{"title":"Improving defense innovation programs to enhance force readiness","authors":"A. Bresler","doi":"10.1108/JDAL-06-2018-0010","DOIUrl":"https://doi.org/10.1108/JDAL-06-2018-0010","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to evaluate Department of Defense (DoD)-backed innovation programs as a means of enhancing the adoption of new technology throughout the armed forces.\u0000\u0000\u0000Design/methodology/approach\u0000The distribution of 1.29 million defense contract awards over seven years was analyzed across a data set of more than 8,000 DoD-backed innovation program award recipients. Surveys and interviews of key stakeholder groups were conducted to contextualize the quantitative results and garner additional insights.\u0000\u0000\u0000Findings\u0000Nearly half of DoD innovation program participants achieve no meaningful growth in direct defense business after program completion, and most small, innovative companies that win follow-on defense contracts solely support their initial sponsor branch. Causes for these program failures include the fact that programs do not market participants’ capabilities to the defense community and do not track participant companies after program completion.\u0000\u0000\u0000Practical implications\u0000Because the DoD does not market the capabilities of its innovation program participants internally, prospective DoD customers conduct redundant market research or fail to modernize. Program participants become increasingly unwilling to invest in the DoD market long term after the programs fail to deliver their expected benefits.\u0000\u0000\u0000Originality/value\u0000Limited scholarship evaluates the efficacy of DoD-backed innovation programs as a means of enhancing force readiness. This research not only uses a vast data set to demonstrate the failures of these programs but also presents concrete recommendations for improving them – including establishing an “Innovators Database” to track program participants and an incentive to encourage contracting entities and contractors to engage with them.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77968259","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}
{"title":"Predicting army reserve unit manning using market demographics","authors":"Nathan L. Parker","doi":"10.1108/JDAL-09-2017-0020","DOIUrl":"https://doi.org/10.1108/JDAL-09-2017-0020","url":null,"abstract":"\u0000Purpose\u0000This 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.\u0000\u0000\u0000Design/methodology/approach\u0000This 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.\u0000\u0000\u0000Findings\u0000This 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.\u0000\u0000\u0000Originality/value\u0000This 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.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85247087","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}
{"title":"Finding the fuel of the Arab Spring fire: a historical data analysis","authors":"D. Ahner, Luke Brantley","doi":"10.1108/JDAL-03-2018-0008","DOIUrl":"https://doi.org/10.1108/JDAL-03-2018-0008","url":null,"abstract":"\u0000Purpose\u0000This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.\u0000\u0000\u0000Design/methodology/approach\u0000Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent.\u0000\u0000\u0000Findings\u0000Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.\u0000\u0000\u0000Originality/value\u0000Of particular note is that the paper addresses the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015 through data analytics. This paper considers various open-source, readily available data for inclusion in multiple models of identified Arab Spring nations in addition to implementing a novel imputation methodology useful to conflict prediction studies in the future.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85207882","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}
{"title":"Cost performance index stability: insights from environmental remediation projects","authors":"Denis S. Clayson, A. Thal, E. White","doi":"10.1108/JDAL-11-2017-0024","DOIUrl":"https://doi.org/10.1108/JDAL-11-2017-0024","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study was to investigate the stability of the cost performance index (CPI) for environmental remediation projects as the topic is not addressed in the literature. CPI is defined as the earned value of work performed divided by the actual cost of the work, and CPI stability represents the point in time in a project after which the CPI varies by less than 20 percent (measured in different ways).\u0000\u0000\u0000Design/methodology/approach\u0000After collecting monthly earned value management (EVM) data for 136 environmental remediation projects from a United States federal agency in fiscal years 2012 and 2013, the authors used the nonparametric Wilcoxon signed-rank test to analyze CPI stability. The authors also used nonparametric statistical comparisons to identify any significant relationships between CPI stability and independent variables representing project and contract characteristics.\u0000\u0000\u0000Findings\u0000The CPI for environmental projects did not stabilize until the projects were 41 percent complete with respect to project duration. The most significant factors contributing to CPI stability were categorized into the following managerial insights: contractor qualifications, communication, stakeholder engagement, contracting strategy, competition, EVM factors, and macro project factors.\u0000\u0000\u0000Originality/value\u0000As CPI stability for environmental remediation projects has not been reported in the literature, this research provides new insights to help project managers understand when the CPIs of environmental remediation projects stabilize and which factors have the most impact on CPI stability.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90147908","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}
J. Ellis, E. White, J. Ritschel, Shawn M. Valentine, B. Lucas, Ian S. Cordell
{"title":"Likelihood and cost impact of engineering change requirements for DoD contracts","authors":"J. Ellis, E. White, J. Ritschel, Shawn M. Valentine, B. Lucas, Ian S. Cordell","doi":"10.1108/JDAL-02-2018-0002","DOIUrl":"https://doi.org/10.1108/JDAL-02-2018-0002","url":null,"abstract":"\u0000Purpose\u0000There appears to be no empirical-based method in the literature for estimating if an engineering change proposal (ECP) will occur or the dollar amount incurred. This paper aims to present an empirically based approach to address this shortfall.\u0000\u0000\u0000Design/methodology/approach\u0000Using the cost assessment data enterprise database, 533 contracts were randomly selected via a stratified sampling plan to build two regression models: one to predict the likelihood of a contract experiencing an ECP and the other to determine the expected median per cent increase in baseline contract cost if an ECP was likely. Both models adopted a stepwise approach. A validation set was placed aside prior to any model building.\u0000\u0000\u0000Findings\u0000Not every contract incurs an ECP; approximately 80 per cent of the contracts in the database did not have an ECP. The likelihood of an ECP and the additional amount incurred appears to be statistically independent of acquisition phase, branch of service, commodity, contract type or any other factor except for the basic contract amount and the number of contract line item numbers; both of these later variables equally affected the contract percentage increase because of an ECP. The combined model overall bested current anecdotal approaches to ECP withhold.\u0000\u0000\u0000Originality/value\u0000This paper both serves as a published reference point for ECP withholds in the archival forum and presents an empirically based method for determining per cent ECP withhold to use.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82817299","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}
{"title":"Deployment-to-dwell metrics and supply-based force sustainment","authors":"S. E. Evans, Gregory Steeger","doi":"10.1108/JDAL-05-2017-0009","DOIUrl":"https://doi.org/10.1108/JDAL-05-2017-0009","url":null,"abstract":"\u0000Purpose\u0000In the present fast-paced and globalized age of war, special operations forces have a comparative advantage over conventional forces because of their small, highly-skilled units. Largely because of these characteristics, special operations forces spend a disproportionate amount of time deployed. The amount of time spent deployed affects service member’s quality of life and their level of preparedness for the full spectrum of military operations. In this paper, the authors ask the following question: How many force packages are required to sustain a deployed force package, while maintaining predetermined combat-readiness and quality-of-life standards?\u0000\u0000\u0000Design/methodology/approach\u0000The authors begin by developing standardized deployment-to-dwell metrics to assess the effects of deployments on service members’ quality of life and combat readiness. Next, they model deployment cycles using continuous time Markov chains and derive closed-form equations that relate the amount of time spent deployed versus at home station, rotation length, transition time and the total force size.\u0000\u0000\u0000Findings\u0000The expressions yield the total force size required to sustain a deployed capability.\u0000\u0000\u0000Originality/value\u0000Finally, the authors apply the method to the US Air Force Special Operations Command. This research has important implications for the force-structure logistics of any military force.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79727999","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}
{"title":"Analyzing operating and support costs for Air Force aircraft","authors":"S. Hewitson, J. Ritschel, E. White, Gregory Brown","doi":"10.1108/JDAL-03-2018-0006","DOIUrl":"https://doi.org/10.1108/JDAL-03-2018-0006","url":null,"abstract":"\u0000Purpose\u0000Recent legislation resulted in an elevation of operating and support (O&S) costs’ relative importance for decision-making in Department of Defense programs. However, a lack of research in O&S hinders a cost analyst’s abilities to provide accurate sustainment estimates. Thus, the purpose of this paper is to investigate when Air Force aircraft O&S costs stabilize and to what degree. Next, a parametric O&S model is developed to predict median O&S costs for use as a new tool for cost analyst practitioners.\u0000\u0000\u0000Design/methodology/approach\u0000Utilizing the Air Force total ownership cost database, 44 programs consisting of 765 observations from 1996 to 2016 are analyzed. First, stability is examined in three areas: total O&S costs, the six O&S cost element structures and by aircraft type. Next, stepwise regression is used to predict median O&S costs per total active inventory (CPTAI) and identify influential variables.\u0000\u0000\u0000Findings\u0000Stability results vary by category but generally are found to occur approximately five years from initial operating capability. The regression model explains 89.01 per cent of the variance in the data set when predicting median O&S CPTAI. Aircraft type, location of lead logistics center and unit cost are the three largest contributing factors.\u0000\u0000\u0000Originality/value\u0000Results from this research provide insight to cost analysts on when to start using actual O&S costs as a baseline for estimates in lieu of analogous cost program data and also derives a new parametric O&S estimating tool designed as a cross-check to current estimating methodologies.\u0000","PeriodicalId":32838,"journal":{"name":"Journal of Defense Analytics and Logistics","volume":"113 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79460757","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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