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A Machine Learning Model for Predict...

Dehlavi, Kamran .

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A Machine Learning Model for Predicting the Procurement Lifetime of Electronic Units in Sustainment-Dominated Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Machine Learning Model for Predicting the Procurement Lifetime of Electronic Units in Sustainment-Dominated Systems./
Author:	Dehlavi, Kamran .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	171 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Contained By:	Dissertations Abstracts International81-06B.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27664295
ISBN:	9781392895894

A Machine Learning Model for Predicting the Procurement Lifetime of Electronic Units in Sustainment-Dominated Systems.
Dehlavi, Kamran .

A Machine Learning Model for Predicting the Procurement Lifetime of Electronic Units in Sustainment-Dominated Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 171 p.

Source: Dissertations Abstracts International, Volume: 81-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2020.

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

The focus of this praxis was on Diminished Manufacturing Sources and Material Shortages (DMSMS) type technology obsolescence of military systems. Military's inability to predict the procurement lifetime of their electronic units leads to reactive obsolescence management, and ultimately increased sustainment cost and readiness degradations. The program managers and operation leads at General Dynamics Information Technology (GDIT) would like to improve the process used to predict DMSMS obsolescence for their Navy Command, Control, communication, computer, and Intelligence (C4I) program subsystems they are under contract to sustain. This paper has responded to GDIT's process improvement initiative by recommending a machine-learning algorithm that can be used to develop a predictive model capable of making inferences about procurement lifetime of electronic units in sustainment-dominated (SD) systems. The researcher hypothesized that manufacturer, unit categorization, market value, reliability data, and unit age are the most significant predictor of electronic products procurement lifetime in SD systems. All the pros and cons were considered to improve the existing DMSMS obsolescence processes at GDIT. The researcher hypothesized again that the proposed machine learning prediction model in this praxis will enable provisioning solutions to obsolescence risk, which will yield in higher cost avoidance than current reactive engineering solutions. Historical data were collected from GDIT repository of a Navy program obsolescence and configuration management data. The researcher used multiple linear regression with backward elimination method for determining the most important factors in determining procurement lifetime of electronic units in SD systems. Also, utilized machine learning methods such as Random Forest (RF), Neural Networks (NN), and K-Nearest Neighbors (KNN) for determining the most applicable model for predicting procurement lifetime. Finally, presented a case to show the benefits of a DMSMS predictive tool in terms of cost avoidance. Based on the statistical data, the researcher illustrated that a machine learning based prediction model could forecast procurement lifetime of Navy's C4I electronic units with higher accuracy than manufacturer's quotes. Recommendations for future work included using larger sample size and repeating machine learning algorithms on different SD systems.

ISBN: 9781392895894Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Engineering management

A Machine Learning Model for Predicting the Procurement Lifetime of Electronic Units in Sustainment-Dominated Systems.
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520 $a The focus of this praxis was on Diminished Manufacturing Sources and Material Shortages (DMSMS) type technology obsolescence of military systems. Military's inability to predict the procurement lifetime of their electronic units leads to reactive obsolescence management, and ultimately increased sustainment cost and readiness degradations. The program managers and operation leads at General Dynamics Information Technology (GDIT) would like to improve the process used to predict DMSMS obsolescence for their Navy Command, Control, communication, computer, and Intelligence (C4I) program subsystems they are under contract to sustain. This paper has responded to GDIT's process improvement initiative by recommending a machine-learning algorithm that can be used to develop a predictive model capable of making inferences about procurement lifetime of electronic units in sustainment-dominated (SD) systems. The researcher hypothesized that manufacturer, unit categorization, market value, reliability data, and unit age are the most significant predictor of electronic products procurement lifetime in SD systems. All the pros and cons were considered to improve the existing DMSMS obsolescence processes at GDIT. The researcher hypothesized again that the proposed machine learning prediction model in this praxis will enable provisioning solutions to obsolescence risk, which will yield in higher cost avoidance than current reactive engineering solutions. Historical data were collected from GDIT repository of a Navy program obsolescence and configuration management data. The researcher used multiple linear regression with backward elimination method for determining the most important factors in determining procurement lifetime of electronic units in SD systems. Also, utilized machine learning methods such as Random Forest (RF), Neural Networks (NN), and K-Nearest Neighbors (KNN) for determining the most applicable model for predicting procurement lifetime. Finally, presented a case to show the benefits of a DMSMS predictive tool in terms of cost avoidance. Based on the statistical data, the researcher illustrated that a machine learning based prediction model could forecast procurement lifetime of Navy's C4I electronic units with higher accuracy than manufacturer's quotes. Recommendations for future work included using larger sample size and repeating machine learning algorithms on different SD systems.
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