Engineering Knowledge Management Systems

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

🛠️ How to Actually Apply Process Safety Knowledge Management (PSKM) "According to the 2024 CCPS Guidelines for Process Safety Knowledge Management" ♦️ We all agree that many organizations struggle to move beyond document storage into true Process Safety Knowledge Management (PSKM). 🔥 So how do we actually apply PSKM in real-world facilities? 1️⃣ Identify Critical Knowledge Assets Start with: - Relief design calculations (API 520/521) - Basis of design documents - Hazard and operability studies (PHA, HAZOP, LOPA) - Operating and emergency procedures - MOC and investigation records - SDS, vendor manuals, and utility info 2️⃣ Assign Knowledge Ownership Each asset should have: - A defined owner (typically a SME or engineer) - Review/refresh cycle (e.g., post-MOC, every 2 years) - Clear link to MOC, audit, and performance systems 🎇 To avoid confusion, create a RACI chart for all major process safety knowledge types. 📊 RACI = Responsible: Does the work (e.g., revise the procedure) Accountable: Final sign-off; owns the outcome Consulted: Provides input (e.g., HSE, Operations) Informed: Needs to know, but doesn’t directly act 3️⃣ Embed Knowledge into Core Safety Activities Tie knowledge into: ✅ PHAs: review old scenarios and design decisions ✅ MOC: revise docs during change ✅ Training: align with latest knowledge ✅ Investigations: use design basis as root-cause context 4️⃣ Design for Access and Application ✅ Smart knowledge delivery uses: - Tagging by equipment, hazard, or unit - Layered views (summary + technical detail + links to MOC or PHAs) - Portable formats for field use - Clear hazard indicators embedded in documents 5️⃣ Audit It Like You Audit Safeguards Just like you test relief valves or review SILs, your knowledge system must be: - Measured (How often is it accessed? Is it current?) - Reviewed (Are lessons learned feeding back into SOPs?) - Actioned (Are users trained on the current version?) ♻️ Use the Right Tools to Support PSKM: No single software can do it all. But a smart ecosystem can. 1️⃣ Document Control Systems: 🔧 Versioning, approvals, change tracking 📌 Tools: Intelex, M-Files, OpenText, MasterControl 2️⃣ MOC Platforms: 🔁 Link changes to doc revisions 📌 Tools: Enablon, SAP MOC, SpheraCloud 3️⃣ Risk & PHA Management Systems: 📊 Store and search past scenarios, safeguards 📌 Tools: PHAWorks, EtQ, PSO 4️⃣ Learning Systems: 🎓 Training materials pull from latest knowledge 🎓 Tracks competency linked to current docs 📌 Tools: ISOtrain, Cornerstone, Docebo 5️⃣ Dashboards & Analytics 📈 Top 10 most-accessed safety docs 📈 % of expired vs. current knowledge 📌 Tools: Power BI, Tableau, Qlik 👉 To read "Guidelines for Process Safety Knowledge Management 2024": https://lnkd.in/eZebeVRY #ProcessSafety #KnowledgeManagement #PSKM #CCPS #RBPS #ChemicalEngineering #PHA #MOC #ProcessSafetyCulture #SafetyLeadership #OperationalExcellence #IncidentInvestigation #DigitalTransformation

𝗜𝗳 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘄𝗮𝗹𝗸𝘀 𝗼𝘂𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝗻𝘁, 𝘀𝗼 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲. For years, the most valuable lessons in manufacturing lived in people’s heads, sat in spreadsheets, or got buried in reports no one read. KM systems existed — but insights stayed siloed. Maintenance logs didn’t inform production. Quality data never reached design. And when experts left, their know-how left too. KBE helped. Rules were codified, designs automated, tasks accelerated. But it stayed narrow. Useful for engineering but useless for the wider enterprise. Now Smart Factories are rewriting the rules. The “smart” in Smart Manufacturing is no longer just IoT, AI, or digital twins — it’s Knowledge Management evolving into the core ingredient that makes factories adaptive, resilient, and truly smart. This transformation is a true 𝗛𝘂𝗺𝗮𝗻–𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻–𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 (𝗛𝗢𝗧)  shift: • Organizations evolve into networks that learn. • Employees grow into knowledge partners. • Technology connects, structures, and scales those learnings. 𝗔𝘁 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗹𝗲𝘃𝗲𝗹: From hierarchies to networks. Lessons flow horizontally across production, logistics, and supply chains — and vertically into ERP and PLM, the true nerve centers. PLM connects design and engineering with shop-floor feedback, ensuring learnings inform product evolution, not just production routines. The enterprise itself becomes a learning system as every captured lesson strengthens resilience, speed, and customer value. 𝗔𝘁 𝘁𝗵𝗲 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗹𝗲𝘃𝗲𝗹: From operators to knowledge workers. Every workaround, every fix, every idea feeds the system. From machine cooperation to human–machine collaboration. Cobots and AI extend capability, people bring judgment. From one-time training to continuous learning. KM guides workers in real time, embedding best practices into daily decisions. 𝗔𝘁 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗹𝗲𝘃𝗲𝗹: 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 – Every event, fix, and insight is logged with context. 𝗘𝗻𝗿𝗶𝗰𝗵 – AI structures it, links it, and connects it with past cases. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 – Learnings move across functions and levels. 𝗔𝗽𝗽𝗹𝘆 – Best practices feed back into workflows, tools, and training. 𝗘𝘃𝗼𝗹𝘃𝗲 – Each cycle makes the system smarter. Documentation, once a burden, is now the nervous system. It captures memory, creates best practices, and feeds them forward — so tomorrow’s decisions are always better than yesterday’s. In Smart Manufacturing, knowledge isn’t just an asset but the very definition of smart. Winners will be those who capture lessons, turn them into best practices, and scale them across the ecosystem. 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝗻 𝗼𝗻𝗲 𝗹𝗶𝗻𝗲: Tribal Know-How → Scattered Learnings → Codified Rules → Knowledge-Enhanced Practices → Ecosystem-Wide Best Practices Ref:https://lnkd.in/dTiqaCQu

For enterprises Knowledge as a Service (KaaS) is getting crucial for AI readiness. The knowledge layer needs to sit on top of existing enterprise systems, making organizational knowledge accessible, maintainable, and AI-ready while preserving existing operational capabilities and governance. Let me try to bring clarity to KaaS Knowledge Discovery and Mapping Map all operational databases and their relationships Identify data warehouses and their current analytical models Document unstructured data sources (documents, emails, process documentation, pictures, videos etc.) Catalog existing business intelligence reports and dashboards Knowledge Flow Analysis Map how data flows between different systems Identify key business processes and their data dependencies Document decision points that require knowledge access Knowledge Structure Development Categorize data based on business context and usage Identify critical knowledge areas and their relationships Create taxonomy for organizing enterprise knowledge Establish metadata framework for knowledge assets Knowledge Model Creation Design knowledge graphs connecting different data sources Create semantic relationships between business concepts Develop ontology for business domain knowledge Map data lineage across systems Technical Implementation Deploy knowledge management platform Implement connectors to operational databases and data warehouses Set up real-time data synchronization mechanisms Create APIs for knowledge access and retrieval Processing Pipeline Develop ETL processes for knowledge extraction Implement AI-powered categorization systems Create automated tagging and classification workflows Set up validation and quality control mechanisms Knowledge Transformation Enrich operational data with business context Create relationships between different knowledge components Implement version control and lifecycle management Integration Layer Connect knowledge platform with existing BI tools Enable knowledge discovery through search interfaces Implement role-based access control Create audit trails for knowledge usage AI Readiness Knowledge Componentization Break down complex information into AI-digestible components Create training datasets for AI models Implement RAG (Retrieval Augmented Generation) capabilities Develop knowledge validation workflows AI Integration Set up AI models for knowledge processing Implement machine learning for continuous improvement Create feedback loops for knowledge refinement Enable automated knowledge updates Operational Excellence Monitoring Setup Implement usage tracking and analytics Create performance dashboards Set up alerting for knowledge quality issues Monitor system performance and utilization Governance Implementation Establish knowledge management policies Define roles and responsibilities Create maintenance procedures Implement compliance controls #GenerativeAI #EnterpriseAI #LLMIntegration #AIImplementation #Innovation

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

The Hidden Cost of Engineering Knowledge Fragmentation Engineering teams are drowning in data. PLM systems hold millions of documents—CAD files, technical specs, test reports—but finding the right information at the right time remains a huge challenge. 🚧 The Problem? Engineers waste countless hours searching across siloed systems. Critical IP gets lost in legacy documents. Teams reinvent the wheel, unknowingly redoing work that already exists. Institutional knowledge disappears as employees leave. These inefficiencies slow down innovation, increase costs, and delay product launches. ⚙️ How can we fix this? AI-driven search and knowledge transformation can extract, organize, and connect engineering knowledge—making it discoverable within seconds. At BrahmaSumm, we’re tackling this challenge by: ✅ Using semantic search and clustering to surface relevant knowledge. ✅ Automating tribal knowledge capture to preserve expertise. ✅ Integrating with PLM systems for seamless access to engineering data. ✅ Running on efficient infrastructure—designed for scalability and security. The goal? Turn engineering documentation into an asset, not a bottleneck. Would love to hear how your teams are handling knowledge discovery today. What strategies have worked (or not worked) for you?