Andreas Dellnitz, Damian Pozo, Jochen Bauer, Andreas Kleine
Universities follow a long tradition of assigning students to courses based on student preferences while taking into account constraints such as the rooms to be used. In this context, theoretical approaches aid us in developing algorithms that can be helpful in practice. Our contribution to this subject is a practice summary in which we discuss the most important findings of a project to develop a MATLAB-based stand-alone software system to solve the seminar assignment problem at the FernUniversität in Hagen, Germany. The use of our software at three departments has already enabled annual savings of nearly 20,000 euros in personnel costs, which corresponds to a reduction of around 500 person-hours per year. Apart from important technical aspects of our work, the reported savings potential provides valuable information for making decisions on future similar software projects. History: This paper was refereed.
{"title":"Practice Summary: Seminar Assignments in a University—MATLAB-Based Decision Support","authors":"Andreas Dellnitz, Damian Pozo, Jochen Bauer, Andreas Kleine","doi":"10.1287/inte.2023.1157","DOIUrl":"https://doi.org/10.1287/inte.2023.1157","url":null,"abstract":"Universities follow a long tradition of assigning students to courses based on student preferences while taking into account constraints such as the rooms to be used. In this context, theoretical approaches aid us in developing algorithms that can be helpful in practice. Our contribution to this subject is a practice summary in which we discuss the most important findings of a project to develop a MATLAB-based stand-alone software system to solve the seminar assignment problem at the FernUniversität in Hagen, Germany. The use of our software at three departments has already enabled annual savings of nearly 20,000 euros in personnel costs, which corresponds to a reduction of around 500 person-hours per year. Apart from important technical aspects of our work, the reported savings potential provides valuable information for making decisions on future similar software projects. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"95 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85890461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Anoop, A. M. Reddy, Mandeep Singh Bhatia, A. K. Jain, R. Gopalakrishnan, Merajus Salekin, Samay Pritam Singh, R. V. Satwik, Sudarshan Pulapadi, Chandrashekhar Bobade, I. S. Kumar, Shivasubramanian Gopalakrishnan, K. Appaiah, N. Rangaraj, M. Belur
During the year 2020, Indian Railways undertook an extensive timetabling exercise for its entire network. The timetable for its six principal routes known as the golden quadrilateral + diagonals (GQD) was generated using a rail traffic simulation tool. The simulation tool and the methodology had to be customized to handle the complex technical requirements of the GQD network, which spans more than 9,000 km. Challenges related to using and integrating data into the simulator also had to be addressed. This was the first time that a simulation software tool of this kind was used for timetabling in Indian Railways, and hence, there were uncertainties regarding the timely delivery, which gave rise to additional challenges to the overall effort. This paper focuses on these challenges and the managerial and human aspects of this massive timetabling exercise. It also explains how this project leverages the benefits of combining top-down and bottom-up approaches in timetabling and how it sets a new paradigm for network-wide timetabling in Indian Railways. History: This paper was refereed. Funding: This work was supported financially through the Indian Railways–sponsored project titled “Implementing Zero Based Timetabling for Major IR Routes by IIT Bombay through Simulation Model of Mixed Rail Traffic” [Grant RD/0120-WRAIL00-002].
{"title":"Rationalized Timetabling Using a Simulation Tool: A Paradigm Shift in Indian Railways","authors":"K. Anoop, A. M. Reddy, Mandeep Singh Bhatia, A. K. Jain, R. Gopalakrishnan, Merajus Salekin, Samay Pritam Singh, R. V. Satwik, Sudarshan Pulapadi, Chandrashekhar Bobade, I. S. Kumar, Shivasubramanian Gopalakrishnan, K. Appaiah, N. Rangaraj, M. Belur","doi":"10.1287/inte.2023.1158","DOIUrl":"https://doi.org/10.1287/inte.2023.1158","url":null,"abstract":"During the year 2020, Indian Railways undertook an extensive timetabling exercise for its entire network. The timetable for its six principal routes known as the golden quadrilateral + diagonals (GQD) was generated using a rail traffic simulation tool. The simulation tool and the methodology had to be customized to handle the complex technical requirements of the GQD network, which spans more than 9,000 km. Challenges related to using and integrating data into the simulator also had to be addressed. This was the first time that a simulation software tool of this kind was used for timetabling in Indian Railways, and hence, there were uncertainties regarding the timely delivery, which gave rise to additional challenges to the overall effort. This paper focuses on these challenges and the managerial and human aspects of this massive timetabling exercise. It also explains how this project leverages the benefits of combining top-down and bottom-up approaches in timetabling and how it sets a new paradigm for network-wide timetabling in Indian Railways. History: This paper was refereed. Funding: This work was supported financially through the Indian Railways–sponsored project titled “Implementing Zero Based Timetabling for Major IR Routes by IIT Bombay through Simulation Model of Mixed Rail Traffic” [Grant RD/0120-WRAIL00-002].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"37 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80950410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Supply chains in the construction industry are less efficient than in other industries (e.g., retail or automotive). The reason for the less optimized supply chains is often the lack of accurate, up-to-date information. For a leading supplier of building materials and construction systems, we perform a value of information analysis to guide future investments in costly sensors for silo fill-level monitoring. Silo inventory is vendor-managed as construction sites withdraw material from silos as they need it. Ensuring continuous availability of material requires proactive replenishment across products and customers. We establish the optimal purchase level of (partial) information for different hardware costs and service levels. Based on the current ensured service level regime, the minimum total costs are achieved with approximately 50% sensor-equipped silos for medium and high annual sensor costs. The findings on the appropriate use of information technology to improve decision making with partial information are generally relevant for suppliers looking to stand out with service guarantees and short delivery times. History: This paper was refereed.
{"title":"Information Value Analysis for Real-Time Silo Fill-Level Monitoring","authors":"Toni Greif, N. Stein, C. Flath","doi":"10.1287/inte.2023.1156","DOIUrl":"https://doi.org/10.1287/inte.2023.1156","url":null,"abstract":"Supply chains in the construction industry are less efficient than in other industries (e.g., retail or automotive). The reason for the less optimized supply chains is often the lack of accurate, up-to-date information. For a leading supplier of building materials and construction systems, we perform a value of information analysis to guide future investments in costly sensors for silo fill-level monitoring. Silo inventory is vendor-managed as construction sites withdraw material from silos as they need it. Ensuring continuous availability of material requires proactive replenishment across products and customers. We establish the optimal purchase level of (partial) information for different hardware costs and service levels. Based on the current ensured service level regime, the minimum total costs are achieved with approximately 50% sensor-equipped silos for medium and high annual sensor costs. The findings on the appropriate use of information technology to improve decision making with partial information are generally relevant for suppliers looking to stand out with service guarantees and short delivery times. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"13 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77720704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patient room cleaning is an often-neglected hospital process. But it has serious implications for clinical outcomes and patient satisfaction. Delays in turning rooms around between patients can impair hospital capacity and capability to speed up patient treatment. Inefficient assignment of rooms to the cleaning staff also can lead to higher incidence of infection transmission. We systematically developed and piloted an efficient room assignment logic for this critical process through an integer-programming staff optimization model at McLeod Health, a leading hospital system in South Carolina. We identified an opportunity for a 20% reduction in staffing level to handle the prevalent demand and annual staffing cost savings of about $575,000 or to handle a 30% greater demand in dirty rooms using the current staffing levels. McLeod Health integrated our recommendations into their staffing strategy immediately. History: This paper was refereed. Funding: This paper is based on a consulting project funded through the University of South Carolina Operations and Supply Chain Center at McLeod Health under the sponsorship of Donna Isgett.
{"title":"McLeod Health Optimizes Staffing for Patient Room Cleaning","authors":"S. Ahire","doi":"10.1287/inte.2022.1155","DOIUrl":"https://doi.org/10.1287/inte.2022.1155","url":null,"abstract":"Patient room cleaning is an often-neglected hospital process. But it has serious implications for clinical outcomes and patient satisfaction. Delays in turning rooms around between patients can impair hospital capacity and capability to speed up patient treatment. Inefficient assignment of rooms to the cleaning staff also can lead to higher incidence of infection transmission. We systematically developed and piloted an efficient room assignment logic for this critical process through an integer-programming staff optimization model at McLeod Health, a leading hospital system in South Carolina. We identified an opportunity for a 20% reduction in staffing level to handle the prevalent demand and annual staffing cost savings of about $575,000 or to handle a 30% greater demand in dirty rooms using the current staffing levels. McLeod Health integrated our recommendations into their staffing strategy immediately. History: This paper was refereed. Funding: This paper is based on a consulting project funded through the University of South Carolina Operations and Supply Chain Center at McLeod Health under the sponsorship of Donna Isgett.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"7 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89032966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Auriel M. V. Fournier, R. Wilson, J. Gleason, E. Adams, Janell M. Brush, R. Cooper, S. Demaso, Melanie J. L. Driscoll, P. Frederick, P. Jodice, M. Ottinger, David B. Reeves, Michael A. Seymour, S. Sharuga, J. Tirpak, William G. Vermillion, T. Zenzal, J. Lyons, M. Woodrey
Conservation planning for large ecosystems has multiple benefits but is often challenging to implement because of the multiple jurisdictions, species, and habitats involved. In addition, decision making at large spatial scales can be hampered because many approaches do not explicitly incorporate potentially competing values and concerns of stakeholders. After the Deepwater Horizon oil spill, establishing baselines was challenging because of (1) variation in study designs, (2) inconsistent use of explicit objectives and hypotheses, (3) inconsistent use of standardized monitoring protocols, and (4) variation in spatial and temporal scope associated with avian monitoring projects before the spill. Herein, we show how the Gulf of Mexico Avian Monitoring Network members used structured decision making to identify bird monitoring priorities. We used multiple tools and techniques to clearly define the problem and stakeholder objectives and to identify bird monitoring priorities at the scale of the entire northern Gulf of Mexico region. Although our example is specific to the northern Gulf of Mexico, this approach provides an example of how stakeholder values can be incorporated into the coordination process of broad-scale monitoring programs to address management, restoration, and scientific questions in other ecosystems and for other taxa. History: This paper was refereed. Funding: Thanks to the National Fish and Wildlife Foundation [Grant 324423], which supported A. Fournier as a postdoctoral research associate at Mississippi State University. M. Woodrey was supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch Project funds, the Mississippi Agricultural and Forestry Experiment Station, National Oceanographic and Atmospheric Administration [Grant NA16NOS4200088 to the Mississippi Department of Marine Resources’ Grand Bay National Estuarine Research Reserve], and the Mississippi Department of Marine Resources [Grant 8200025414]. This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station. T. Zenzal was supported by the National Oceanic and Atmospheric Administration RESTORE Act Science Program [Grant NA17NOS4510092].
{"title":"Structured Decision Making to Prioritize Regional Bird Monitoring Needs","authors":"Auriel M. V. Fournier, R. Wilson, J. Gleason, E. Adams, Janell M. Brush, R. Cooper, S. Demaso, Melanie J. L. Driscoll, P. Frederick, P. Jodice, M. Ottinger, David B. Reeves, Michael A. Seymour, S. Sharuga, J. Tirpak, William G. Vermillion, T. Zenzal, J. Lyons, M. Woodrey","doi":"10.1287/inte.2022.1154","DOIUrl":"https://doi.org/10.1287/inte.2022.1154","url":null,"abstract":"Conservation planning for large ecosystems has multiple benefits but is often challenging to implement because of the multiple jurisdictions, species, and habitats involved. In addition, decision making at large spatial scales can be hampered because many approaches do not explicitly incorporate potentially competing values and concerns of stakeholders. After the Deepwater Horizon oil spill, establishing baselines was challenging because of (1) variation in study designs, (2) inconsistent use of explicit objectives and hypotheses, (3) inconsistent use of standardized monitoring protocols, and (4) variation in spatial and temporal scope associated with avian monitoring projects before the spill. Herein, we show how the Gulf of Mexico Avian Monitoring Network members used structured decision making to identify bird monitoring priorities. We used multiple tools and techniques to clearly define the problem and stakeholder objectives and to identify bird monitoring priorities at the scale of the entire northern Gulf of Mexico region. Although our example is specific to the northern Gulf of Mexico, this approach provides an example of how stakeholder values can be incorporated into the coordination process of broad-scale monitoring programs to address management, restoration, and scientific questions in other ecosystems and for other taxa. History: This paper was refereed. Funding: Thanks to the National Fish and Wildlife Foundation [Grant 324423], which supported A. Fournier as a postdoctoral research associate at Mississippi State University. M. Woodrey was supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch Project funds, the Mississippi Agricultural and Forestry Experiment Station, National Oceanographic and Atmospheric Administration [Grant NA16NOS4200088 to the Mississippi Department of Marine Resources’ Grand Bay National Estuarine Research Reserve], and the Mississippi Department of Marine Resources [Grant 8200025414]. This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station. T. Zenzal was supported by the National Oceanic and Atmospheric Administration RESTORE Act Science Program [Grant NA17NOS4510092].","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79143082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes how Amazon redesigned the robotic picking algorithm used in Amazon Robotics (AR) fulfillment centers (FCs) to enable humans and robots to work together effectively. In AR FCs, robotic drives fetch storage pods filled with inventory for associates to pick. The picking algorithm needs to decide which specific units of inventory on which pods should be picked to fulfill customer order shipments. We want to do so in a way that is most efficient and distance traveled by drives per unit picked is the key performance metric. This new algorithm reduced the distance traveled by drives per unit picked by 62% without negative operational impact and has since been implemented in all AR FCs. This improvement reduced the number of drives required in AR FCs by 31%, which amounted to half a billion dollars in savings. The redesigned algorithm enabled seamless collaboration between associates and robots, and its effectiveness in scaling up convinced Amazon to make AR FCs the standard for new FCs, allowing Amazon to reduce the storage footprint by about 29% compared with non-AR FCs. History: This paper was refereed.
{"title":"Algorithm for Robotic Picking in Amazon Fulfillment Centers Enables Humans and Robots to Work Together Effectively","authors":"R. Allgor, Tolga Çezik, Daniel Chen","doi":"10.1287/inte.2022.1143","DOIUrl":"https://doi.org/10.1287/inte.2022.1143","url":null,"abstract":"This paper describes how Amazon redesigned the robotic picking algorithm used in Amazon Robotics (AR) fulfillment centers (FCs) to enable humans and robots to work together effectively. In AR FCs, robotic drives fetch storage pods filled with inventory for associates to pick. The picking algorithm needs to decide which specific units of inventory on which pods should be picked to fulfill customer order shipments. We want to do so in a way that is most efficient and distance traveled by drives per unit picked is the key performance metric. This new algorithm reduced the distance traveled by drives per unit picked by 62% without negative operational impact and has since been implemented in all AR FCs. This improvement reduced the number of drives required in AR FCs by 31%, which amounted to half a billion dollars in savings. The redesigned algorithm enabled seamless collaboration between associates and robots, and its effectiveness in scaling up convinced Amazon to make AR FCs the standard for new FCs, allowing Amazon to reduce the storage footprint by about 29% compared with non-AR FCs. History: This paper was refereed.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"54 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87240184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tamara Adams, Alessandro Ferrucci, Pedro Carvalho, Sothiara Em, Benjamin Whitley, Ryan Cecchi, Teresa E. Hicks, Alexander Wooten, J. Cuffe, Stephanie Studds, I. Lustig, Steve Sashihara
The U.S. Census Bureau conducts a census of population and housing every 10 years as mandated in the U.S. Constitution. Following up in person with households that do not respond online, by phone, or by mail, which is known as nonresponse follow-up (NRFU), represents a major component of this effort. For the 2010 Census, the Census Bureau equipped enumerators with paper maps and notebooks filled with questionnaires and required enumerators to go door to door and collect decennial census data. The enumerators met daily with their supervisors to return completed questionnaires and update payroll information. For the 2020 Census, an advanced analytics solution, utilizing machine learning and optimization techniques, drove a reengineering of the entire field operations process, leading to substantially reduced costs and improved productivity. These reengineering efforts included business processes and technology centered around the development of this solution, the MOJO Optimizer, and resulted in an 80% increase in the number of cases completed per hour (from 1.05 to 1.92) and a 27% decrease in the number of miles reimbursed per case (from 5.05 to 3.68) compared with the 2010 Census NRFU. Capitalizing on the massive innovations realized during decennial census operations, the Census Bureau intends to use this technology to revolutionize its over 90 active surveys.
{"title":"Advanced Analytics Drives Reengineering of Field Operations for the 2020 U.S. Census","authors":"Tamara Adams, Alessandro Ferrucci, Pedro Carvalho, Sothiara Em, Benjamin Whitley, Ryan Cecchi, Teresa E. Hicks, Alexander Wooten, J. Cuffe, Stephanie Studds, I. Lustig, Steve Sashihara","doi":"10.1287/inte.2022.1146","DOIUrl":"https://doi.org/10.1287/inte.2022.1146","url":null,"abstract":"The U.S. Census Bureau conducts a census of population and housing every 10 years as mandated in the U.S. Constitution. Following up in person with households that do not respond online, by phone, or by mail, which is known as nonresponse follow-up (NRFU), represents a major component of this effort. For the 2010 Census, the Census Bureau equipped enumerators with paper maps and notebooks filled with questionnaires and required enumerators to go door to door and collect decennial census data. The enumerators met daily with their supervisors to return completed questionnaires and update payroll information. For the 2020 Census, an advanced analytics solution, utilizing machine learning and optimization techniques, drove a reengineering of the entire field operations process, leading to substantially reduced costs and improved productivity. These reengineering efforts included business processes and technology centered around the development of this solution, the MOJO Optimizer, and resulted in an 80% increase in the number of cases completed per hour (from 1.05 to 1.92) and a 27% decrease in the number of miles reimbursed per case (from 5.05 to 3.68) compared with the 2010 Census NRFU. Capitalizing on the massive innovations realized during decennial census operations, the Census Bureau intends to use this technology to revolutionize its over 90 active surveys.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"13 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84403368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This special issue of the INFORMS Journal on Applied Analytics (formerly Interfaces) is devoted to the finalists of the 52nd annual competition for the Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science, the profession’s most prestigious award for deployed work. As in previous years, the finalists this year cover a wide range of industries and functions.
{"title":"Introduction: 2022 Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science","authors":"Carrie Beam, Pelin Pekgün","doi":"10.1287/inte.2022.1148","DOIUrl":"https://doi.org/10.1287/inte.2022.1148","url":null,"abstract":"This special issue of the INFORMS Journal on Applied Analytics (formerly Interfaces) is devoted to the finalists of the 52nd annual competition for the Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science, the profession’s most prestigious award for deployed work. As in previous years, the finalists this year cover a wide range of industries and functions.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"17 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88024381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Basso, M. Goic, Marcelo Olivares, Denis Sauré, Charles Thraves, Aldo G. Carranza, G. Weintraub, Julio Covarrubia, Cristian Escobedo, Natalia Jara, Antonio Moreno, Demian Arancibia, M. Fuenzalida, J. P. Uribe, Felipe Zúñiga, M. Zúñiga, M. O’Ryan, E. Santelices, J. P. Torres, M. Badal, Mirko Bozanic, Sebastián Cancino-Espinoza, Eduardo Lara, Ignasi Neira
During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time; (2) helped allocate limited intensive care unit (ICU) capacity; (3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases; and (4) implemented a nationwide serology surveillance program that significantly influenced Chile’s decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field; the effective communication of information to the population; and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD. Funding: This work was supported by the ANID PIA/APOYO [Grant AFB180003 and AFB220003], used to hire research assistants for developing solutions, information systems, data management, and training of field staff. Fieldwork was funded by the Ministry of Health of the Government of Chile. M. Goic acknowledges the financial support of Fondecyt [Project 1221711] and the Institute for Research in Market Imperfections and Public Policy [Grant IS130002 ANID]. D. Sauré acknowledges the financial support of Fondecyt [Project 1211407]. Finally, G. Weintraub and A. Carranza thank the Stanford RISE COVID-19 Crisis Response Faculty Seed Grant Program for helpful financial support.
{"title":"Analytics Saves Lives During the COVID-19 Crisis in Chile","authors":"L. Basso, M. Goic, Marcelo Olivares, Denis Sauré, Charles Thraves, Aldo G. Carranza, G. Weintraub, Julio Covarrubia, Cristian Escobedo, Natalia Jara, Antonio Moreno, Demian Arancibia, M. Fuenzalida, J. P. Uribe, Felipe Zúñiga, M. Zúñiga, M. O’Ryan, E. Santelices, J. P. Torres, M. Badal, Mirko Bozanic, Sebastián Cancino-Espinoza, Eduardo Lara, Ignasi Neira","doi":"10.1287/inte.2022.1149","DOIUrl":"https://doi.org/10.1287/inte.2022.1149","url":null,"abstract":"During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time; (2) helped allocate limited intensive care unit (ICU) capacity; (3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases; and (4) implemented a nationwide serology surveillance program that significantly influenced Chile’s decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field; the effective communication of information to the population; and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD. Funding: This work was supported by the ANID PIA/APOYO [Grant AFB180003 and AFB220003], used to hire research assistants for developing solutions, information systems, data management, and training of field staff. Fieldwork was funded by the Ministry of Health of the Government of Chile. M. Goic acknowledges the financial support of Fondecyt [Project 1221711] and the Institute for Research in Market Imperfections and Public Policy [Grant IS130002 ANID]. D. Sauré acknowledges the financial support of Fondecyt [Project 1211407]. Finally, G. Weintraub and A. Carranza thank the Stanford RISE COVID-19 Crisis Response Faculty Seed Grant Program for helpful financial support.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"14 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84369633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tugce G. Martagan, I. Adan, M. Baaijens, Coen Dirckx, Oscar Repping, Bram van Ravenstein, PK Yegneswaran
Merck Animal Health offers veterinarians, farmers, pet owners and governments one of the widest ranges of veterinary pharmaceuticals, vaccines and health management solutions. After four years of collaboration where vision met opportunity, a portfolio of optimization and decision support applications were implemented that substantially improved biomanufacturing effectiveness. Biomanufacturing uses living organisms (i.e., viruses and bacteria) to grow active ingredients in vaccines and therapeutics. This high-tech manufacturing process generates challenges not found in many other industries. Additionally, the high cost of equipment and labor-intensive nature of operations precluded the ability to just add capacity. Operations research was critical to meet these challenges. The implementation of the developed models had a significant impact by generating $200 million of additional revenue without the need for additional raw materials, energy resources, or new equipment. The work has been vital to the increased biomanufacturing efficiency for the company.
{"title":"Merck Animal Health Uses Operations Research Methods to Transform Biomanufacturing Productivity for Lifesaving Medicines","authors":"Tugce G. Martagan, I. Adan, M. Baaijens, Coen Dirckx, Oscar Repping, Bram van Ravenstein, PK Yegneswaran","doi":"10.1287/inte.2022.1147","DOIUrl":"https://doi.org/10.1287/inte.2022.1147","url":null,"abstract":"Merck Animal Health offers veterinarians, farmers, pet owners and governments one of the widest ranges of veterinary pharmaceuticals, vaccines and health management solutions. After four years of collaboration where vision met opportunity, a portfolio of optimization and decision support applications were implemented that substantially improved biomanufacturing effectiveness. Biomanufacturing uses living organisms (i.e., viruses and bacteria) to grow active ingredients in vaccines and therapeutics. This high-tech manufacturing process generates challenges not found in many other industries. Additionally, the high cost of equipment and labor-intensive nature of operations precluded the ability to just add capacity. Operations research was critical to meet these challenges. The implementation of the developed models had a significant impact by generating $200 million of additional revenue without the need for additional raw materials, energy resources, or new equipment. The work has been vital to the increased biomanufacturing efficiency for the company.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"21 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78799148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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