SBIR-STTR Award

The Department of Defense (DOD) currently exposes research, development, testing, and evaluation (RDT&E) outputs to the Defense Technical Information Center (DTIC), which is estimated to serve about 4-5 million documents to 4000 users. DTICs goal is to consolidate, unify, manage, control, search, analyze, and disseminate scientific and technical data using a single tool. Currently, the system lacks certain auditing capabilities surrounding the integrity, authenticity, authoritative source, and access auditing and control capabilities for the data. Consequently, what is required moving forward is a system that can integrate data from DTIC and other repositories in a way that can apply such measures in a flexible and extensible way, so that new users and new uses of the data can be managed and scaled effectively. The Authenticity Ledger for Auditable Military Enclaved Data Access (ALAMEDA) proposal aims to design such a system. By leveraging our previous experience in designing and implementing our SIMBA Chain platform in collaboration with the University of Notre Dame, we will architect, design and prototype a demonstration system that can interact with existing repositories to ingest data, store data in a non repudiable way using a private Blockchain network and associated off-chain data store. The proposed system makes it possible to efficiently share documents and scientific data sets that will retain their integrity and authenticity and which can be accessed through an extensible access control layer providing flexible APIs for adding groups, users and permissions to the documents they share. Users or any authoritative source will be able to decide whom they share the data with, and how.

Benefit:
SIMBA Chain will deliver ALAMEDA as product offering to be used on multiple platforms. The competitive advantage of our product is that it will deliver improved traceability, authenticity, tracking, identification, searchability and encryption using Blockchain technology. Our competitive advantage is achieved by building upon and implementing what has been developed from previous SIMBA contracts from DARPA and Department of Energy for Blockchain technology. There is already a sizeable market for information security of $70B/year according to Gartner, which is expected to grow significantly with the increase of mobile devices. Our improved tracking, searchability, and encryption feature is expected to be of interest primarily to the defense and enterprise markets where a large amount of sensitive information is increasingly exchanged via mobile and can pose a significant security risk if compromised. Our primary customers would be defense and large enterprise firms: Raytheon, General Dynamics, Boeing, Lockheed Martin and General Electric of which team members of SIMBA Chain have partnered with in the past. We anticipate that we would capture about 0.015% of the $70B/year information security market. We expect to ramp up to sales revenue of approximately $10,000,000/year based on an anticipated selling price of $5,000 per month per organization plus transactions fees.

Keywords:
Data Storage, Data Storage, Control, traceability, Blockchain, Smart Contracts, Command, Authenticity, Automation

During the Authenticity Ledger for Auditable Military Enclaved Data Access (ALAMEDA) Phase 1 effort, SIMBA Chain worked with USMC Depot Albany stakeholders to define the use case for a blockchain-based prototype to monitor the inventory and movement of physical assets at the Depot. SIMBA Chain and USMC Albany stakeholders selected the M2A1 .50 Caliber use case to integrate Bill of Materials (BOM) data and data from public sources onto a blockchain to create a permanent, verifiable record of exchange. The ALAMEDA Phase 1 effort resulted in the architecture, design, and prototype of a blockchain-based system that could import a BOM spreadsheet, integrate its data with data from public sources and store the aggregated data in a non-repudiable way using a blockchain, while maintaining the blockchain entity relationships (depots, suppliers, assemblies, parts, etc) using a graph structure. This system is capable of correlating data from the BOM with data from public repositories to provide a robust dataset that provides the proof of concept single source of truth ledger to support monitoring the inventory and movement of physical assets. Phase 1 focused on a single M2A1 BOM use case and provided data analysis tools that provided initial statistical insights into the BOM data. It also allowed sophisticated querying of the blockchain data using SIMBA Chains GraphQL system, which enabled complex questions to be answered. The need the prototype aims to address is Demand Sensing so parts are available when they are required. Long lead time for required parts can cause delays. Earlier detection of shortages will mean earlier recoveries. In this Phase 2 project, we will address demand sensing for the Fleet Readiness Center Southeast (FRCSE) in Jacksonville, Florida. FRCSE is one of eight fleet readiness centers commissioned by the U.S. Navy and one of only three centers that perform Maintenance, Repair, and Overhaul (MRO) for the F/A-18 tailhook, which will provide the use case for the project.

Benefit:
The first four tasks are scheduled for the Base period and will deliver the blockchain-based prototype with component data integrated from FRCSE, public data sources, and DLA. The Option period will incorporate additional use cases to test the system and provide research and development for demand sensing and supply chain risk mitigation algorithms, including the incorporation of weather and traffic data that have direct impacts to the supply chain. In the following sections we will provide details of these tasks. To set the scene for the data perspective, component data that resides on the blockchain will require aggregation from multiple data sources, including: FRCSE BOM and ERP systems, Public Data Sources e.g. PUBLOG, Federal Procurement Data System - Next Generation (FPDS-NG), GIDEP, Dun & Bradstreet, DLA Enterprise Business Systems (EBS) / ERP Systems and public weather and traffic information. We envisage data importers for each of the data sources that will act as adapters that will be integrated into the SIMBA Concurrent Extract Load Transform (CELT) engine. Each adapter will be designed and developed to extract data and feed the data into the engine. (Automate Component Data Import from Public Data Sources) Adapters will be designed and developed to integrate data from public data sources, including PUBLOG, FPDS-NG, GIDEP, and Dun and Bradstreet. PUBLOG is a Logistics Information Service intended for use by public entities requiring National Stock Number (NSN) and other cataloging information including Federal Supply Classification (FSC) data and Commercial and Government Entity (CAGE) codes. FPDS-NG is the authoritative source for government wide contract award data and contains detailed information on contract actions over $3,000 (FY2004 and later data). GIDEP is a cooperative activity between government and industry participants seeking to reduce or eliminate expenditures of resources by sharing technical information during research, design, development, production and operational life-cycle phases of systems, facilities and equipment. These public data sources will enable us to harvest auxiliary information about suppliers, entities and parts, to establish a single source of truth and immutable historical record that comprehensively represents the supply chain for our FRCSE use cases. (Automate Component Data Import (BOM and ERP Data) Adapters for component data will be designed and developed to extract BOM/ERP data for transformation to load to the blockchain. Loading of the Excel EBOM will be accomplished using the Python Pandas Data Analysis Library 3 and ERP data can be handled using our existing B2MML importer or by directly interfacing from an adapter within our CETL engine. (Recursive Entity Relationship Data Mapping) Once the data is extracted, we plan to design an automated scheme for serializing the input data into multiple transactions by representing the components, assemblies and parts using a recursive algorithm that creates a hierarchical graph.

Keywords:
tracking, distributed ledger, supply chain, ERP, integrity, Logistics, Blockchain, Trust