Requirements for parts, materials, and processes are the building blocks of design, engineering, and manufacturing. Unfortunately today, most of these requirements are “locked up” in legacy PDF documents -- internal standards, customer specs, industry standards (like ASTM, SAE, MIL), regulatory information, test methods, etc. Engineers and technical professionals go through absurd manual labor to find, extract, and share requirements in a way that fits into modern engineering workflow (yes, copy/paste is still the norm!).
Because of these primitive static documents, companies are spending hundreds of millions of dollars annually on avoidable costs, errors, and inefficiencies.
This presentation describes how a new digital model platform called SWISS (Semantic Web for Interoperable Specs and Standards) uses Semantic AI to turn static engineering documents into model-based "digital twin documents" organized into a knowledge graph and accessible from any enterprise application through a robust API.
Once digital twin documents are created, precise requirements (including subjects, attributes, and values) from an entire web of documents can be extracted in seconds with a single click. Digital twin documents can be interoperable with every related document or piece of data, even external standards like SAE, ASTM, and more. Intelligent data objects such as requirements, test methods, and material and process attributes can be identified and shared with authorized users in enterprise applications such as PLM/PDM, MES, and more.
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