{"title":"利用市场人口统计数据预测陆军预备役部队编制","authors":"Nathan L. Parker","doi":"10.1108/JDAL-09-2017-0020","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.\n\n\nDesign/methodology/approach\nThis research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.\n\n\nFindings\nThis research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.\n\n\nOriginality/value\nThis research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.\n","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting army reserve unit manning using market demographics\",\"authors\":\"Nathan L. Parker\",\"doi\":\"10.1108/JDAL-09-2017-0020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThis research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.\\n\\n\\nDesign/methodology/approach\\nThis research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.\\n\\n\\nFindings\\nThis research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.\\n\\n\\nOriginality/value\\nThis research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.\\n\",\"PeriodicalId\":32838,\"journal\":{\"name\":\"Journal of Defense Analytics and Logistics\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Defense Analytics and Logistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/JDAL-09-2017-0020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Defense Analytics and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/JDAL-09-2017-0020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Decision Sciences","Score":null,"Total":0}
Predicting army reserve unit manning using market demographics
Purpose
This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.
Design/methodology/approach
This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.
Findings
This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.
Originality/value
This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.