Project Announcement: Logistics Zero Trust Data Fabric (ZTDF)

The US Department of Defense is partnering with industry and academia on an initiative to demonstrate enhanced security and semantic interoperability in the supply chain network, with a focus on enabling domain-driven use cases.

This initiative will leverage a system known as Zero Trust Data Fabric (ZTDF) along with a current Product Lifecycle Management (PLM) system that will furnish the logistics domain for the ZTDF. This approach will allow organizations to collect and analyze data from various sources, anticipate maintenance needs, minimize downtime, and improve readiness and mission success and will provide lessons learned that can be applied across other services.

The overall objective of this initiative is to enable a federated ZTDF ecosystem and demonstrate its utility with a large organization’s PLM as the Authoritative Source of Truth (ASOT), focusing on a Bill of Materials (BOM) use case.  Key objectives have been identified to achieve the overall goal:

• Develop and prototype a zero-trust architecture that verifies and authenticates all data transport requests across the supply chain network utilizing encryption techniques to protect data in transit, preventing unauthorized access and data breaches, and developing a secure data exchange protocol that enables seamless and secure data transfer across all parties in the supply chain, with a focus on data standardization and normalization.
• Apply data governance, policy, and access controls at the logistics domain level and intra-domains (manufacturing & sustainment) to ensure interoperability.
• Implement an initial logistics data mesh through identifying key data sources and systems used by DoD, OEMs, and the American industrial base to build a comprehensive understanding of the data landscape.
• Take measures to operationalize data for machine learning and artificial intelligence using the data product being developed and implement data quality checks and validation processes to ensure the accuracy and completeness of the data. Apply advanced analytics techniques such as predictive modeling and machine learning to identify patterns and insights that can inform supply chain decisions.

If you feel your organization has the technical capabilities and would like to be considered for this project, please complete the form below and upload your organization’s technical capabilities statement.

Interested Submissions Due by 8/15/2023.

We encourage participation of Disadvantaged Business Enterprises (DBEs), including Minority Business Enterprises (MBEs) and Women’s Business Enterprises (WBEs).