A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence
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Authors
DeLaurentis, Daniel A.
Guariniello, Cesare
Balasubramani, Prajwal
Subjects
Advisors
Date of Issue
2021-12
Date
12/02/21
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
System-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of this work is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how the sharing of connected data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective based on extensive literature review and generating appropriate predictive and prescriptive methodologies that can be used for SoS analysis: Additionally, we propose to utilize machine learning techniques to predict the SoS capability achievable by sharing pertinent datasets and to prescribe the information links between systems to enable this sharing. Two case studies demonstrate the use of the framework and prospects for meeting the objective. Highlights of our study are summarized next.
Type
Report
Description
Acquisition Research Program Sponsored Report Series
Sponsored Acquisition Research & Technical Reports
Sponsored Acquisition Research & Technical Reports
Series/Report No
Department
Organization
Identifiers
NPS Report Number
PUR-SE-22-004
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.