Ivan Lugovoi, Dimitrios A. Andritsos, Claire Senot
Problem definition: Process innovation is commonly claimed to be a major source of competitive advantage for firms. Despite this perceived influence, it has received substantially less attention than product innovation, and much uncertainty remains about its true association with firm performance. We investigate the relationship between a pharmaceutical firm’s portfolio of manufacturing process innovations and its economic performance. Academic/practical relevance: We uniquely conduct a multidimensional evaluation of a firm’s portfolio of manufacturing process innovations at the product level. This allows a quantitative evaluation of both the relative benefit of the different dimensions of a portfolio as well as the potential complementarities between these in different technological landscapes. Methodology: Through a collaboration with expert patent attorneys, we develop a unique longitudinal data set that combines secondary data and evaluations of a firm’s portfolio of process patents along two key dimensions: novelty and scope. We conduct econometric analyses for a large-scale sample of active pharmaceutical ingredients (APIs) whose product patents have expired and for which process innovation is thus the main source of competitive advantage. Results: We find a positive association between the presence of manufacturing process innovation and firm performance. However, although portfolio’s scope appears to always be beneficial to performance, the effect of novelty alone depends on the ruggedness of the technological landscape: negative in smoother landscapes and positive in more rugged landscapes. Results further suggest that novelty and scope of a portfolio of process innovations are complementary across technological landscapes. Managerial implications: Our results provide important practical insights that can inform the organization and execution of the research and development process across high-technology industries. In particular, although process innovations can be economically beneficial, investing in high-novelty process innovations without a corresponding high scope could jeopardize payoffs, especially in technological landscapes that are relatively smooth.
{"title":"Manufacturing Process Innovation in the Pharmaceutical Industry","authors":"Ivan Lugovoi, Dimitrios A. Andritsos, Claire Senot","doi":"10.1287/msom.2021.1035","DOIUrl":"https://doi.org/10.1287/msom.2021.1035","url":null,"abstract":"Problem definition: Process innovation is commonly claimed to be a major source of competitive advantage for firms. Despite this perceived influence, it has received substantially less attention than product innovation, and much uncertainty remains about its true association with firm performance. We investigate the relationship between a pharmaceutical firm’s portfolio of manufacturing process innovations and its economic performance. Academic/practical relevance: We uniquely conduct a multidimensional evaluation of a firm’s portfolio of manufacturing process innovations at the product level. This allows a quantitative evaluation of both the relative benefit of the different dimensions of a portfolio as well as the potential complementarities between these in different technological landscapes. Methodology: Through a collaboration with expert patent attorneys, we develop a unique longitudinal data set that combines secondary data and evaluations of a firm’s portfolio of process patents along two key dimensions: novelty and scope. We conduct econometric analyses for a large-scale sample of active pharmaceutical ingredients (APIs) whose product patents have expired and for which process innovation is thus the main source of competitive advantage. Results: We find a positive association between the presence of manufacturing process innovation and firm performance. However, although portfolio’s scope appears to always be beneficial to performance, the effect of novelty alone depends on the ruggedness of the technological landscape: negative in smoother landscapes and positive in more rugged landscapes. Results further suggest that novelty and scope of a portfolio of process innovations are complementary across technological landscapes. Managerial implications: Our results provide important practical insights that can inform the organization and execution of the research and development process across high-technology industries. In particular, although process innovations can be economically beneficial, investing in high-novelty process innovations without a corresponding high scope could jeopardize payoffs, especially in technological landscapes that are relatively smooth.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"6 1","pages":"1760-1778"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85229639","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 investigates the issue of sharing the private demand information of a manufacturer that sells a product to retailers competing on prices and service efforts. Academic/practical relevance: In the existing literature, which ignores service effort competition, it is known that demand signaling induces an informed manufacturer to distort the wholesale price downward, which benefits the retailers, and so, they do not have any incentive to receive the manufacturer’s private information. In practice, many manufacturers share demand information with their retailers that compete on prices and service efforts (e.g., demand-enhancing retail activities), a setting that has not received much attention from the literature. Methodology: We develop a game-theoretic model with one manufacturer selling to two competing retailers and solve for the equilibrium of the game. Results: We show how an informed manufacturer may distort the wholesale price upward or downward to signal demand information to the retailers, depending on the cost of service effort, the intensity of effort competition, and the number of uninformed retailers. We fully characterize the impact of such wholesale price distortion on the firms’ incentive to share information and derive the conditions under which the manufacturer shares information with none, one, or both of the retailers. We derive conditions under which a higher cost of service effort makes the retailers or the manufacturer better off. Managerial implications: Our results provide novel insights about how service effort competition impacts the incentives for firms in a supply chain to share a manufacturer’s private demand information. For instance, when the cost of effort is high or service effort competition is intense, a manufacturer should share information with none or some, but not all, of the retailers.
{"title":"Sharing Manufacturer's Demand Information in a Supply Chain with Price and Service Effort Competition","authors":"Yunjie Wang, Albert Y. Ha, Shilu Tong","doi":"10.1287/msom.2021.1028","DOIUrl":"https://doi.org/10.1287/msom.2021.1028","url":null,"abstract":"Problem definition: This paper investigates the issue of sharing the private demand information of a manufacturer that sells a product to retailers competing on prices and service efforts. Academic/practical relevance: In the existing literature, which ignores service effort competition, it is known that demand signaling induces an informed manufacturer to distort the wholesale price downward, which benefits the retailers, and so, they do not have any incentive to receive the manufacturer’s private information. In practice, many manufacturers share demand information with their retailers that compete on prices and service efforts (e.g., demand-enhancing retail activities), a setting that has not received much attention from the literature. Methodology: We develop a game-theoretic model with one manufacturer selling to two competing retailers and solve for the equilibrium of the game. Results: We show how an informed manufacturer may distort the wholesale price upward or downward to signal demand information to the retailers, depending on the cost of service effort, the intensity of effort competition, and the number of uninformed retailers. We fully characterize the impact of such wholesale price distortion on the firms’ incentive to share information and derive the conditions under which the manufacturer shares information with none, one, or both of the retailers. We derive conditions under which a higher cost of service effort makes the retailers or the manufacturer better off. Managerial implications: Our results provide novel insights about how service effort competition impacts the incentives for firms in a supply chain to share a manufacturer’s private demand information. For instance, when the cost of effort is high or service effort competition is intense, a manufacturer should share information with none or some, but not all, of the retailers.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"46 1","pages":"1698-1713"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80067410","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 examines whether and, if so, how much an online–off-line return partnership between online and third-party retailers with physical stores (or “location partners”) generates additional value to location partners. Academic/practical relevance: Online shoppers often prefer to return products to stores rather than mailing them back. Many online retailers have recently started to collaborate with location partners to offer the store return option to their customers, and we quantify its economic benefit to a location partner. Methodology: We analyze proprietary data sets from Happy Returns (which provides return services for more than 30 online retailers) and one of its location partners, using a panel difference-in-differences model. In our study, a treatment is the initiation of the return service at each of the location partner’s stores, and an outcome is the store and online channel performance of the location partner. We then explore the mechanisms of underlying customer behavior that drive these outcomes. Results: We find that the partnership increases the number of unique customers, items sold, and net revenue in both store and online channels. We identify two drivers for this improved performance: (1) the location partner acquires new customers in both store and online channels, and (2) existing customers change their shopping patterns only in the store channel after using the return service; in particular, they visit stores more often, purchase more items, and generate higher revenue after their first return service. Managerial implications: To our knowledge, we provide the first direct empirical evidence of value to location partners from a return partnership, and as these partnerships become more prevalent, our findings have important managerial implications for location partners and online retailers alike.
{"title":"Value of Online-Off-line Return Partnership to Off-line Retailers","authors":"Elina H. Hwang, Leela Nageswaran, Soo-Haeng Cho","doi":"10.1287/msom.2021.1026","DOIUrl":"https://doi.org/10.1287/msom.2021.1026","url":null,"abstract":"Problem definition: This paper examines whether and, if so, how much an online–off-line return partnership between online and third-party retailers with physical stores (or “location partners”) generates additional value to location partners. Academic/practical relevance: Online shoppers often prefer to return products to stores rather than mailing them back. Many online retailers have recently started to collaborate with location partners to offer the store return option to their customers, and we quantify its economic benefit to a location partner. Methodology: We analyze proprietary data sets from Happy Returns (which provides return services for more than 30 online retailers) and one of its location partners, using a panel difference-in-differences model. In our study, a treatment is the initiation of the return service at each of the location partner’s stores, and an outcome is the store and online channel performance of the location partner. We then explore the mechanisms of underlying customer behavior that drive these outcomes. Results: We find that the partnership increases the number of unique customers, items sold, and net revenue in both store and online channels. We identify two drivers for this improved performance: (1) the location partner acquires new customers in both store and online channels, and (2) existing customers change their shopping patterns only in the store channel after using the return service; in particular, they visit stores more often, purchase more items, and generate higher revenue after their first return service. Managerial implications: To our knowledge, we provide the first direct empirical evidence of value to location partners from a return partnership, and as these partnerships become more prevalent, our findings have important managerial implications for location partners and online retailers alike.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"12 2 1","pages":"1630-1649"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83627547","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 study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources.
{"title":"Using Mobile Device Data to Understand the Effect of Stay-at-Home Orders on Residents' Mobility","authors":"Guihua Wang","doi":"10.1287/msom.2021.1014","DOIUrl":"https://doi.org/10.1287/msom.2021.1014","url":null,"abstract":"Problem definition: This study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"132 1","pages":"2882-2900"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86338931","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}
Daniel W. Steeneck, Fredrik Eng-Larsson, F. Jauffred
Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.
{"title":"Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability","authors":"Daniel W. Steeneck, Fredrik Eng-Larsson, F. Jauffred","doi":"10.1287/msom.2021.1015","DOIUrl":"https://doi.org/10.1287/msom.2021.1015","url":null,"abstract":"Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"136 1","pages":"1578-1594"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89213234","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}
Narendra Singh, Karthik Ramachandran, Ravi Subramanian
Problem definition: An increased incidence of quality issues, resulting in defective product returns (DPRs), is a concern for firms bringing innovative products to market. Although a firm can recover value from DPRs through refurbishing, consumers are known to act strategically in anticipation of the future availability of refurbished units. We study a firm’s strategy for offering a new product and refurbished DPRs to strategic consumers across time. Academic/practical relevance: Aided by emerging shopping tools, an increasing number of consumers consider buying refurbished versions of products rather than their new counterparts. A novel contribution of our work is the recognition of the refurbishing of DPRs as a possible solution to the time inconsistency problem that arises when a firm offers products to strategic consumers across time. We characterize how the product line decisions and profit of the firm are influenced by the defect rate, the perceived quality of refurbished DPRs, and consumers’ hassle cost of returns. Methodology: We develop a two-period game-theoretic model to characterize the firm offering the new product and refurbished DPRs to strategic consumers across time. Results: The refurbishing of DPRs helps the firm implicitly commit to limiting the quantity of the new product offered in the future, allowing the firm to charge a premium for the new product today. As a result, firm profit may even increase with the defect rate. These results persist across various model extensions. Managerial implications: Whereas the firm’s profit is the highest when there are no defects, opportunities to achieve marginal reductions in defect rates may not be worth the investment and may even be counterproductive. Also, efforts toward enhancing the perceived quality of the refurbished product or decreasing the hassle cost for consumers may better serve the firm than efforts toward marginally improving defect rates.
{"title":"Intertemporal Product Management with Strategic Consumers: The Value of Defective Product Returns","authors":"Narendra Singh, Karthik Ramachandran, Ravi Subramanian","doi":"10.1287/msom.2021.0972","DOIUrl":"https://doi.org/10.1287/msom.2021.0972","url":null,"abstract":"Problem definition: An increased incidence of quality issues, resulting in defective product returns (DPRs), is a concern for firms bringing innovative products to market. Although a firm can recover value from DPRs through refurbishing, consumers are known to act strategically in anticipation of the future availability of refurbished units. We study a firm’s strategy for offering a new product and refurbished DPRs to strategic consumers across time. Academic/practical relevance: Aided by emerging shopping tools, an increasing number of consumers consider buying refurbished versions of products rather than their new counterparts. A novel contribution of our work is the recognition of the refurbishing of DPRs as a possible solution to the time inconsistency problem that arises when a firm offers products to strategic consumers across time. We characterize how the product line decisions and profit of the firm are influenced by the defect rate, the perceived quality of refurbished DPRs, and consumers’ hassle cost of returns. Methodology: We develop a two-period game-theoretic model to characterize the firm offering the new product and refurbished DPRs to strategic consumers across time. Results: The refurbishing of DPRs helps the firm implicitly commit to limiting the quantity of the new product offered in the future, allowing the firm to charge a premium for the new product today. As a result, firm profit may even increase with the defect rate. These results persist across various model extensions. Managerial implications: Whereas the firm’s profit is the highest when there are no defects, opportunities to achieve marginal reductions in defect rates may not be worth the investment and may even be counterproductive. Also, efforts toward enhancing the perceived quality of the refurbished product or decreasing the hassle cost for consumers may better serve the firm than efforts toward marginally improving defect rates.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"11 1","pages":"1146-1164"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87285375","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: Physical distancing requirements during the COVID-19 pandemic have dramatically reduced the effective capacity of university campuses. Under these conditions, we examine how to make the most of newly scarce resources in the related problems of curriculum planning and course timetabling. Academic/practical relevance: We propose a unified model for university course scheduling problems under a two-stage framework and draw parallels between component problems while showing how to accommodate individual specifics. During the pandemic, our models were critical to measuring the impact of several innovative proposals, including expanding the academic calendar, teaching across multiple rooms, and rotating student attendance through the week and school year. Methodology: We use integer optimization combined with enrollment data from thousands of past students. Our models scale to thousands of individual students enrolled in hundreds of courses. Results: We projected that if Massachusetts Institute of Technology moved from its usual two-semester calendar to a three-semester calendar, with each student attending two semesters in person, more than 90% of student course demand could be satisfied on campus without increasing faculty workloads. For the Sloan School of Management, we produced a new schedule that was implemented in fall 2020. The schedule allowed half of Sloan courses to include an in-person component while adhering to safety guidelines. Despite a fourfold reduction in classroom capacity, it afforded two thirds of Sloan students the opportunity for in-person learning in at least half their courses. Managerial implications: Integer optimization can enable decision making at a large scale in a domain that is usually managed manually by university administrators. Our models, although inspired by the pandemic, are generic and could apply to any scheduling problem under severe capacity constraints.
{"title":"Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning","authors":"C. Barnhart, D. Bertsimas, A. Delarue, Julia Yan","doi":"10.1287/msom.2021.0996","DOIUrl":"https://doi.org/10.1287/msom.2021.0996","url":null,"abstract":"Problem definition: Physical distancing requirements during the COVID-19 pandemic have dramatically reduced the effective capacity of university campuses. Under these conditions, we examine how to make the most of newly scarce resources in the related problems of curriculum planning and course timetabling. Academic/practical relevance: We propose a unified model for university course scheduling problems under a two-stage framework and draw parallels between component problems while showing how to accommodate individual specifics. During the pandemic, our models were critical to measuring the impact of several innovative proposals, including expanding the academic calendar, teaching across multiple rooms, and rotating student attendance through the week and school year. Methodology: We use integer optimization combined with enrollment data from thousands of past students. Our models scale to thousands of individual students enrolled in hundreds of courses. Results: We projected that if Massachusetts Institute of Technology moved from its usual two-semester calendar to a three-semester calendar, with each student attending two semesters in person, more than 90% of student course demand could be satisfied on campus without increasing faculty workloads. For the Sloan School of Management, we produced a new schedule that was implemented in fall 2020. The schedule allowed half of Sloan courses to include an in-person component while adhering to safety guidelines. Despite a fourfold reduction in classroom capacity, it afforded two thirds of Sloan students the opportunity for in-person learning in at least half their courses. Managerial implications: Integer optimization can enable decision making at a large scale in a domain that is usually managed manually by university administrators. Our models, although inspired by the pandemic, are generic and could apply to any scheduling problem under severe capacity constraints.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"33 1","pages":"727-745"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83388226","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}
A. Alban, Philippe Blaettchen, H. D. Vries, L. V. Wassenhove
Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower.
{"title":"Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption","authors":"A. Alban, Philippe Blaettchen, H. D. Vries, L. V. Wassenhove","doi":"10.1287/msom.2021.1020","DOIUrl":"https://doi.org/10.1287/msom.2021.1020","url":null,"abstract":"Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"85 1","pages":"2944-2961"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83143299","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: Health information technology (HIT) interoperability refers to the ability of different electronic health record systems and software applications to communicate, exchange data,...
{"title":"The Value of Health Information Technology Interoperability: Evidence from Interhospital Transfer of Heart Attack Patients","authors":"Yao Li, Lauren Xiaoyuan Lu, S. F. Lu, Jian Chen","doi":"10.1287/MSOM.2021.1007","DOIUrl":"https://doi.org/10.1287/MSOM.2021.1007","url":null,"abstract":"Problem definition: Health information technology (HIT) interoperability refers to the ability of different electronic health record systems and software applications to communicate, exchange data,...","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"1 1","pages":"827-845"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73909161","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 investigates the channel choice problem of an online platform that exerts service effort to enhance the demand in its sales channels. Academic/practical relevance: In the existing literature on the channel structure of an online retail platform, it is usually assumed that a manufacturer sells through either the platform’s agency or reselling channel but not both. In practice, many manufacturers sell the same products through both channels of the same online retail platform, a phenomenon that cannot be explained by the existing theory. Moreover, online retail platforms routinely invest in retail services that enhance the demand in their sales channels. Methodology: We develop a game-theoretic model to investigate the equilibrium channel choice, wholesale price, and retail quantity decisions. We also conduct sensitivity analysis to evaluate the impact of some parameters on the equilibrium. Results: We derive conditions under which each of the three channel structures (agency channel, reselling channel, and dual channel) emerges in equilibrium. We show that the wholesale price in the reselling channel is reduced because of the addition of the agency channel even when both channels are equally efficient, which extends the wholesale price effect because of the addition of a less efficient direct channel in the supplier encroachment literature. Our analysis highlights the flexibility of a dual channel for firms to shift sales between the two channels, which could increase the retail platform’s incentive to exert service effort. Managerial implications: Our study provides useful insights to managers to understand and make channel choice decisions in supply chains with manufacturers selling through online retail platforms.
{"title":"Channel Structures of Online Retail Platforms","authors":"Albert Y. Ha, Shilu Tong, Yunjie Wang","doi":"10.1287/msom.2021.1011","DOIUrl":"https://doi.org/10.1287/msom.2021.1011","url":null,"abstract":"Problem definition: This paper investigates the channel choice problem of an online platform that exerts service effort to enhance the demand in its sales channels. Academic/practical relevance: In the existing literature on the channel structure of an online retail platform, it is usually assumed that a manufacturer sells through either the platform’s agency or reselling channel but not both. In practice, many manufacturers sell the same products through both channels of the same online retail platform, a phenomenon that cannot be explained by the existing theory. Moreover, online retail platforms routinely invest in retail services that enhance the demand in their sales channels. Methodology: We develop a game-theoretic model to investigate the equilibrium channel choice, wholesale price, and retail quantity decisions. We also conduct sensitivity analysis to evaluate the impact of some parameters on the equilibrium. Results: We derive conditions under which each of the three channel structures (agency channel, reselling channel, and dual channel) emerges in equilibrium. We show that the wholesale price in the reselling channel is reduced because of the addition of the agency channel even when both channels are equally efficient, which extends the wholesale price effect because of the addition of a less efficient direct channel in the supplier encroachment literature. Our analysis highlights the flexibility of a dual channel for firms to shift sales between the two channels, which could increase the retail platform’s incentive to exert service effort. Managerial implications: Our study provides useful insights to managers to understand and make channel choice decisions in supply chains with manufacturers selling through online retail platforms.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"93 1","pages":"1547-1561"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77310162","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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