Systems Engineering Approach

Adopt a systems approach to engineering design before your next capital project slips.     The pain you live with, disconnected people, processes, and data, shows up as late surprises, change chaos, and compliance gaps. In large capital assets, that fragmentation has a real price: average projects see cost overruns near $1.2 billion with delays from six months to two years, while construction productivity has inched around 1% over two decades compared to 3.6% in manufacturing. Add the push toward modular construction (about $175 billion by 2025) and a once-in-a-generation surge in capital spending through 2027, and the stakes could not be higher.     Systems-driven design is the discipline that closes the loop. It moves requirements from scattered docs into a single source of truth connected to every design domain, electrical, mechanical, software, so changes propagate in real time and compliance can be checked as decisions are made, not after the fact. It brings end-to-end traceability, the voice of the customer baked into each step, and a repeatable way to manage change as regulations evolve.     Try this simple play on your next project:  • Make requirements your single source of truth across plant, process, and equipment data.  • Connect requirements to every design activity and validate continuously as changes flow.  • Classify and reuse sub-systems, requirements, history, and change included, to cut rework.     When this approach is in place, hidden design issues surface early, cross-functional collaboration improves, and re-invention gives way to reuse. That’s how assets are more likely to work the first time with minimal rework later. 

From Requirements to Customer Product, or the Benefits of Integrating Systems Engineering and Product Engineering Many product development challenges start with a disconnect: Requirements are defined in one tool, systems are designed somewhere else, and the engineering product structure lives in yet another system. The result is lost traceability, unclear responsibilities, and product structures that do not reflect the intended architecture. A more effective approach is to bring together Systems Engineering and Product Engineering in a continuous, integrated environment: Requirements → System Breakdown Structure (SBS) → 150% EBOM → Configured 100% products. The journey starts with requirements. These capture what the product must do: Performance targets, regulatory constraints, operational needs, and customer expectations. Requirements describe capabilities, not components. From these requirements, systems engineers develop the System Breakdown Structure (SBS). The SBS decomposes the product into systems and subsystems based on functional responsibility; propulsion, control, energy, structure, electronics, and so on. Each system becomes responsible for fulfilling a specific set of requirements and defining the interfaces to other systems. Here the product architecture begins to take shape. Product engineering then translates this architecture into the physical product structure. Each system defined in the SBS is implemented as a module or assembly in the Engineering Bill of Materials (EBOM). To support product families and variants, this is typically represented as a 150% EBOM, containing all modules and variant options across the platform. From the 150% EBOM configuration logic then selects the appropriate modules to create a specific 100% product EBOM for a customer order, region or production variant. When this process is executed in an integrated environment, powerful benefits emerge. Requirements remain traceable to the systems that fulfill them. Systems remain linked to the modules and assemblies that implement them. Changes in requirements or architecture can be traced directly to the affected product structures and configurations, and determining technical and financial impacts becomes quick and easy. This integration also supports better modularization based on changing requirements. Systems engineering defines clear functional boundaries and interfaces, which translate into well-defined product modules in the EBOM. In short, integrating systems engineering with product engineering creates a continuous digital thread: Requirements → Systems → Modules → Product Family → Customer Specific Product Configuration. And that integration is what ultimately enables companies to build complex, configurable products faster, with better control over architecture, variants, and lifecycle changes and ultimately quickly configure a product that meets specific customer requirements.

How often do we really step back and see the full picture of a problem? Take sustainability initiatives, for example. They’re not just a collection of isolated projects or actions; they’re deeply woven into an organization’s core operations. And it’s this systems thinking approach, rooted in Lean Six Sigma, that gives us a way to look at the big picture. Rather than focusing only on immediate tasks, we consider every step in the lifecycle, connecting dots between people, resources, and processes. Studies show that sustainability initiatives grounded in systems thinking yield more resilient outcomes. I n fact, a 2023 McKinsey study found that organizations with an integrated approach to sustainability had a 20% greater rate of project success than those using isolated, short-term fixes. If you’re considering a systems approach, think about these key elements: -Define your end goal clearly: Where do you want this initiative to lead? -Engage all stakeholders: Every voice, from leadership to frontline workers, plays a part. -Break down big problems into manageable tasks: This makes complex challenges easier to tackle. -Continuously evaluate the structure: Keep refining how each part connects within the system. -Justify each major step: Having a solid rationale builds trust and clarity. The global challenges we face—like climate change, resource scarcity, and social equity—require this type of thinking. By applying a systems view to sustainability, we’re not only working to solve the immediate issue but also creating a resilient foundation for the future. Where do you see systems thinking fitting into your projects?

I thought systems engineers were just glorified project managers. ↳ I assumed they were unnecessary overhead. ↳ I believed they only slowed down the development process. ↳ I was convinced our team could handle everything without them. Boy, was I wrong. Let me take you back to the project that changed my mind... We were developing a cutting-edge automotive safety system. Deadlines were looming, budgets were tight, and interdepartmental conflicts were rife. It was a perfect storm of chaos. Our VP suggested bringing in a systems engineer. I rolled my eyes. "Great," I thought. "Another 'expert' to tell us how to do our jobs." But here's what actually happened: 1. The systems engineer mapped out the entire project ecosystem. 2. Cross-functional communication improved dramatically. 3. Potential risks were identified and mitigated before they became issues. 4. Integration challenges were solved proactively. The result? We delivered the project 6 weeks early and 12% under budget. But don't just take my word for it. Let's look at some hard data: - A study by the International Council on Systems Engineering found that projects with effective systems engineering are 50% more likely to meet their objectives. - The National Defense Industrial Association reported that high-performing projects using systems engineering had a 57% success rate, compared to just 15% for those with low systems engineering capability. - NASA credits systems engineering for reducing their project failure rate from 1 in 4 to less than 1 in 100. The numbers don't lie. Systems engineers are the unsung heroes of complex projects. They're the glue that holds interdisciplinary teams together, the visionaries who see the big picture, and the problem-solvers who tackle challenges before they become showstoppers. My skepticism has transformed into advocacy. Now, I wouldn't dream of starting a complex project without a systems engineer on board. Have you had a similar experience? Did a systems engineer save your project from disaster? Share your stories below. Let's start a conversation about the hidden superpowers of systems engineering in the automotive industry. #SystemsEngineering #AutomotiveInnovation #ProjectSuccess #EngineeringLeadership

Looking for free Systems Engineering training? Here are some great options. A lot of people trying to grow into Systems Engineering or Systems Security Engineering keep asking the same thing: “Where can I find good, free, practical systems engineering training?” Here are several high-quality, no-cost courses worth bookmarking: 1. Open University — Systems Engineering: Challenging Complexity Free to audit. A solid introduction to systems thinking and complexity. https://lnkd.in/exhPDyYy 2. MIT OpenCourseWare — Fundamentals of Systems Engineering (16.842) Full lectures, readings, and examples straight from MIT. Great engineering depth. https://lnkd.in/eXXpm_Jb 3. Alison — Introduction to Engineering System Design and Processes Beginner-friendly and useful for onboarding non-SE roles. https://lnkd.in/e6jw4fU7 4. Coursera (UNSW) — Introduction to Systems Engineering Free to audit, well-structured, and strong on lifecycle fundamentals. https://lnkd.in/eZyKMeCP 5. NASA — • Tutorial landing page: https://lnkd.in/eGS8QgNF • Introduction Module (via Scribd): https://lnkd.in/eVhN-FmA • NASA Systems Engineering Handbook (PDF): https://lnkd.in/eQcUet-J These won’t replace the depth of ISO/IEC 15288, INCOSE, or NIST SP 800-160, but they’re excellent entry points; especially for cybersecurity practitioners trying to understand how real engineering thinking enables resilience. #systemsengineering #systemssecurityengineering #cyberresilience #engineeringleadership #missionassurance #engineeringeducation

Most engineers fail system design interviews in the first 10 minutes. Not because they can't design systems. Not because they haven't scaled systems. But because they start building before thinking. I've seen it happen with very senior engineers too. I once watched an experienced engineer walk into a system design interview and immediately say: “We’ll run this on Kubernetes with autoscaling across regions, put an SQS queue in front, use Redis for caching, and shard the database…” I paused and asked: “How many users does this system serve?” Silence. They were designing for internet-scale when the question was about a small internal tool for ~100 users. Here’s the secret about system design interviews nobody tells you: → It's not about how fast you can say “Kubernetes, Kafka, Redis.” → It's about whether you can think like an engineer. When designing real systems, we don’t dive straight into solutions. We clarify the problem first. And system design interviews should be no different. So in your next system design interview, try this simple framework: [1] Clarify the problem (don't skip this please) → What problem are we solving? → Who are the users and how many? → Read/write patterns and constraints? → Latency and availability requirements? → What’s in scope vs out of scope? [2] Define requirements → Functional: what the system must do → Non-functional: SLA, scalability, latency, availability, durability, security, cost constraints, compliance [3] Propose a high-level design → Keep it simple → Walk through the core data flow and use cases of the system. → Discuss trade-offs throughout → Get alignment from your interview before diving deep (so important). [4] Dive deep on the important parts of system → Confirm the focus area with the interviewer. → This may include: Data models & storage, API design, consistency model, scaling, security → Explain trade-offs clearly [5] Improve & wrap up → Call out bottlenecks and failure modes. → Discuss how you'd implement observability across your system. → Deployments, CI/CD → Summarise the design and decisions. → Tie your solution back to the requirements, this is super crucial. System design isn’t about sounding smart. It’s about solving the right problem in the right way. Slow down. Ask first. Design second. Save this for your next interview, and refer back to it before you walk into the room. P.S. Sharing Neo Kim’s System Design red flags list as well, it's incredibly helpful for interview prep. #softwareengineering #interviews

System of systems thinking is rad. A complex system is vulnerable and limited. If it's destroyed, its constituent parts are non-functional. A system of systems is resilient. Constituent parts operate independently. Think of a giant supermarket vs a local farmers' market. The supermarket is a complex system—efficient but fragile. One power outage or supply chain disruption, and the whole thing grinds to a halt. The farmers' market is a system of systems—each vendor operates independently but works with others. If some vendors can't make it, the market adapts and carries on. Bad weather hits one farm's crops? Others fill the gap. I think a system-of-systems approach needs to be applied to AI on a broad scale. Sae Schatz has the right idea. On the Warfighter Podcast with Tom Constable and Colin H., she advocates for “a modular open systems approach, where we make each Lego block within the larger system its own standalone device.” For complex, dynamic environments, there's no single stand-alone product that will fit the bill. If your use case is basic, sure, go ahead and use ChatGPT or some other product. For environments that need a specific capabilities set assembled to spec, with the ability to rapidly deploy new capabilities or reconfigure, a system of systems approach is what you need. Plug and play interoperable modules into a larger ensemble, which itself can be a module in even a larger ensemble. It's mindblowing what capabilities this can enable. Take drug discovery. One system analyzes molecular structures and binding properties. Another processes genetic pathway data. A third examines clinical trial outcomes and patient data. A fourth scans scientific literature and research papers. Each runs independently, mastering its domain. But combine them into a higher-level system, and patterns emerge. That molecular structure matches this genetic pathway, connecting to those clinical results, backed by emerging research trends. Stack this ensemble into an even larger system that correlates with global disease patterns and antibiotic resistance, and you've got an AI that spots promising drug candidates while anticipating future needs. No single AI model could handle this complexity. At Talbot West, we've developed a term for system-of-systems AI ensembles. We call it Cognitive Hive AI (CHAI). It's the future, and we're proud to be part of it. I wrote an article expanding on the thoughts of this post. Link in comments. #artificialintelligence #systemofsystems #chai #cognitivehiveai #talbotwest