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Title: A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence
Authors: Daniel A. DeLaurentis, Cesare Guariniello
Prajwal Balasubramani
Keywords: system of systems
machine learning
predictive analytics
artificial intelligence
multi-domain scenario
Issue Date: 2-Dec-2021
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Systems Engineering;PUR-SE-22-004
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.
Description: Systems Engineering
Appears in Collections:Sponsored Acquisition Research & Technical Reports

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