Systems Engineering Integration Techniques

Breaking Quantum News: Real algorithms, real data, real quantum machines HSBC, in partnership with IBM, has delivered the world’s first quantum-enabled algorithmic trading trial. Using live, production-scale data from the European corporate bond market, HSBC integrated IBM’s quantum processors with classical systems—achieving up to a 34% improvement in predicting the probability of winning trades compared with classical methods alone. Why it matters: - Bond trading is one of the most complex, data-heavy challenges in finance. - Classical models struggle to capture hidden pricing signals in noisy markets. - By augmenting workflows with IBM Quantum Heron, HSBC uncovered insights classical systems could not. As Philip Intallura Ph.D, HSBC’s Global Head of Quantum Technologies, put it: “This is a tangible example of how today’s quantum computers could solve a real-world business problem at scale and offer a competitive edge.” And as IBM’s Jay Gambetta emphasized: breakthroughs come from combining deep financial expertise with cutting-edge quantum algorithms—demonstrating what becomes possible as quantum advances. This is not hype. It’s not distant. Quantum is entering the market—today. #QuantumComputing #Finance #Innovation #PQC #QuantumReady

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

Researchers at Northwestern University (USA) have made a significant breakthrough in quantum communication by successfully teleporting a quantum state of light—a qubit carried by a photon—through approximately 30 kilometers of optical fiber while simultaneously transmitting high-speed classical data traffic. Key details include: - The fiber length used was around 30.2 km. - It carried a classical signal of approximately 400 Gbps in the C-band alongside the quantum channel. - The quantum channel operated in the O-band, utilizing special filtering and narrow-temporal/spectral techniques to shield delicate photons from noise, such as spontaneous Raman scattering from the classical channel. This experiment confirms that quantum teleportation of a quantum state can coexist with classical internet traffic in the same fiber infrastructure. It's important to clarify that "teleportation" in quantum communication does not involve moving the physical photon or "beaming" objects as depicted in science fiction. Instead, it refers to the transfer of the quantum state of a qubit from one location to another using an entanglement-based protocol, coupled with classical communication. The original qubit is destroyed during this process and recreated at the destination. While quantum teleportation enables inherently secure quantum communication channels—since measurement disturbs quantum states—practical deployment still faces challenges, including node security, classical channel security, side-channels, and error rates. This marks a significant step toward quantum-secure networks, though it is not yet a complete "unhackable" solution. This experiment suggests that we may not require entirely separate fiber infrastructure dedicated solely to quantum communications; existing telecom fiber could be effectively utilized. It enhances the feasibility of developing quantum networks and, eventually, a "quantum internet" that integrates with classical infrastructure. From a security and cyber perspective, it supports the architecture of quantum-secure communications, including quantum key distribution and entanglement-based signaling. Overall, this represents a major technological milestone in photonics, quantum information science, and telecom integration.

As more teams bring in models like OpenAI, Claude, or Gemini and connect them to CRMs, analytics tools, or internal apps, things start to break. Not on the surface. But deep down, in the wiring: • Interoperability becomes a nightmare.    • Every model needs to talk to every system.    • Suddenly, you're managing M × N connections, and it's a mess.    That’s where something like the Model Context Protocol (MCP) makes a difference. With MCP: • Each model integrates once. • Each tool integrates once. • And the system just works.    You go from M × N complexity to M + N clarity. It’s not just a cleaner setup—it’s what makes AI systems scale without collapsing under their own weight. I’ve broken this down visually below for anyone trying to make sense of the value of MCP, whether you're writing the code or managing the roadmap. Do you think protocol-first design is where AI infrastructure is headed?

Quantum Teleportation Achieved Over Internet for the First Time Researchers in the U.S. have successfully teleported a quantum state of light through over 30 kilometers (18 miles) of fiber optic cable while coexisting with regular internet traffic. This achievement marks a monumental step toward integrating quantum communication systems into existing telecommunications infrastructure, paving the way for future quantum internet networks. Key Highlights: • Teleportation Explained: Quantum teleportation involves transferring the quantum state of one particle to another distant particle, effectively replicating its state without physically moving the particle itself. • Overcoming Challenges: The experiment succeeded despite the interference from traditional internet data flowing through the same cables, showcasing an unprecedented level of stability and accuracy in a real-world environment. • Infrastructure Integration: The ability to teleport quantum states using existing fiber optic networks suggests that quantum and classical communication systems can share infrastructure, greatly reducing costs and accelerating deployment timelines. Why This Matters: • Quantum Internet Potential: Quantum networks promise ultra-secure encryption, seamless quantum computer connections, and advanced distributed sensing systems. • Real-World Feasibility: Demonstrating quantum teleportation in active fiber optic networks proves the technology can be scaled and deployed in real-world conditions. • Data Security: Quantum encryption methods, leveraging principles such as quantum key distribution (QKD), could make communications virtually unhackable. Researcher Insights: “This is incredibly exciting because nobody thought it was possible,” said Prem Kumar, a computing engineer at Northwestern University who led the study. “Our work shows a path towards next-generation quantum and classical networks sharing a unified fiber optic infrastructure. Basically, it opens the door to pushing quantum communications to the next level.” Implications for the Future: • Secure Communications: Enhanced encryption and ultra-secure networks could revolutionize cybersecurity. • Quantum Cloud Computing: Seamless connectivity between quantum computers across long distances could unlock unprecedented computational capabilities. • Scalable Deployment: Utilizing existing infrastructure minimizes costs and accelerates integration into global communication networks. While we’re still far from the Star Trek-style teleportation of physical objects, this achievement represents a profound advancement in quantum network engineering, bringing the vision of a global quantum internet significantly closer to reality.

Venture capital and media attention fixate on foundation model capabilities, but the competitive battleground in AI has shifted to the unsexy, boring parts of AI - things like orchestration layers, retrieval systems and connective infrastructure. Organisations do not deploy “a model”. They deploy workflows integrating models with proprietary data, existing software systems, human review processes, compliance controls and operational monitoring. The sophistication of this second-order infrastructure increasingly determines who wins in AI deployment. The Model Context Protocol exemplifies this shift. By providing a standardised interface for AI systems to connect with external tools and data sources, MCP solves the “M times N” problem that plagued earlier integration efforts. Connecting M models to N tools previously required M times N custom integrations, each demanding bespoke engineering, testing and maintenance. MCP reduces this to M plus N by providing a common protocol. The seemingly technical detail of interoperability standards enables the ecosystem effects that allow agentic AI to scale across organisations and use cases. Retrieval-Augmented Generation represents another critical infrastructure layer. Generic models know only what appears in their training data. Enterprise value requires grounding AI responses in current, proprietary organisational information. RAG systems retrieve relevant context from document stores, databases and knowledge graphs, then inject that context into the model’s reasoning process. The engineering required to make this work reliably encompasses vector databases, embedding models, semantic search, ranking systems, access controls and cache management. These components are invisible to end users but determine whether an AI system produces valuable insights or expensive nonsense. The orchestration market has grown explosively as organisations recognise that managing multiple specialised models and tools requires sophisticated coordination. Rather than forcing every query through a single expensive frontier model, orchestration systems route requests intelligently. Simple queries go to fast, cheap models. Complex reasoning tasks go to sophisticated models. Specialised tasks go to fine-tuned domain models. This arbitrage across model capabilities and costs determines the unit economics of AI deployment. These systems sit between enterprise users and external AI providers, enforcing usage policies, managing costs, logging interactions for audit and blocking potentially harmful outputs. Deploying AI without a gateway has become as negligent as deploying web servers without firewalls. The governance, compliance and risk management capabilities embedded in these infrastructure layers determine whether enterprises can scale AI deployment while maintaining controle. The companies building superior connective tissue will matter more than those training marginally better models.

🚀 𝐍𝐞𝐰 𝐏𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧! 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐑𝐀 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐈𝐨𝐓 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬, 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬, 𝐚𝐧𝐝 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Proud to share our newest peer-reviewed article in Information (MDPI), co-authored with Miguel Ángel Ortega Velázquez, Iris Cuevas Martinez, and Dr. Antonio J. Jara (myself as ISACA CISM/CISA/AAIA). 𝘛𝘩𝘪𝘴 𝘸𝘰𝘳𝘬 𝘢𝘳𝘳𝘪𝘷𝘦𝘴 𝘢𝘵 𝘢 𝘤𝘳𝘶𝘤𝘪𝘢𝘭 𝘮𝘰𝘮𝘦𝘯𝘵, 𝘢𝘴 𝘵𝘩𝘦 𝘌𝘜 𝘊𝘺𝘣𝘦𝘳 𝘙𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 𝘈𝘤𝘵 (𝘊𝘙𝘈) 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭 𝘳𝘦𝘨𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘐𝘰𝘛 𝘮𝘢𝘯𝘶𝘧𝘢𝘤𝘵𝘶𝘳𝘦𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘪𝘯𝘨 𝘺𝘦𝘢𝘳𝘴. 🔥 𝐓𝐨𝐩 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 1️⃣ 𝐀 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐦𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐥𝐞𝐠𝐚𝐥 𝐂𝐑𝐀 𝐭𝐞𝐱𝐭 𝐢𝐧𝐭𝐨 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: We introduce a two-phase framework: • Phase 1: Systematically transform CRA Articles 13–14 and Annexes into atomic, testable engineering requirements. • Phase 2: Apply Analytic Hierarchy Process (AHP) quantitative scoring to produce a defensible readiness metric. 2️⃣ 𝐀 𝐟𝐮𝐥𝐥 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞-𝐛𝐚𝐬𝐞𝐝 𝐂𝐑𝐀 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐟𝐨𝐫 𝐈𝐨𝐓 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬: From secure design to post-market obligations, the paper provides an actionable DevSecOps-aligned checklist. 3️⃣ 𝐀 𝐝𝐞𝐟𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐫𝐢𝐬𝐤-𝐛𝐚𝐬𝐞𝐝 𝐰𝐞𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 (𝐀𝐇𝐏): We derive consistent domain weights, ensuring mathematically validated prioritization of CRA domains. 4️⃣ 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 through the TRUEDATA project funded by INCIBE - Instituto Nacional de Ciberseguridad: We applied the full model to a large industrial OT cybersecurity project (water infrastructure) with Neoradix Solutions AirTrace Bersey UCAM Universidad Católica San Antonio de Murcia at the pilots with the support of the Confederación Hidrográfica del Segura, O.A., Mancomunidad De Los Canales De Taibilla, and FRANCISCO ARAGÓN. 5️⃣ 𝐂𝐥𝐞𝐚𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞. The paper provides best practices for SBOM automation, PSIRT & CVD setup, Secure-by-design, OTA, monitoring, attestation, documentation and conformity assessment Our aim from Libelium with this paper is to give the industry a practical, structured, and evidence-based way to operationalize compliance and strengthen cybersecurity by design. 𝐓𝐑𝐔𝐄𝐃𝐀𝐓𝐀 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐞𝐬 𝐡𝐨𝐰 𝐭𝐡𝐞 𝐦𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐚𝐩𝐩𝐥𝐢𝐞𝐬 𝐭𝐨 𝐡𝐢𝐠𝐡-𝐬𝐭𝐚𝐤𝐞𝐬 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. 𝐓𝐡𝐞 𝐂𝐑𝐀 𝐢𝐬 𝐧𝐨𝐭 “𝐣𝐮𝐬𝐭 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐫𝐞𝐠��𝐥𝐚𝐭𝐢𝐨𝐧”, 𝐢𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐛𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐈𝐨𝐓 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐄𝐮𝐫𝐨𝐩𝐞. 👉 Download here: https://lnkd.in/dQu54qE2 European Union Agency for Cybersecurity (ENISA) Felix A. Barrio (PhD, CISM) Global Cybersecurity Forum SITE سايت Betania Allo Axon Partners Group ISACA ISACA VALENCIA

𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵? 𝗛𝗲𝗿𝗲’𝘀 𝗠𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Industrial operations run our daily lives—think metro trains, water systems, power grids, even the checkout at your supermarket. All of this is powered by Operational Technology (OT), which directly impacts physical processes and public safety. But OT systems are under attack more than ever. Many still run on 20-year-old software, are tough to update, and can’t just be “patched” like regular IT systems. Real-world consequences can be huge: from power outages to critical failures in hospitals and transport. So, where do you even begin with OT security? Here’s my take (as discussed with Prabh in his latest podcast): 1. Understand What You Have: Start with an asset inventory. Visibility is everything. You can’t protect what you don’t know exists. 2. Identify Risks: Figure out what could go wrong. Every asset, old or new, has its own risks—especially those running legacy software. 3. Involve Your Operations Team: OT staff are focused on keeping the plant running. Bring them into the conversation from Day 1. Awareness and buy-in are key. 4. Tailor Your Approach: There’s no copy-paste. Every factory, plant, or substation is unique. Build processes that fit your environment, not just what the textbook says. 5. Prioritize the Basics: ✏️ Incident response plans: Who does what when things go wrong? ✏️ Control remote access: Limit those USB sticks, dongles, and remote sessions. ✏️ Access control: Don’t give everyone full admin rights. ✏️ Network segmentation: Create “islands” to limit the spread if something goes wrong. ✏️ Training: Make cybersecurity real for your OT staff. One weak link can break everything. 6. Use the Right Frameworks: IEC 62443 is a great start, covering people, process, and technology. Pair it with industry guidance like NIST 800-82. 7. Continuous Improvement: Cybersecurity isn’t a one-off project. Monitor, learn, and adapt. OT threats evolve—your defenses should too. Why does all this matter? Because OT is critical. Downtime isn’t just about lost money—it can risk lives. And with more cyber threats targeting OT, our collective vigilance matters now more than ever. I’ve built the OT Security Huddle community for this reason: to share, discuss, and solve real OT security problems together. Whether you’re just getting started or deep into your journey, you’re not alone. Watch my full conversation with Prabh Nair for all the details—link below! https://lnkd.in/gjYCnt7j #OTSecurity #Cybersecurity #IEC62443 #CriticalInfrastructure #IndustrialSecurity

Do you have mature CM processes that actually work across the entire lifecycle? Because CM process maturity isn’t about having procedures on SharePoint. It’s about whether the process is 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝗱, 𝗮𝗽𝗽𝗹𝗶𝗲𝗱, 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹𝗹𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱, and whether it enables the changing needs of an organization. 👉 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: If your CM process is not standardized, adaptable, compliant, and pragmatic, maturity will remain a paper exercise. Start with the foundation. A mature CM organization has 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱, 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱, 𝗮𝗻𝗱 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗖𝗠 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 under formal change control and accessible to all stakeholders. That process is explicitly based on recognized standards such as CM2 and SAE-EIA-649. But maturity doesn’t stop at documentation. The CM process must be defined in a way that: 🔹 Accommodates product and project lifecycle differences 🔹 Preserves company-wide CM principles 🔹 Applies consistently to products, facilities, and even administrative information from single sources of truth 🔹 Uses KPIs to monitor performance and guide continual improvement 📊 Where many organizations struggle is with 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. True process maturity means CM is applied: 🔹 Across all lifecycle phases — from concept to decommission 🔹 To products and facilities 🔹 To CM itself, including changes to CM strategy, processes, and documentation 🔹 With measurable success, supported by defined KPIs Then comes the heart of CM discipline, 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Mature processes are clearly defined: 🔹 Ownership of configuration information 🔹 Closed-loop change traceability for all configuration information 🔹 Embedded customer and supplier involvement when required 🔹 Transparent status accounting (as-designed, as-built, as-maintained, etc.) And yes, the classic CM pillars still matter: 🔹 Configuration Planning with clear maturity expectations 🔹 Configuration Identification with naming, numbering, baselining, traceability, and Model-Based Engineering support 🔹 Change Management with impact analysis, governance, and differentiated change tracks 🔹 Status Accounting that reflects reality, not intent 🔹 Verification and configuration audits before release to customers A CM process is only mature if it 𝘀𝘂𝗿𝘃𝗶𝘃𝗲𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝗰𝗵𝗮𝗻𝗴𝗲, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲. Not because it’s rigid, but because it’s 𝘄𝗲𝗹𝗹-𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲, 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲, 𝗳𝗶𝘁 𝗳𝗼𝗿 𝗽𝘂𝗿𝗽𝗼𝘀𝗲, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹𝗹𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱. 👉 Where does your CM process struggle most today: definition, application, or measurement? I’d be interested to hear your view. Note: the following post around maturity assessments will focus on Knowledge and Support, and Tools. #ConfigurationManagement #CM2 #PLM #CM #MaturityAssessment #ProductLifecycleManagement #MaturityModel