Reasoning about Global Supply Networks
Abstract
Abstract # Approved for Public ReleaseReasoning About Global Supply Networks In resp. to N00014-23-S-B001, David Phillips, 311 Paulo Shakarian, Arizona State University pshak02@asu.eduThe microelectronics shortages, 2022 Russian-Ukrainian war, and COVID-19 pandemic have all illustrated the impact of macro-scale global supply networks (GSNs) in various ways. This proposal seeks to create a framework to understand the dynamics of GSNs, understand various indicators that may emerge, and how such indicators relate to externalevents. We note that this proposal seeks understanding on macro-level supply networks. This contrasts with and is complementary to work on micro-scale supply chains. Work studying micro-scale supply chains these focus on issues of procurement, meeting demand, business optimization, and organizational resiliency. This proposal studies GSNs at the macro scale to understand a different set of concerns. These include political instability, global flows of goods, macroeconomic changes, and analyzing second and third order effects of a crisis: areas of importance to Naval intelligence and strategic planning. Approaches to these issues have historically beenhampered by a lack of data, however, recent advances in data collection have now enabled low-cost, commercially available, large-scale data stores of supply-demand networks derived from shipping and opensource data. Additionally, advances in time-series analysis of complex networks and temporal logic can now form a solid foundation for detection and prediction tasks in time-series graph data.In this proposal, we look to leverage these two advancements together to establish a novel groundwork for formal reasoning about GSNs. Key practical benefits of this work include (1.) identification of changes in the GSN that can impact components of a military system, affecting readiness, (2.) identification of indicators in the GSN that could harm the economy of a country and potentially lead to instability, and (3.) identification of patterns in the GSN that are indicative of adversarial action such as sanctions violations. These use cases are of specific relevance to the Navy, and each can directly impact military decision making at multiple levels. Further, the geospatial nature of a GSN can also inform the deployment of military resources to support contingencies that may arise out of GSN-driven forecasts. The work of this proposal will provide a general framework to address these use cases leading to GSN derived information functioning in a manner similar to a source of intelligence. Moreover, to effectively function as a form of intelligence, a general framework, as opposed to multiple disparate models, is desirable to ensure analyst adoption. In this work, we propose such a framework for #Reasoning About Global Supply Networks.# This platform will have the capability to address a superset of all the aforementioned examples. Additionally, for such a framework to be successful, it should lead to tools used by intelligence and logistics professionals. Hence, such a framework should be explainable (i.e., there needs to be an understanding of how the system arrived at a given result), be able to identify unique patterns over time, and inherently consider the network structure of theglobal supply network in a scalable manner. Therefore, to meet these criteria, we will leverage work from temporal logic, network science, causality, and rule learning to accomplish the following research objectives: (1.) create a GSN reasoning framework and dataset, (2.) create methods to extract GSN indicators, (3.) identifying temporal patterns of GSN indicators, and (4.) identify temporalrelationships between the GSN and externalities.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 15, 2023
- Source ID
- N000142312580
Entities
People
- Paulo Shakarian
Organizations
- Arizona State University
- Office of Naval Research
- United States Navy