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DEVELOPMEMNT OF A MODELING AND SIMULATION TRAINING NEEDS MODEL FOR SELECTED DEFENSE ACQUISITION WORKFORCE COMMUNITIES
Title
DEVELOPMEMNT OF A MODELING AND SIMULATION TRAINING NEEDS MODEL FOR SELECTED DEFENSE ACQUISITION WORKFORCE COMMUNITIES.
Creator
PEH, LIK CHUN, PROCTOR, MICHAEL, University of Central Florida
Abstract / Description
The DoD Modeling and Simulation Steering Committee (M&S SC) identified Modeling and Simulation (M&S) as an educational objective for the Acquisition, Technology and Logistics (AT&L) workforce. Notably, past usages of M&S in system acquisitions for both DoD and commercial industry have demonstrated improvements in efficiency and effectiveness over traditional acquisition techniques. However, to achieve expected and consistent performance by this workforce in these new techniques, the M&S...
Show moreThe DoD Modeling and Simulation Steering Committee (M&S SC) identified Modeling and Simulation (M&S) as an educational objective for the Acquisition, Technology and Logistics (AT&L) workforce. Notably, past usages of M&S in system acquisitions for both DoD and commercial industry have demonstrated improvements in efficiency and effectiveness over traditional acquisition techniques. However, to achieve expected and consistent performance by this workforce in these new techniques, the M&S essential skill requirements for this workforce may be extensive. This research aims to validate the content and level of competency in selected M&S tools and technology necessary for consistent workforce performance. The notion here is to achieve greater efficiency and effectiveness in the acquisition process through thresholds of competency that must be resident in or available to the acquisition workforce. This research proposes a matrix of training objectives and levels of competency for portions of the AT&L workforce that was validated through survey by individuals who are leading experts in both M&S and acquisition. This effort combines rigorously defined learning objectives and parameters by academia with practical learning insights from the military and industry ground perspectives. The resultant Joint Learning Model aims to identify the workforce educational foundations necessary to achieve more widespread efficiency and effectiveness in current and future DoD acquisitions.
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Date Issued
2008
Identifier
CFE0002110, ucf:47536
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002110
A Methodology on Weapon Combat Effectiveness Analytics using Big Data and Live, Virtual, or/and Constructive Simulations
Title
A Methodology on Weapon Combat Effectiveness Analytics using Big Data and Live, Virtual, or/and Constructive Simulations.
Creator
Jung, Won Il, Lee, Gene, Rabelo, Luis, Elshennawy, Ahmad, Ahmad, Ali, University of Central Florida
Abstract / Description
The Weapon Combat Effectiveness (WCE) analytics is very expensive, time-consuming, and dangerous in the real world because we have to create data from the real operations with a lot of people and weapons in the actual environment. The Modeling and Simulation (M(&)S) of many techniques is used for overcoming these limitations. Although the era of big data has emerged and achieved a great deal of success in a variety of fields, most WCE research using the Defense Modeling and Simulation (DM(&)S...
Show moreThe Weapon Combat Effectiveness (WCE) analytics is very expensive, time-consuming, and dangerous in the real world because we have to create data from the real operations with a lot of people and weapons in the actual environment. The Modeling and Simulation (M(&)S) of many techniques is used for overcoming these limitations. Although the era of big data has emerged and achieved a great deal of success in a variety of fields, most WCE research using the Defense Modeling and Simulation (DM(&)S) techniques were studied without the help of big data technologies and techniques. The existing research has not considered various factors affecting WCE. This is because current research has been restricted by only using constructive simulation, a single weapon system, and limited scenarios. Therefore, the WCE analytics using existing methodologies have also incorporated the same limitations, and therefore, cannot help but get biased results.To solve the above problem, this dissertation is to initially review and compose the basic knowledge for the new WCE analytics methodology using big data and DM(&)S to further serve as the stepping-stone of the future research for the interested researchers. Also, this dissertation presents the new methodology on WCE analytics using big data generated by Live, Virtual, or/and Constructive (LVC) simulations. This methodology can increase the fidelity of WCE analytics results by considering various factors. It can give opportunities for application of weapon acquisition, operations analytics and plan, and objective level development on each training factor for the weapon operators according to the selection of Measures of Effectiveness (MOEs) and Measures of Performance (MOPs), or impact factors, based on the analytics goal.
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Date Issued
2018
Identifier
CFE0007025, ucf:52870
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007025