Problem definition: We examine the impact of four classes of workplace interruptions on short-term (working hours) and long-term (across-shifts) worker performance in an agribusiness setting. The interruptions are organized in a two-by-two framework in which they result (or do not result) in a physical task requirement and lead to a varying degree of attention shift from the primary task. Academic/practical relevance: Prior operations management literature primarily examines the long-term effects of a single class of interruption that reduces performance. Our study contributes to this literature by examining multiple classes of interruptions that lead to positive and negative outcomes over the short-term in addition to the long-term. Further, our study also contributes to understanding the impact of general task transitions (interruptions) on worker performance, and the interrupting tasks include tasks that are not part of workers’ primary job duties. Our study is relevant to work settings that envelop high manual labor and experience interruptions regularly. Finally, we offer strategies to improve operational performance. Methodology: Using a granular data set on worker productivity from 211 harvesters yielding 117,581 truckloads of fruit harvested for 9,819 worker shifts, we utilize an instrumental variable approach with two-stage residual inclusion estimation on a mix of linear and nonlinear models to examine and quantify the impact of interruptions on both short- and long-term worker productivity. Results: We identify a new interruption class, a pause—interruptions that provide the physical respite and limit the degree of attention shift from the primary task. We find that pauses improve worker productivity in the short- and long-term. Next, we find that scheduled breaks hurt (improve) the worker’s productivity in the short-term (long-term). Finally, we find that harvester breakdown and travel across field interruptions that drain physical resources and cause attention shift hurt worker productivity in the short- and long-term. We quantify the impact (in our field context) of a five-minute increase in each of these work interruptions on average worker productivity. Managerial implications: Our study demonstrates that various work interruptions can have positive or negative effects on workers’ productivity. We suggest that introducing brief pauses in a workday and simultaneously reminding (before initiating the pause) employees about the tasks yet to be completed or goals to be achieved for the rest of the shift can help maintain their focus on the work and yield high-performance benefits. We also suggest strategies that limit the restart costs and increase the predictability of interruptions that hurt performance. For example, in regards to scheduled breaks, planning the break after completing a subtask or reaching a subgoal can limit their adverse effects. Further, informing workers on the possibility of interruption circumstances at the beg
{"title":"The Microstructure of Work: Understanding Productivity Benefits and Costs of Interruptions","authors":"Pradeep K. Pendem, Paul I. Green, B. Staats","doi":"10.1287/msom.2021.1053","DOIUrl":"https://doi.org/10.1287/msom.2021.1053","url":null,"abstract":"Problem definition: We examine the impact of four classes of workplace interruptions on short-term (working hours) and long-term (across-shifts) worker performance in an agribusiness setting. The interruptions are organized in a two-by-two framework in which they result (or do not result) in a physical task requirement and lead to a varying degree of attention shift from the primary task. Academic/practical relevance: Prior operations management literature primarily examines the long-term effects of a single class of interruption that reduces performance. Our study contributes to this literature by examining multiple classes of interruptions that lead to positive and negative outcomes over the short-term in addition to the long-term. Further, our study also contributes to understanding the impact of general task transitions (interruptions) on worker performance, and the interrupting tasks include tasks that are not part of workers’ primary job duties. Our study is relevant to work settings that envelop high manual labor and experience interruptions regularly. Finally, we offer strategies to improve operational performance. Methodology: Using a granular data set on worker productivity from 211 harvesters yielding 117,581 truckloads of fruit harvested for 9,819 worker shifts, we utilize an instrumental variable approach with two-stage residual inclusion estimation on a mix of linear and nonlinear models to examine and quantify the impact of interruptions on both short- and long-term worker productivity. Results: We identify a new interruption class, a pause—interruptions that provide the physical respite and limit the degree of attention shift from the primary task. We find that pauses improve worker productivity in the short- and long-term. Next, we find that scheduled breaks hurt (improve) the worker’s productivity in the short-term (long-term). Finally, we find that harvester breakdown and travel across field interruptions that drain physical resources and cause attention shift hurt worker productivity in the short- and long-term. We quantify the impact (in our field context) of a five-minute increase in each of these work interruptions on average worker productivity. Managerial implications: Our study demonstrates that various work interruptions can have positive or negative effects on workers’ productivity. We suggest that introducing brief pauses in a workday and simultaneously reminding (before initiating the pause) employees about the tasks yet to be completed or goals to be achieved for the rest of the shift can help maintain their focus on the work and yield high-performance benefits. We also suggest strategies that limit the restart costs and increase the predictability of interruptions that hurt performance. For example, in regards to scheduled breaks, planning the break after completing a subtask or reaching a subgoal can limit their adverse effects. Further, informing workers on the possibility of interruption circumstances at the beg","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"28 1","pages":"2202-2220"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75906229","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}
Yingchao Lan, Aravind Chandrasekaran, D. Goradia, D. Walker
Problem definition: This study explores the performance implications of collaboration structures in an integrated healthcare delivery system, namely, an accountable care organization (ACO). ACOs consist of providers from various stages of the care continuum (preacute, acute, and postacute) that voluntarily assume collective responsibility for the quality and cost of care for a defined patient population. Academic/practical relevance: ACOs’ unsatisfied outcomes are largely due to a lack of provider collaboration. There is a dearth of empirical studies on how to develop collaboration structures. Studies in the healthcare operations management primarily have focused on collaboration within a single organization, shedding little light on this problem. We address this issue by exploring two distinct dimensions of collaboration: partnership scope and scale. Partnership scope measures the presence of providers from the preacute, acute, and/or postacute care-continuum stages, whereas partnership scale measures the presence of providers within a single care-continuum stage. Methodology: We assemble a unique data set of provider types, collaboration structures, and system-level performance for 528 Medicare Shared Savings Program (MSSP) ACOs from 2013–2016. To investigate the impact of partnership scope and scale on ACO performance, we then use econometric estimation approaches that account for endogeneity in collaboration structure decisions. ACO performance is measured by experiential quality and 30-day readmission rates. As additional tests, we also investigate our research questions by assembling data sets at both the hospital level (20,975 hospital-year panel data spanning 2009 to 2015) and patient level (859,145 Medicare patients admitted to 39 California hospitals over a four-year period from 2012 to 2015). Results: We find that synergies exist between partnership scope and scale with respect to ACO performance. Specifically, an average-sized ACO can realize 3.2% more improvement in experiential quality and a 6.6% greater reduction in 30-day readmission rates through partnership scope and scale synergies in the preacute care stage. We also show that the benefits of increasing partnership scope are consistent across providers and patient-level analysis. Further, we find that these benefits come at some cost, suggesting an initial cost-quality trade-off when developing collaboration structures. Managerial implications: Our results offer important insights into the healthcare operations management literature on designing effective healthcare delivery systems extending beyond a single organization.
{"title":"Collaboration Structures in Integrated Healthcare Delivery Systems: An Exploratory Study of Accountable Care Organizations","authors":"Yingchao Lan, Aravind Chandrasekaran, D. Goradia, D. Walker","doi":"10.1287/msom.2021.1038","DOIUrl":"https://doi.org/10.1287/msom.2021.1038","url":null,"abstract":"Problem definition: This study explores the performance implications of collaboration structures in an integrated healthcare delivery system, namely, an accountable care organization (ACO). ACOs consist of providers from various stages of the care continuum (preacute, acute, and postacute) that voluntarily assume collective responsibility for the quality and cost of care for a defined patient population. Academic/practical relevance: ACOs’ unsatisfied outcomes are largely due to a lack of provider collaboration. There is a dearth of empirical studies on how to develop collaboration structures. Studies in the healthcare operations management primarily have focused on collaboration within a single organization, shedding little light on this problem. We address this issue by exploring two distinct dimensions of collaboration: partnership scope and scale. Partnership scope measures the presence of providers from the preacute, acute, and/or postacute care-continuum stages, whereas partnership scale measures the presence of providers within a single care-continuum stage. Methodology: We assemble a unique data set of provider types, collaboration structures, and system-level performance for 528 Medicare Shared Savings Program (MSSP) ACOs from 2013–2016. To investigate the impact of partnership scope and scale on ACO performance, we then use econometric estimation approaches that account for endogeneity in collaboration structure decisions. ACO performance is measured by experiential quality and 30-day readmission rates. As additional tests, we also investigate our research questions by assembling data sets at both the hospital level (20,975 hospital-year panel data spanning 2009 to 2015) and patient level (859,145 Medicare patients admitted to 39 California hospitals over a four-year period from 2012 to 2015). Results: We find that synergies exist between partnership scope and scale with respect to ACO performance. Specifically, an average-sized ACO can realize 3.2% more improvement in experiential quality and a 6.6% greater reduction in 30-day readmission rates through partnership scope and scale synergies in the preacute care stage. We also show that the benefits of increasing partnership scope are consistent across providers and patient-level analysis. Further, we find that these benefits come at some cost, suggesting an initial cost-quality trade-off when developing collaboration structures. Managerial implications: Our results offer important insights into the healthcare operations management literature on designing effective healthcare delivery systems extending beyond a single organization.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"39 1","pages":"1796-1820"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85884911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the quality competition between two hospitals under the plan of one insurer. The insurer aims to maximize the overall achievable quality in the system by selecting either the fee-for-service (FFS) or the bundled payment (BP) scheme to compensate each hospital. The demand, the costs, and the probability of successfully treating patients depend on a hospital’s chosen quality. Under such a setting, we use game theoretic models to study the choice of insurer and the impact of payment schemes on the equilibrium qualities of hospitals. Our models seek answers to the following questions. Is BP (FFS) payment scheme always associated with high (low) equilibrium quality? What factors affect the equilibrium qualities of competing hospitals and the choice of the insurer and how? Academic/practical relevance: Competing hospitals under same or different payment models from an insurer is rather common for healthcare delivery in the United States. However, there is a lack of understanding about the impact of payment schemes on quality outcomes from both the hospital’s and the insurer’s perspective. To the best of our knowledge, ours is the first paper to incorporate competition and the insurer’s perspective to study the impact of different payment models on quality. Methodology: Game theory, equilibrium analysis. Results: We show that BP (FFS) is not always associated with high (low) equilibrium quality and that the equilibrium qualities of the two hospitals are strategic complements. We characterize when the insurer might assign the BP to both hospitals, the FFS to both hospitals, or the BP to one and the FFS to the other hospital. Managerial implications: Our findings provide prescriptive guidelines to an insurer choosing payment models. We also show that a specific quality outcome is achievable not by choosing any payment model but by a careful design of its parameters. Our numerical studies show that hospital readmission reduction initiatives are most effective when run in conjunction with cost reduction initiatives.
{"title":"Competition Between Hospitals Under Bundled Payments and Fee-for-Service: An Equilibrium Analysis of Insurer's Choice","authors":"Zheng Han, Mazhar Arikan, S. Mallik","doi":"10.1287/msom.2021.1039","DOIUrl":"https://doi.org/10.1287/msom.2021.1039","url":null,"abstract":"Problem definition: We consider the quality competition between two hospitals under the plan of one insurer. The insurer aims to maximize the overall achievable quality in the system by selecting either the fee-for-service (FFS) or the bundled payment (BP) scheme to compensate each hospital. The demand, the costs, and the probability of successfully treating patients depend on a hospital’s chosen quality. Under such a setting, we use game theoretic models to study the choice of insurer and the impact of payment schemes on the equilibrium qualities of hospitals. Our models seek answers to the following questions. Is BP (FFS) payment scheme always associated with high (low) equilibrium quality? What factors affect the equilibrium qualities of competing hospitals and the choice of the insurer and how? Academic/practical relevance: Competing hospitals under same or different payment models from an insurer is rather common for healthcare delivery in the United States. However, there is a lack of understanding about the impact of payment schemes on quality outcomes from both the hospital’s and the insurer’s perspective. To the best of our knowledge, ours is the first paper to incorporate competition and the insurer’s perspective to study the impact of different payment models on quality. Methodology: Game theory, equilibrium analysis. Results: We show that BP (FFS) is not always associated with high (low) equilibrium quality and that the equilibrium qualities of the two hospitals are strategic complements. We characterize when the insurer might assign the BP to both hospitals, the FFS to both hospitals, or the BP to one and the FFS to the other hospital. Managerial implications: Our findings provide prescriptive guidelines to an insurer choosing payment models. We also show that a specific quality outcome is achievable not by choosing any payment model but by a careful design of its parameters. Our numerical studies show that hospital readmission reduction initiatives are most effective when run in conjunction with cost reduction initiatives.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"1 1","pages":"1821-1842"},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83090555","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 empirically examine a complementary behavioral source of the bullwhip effect that has been previously overlooked in the literature: that individuals order more aggressively (i.e., overreact) when they face shortages than when they hold inventory. Methodology/Results: We conduct a behavioral experiment using the beer distribution game. We estimate decision rules using multilevel modeling approaches that overcome several drawbacks of the estimation methods used in the earlier literature. We find robust evidence that, contrary to the overreaction when in backlog hypothesis and reports from popular press, decision makers order less aggressively and become insensitive to the scope of the problem when in backlog—a scope neglect phenomenon. Managerial implications: We propose a dual-process theoretical account predicated on affective reactions to explain this scope neglect. Our results suggest that affective reactions under novel operating conditions or dramatic events in supply chains, such as the COVID-19 pandemic, can overwhelm cognitive processing of managers and make them fail to recognize the full scope of the problems faced and update decision models accordingly. Understanding the cognitive-affective drivers of ordering behaviors that generate supply chain instability is important in designing interventions to mitigate their negative effects.
{"title":"Do Managers Overreact When in Backlog? Evidence of Scope Neglect from a Supply Chain Experiment","authors":"Rogelio Oliva, Huseyn Abdulla, Paulo Gonçalves","doi":"10.1287/msom.2021.1072","DOIUrl":"https://doi.org/10.1287/msom.2021.1072","url":null,"abstract":"Problem definition: We empirically examine a complementary behavioral source of the bullwhip effect that has been previously overlooked in the literature: that individuals order more aggressively (i.e., overreact) when they face shortages than when they hold inventory. Methodology/Results: We conduct a behavioral experiment using the beer distribution game. We estimate decision rules using multilevel modeling approaches that overcome several drawbacks of the estimation methods used in the earlier literature. We find robust evidence that, contrary to the overreaction when in backlog hypothesis and reports from popular press, decision makers order less aggressively and become insensitive to the scope of the problem when in backlog—a scope neglect phenomenon. Managerial implications: We propose a dual-process theoretical account predicated on affective reactions to explain this scope neglect. Our results suggest that affective reactions under novel operating conditions or dramatic events in supply chains, such as the COVID-19 pandemic, can overwhelm cognitive processing of managers and make them fail to recognize the full scope of the problems faced and update decision models accordingly. Understanding the cognitive-affective drivers of ordering behaviors that generate supply chain instability is important in designing interventions to mitigate their negative effects.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"29 1","pages":"1997-2009"},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82258685","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 continuously soaring prices of new drugs and their uncertain effectiveness in clinical practice have put substantial risks on insurers/payers. To induce insurer coverage of their new drugs, manufacturers start to propose an innovative outcome-based reimbursement (OBR) scheme under which manufacturers refund insurers (and possibly patients) if the drugs fail to achieve a prespecified treatment target. We investigate the impact of OBR on insurers, manufacturers, and patients. Academic/practical relevance: Although OBR sounds intuitively appealing, its true impact is under much debate and depends particularly on the design of OBR. Our study sheds light on the optimal design of OBR and the debate around OBR, considering key trade-offs and key elements not covered in prior literature. Methodology: We develop a Stackelberg game under which the manufacturer designs a rebate scheme for its drug, either non-OBR or OBR, considering the trade-off between a favorable formulary position and the rebate provided. The insurer subsequently determines its formulary for the drug as well as other alternative drugs within the same disease category considering the trade-off between its spending and patient health benefits. Using data on 14 drugs treating a common disease, hyperlipidemia, we estimate through a multinomial logit model the demand of the 14 drugs and conduct counterfactual analyses on the impact of OBR. Results: Under the optimal OBR, the manufacturer lowers the insurer’s risk but inflates the wholesale price (hence, may not reduce insurer spending). OBR also induces a better formulary position for the manufacturer, which, hence, improves patient access to new drugs. Further, rebates to the insurer and patients affect demand through different mechanisms. Including patient rebates in OBR lowers patient expenses and increases drug demand but further increases insurer spending. Managerial implications: We demonstrate the structure of an optimal formulary and its application in practice. We caution insurers/payers who are seeking OBR to reduce their spending.
{"title":"Outcome-Based Reimbursement: The Solution to High Drug Spending?","authors":"Liang Xu, Hongmin Li, H Zhao","doi":"10.1287/msom.2021.1051","DOIUrl":"https://doi.org/10.1287/msom.2021.1051","url":null,"abstract":"Problem definition: The continuously soaring prices of new drugs and their uncertain effectiveness in clinical practice have put substantial risks on insurers/payers. To induce insurer coverage of their new drugs, manufacturers start to propose an innovative outcome-based reimbursement (OBR) scheme under which manufacturers refund insurers (and possibly patients) if the drugs fail to achieve a prespecified treatment target. We investigate the impact of OBR on insurers, manufacturers, and patients. Academic/practical relevance: Although OBR sounds intuitively appealing, its true impact is under much debate and depends particularly on the design of OBR. Our study sheds light on the optimal design of OBR and the debate around OBR, considering key trade-offs and key elements not covered in prior literature. Methodology: We develop a Stackelberg game under which the manufacturer designs a rebate scheme for its drug, either non-OBR or OBR, considering the trade-off between a favorable formulary position and the rebate provided. The insurer subsequently determines its formulary for the drug as well as other alternative drugs within the same disease category considering the trade-off between its spending and patient health benefits. Using data on 14 drugs treating a common disease, hyperlipidemia, we estimate through a multinomial logit model the demand of the 14 drugs and conduct counterfactual analyses on the impact of OBR. Results: Under the optimal OBR, the manufacturer lowers the insurer’s risk but inflates the wholesale price (hence, may not reduce insurer spending). OBR also induces a better formulary position for the manufacturer, which, hence, improves patient access to new drugs. Further, rebates to the insurer and patients affect demand through different mechanisms. Including patient rebates in OBR lowers patient expenses and increases drug demand but further increases insurer spending. Managerial implications: We demonstrate the structure of an optimal formulary and its application in practice. We caution insurers/payers who are seeking OBR to reduce their spending.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"18 1","pages":"2029-2047"},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89058677","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: Lack of patient adherence to treatment protocols is a main barrier to reducing the global disease burden of tuberculosis (TB). We study the operational design of a treatment adherence support (TAS) platform that requires patients to verify their treatment adherence on a daily basis. Academic/practical relevance: Experimental results on the effectiveness of TAS programs have been mixed; and rigorous research is needed on how to structure these motivational programs, particularly in resource-limited settings. Our analysis establishes that patient engagement can be increased by personal sponsor outreach and that patient behavior data can be used to identify at-risk patients for targeted outreach. Methodology: We partner with a TB TAS provider and use data from a completed randomized controlled trial. We use administrative variation in the timing of peer sponsor outreach to evaluate the impact of personal messages on subsequent patient verification behavior. We then develop a rolling-horizon machine learning (ML) framework to generate dynamic risk predictions for patients enrolled on the platform. Results: We find that, on average, sponsor outreach to patients increases the odds ratio of next-day treatment adherence verification by 35%. Furthermore, patients’ prior verification behavior can be used to accurately predict short-term (treatment adherence verification) and long-term (successful treatment completion) outcomes. These results allow the provider to target and implement behavioral interventions to at-risk patients. Managerial implications: Our results indicate that, compared with a benchmark policy, the TAS platform could reach the same number of at-risk patients with 6%–40% less capacity, or reach 2%–20% more at-risk patients with the same capacity, by using various ML-based prioritization policies that leverage patient engagement data. Personal sponsor outreach to all patients is likely to be very costly, so targeted TAS may substantially improve the cost-effectiveness of TAS programs.
{"title":"Improving Tuberculosis Treatment Adherence Support: The Case for Targeted Behavioral Interventions","authors":"J. Boutilier, J. Jónasson, Erez Yoeli","doi":"10.1287/msom.2021.1046","DOIUrl":"https://doi.org/10.1287/msom.2021.1046","url":null,"abstract":"Problem definition: Lack of patient adherence to treatment protocols is a main barrier to reducing the global disease burden of tuberculosis (TB). We study the operational design of a treatment adherence support (TAS) platform that requires patients to verify their treatment adherence on a daily basis. Academic/practical relevance: Experimental results on the effectiveness of TAS programs have been mixed; and rigorous research is needed on how to structure these motivational programs, particularly in resource-limited settings. Our analysis establishes that patient engagement can be increased by personal sponsor outreach and that patient behavior data can be used to identify at-risk patients for targeted outreach. Methodology: We partner with a TB TAS provider and use data from a completed randomized controlled trial. We use administrative variation in the timing of peer sponsor outreach to evaluate the impact of personal messages on subsequent patient verification behavior. We then develop a rolling-horizon machine learning (ML) framework to generate dynamic risk predictions for patients enrolled on the platform. Results: We find that, on average, sponsor outreach to patients increases the odds ratio of next-day treatment adherence verification by 35%. Furthermore, patients’ prior verification behavior can be used to accurately predict short-term (treatment adherence verification) and long-term (successful treatment completion) outcomes. These results allow the provider to target and implement behavioral interventions to at-risk patients. Managerial implications: Our results indicate that, compared with a benchmark policy, the TAS platform could reach the same number of at-risk patients with 6%–40% less capacity, or reach 2%–20% more at-risk patients with the same capacity, by using various ML-based prioritization policies that leverage patient engagement data. Personal sponsor outreach to all patients is likely to be very costly, so targeted TAS may substantially improve the cost-effectiveness of TAS programs.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"06 1","pages":"2925-2943"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86516267","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: With heightened global uncertainty, supply chain managers are under increasing pressure to craft strategies that accommodate both supply and demand risks. Although product flexibility is a well-understood strategy to accommodate risk, there is no clear guidance on the optimal flexibility configuration of a supply network that comprises both unreliable primary suppliers and reliable backup suppliers. Academic/practical relevance: Existing literature examines the value of flexibility with primary and backup suppliers independently. For a risk-neutral firm, research shows that (a) incorporating flexibility in a primary supplier by replacing two dedicated ones (in absence of backup supply) is always beneficial and that (b) adding flexibility to a reliable backup supplier (in absence of product flexibility in primary suppliers) is always valuable. It is unclear, however, how flexibility should be incorporated into a supply network with both unreliable primary suppliers and reliable backup suppliers. This research studies whether flexibility should be incorporated in a primary supplier, a backup supplier, or both. Methodology: We develop a normative model to analyze when flexibility benefits and when it hurts. Results: Compared with a base case of no flexibility, we prove that incorporating flexibility in either primary or backup suppliers is always beneficial. However, incorporating flexibility in both primary and backup suppliers can be counterproductive because the supply chain performance can decline with saturated flexibility, even if flexibility is costless. A key reason is that the risk-aggregation effect of consolidating flexibility in an unreliable supplier becomes more salient when flexibility is already embedded in a backup supplier. Managerial implications: This research refines the existing understanding of flexibility by illustrating that flexibility is not always beneficial. When there is a choice, a firm should prioritize incorporating flexibility in a reliable backup supplier.
{"title":"Product Flexibility Strategy Under Supply and Demand Risk","authors":"Yimin Wang, S. Webster","doi":"10.1287/msom.2021.1037","DOIUrl":"https://doi.org/10.1287/msom.2021.1037","url":null,"abstract":"Problem definition: With heightened global uncertainty, supply chain managers are under increasing pressure to craft strategies that accommodate both supply and demand risks. Although product flexibility is a well-understood strategy to accommodate risk, there is no clear guidance on the optimal flexibility configuration of a supply network that comprises both unreliable primary suppliers and reliable backup suppliers. Academic/practical relevance: Existing literature examines the value of flexibility with primary and backup suppliers independently. For a risk-neutral firm, research shows that (a) incorporating flexibility in a primary supplier by replacing two dedicated ones (in absence of backup supply) is always beneficial and that (b) adding flexibility to a reliable backup supplier (in absence of product flexibility in primary suppliers) is always valuable. It is unclear, however, how flexibility should be incorporated into a supply network with both unreliable primary suppliers and reliable backup suppliers. This research studies whether flexibility should be incorporated in a primary supplier, a backup supplier, or both. Methodology: We develop a normative model to analyze when flexibility benefits and when it hurts. Results: Compared with a base case of no flexibility, we prove that incorporating flexibility in either primary or backup suppliers is always beneficial. However, incorporating flexibility in both primary and backup suppliers can be counterproductive because the supply chain performance can decline with saturated flexibility, even if flexibility is costless. A key reason is that the risk-aggregation effect of consolidating flexibility in an unreliable supplier becomes more salient when flexibility is already embedded in a backup supplier. Managerial implications: This research refines the existing understanding of flexibility by illustrating that flexibility is not always beneficial. When there is a choice, a firm should prioritize incorporating flexibility in a reliable backup supplier.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"7 1","pages":"1779-1795"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89752976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the assortment optimization problem of a retailer that operates a physical store and an online store. The products that can be offered are described by their features. Customers purchase among the products that are offered in their preferred store. However, customers who purchase from the online store can first test out products offered in the physical store. These customers revise their preferences for online products based on the features that are shared with the in-store products. The full assortment is offered online, and the goal is to select an assortment for the physical store to maximize the retailer’s total expected revenue. Academic/practical relevance: The physical store’s assortment affects preferences for online products. Unlike traditional assortment optimization, the physical store’s assortment influences revenue from both stores. Methodology: We introduce a features tree to organize products by features. The nonleaf vertices on the tree correspond to features, and the leaf vertices correspond to products. The ancestors of a leaf correspond to features of the product. Customers choose among the products within their store’s assortment according to the multinomial logit model. We consider two settings; either all customers purchase online after viewing products in the physical store, or we have a mix of customers purchasing from each store. Results: When all customers purchase online, we give an efficient algorithm to find the optimal assortment to display in the physical store. With a mix of customers, the problem becomes NP-hard, and we give a fully polynomial-time approximation scheme. We numerically demonstrate that we can closely approximate the case where products have arbitrary combinations of features without a tree structure and that our fully polynomial-time approximation scheme performs remarkably well. Managerial implications: We characterize conditions under which it is optimal to display expensive products with underrated features and expose inexpensive products with overrated features.
{"title":"Omnichannel Assortment Optimization Under the Multinomial Logit Model with a Features Tree","authors":"Venus Lo, Huseyin Topaloglu","doi":"10.1287/msom.2021.1001","DOIUrl":"https://doi.org/10.1287/msom.2021.1001","url":null,"abstract":"Problem definition: We consider the assortment optimization problem of a retailer that operates a physical store and an online store. The products that can be offered are described by their features. Customers purchase among the products that are offered in their preferred store. However, customers who purchase from the online store can first test out products offered in the physical store. These customers revise their preferences for online products based on the features that are shared with the in-store products. The full assortment is offered online, and the goal is to select an assortment for the physical store to maximize the retailer’s total expected revenue. Academic/practical relevance: The physical store’s assortment affects preferences for online products. Unlike traditional assortment optimization, the physical store’s assortment influences revenue from both stores. Methodology: We introduce a features tree to organize products by features. The nonleaf vertices on the tree correspond to features, and the leaf vertices correspond to products. The ancestors of a leaf correspond to features of the product. Customers choose among the products within their store’s assortment according to the multinomial logit model. We consider two settings; either all customers purchase online after viewing products in the physical store, or we have a mix of customers purchasing from each store. Results: When all customers purchase online, we give an efficient algorithm to find the optimal assortment to display in the physical store. With a mix of customers, the problem becomes NP-hard, and we give a fully polynomial-time approximation scheme. We numerically demonstrate that we can closely approximate the case where products have arbitrary combinations of features without a tree structure and that our fully polynomial-time approximation scheme performs remarkably well. Managerial implications: We characterize conditions under which it is optimal to display expensive products with underrated features and expose inexpensive products with overrated features.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"17 1","pages":"1220-1240"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86344863","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: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-location experiments demonstrate that local thresholds perform better than global thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, global threshold outperforms local thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case. Managerial implications: Our methods provide effective algorithms to manage fulfillment costs for online orders, demonstrating a significant reduction over policies that treat each location separately and reflecting the significant advantage of incorporating shipping in computing thresholds. Numerical studies provide insights as to why local thresholds perform well in a wide variety of situations.
{"title":"Effective Online Order Acceptance Policies for Omnichannel Fulfillment","authors":"Su Jia, Jeremy Karp, R. Ravi, S. Tayur","doi":"10.2139/ssrn.3932692","DOIUrl":"https://doi.org/10.2139/ssrn.3932692","url":null,"abstract":"Problem definition: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-location experiments demonstrate that local thresholds perform better than global thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, global threshold outperforms local thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case. Managerial implications: Our methods provide effective algorithms to manage fulfillment costs for online orders, demonstrating a significant reduction over policies that treat each location separately and reflecting the significant advantage of incorporating shipping in computing thresholds. Numerical studies provide insights as to why local thresholds perform well in a wide variety of situations.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"29 1","pages":"1650-1663"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89985884","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 examine a firm’s investigation and recall decisions when a defect occurs and provide policy implications on how to deter long delayed recalls. Practical relevance: When a safety defect occurs, manufacturers often use product recalls to mitigate potential consequences. Although consumers expect on-time recalls for product defects, anecdotal examples suggest that firms may be passive in investigating potential defects and/or severely delay their recall decisions. Understanding how firms make their recall timing decisions has important business and social implications. Methodology: We study decisions on investigation efforts and recall timings for a profit-maximizing manufacturer by incorporating a Bass diffusion model to capture sales patterns for products with long life cycles. We then test our implications using data from the automobile industry and find supporting evidence. Results: We first find that a firm will consider a delayed recall when the defect is noticed early, when sales suffer more negative impacts from (external) media exposure on a recall, and when the product has a relatively high margin-to-recall-cost ratio. Second, a firm that will consider a delayed recall exerts a smaller investigation effort, and it will further reduce the effort when the defect is more likely to lead to a recall. When we consider the case in which a firm’s learning effect and information updating occur in an investigation and recall process, our results remain consistent. Managerial implications: Our model not only helps us understand how firms make their decisions when defects occur but also offers governments and regulatory bodies new instruments (e.g., investigation efforts, penalty design, information disclosure, firm supervision) to help firms be proactive should a defect occur, thereby reducing potential casualties associated with delays in a recall progress.
{"title":"Before It's Too Late: Product Recall Delays and Policy Design","authors":"W. Mao, Zhanyu Dong, Hsiao-Hui Lee","doi":"10.1287/msom.2021.1042","DOIUrl":"https://doi.org/10.1287/msom.2021.1042","url":null,"abstract":"Problem definition: We examine a firm’s investigation and recall decisions when a defect occurs and provide policy implications on how to deter long delayed recalls. Practical relevance: When a safety defect occurs, manufacturers often use product recalls to mitigate potential consequences. Although consumers expect on-time recalls for product defects, anecdotal examples suggest that firms may be passive in investigating potential defects and/or severely delay their recall decisions. Understanding how firms make their recall timing decisions has important business and social implications. Methodology: We study decisions on investigation efforts and recall timings for a profit-maximizing manufacturer by incorporating a Bass diffusion model to capture sales patterns for products with long life cycles. We then test our implications using data from the automobile industry and find supporting evidence. Results: We first find that a firm will consider a delayed recall when the defect is noticed early, when sales suffer more negative impacts from (external) media exposure on a recall, and when the product has a relatively high margin-to-recall-cost ratio. Second, a firm that will consider a delayed recall exerts a smaller investigation effort, and it will further reduce the effort when the defect is more likely to lead to a recall. When we consider the case in which a firm’s learning effect and information updating occur in an investigation and recall process, our results remain consistent. Managerial implications: Our model not only helps us understand how firms make their decisions when defects occur but also offers governments and regulatory bodies new instruments (e.g., investigation efforts, penalty design, information disclosure, firm supervision) to help firms be proactive should a defect occur, thereby reducing potential casualties associated with delays in a recall progress.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"9 1","pages":"1926-1938"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82566950","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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