved (1) assigning a mode to each offered class as remote, residential (in-person), or hybrid and (2) reassigning classrooms under severely reduced capacities to the non-remote classes. These decisions need to be made quickly and under several constraints and competing priorities, such as restrictions on changes to the timetable of classes, trade-offs between classroom density and educational benefits of in-person versus online instruction, and administrative preferences for course modes and classrooms reassignments. Methodology: We solve a flexible integer program and use hierarchical optimization to handle the multiple criteria according to priorities. We apply our methods using actual Georgia Institute of Technology (GT) student registration data, COVID-19–adjusted classroom and laboratory capacities, and departmental course mode delivery preferences. We generate optimal classroom assignments for all GT classes at the Atlanta campus and quantify the trade-offs among the competing priorities. Results: When classroom capacities decreased to 20%–25% of their normal seating capacities, optimization afforded students 15.5% more in-person contact hours compared with no room reassignments (NRRs). Among sections with an in-person preference, our model satisfies 87% of mode preferences, whereas only 47% are satisfied under NRR. Additionally, in a scenario in which all classes are preferred to be delivered in person, our model can satisfy 90% of mode preferences compared with 37% under NRR. Managerial implications: Multiobjective optimization is well suited for classroom assignment problems that campus planners usually manage sequentially and manually. Our models are computationally efficient and flexible, with the ability to handle multiple objectives with different priorities and build a new class-classrooms assignment or optimize an existing one, and they can apply under normal or sudden capacity scarcity constraints.
{"title":"Multicriteria Course Mode Selection and Classroom Assignment Under Sudden Space Scarcity","authors":"Mehran Navabi-Shirazi, Mohamed El Tonbari, N. Boland, D. Nazzal, L. Steimle","doi":"10.1287/msom.2022.1131","DOIUrl":"https://doi.org/10.1287/msom.2022.1131","url":null,"abstract":"Problem definition: Although physical (or “social”) distancing is an important public health intervention during airborne pandemics, physical distancing dramatically reduces the effective capacity of classrooms. Academic/practical relevance: During the COVID-19 pandemic, this presented a unique problem to campus planners who hoped to deliver a meaningful amount of in-person instruction in a way that respected physical distancing. This process involved (1) assigning a mode to each offered class as remote, residential (in-person), or hybrid and (2) reassigning classrooms under severely reduced capacities to the non-remote classes. These decisions need to be made quickly and under several constraints and competing priorities, such as restrictions on changes to the timetable of classes, trade-offs between classroom density and educational benefits of in-person versus online instruction, and administrative preferences for course modes and classrooms reassignments. Methodology: We solve a flexible integer program and use hierarchical optimization to handle the multiple criteria according to priorities. We apply our methods using actual Georgia Institute of Technology (GT) student registration data, COVID-19–adjusted classroom and laboratory capacities, and departmental course mode delivery preferences. We generate optimal classroom assignments for all GT classes at the Atlanta campus and quantify the trade-offs among the competing priorities. Results: When classroom capacities decreased to 20%–25% of their normal seating capacities, optimization afforded students 15.5% more in-person contact hours compared with no room reassignments (NRRs). Among sections with an in-person preference, our model satisfies 87% of mode preferences, whereas only 47% are satisfied under NRR. Additionally, in a scenario in which all classes are preferred to be delivered in person, our model can satisfy 90% of mode preferences compared with 37% under NRR. Managerial implications: Multiobjective optimization is well suited for classroom assignment problems that campus planners usually manage sequentially and manually. Our models are computationally efficient and flexible, with the ability to handle multiple objectives with different priorities and build a new class-classrooms assignment or optimize an existing one, and they can apply under normal or sudden capacity scarcity constraints.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"32 1","pages":"3252-3268"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85561891","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: Lawmakers have begun to introduce “fair schedule” legislations that require employers to provide shift workers with more predictable and consistent work schedules. Business owners are concerned that the resultant loss of scheduling flexibility could reduce overall operational efficiency. We argue this is not necessarily the case. Academic/practical relevance: Although recent studies suggest that increasing schedule predictability by reducing “just-in-time” scheduling can increase productivity, few have examined the effects of schedule consistency on worker productivity. Our study fills this void by investigating the impact of schedule consistency on cashier productivity in grocery retailing. Methodology: We estimate econometric models using transaction level scanner data including more than 1.2 million shopping baskets processed by 126 cashiers working for a local grocer. Work schedule consistency is operationalized via two metrics: (1) hour-of-the-day consistency measuring whether a cashier is consistently scheduled to work in the same hours of the day, and (2) day-of-the-week consistency measuring whether a cashier is consistently scheduled to work on the same days of the week. Results: We find that, on average, hour-of-the-day consistency and day-of-the-week consistency increase cashier productivity by 0.95% and 1.63%, respectively. These effects are much stronger for inexperienced cashiers (e.g., an average productivity boost of 3.39% and 7.93%, respectively, for the new hires). Managerial implications: Our findings suggest that (a) business owners can increase shift workers’ productivity by providing them with more consistent work schedules, and (b) the productivity of less-experienced shift workers, especially new hires, is more vulnerable to inconsistent work schedules, highlighting the potential for operational efficiency gains from greater schedule consistency, especially for businesses employing a high portion of inexperienced shift workers.
{"title":"The Impact of Schedule Consistency on Shift Worker Productivity: An Empirical Investigation","authors":"G. Lu, R. Du, David Xiaosong Peng","doi":"10.1287/msom.2022.1132","DOIUrl":"https://doi.org/10.1287/msom.2022.1132","url":null,"abstract":"Problem definition: Lawmakers have begun to introduce “fair schedule” legislations that require employers to provide shift workers with more predictable and consistent work schedules. Business owners are concerned that the resultant loss of scheduling flexibility could reduce overall operational efficiency. We argue this is not necessarily the case. Academic/practical relevance: Although recent studies suggest that increasing schedule predictability by reducing “just-in-time” scheduling can increase productivity, few have examined the effects of schedule consistency on worker productivity. Our study fills this void by investigating the impact of schedule consistency on cashier productivity in grocery retailing. Methodology: We estimate econometric models using transaction level scanner data including more than 1.2 million shopping baskets processed by 126 cashiers working for a local grocer. Work schedule consistency is operationalized via two metrics: (1) hour-of-the-day consistency measuring whether a cashier is consistently scheduled to work in the same hours of the day, and (2) day-of-the-week consistency measuring whether a cashier is consistently scheduled to work on the same days of the week. Results: We find that, on average, hour-of-the-day consistency and day-of-the-week consistency increase cashier productivity by 0.95% and 1.63%, respectively. These effects are much stronger for inexperienced cashiers (e.g., an average productivity boost of 3.39% and 7.93%, respectively, for the new hires). Managerial implications: Our findings suggest that (a) business owners can increase shift workers’ productivity by providing them with more consistent work schedules, and (b) the productivity of less-experienced shift workers, especially new hires, is more vulnerable to inconsistent work schedules, highlighting the potential for operational efficiency gains from greater schedule consistency, especially for businesses employing a high portion of inexperienced shift workers.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"130 1","pages":"2780-2796"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89207012","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: Using labor supply data from a large online education platform with more than 100,000 gig workers, we investigate how online gig workers changed their behavior after the outbreak of the coronavirus disease 2019 (COVID-19) pandemic and what drove the changes. Methodology and Results: Online gig workers sharply increased their labor supply on the platform by 23% from the announcement of national emergency to the end of April (stage 1); the increase became smaller in May and June (stage 2) and disappeared in July and August (stage 3). Year to year difference-in-difference analyses show that these findings are robust after controlling for seasonality and worker heterogeneity. Results: We show that the increase in gig workers’ labor supply is not driven by a higher demand or excessive entry of new workers during the pandemic. A series of mediation analyses indicates that unemployment and nonpharmaceutical interventions (NPIs) rather than the risk of contracting COVID-19 can better explain why online gig workers increased their labor supply. The impact of unemployment is smaller than that of NPI policies, indicating that the increase in gig workers’ labor supply is more driven by temporary changes in working arrangements because of the policies rather than relatively long-term changes in employment situations. We also examine how online gig workers change their quality of work and how their earning potential on the platform relates to their changes in behavior during the pandemic. Managerial implications: Our findings provide insights for the management of online gig workers during major disruptions, like the COVID-19 pandemic.
{"title":"The Impact of the COVID-19 Pandemic on the Behavior of Online Gig Workers","authors":"X. Cao, Dennis J. Zhang, Lei Huang","doi":"10.1287/msom.2022.1113","DOIUrl":"https://doi.org/10.1287/msom.2022.1113","url":null,"abstract":"Problem definition: Using labor supply data from a large online education platform with more than 100,000 gig workers, we investigate how online gig workers changed their behavior after the outbreak of the coronavirus disease 2019 (COVID-19) pandemic and what drove the changes. Methodology and Results: Online gig workers sharply increased their labor supply on the platform by 23% from the announcement of national emergency to the end of April (stage 1); the increase became smaller in May and June (stage 2) and disappeared in July and August (stage 3). Year to year difference-in-difference analyses show that these findings are robust after controlling for seasonality and worker heterogeneity. Results: We show that the increase in gig workers’ labor supply is not driven by a higher demand or excessive entry of new workers during the pandemic. A series of mediation analyses indicates that unemployment and nonpharmaceutical interventions (NPIs) rather than the risk of contracting COVID-19 can better explain why online gig workers increased their labor supply. The impact of unemployment is smaller than that of NPI policies, indicating that the increase in gig workers’ labor supply is more driven by temporary changes in working arrangements because of the policies rather than relatively long-term changes in employment situations. We also examine how online gig workers change their quality of work and how their earning potential on the platform relates to their changes in behavior during the pandemic. Managerial implications: Our findings provide insights for the management of online gig workers during major disruptions, like the COVID-19 pandemic.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"3 1","pages":"2611-2628"},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86958005","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: Carbon abatement opportunities are diverse, making it difficult to classify them. Do latent classes of carbon abatement opportunities exist and is there a type that is financially and environmentally superior? Methodology/results: In this study, we classify 16,525 implemented carbon abatement projects using text analysis. We benchmark our clustering method to the latent Dirichlet allocation model and verify our classifications using a crowd-sourcing platform. We then compare the payback period, financial hurdle (measured in upfront cost), savings, and carbon emissions reduction by type. Our results show that latent classes exist, and they statistically differ in the metrics we examine. Our regression results show that the type of project explains more of the variation in the financial and environmental outcomes than the firm-level financial controls we included. We find that liquidity (measured using cash-to-asset and current ratios) is associated with the number of reported projects, but the magnitude and direction varies by type. Our extension shows that marginal abatement costs statistically differ by type with a few exceptions. Lastly, we show that our classification is robust to sector-level variation. Managerial implications: Although the results show that no single type of opportunity dominates in all four metrics, our classification provides a ranking of the types firms should pursue depending on their goals. Our results suggest that firms likely place different weights across these four metrics. This means that policies targeted at making investment costs more attractive (e.g., subsidies or better financing) may not have the same impact on firms that put more weight on savings compared with those more sensitive to costs. A classification of opportunities can contribute toward understanding whether a unifying theory or pattern across carbon abatement activities may exist or not.
{"title":"A Classification of Carbon Abatement Opportunities of Global Firms","authors":"Christian C. Blanco","doi":"10.1287/msom.2022.1115","DOIUrl":"https://doi.org/10.1287/msom.2022.1115","url":null,"abstract":"Problem definition: Carbon abatement opportunities are diverse, making it difficult to classify them. Do latent classes of carbon abatement opportunities exist and is there a type that is financially and environmentally superior? Methodology/results: In this study, we classify 16,525 implemented carbon abatement projects using text analysis. We benchmark our clustering method to the latent Dirichlet allocation model and verify our classifications using a crowd-sourcing platform. We then compare the payback period, financial hurdle (measured in upfront cost), savings, and carbon emissions reduction by type. Our results show that latent classes exist, and they statistically differ in the metrics we examine. Our regression results show that the type of project explains more of the variation in the financial and environmental outcomes than the firm-level financial controls we included. We find that liquidity (measured using cash-to-asset and current ratios) is associated with the number of reported projects, but the magnitude and direction varies by type. Our extension shows that marginal abatement costs statistically differ by type with a few exceptions. Lastly, we show that our classification is robust to sector-level variation. Managerial implications: Although the results show that no single type of opportunity dominates in all four metrics, our classification provides a ranking of the types firms should pursue depending on their goals. Our results suggest that firms likely place different weights across these four metrics. This means that policies targeted at making investment costs more attractive (e.g., subsidies or better financing) may not have the same impact on firms that put more weight on savings compared with those more sensitive to costs. A classification of opportunities can contribute toward understanding whether a unifying theory or pattern across carbon abatement activities may exist or not.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"18 1","pages":"2648-2665"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85662145","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: Consider an online personalized assortment optimization problem in which customers arrive sequentially and make their decisions (e.g., click an ad, purchase a product) following the multinomial logit choice model with unknown parameters. Utilizing a customer’s personal information that is high-dimensional, the firm selects an assortment tailored for each individual customer’s preference. Academic/practical relevance: High dimensionality of a customer’s contextual information is prevalent in real applications, and it creates tremendous computational challenge in online personalized optimization. Methodology: In this paper, an efficient learning algorithm is developed to tackle the computational complexity issue while maintaining satisfactory performance. The algorithm first applies a random projection for dimension reduction and incorporates an online convex optimization procedure for parameter estimation, thus overcoming the issue of linearly increasing computational requirement as data accumulates. Then, it integrates the upper confidence bound method to balance the exploration and revenue exploitation. Results: The theoretical performance of the algorithm in terms of regret is derived under some plausible sparsity assumption on personal information that is observed in real data, and numerical experiments using both synthetic data and a real data set from Yahoo! show that the algorithm performs very well, having scalability and significant advantage in computational time compared with benchmark methods. Managerial implications: Our findings suggest that practitioners should process high-dimensional sparse customer data with an appropriate feature engineering technique, such as random projection (instead of abandoning the sparse portion) to maximize the effectiveness of online optimization algorithms.
{"title":"Online Personalized Assortment Optimization with High-Dimensional Customer Contextual Data","authors":"Sentao Miao, X. Chao","doi":"10.1287/msom.2022.1128","DOIUrl":"https://doi.org/10.1287/msom.2022.1128","url":null,"abstract":"Problem definition: Consider an online personalized assortment optimization problem in which customers arrive sequentially and make their decisions (e.g., click an ad, purchase a product) following the multinomial logit choice model with unknown parameters. Utilizing a customer’s personal information that is high-dimensional, the firm selects an assortment tailored for each individual customer’s preference. Academic/practical relevance: High dimensionality of a customer’s contextual information is prevalent in real applications, and it creates tremendous computational challenge in online personalized optimization. Methodology: In this paper, an efficient learning algorithm is developed to tackle the computational complexity issue while maintaining satisfactory performance. The algorithm first applies a random projection for dimension reduction and incorporates an online convex optimization procedure for parameter estimation, thus overcoming the issue of linearly increasing computational requirement as data accumulates. Then, it integrates the upper confidence bound method to balance the exploration and revenue exploitation. Results: The theoretical performance of the algorithm in terms of regret is derived under some plausible sparsity assumption on personal information that is observed in real data, and numerical experiments using both synthetic data and a real data set from Yahoo! show that the algorithm performs very well, having scalability and significant advantage in computational time compared with benchmark methods. Managerial implications: Our findings suggest that practitioners should process high-dimensional sparse customer data with an appropriate feature engineering technique, such as random projection (instead of abandoning the sparse portion) to maximize the effectiveness of online optimization algorithms.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"2676 1","pages":"2741-2760"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82131469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: This paper empirically investigates how customer email engagement affects the profitability of subscription service providers and retailers. They have been using email engagement to increase customer retention. However, it is unclear whether email engagement improves their profitability. The existing literature focuses on email engagement’s benefit of customer retention but ignores its associated operating cost to serve retained customers. Methodology/results: We analyze the outcome of a field experiment conducted by a large U.S. car wash chain that offers tiered subscription services to consumers and employs an radiofrequency identification-based technology to track subscriber service events. We apply survival analysis and difference-in-differences methods to estimate the effects of email engagement on subscribers’ retention and service consumption. We find that a one-month engagement with two emails separated by a half-month interval increased the likelihood of subscriber retention by 7.4% five months after the experiment started and decreased the subscriber churn odds by 26.3% for the entire five-month duration. Meanwhile, we find that the same engagement increased a subscriber’s per-period service consumption by 7.0%. We provide suggestive evidence for two behavioral mechanisms that explain the effect of email engagement on subscribers’ service consumption. First, the engagement effect decays over time and exhibits fatigue after the second email, suggesting that emails act as reminders to subscribers. Second, the engagement effect persists after engagement ends but weakens over time, suggesting the habit formation of subscribers. By computing subscriber lifetime value and the operating cost of service, we find that email engagement increases profit when deployed on mid-level infrequent-use subscribers and top-level subscribers but decreases profit when deployed on mid-level frequent-use subscribers and basic-level subscribers. Therefore, we recommend that the company use a selective strategy by sending engagement emails to only profitable subscribers. Managerial implications: Our study highlights that email engagement is a double-edged sword; it increases both customer retention and service consumption, and it may decrease profitability when the increased operating cost to serve retained customers outweighs the benefit of customer retention. We recommend that subscription service providers and retailers adopt a data-driven approach to optimize their email engagement strategies.
{"title":"Does Customer Email Engagement Improve Profitability? Evidence from a Field Experiment in Subscription Service Retailing","authors":"Yiwei Wang, Lauren Xiaoyuan Lu, Pengcheng Shi","doi":"10.1287/msom.2022.1122","DOIUrl":"https://doi.org/10.1287/msom.2022.1122","url":null,"abstract":"Problem definition: This paper empirically investigates how customer email engagement affects the profitability of subscription service providers and retailers. They have been using email engagement to increase customer retention. However, it is unclear whether email engagement improves their profitability. The existing literature focuses on email engagement’s benefit of customer retention but ignores its associated operating cost to serve retained customers. Methodology/results: We analyze the outcome of a field experiment conducted by a large U.S. car wash chain that offers tiered subscription services to consumers and employs an radiofrequency identification-based technology to track subscriber service events. We apply survival analysis and difference-in-differences methods to estimate the effects of email engagement on subscribers’ retention and service consumption. We find that a one-month engagement with two emails separated by a half-month interval increased the likelihood of subscriber retention by 7.4% five months after the experiment started and decreased the subscriber churn odds by 26.3% for the entire five-month duration. Meanwhile, we find that the same engagement increased a subscriber’s per-period service consumption by 7.0%. We provide suggestive evidence for two behavioral mechanisms that explain the effect of email engagement on subscribers’ service consumption. First, the engagement effect decays over time and exhibits fatigue after the second email, suggesting that emails act as reminders to subscribers. Second, the engagement effect persists after engagement ends but weakens over time, suggesting the habit formation of subscribers. By computing subscriber lifetime value and the operating cost of service, we find that email engagement increases profit when deployed on mid-level infrequent-use subscribers and top-level subscribers but decreases profit when deployed on mid-level frequent-use subscribers and basic-level subscribers. Therefore, we recommend that the company use a selective strategy by sending engagement emails to only profitable subscribers. Managerial implications: Our study highlights that email engagement is a double-edged sword; it increases both customer retention and service consumption, and it may decrease profitability when the increased operating cost to serve retained customers outweighs the benefit of customer retention. We recommend that subscription service providers and retailers adopt a data-driven approach to optimize their email engagement strategies.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"36 1","pages":"2703-2721"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77545179","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 investigate the effect of using subcontracted workers together with permanent workers on project financial performance. Academic/practical relevance: It is widespread practice, across disparate businesses, to staff project teams with subcontracted workers—and yet, despite the prevalence of this phenomenon, there is scant research on how subcontracted workers impact project performance. Investigating such an effect is important because past findings on the effects of subcontracting in retail or assembly lines cannot be hastily extrapolated to the more qualified workers and more demanding tasks normally associated with project environments. Methodology: Building on previous findings about the higher motivation level of subcontracted versus permanent workers when the latter are protected from individual dismissal by the law, we develop hypotheses to conceptualize how and under what conditions subcontracted workers positively impact project performance. We then test our hypotheses by analyzing 413 projects of a European high-tech firm. Results: We find that with increased use of subcontracted workers comes increased project profit margins. This positive effect is stronger for larger teams and weaker when large project scope changes occur or when higher-skilled workers are subcontracted. We also find this effect to be stronger when subcontracted workers are involved in technical rather than administrative roles and when subcontractors join in the later stages of the project. Managerial implications: This study offers guidelines on how project managers can use subcontracting to increase project margins, highlighting strategic and tactical factors that affect the benefits of using subcontracted labor.
{"title":"The Effect of Subcontracted Labor Mix on Financial Performance: Evidence from High-Tech Project Teams","authors":"Antoaneta Momcheva, E. Avgerinos, F. Salvador","doi":"10.1287/msom.2022.1125","DOIUrl":"https://doi.org/10.1287/msom.2022.1125","url":null,"abstract":"Problem definition: We investigate the effect of using subcontracted workers together with permanent workers on project financial performance. Academic/practical relevance: It is widespread practice, across disparate businesses, to staff project teams with subcontracted workers—and yet, despite the prevalence of this phenomenon, there is scant research on how subcontracted workers impact project performance. Investigating such an effect is important because past findings on the effects of subcontracting in retail or assembly lines cannot be hastily extrapolated to the more qualified workers and more demanding tasks normally associated with project environments. Methodology: Building on previous findings about the higher motivation level of subcontracted versus permanent workers when the latter are protected from individual dismissal by the law, we develop hypotheses to conceptualize how and under what conditions subcontracted workers positively impact project performance. We then test our hypotheses by analyzing 413 projects of a European high-tech firm. Results: We find that with increased use of subcontracted workers comes increased project profit margins. This positive effect is stronger for larger teams and weaker when large project scope changes occur or when higher-skilled workers are subcontracted. We also find this effect to be stronger when subcontracted workers are involved in technical rather than administrative roles and when subcontractors join in the later stages of the project. Managerial implications: This study offers guidelines on how project managers can use subcontracting to increase project margins, highlighting strategic and tactical factors that affect the benefits of using subcontracted labor.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"13 1","pages":"2722-2740"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81124422","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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