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Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments./
Author:	Parchamazad, Kamrooz Kommy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	281 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Contained By:	Dissertations Abstracts International81-03B.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22589802
ISBN:	9781085705264

Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments.
Parchamazad, Kamrooz Kommy.

Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 281 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2019.

This item must not be sold to any third party vendors.

Space Mission engineering is a complex system engineering, which includes parameters and technical requirements that meet mission objectives with consideration of programmatic constraints comprised of cost, schedule and risk. Since cost is a very crucial and determining programmatic constraint, this research will focus on 'Cost Estimation Engineering'. A space mission demands substantial budget and financial support. Assigned budget and financial support provide essential resources, which either will be utilized directly in a mission (e.g. spacecraft hardware) or will support a space mission (e.g. Mission Operation Systems- MOS). Factual cost estimation and optimization are crucial processes for the mission team so that the project can be passed to the next key decision point (KDP) during its development lifecycle. Cost engineering community have practiced triple methods of 'Analogy', 'Parametric' and 'Grassroots' within the space industry extensively during the past few years. These conventional cost estimation methods are suffering from technical shortages, which have led to low-confidence cost estimation results. Firstly, the 'Analogy' method has neither been supported sufficiently by multivariate data analysis techniques to encompass diverse system-level factors nor has it been populated with qualitative and programmatic variables (e.g. Technology Readiness Level - TRL & Concept Maturity Level- CML) for cost estimating processes. Secondly, there is not a bright mathematical application of Complexity Factor (CF) in the 'Analogy' method. Thirdly, 'Parametric' methodology has neglected the role of both qualitative ordinal and categorical parameters in estimation processes. The goal of this research is to perform cost estimation for robotic space missions and spacecraft remote sensing instruments during 'Pre-Phase A Concept Studies', when there are limited conceptual inputs available for proposed space system. This research provides a new architecture and methodology, including a set of abstract and concrete models that address space missions' cost estimation uncertainty in the Pre-Phase A mission life cycle-proposal development and covers the gaps and shortages of previous conventional techniques. Based on this research's proposed generic methods and prototype models, multivariate data analysis and numerical techniques will be widely applicable in the 'Analogy' method and several concepts including Technology Readiness Level (TRL), Concept Maturity Level (CML), Mission Reliability Class, Mission Category, and Spacecraft Architecture Complexity, Mass and Power of the spacecraft and its subsystems will have substantial role in the cost estimation processes. This research incorporates an algorithm, which encompasses a set of techniques including but not limited to vector analysis on principle component factors. The proposed algorithm is applicable at instrument, component, subsystem and system levels for future space missions' cost estimation. Prospective prototype models estimate the cost of a robotic space mission as well as remote sensing instruments onboard of an unmanned spacecraft. The architecture of this research is based on 'Analogous Parametric' and 'Parametric Multivariate' disciplines. Plugging previous space mission variables into the proposed mathematical cost estimation models, and comparing the results with real cost data will validate the proposed methods and will provide high confidence on models applicability for future cost estimations. Historically, cost estimation margin of error of a space mission in Concept Study and Concept Technology Development - Phase A in comparison with the real cost of the mission at the end of Phase E is ranging up to 46.5% (U.S. Government Accountability Office- GAO Report - GAO-19262SP). Proposed research indicates that the average of Mean Error Rate (MER) of models will be under 15.0%. This improvement in cost estimation provides a higher confidence to budgeting authorities and funding decision makers. Recommended methods and models can be replaced as the new generation cost estimation processes for future space missions. Approximately $20 billion NASA budget in 2018 indicates the important role of having reliable and high confidence cost estimation models for future space missions. This research through innovative abstract methods and concrete models provide solutions to this critical challenge.

ISBN: 9781085705264Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Analogous parametric

Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments.
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245 1 0 $a Selection and Development of Cost Estimation Models for Robotic Space Missions and Spacecraft Remote Sensing Instruments.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 281 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
500 $a Advisor: Nagarur, Nagendra N.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Space Mission engineering is a complex system engineering, which includes parameters and technical requirements that meet mission objectives with consideration of programmatic constraints comprised of cost, schedule and risk. Since cost is a very crucial and determining programmatic constraint, this research will focus on 'Cost Estimation Engineering'. A space mission demands substantial budget and financial support. Assigned budget and financial support provide essential resources, which either will be utilized directly in a mission (e.g. spacecraft hardware) or will support a space mission (e.g. Mission Operation Systems- MOS). Factual cost estimation and optimization are crucial processes for the mission team so that the project can be passed to the next key decision point (KDP) during its development lifecycle. Cost engineering community have practiced triple methods of 'Analogy', 'Parametric' and 'Grassroots' within the space industry extensively during the past few years. These conventional cost estimation methods are suffering from technical shortages, which have led to low-confidence cost estimation results. Firstly, the 'Analogy' method has neither been supported sufficiently by multivariate data analysis techniques to encompass diverse system-level factors nor has it been populated with qualitative and programmatic variables (e.g. Technology Readiness Level - TRL & Concept Maturity Level- CML) for cost estimating processes. Secondly, there is not a bright mathematical application of Complexity Factor (CF) in the 'Analogy' method. Thirdly, 'Parametric' methodology has neglected the role of both qualitative ordinal and categorical parameters in estimation processes. The goal of this research is to perform cost estimation for robotic space missions and spacecraft remote sensing instruments during 'Pre-Phase A Concept Studies', when there are limited conceptual inputs available for proposed space system. This research provides a new architecture and methodology, including a set of abstract and concrete models that address space missions' cost estimation uncertainty in the Pre-Phase A mission life cycle-proposal development and covers the gaps and shortages of previous conventional techniques. Based on this research's proposed generic methods and prototype models, multivariate data analysis and numerical techniques will be widely applicable in the 'Analogy' method and several concepts including Technology Readiness Level (TRL), Concept Maturity Level (CML), Mission Reliability Class, Mission Category, and Spacecraft Architecture Complexity, Mass and Power of the spacecraft and its subsystems will have substantial role in the cost estimation processes. This research incorporates an algorithm, which encompasses a set of techniques including but not limited to vector analysis on principle component factors. The proposed algorithm is applicable at instrument, component, subsystem and system levels for future space missions' cost estimation. Prospective prototype models estimate the cost of a robotic space mission as well as remote sensing instruments onboard of an unmanned spacecraft. The architecture of this research is based on 'Analogous Parametric' and 'Parametric Multivariate' disciplines. Plugging previous space mission variables into the proposed mathematical cost estimation models, and comparing the results with real cost data will validate the proposed methods and will provide high confidence on models applicability for future cost estimations. Historically, cost estimation margin of error of a space mission in Concept Study and Concept Technology Development - Phase A in comparison with the real cost of the mission at the end of Phase E is ranging up to 46.5% (U.S. Government Accountability Office- GAO Report - GAO-19262SP). Proposed research indicates that the average of Mean Error Rate (MER) of models will be under 15.0%. This improvement in cost estimation provides a higher confidence to budgeting authorities and funding decision makers. Recommended methods and models can be replaced as the new generation cost estimation processes for future space missions. Approximately $20 billion NASA budget in 2018 indicates the important role of having reliable and high confidence cost estimation models for future space missions. This research through innovative abstract methods and concrete models provide solutions to this critical challenge.
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653 $a Parametric multivariate
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653 $a Robotic space mission
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