Manufacturing Execution System

The purpose of this sources sought notice is to conduct market research and identify potential sources of commercial equipment/services that satisfy the Government’s anticipated needs. BACKGROUND The National Institute of Standards and Technology (NIST) Engineering Laboratory (EL) supports the advancement of Additive Manufacturing (AM). As AM transitions from prototyping to industrial production, the seamless integration of AM machines with enterprise software specifically Manufacturing Execution Systems (MES) is the primary enabler for increasing throughput and addressing scalability issues. However, the standards are missing for interoperable AM and MES integration. The research team at the System Integration Division has a research objective to develop a neutral information model that can be standardized for AM and MES integration. As part of the efforts to develop standard information model to integrate AM and MES systems, the Information Modeling and Testing group will conduct a six-month benchmarking study to compare (a) a standards-based AM-MES integration approach using a neutral information model and validation, versus (b) an AI-assisted point-to-point integration approach using Large Language Model (LLM)-driven documentation ingestion and code generation, and (c) test the performance and efforts using existing NIST-owned AM machines, including EOS 290 and Renishaw RenAM500. The goal is to quantify effort, reliability, and semantic accuracy, and to produce a practical roadmap for AM-MES interoperability. Requirements: NIST is seeking information from sources that may be capable of providing a solution that will achieve the objectives described below: Product Requirements The MES software shall have been tested integrable with EOS 290 and RenishawAM500 The MES software shall have an open API which is sufficient for AI-based integration code generation The MES shall come with emulators for EOS 290 and Renishaw AM 500 for integration tests Technical Support Requirements Task 1: Neutral Data Model & Standards Baseline to build the "Standards-Based" pathway. The contractor shall:  Align their MES API and internal information with the NIST developed AM-MES information model and develop a "Translation Layer" to ingest neutral model-based JSON into their software. Align their EOS 290 and Renishaw AM 500 APIs with the NIST developed AM-MES information model and develop a "Translation Layer" to translate data from EOS 290 and Renishaw AM 500 into neutral model-based JSONL. Task 2: Standards-Based Integration Development to execute the traditional integration. The contractor shall:  Develop Middleware. Build a "Neutral Broker" that collects data from AM systems, converts it to the NIST neutral information model, and pushes it to the MES. Validate Layer. Implement JSON Schema validation based on the NIST neutral information model. Deploy API Endpoint. Deploy and document the endpoints for receiving standards-compliant data packages. Task 3: AI-Driven "Point-to-Point" Integration using AI for code generation to bypass the neutral model for comparison. The contractor shall:  Evaluate and recommend an AI tool with the NISTteam Integrate Documentation and design prompts. Feed AM System APIs and MES API documentation into the approved AI tool. Use AI to generate integration code. Use AI to generate Python/Node.js scripts that directly integrate EOS 290 and Renishaw AM500 to the Contractor’s MES software without using a neutral information model. Review Code. Document how many "hallucinations" or logical errors occur in AI-generated scripts. Task 4: Integration Testing & Benchmarking for comparative execution and data collection. The contractor shall:  Test Standards Pathway. Run additive manufacturing process using the neutral information model to integrate MES software with an EOS 290 emulator and a Renishaw AM500 emulator. Test AI Pathway. Run the same scenarios using the AI-generated point-to-point scripts. Stress test. Intentionally change an AM machine's API field name to see which path (Standards or AI) breaks or recovers faster. Assessing Data Quality. Evaluate the "usability" of the data received in both pathways. HOW TO RESPOND TO THIS NOTICE In responding to this notice, please DO NOT PROVIDE PROPRIETARY INFORMATION . Please include only the following information, readable in either Microsoft Word, Microsoft Excel, or .pdf format, in the response: Submit the response by email to the Primary Point of Contact and, if specified, to the Secondary Point of Contact listed in this notice as soon as possible, and preferably before the closing date and time of this notice.  Please note that to be considered for award under any official solicitation, the entity must be registered and “active” in SAM at the time of solicitation response. Provide the complete name of your compan