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A Classification Model for Early Est...

Lape, Matthew H.,

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A Classification Model for Early Estimation and Validation of US Government R&D Program Costs /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Classification Model for Early Estimation and Validation of US Government R&D Program Costs // Matthew H Lape.
Author:	Lape, Matthew H.,
Description:	1 electronic resource (105 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639828
ISBN:	9798346805861

A Classification Model for Early Estimation and Validation of US Government R&D Program Costs /
Lape, Matthew H.,

A Classification Model for Early Estimation and Validation of US Government R&D Program Costs /Matthew H Lape. - 1 electronic resource (105 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

During the initial stages of planning, US government research and development programs tend to underestimate program costs, resulting in program costs 20-120% higher than estimated and inefficient use of funds. This results from subjective cognitive biases and the absence of scope changes in the estimation process. This research introduces a novel metric, Total Financial Reserve (TFR), to the field, providing sponsors, portfolio managers, and the Department of Defense (DoD) with a comprehensive perspective on the anticipated costs associated with a program. This advancement facilitates improved planning and resource allocation, thereby minimizing the incidence of funding reallocations and program cancellations.Utilizing publicly available data from the Government Accountability Office (GAO), a comprehensive analysis of 20 years of Major Defense Acquisition Programs (MDAP) was conducted, allowing for a comparison between 133 initial baselines and their corresponding final costs. Reference Class Forecasting (RCF) was employed for the first time in the context of cost estimation for MDAP. A model was constructed utilizing a fitted Johnson SU distribution, thereby facilitating the establishment of quartiles for ten reference classes.Through cross-validation, it was demonstrated that this model accurately categorized the 50th and 75th quantiles for all the test data within 10% of the expected distribution. Furthermore, a comparison with established reserve estimation heuristics revealed a substantial improvement in accuracy.

English

ISBN: 9798346805861Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Optimism bias

A Classification Model for Early Estimation and Validation of US Government R&D Program Costs /
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500 $a Advisors: Jones, Michael Committee members: Blackford, J. P.; Yassan, R.
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520 $a During the initial stages of planning, US government research and development programs tend to underestimate program costs, resulting in program costs 20-120% higher than estimated and inefficient use of funds. This results from subjective cognitive biases and the absence of scope changes in the estimation process. This research introduces a novel metric, Total Financial Reserve (TFR), to the field, providing sponsors, portfolio managers, and the Department of Defense (DoD) with a comprehensive perspective on the anticipated costs associated with a program. This advancement facilitates improved planning and resource allocation, thereby minimizing the incidence of funding reallocations and program cancellations.Utilizing publicly available data from the Government Accountability Office (GAO), a comprehensive analysis of 20 years of Major Defense Acquisition Programs (MDAP) was conducted, allowing for a comparison between 133 initial baselines and their corresponding final costs. Reference Class Forecasting (RCF) was employed for the first time in the context of cost estimation for MDAP. A model was constructed utilizing a fitted Johnson SU distribution, thereby facilitating the establishment of quartiles for ten reference classes.Through cross-validation, it was demonstrated that this model accurately categorized the 50th and 75th quantiles for all the test data within 10% of the expected distribution. Furthermore, a comparison with established reserve estimation heuristics revealed a substantial improvement in accuracy.
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653 $a Optimism bias
653 $a Planning fallacy
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653 $a Total Financial Reserve
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720 1 $a Jones, Michael $e degree supervisor.
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