Phase II Amount
$1,498,229
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