System-of-Systems Integration

🚘 How OEM Handles ECUs & SW from Multiple Suppliers 1. Vehicle-Level Feature Planning 💠 OEM defines features & functions for the entire vehicle: 🔸 Safety (ABS, Airbags, ADAS) 🔸 Performance (Engine, EV Battery Management) 🔸 Comfort (Climate Control, Infotainment) 🔸 Connectivity (Telematics, OTA) 💠 These are broken into functional domains → then mapped to ECUs. 2. Sourcing & Supplier Selection 💠 OEM issues RFQ (Request for Quotation) to Tier-1 suppliers. 💠 Suppliers propose ECU hardware + base software + application SW. 💠 OEM selects by cost, expertise, partnerships, and AUTOSAR/ISO 26262 compliance. 3. Interface Standardization 💠 OEM ensures all suppliers adhere to standards: 🔸 AUTOSAR (Classic/Adaptive) 🔸 Communication protocols (CAN, LIN, FlexRay, Ethernet) 🔸 Cybersecurity guidelines (ISO 21434), Functional Safety (ISO 26262) 💠 OEM provides suppliers with: 🔸 Signal & Com Matrix (DBC/ARXML) 🔸 Vehicle architecture requirements 🔸 Diag specs (UDS, DIDs, DTCs) 4. Supplier ECU Development 💠 Each supplier develops their ECU independently: 🔸 HW (Microcontroller, Memory, Interfaces) 🔸 SW (BSW, MCAL, RTE, Application SWCs) 💠 Example: 🔸 ZF develops ABS/ESP, ADAS | Bosch: Engine ECU | Harman: Infotainment 5. OEM ensures all ECUs work seamlessly together. 💠 System Integration 🔸 OEM integrates all ECUs into central network using gateway ECUs. 🔸 Signals from different domains are checked against communication matrices. 💠 Compatibility Testing 🔸 SIL (Software-in-the-loop) with supplier binaries. 🔸 HIL (Hardware-in-the-loop) with real ECU prototypes. 🔸 Network Testing for CAN/Ethernet loads. 💠 Diagnostic Consistency 🔸 OEM ensures common diagnostic strategy (DTC reporting, OBD-II compliance). 💠 Cybersecurity 🔸 OEM integrates security keys, certificates, and secure boot strategies across all ECUs. 6. Change & Update Management 💠 Suppliers deliver ECU SW in frozen versions. 💠 OEM maintains software version control at vehicle level: 🔸 Which SW version is flashed in which ECU 🔸 Dependency between ECUs (compatibility matrix) 💠 Example: Engine ECU update may need Brake ECU update → OEM handles via Change Management. 7. Final Validation by OEM 💠 Vehicle-level validation done by OEM: 🔸 Integration Testing: All ECUs together in lab car. 🔸 System Testing: Real car with road testing. 🔸 Regulatory Testing: Emission, Safety, Homologation. 8. Production & Field Updates 💠 After validation, OEM: 🔸 Flashes ECUs in production line (via UDS/DoIP). 🔸 Manages OTA updates in field (via Telematics ECU). 💠 OEM ensures updates are safe across all suppliers → No compatibility breakage. #Automotive #AUTOSAR #OEM #SoftwareEngineering #AutomotiveSoftware #ConnectedCars #ECU #FunctionalSafety #CyberSecurity #ADAS #SystemIntegration #AutomotiveIndustry

Our team has a new article in #HealthSecurity arguing that epidemic preparedness requires a shift from siloed models to a systems-of-systems (SoS) approach to epidemic intelligence. Drawing on lessons from COVID-19 and a case study of highly pathogenic avian influenza (H5N1), we argue that integrating information about epidemiology, supply chains, behavior, policy, and economic systems—while respecting their autonomy—can improve situational awareness and decision support during outbreaks. The central claim is that pandemics are not single systems, and our intelligence infrastructure shouldn’t treat them as such. https://lnkd.in/eNv_UUMZ Happy to discuss implications for public health agencies, modeling teams, and funders. If you encounter a paywall, please send me a private email for access.

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.

Most integrations fail before the first connector is built. Not because of tools — because of missing clarity. Teams rush to connect systems, then spend months reconciling meaning, ownership, and expectations. Strong integration starts upstream. Before you connect anything, ask: • What decision is this meant to support? • What does this data actually mean in business terms? • Which system is the source of truth? • How fresh does it need to be to stay useful? • Who owns it when something breaks? Integration isn’t just a technical exercise. It’s an agreement about meaning, responsibility, and timing. The teams that get this right don’t just move data — they move decisions forward. Follow Reeves Smith for practical frameworks on data integration and strategy

Every integration you add is a new universe you’re agreeing to live inside. And each universe has its own laws, rituals, bugs, broken edges, paperwork, and weather patterns. Before you even reach the API layer, you’re already fighting gravity: – creating the right kind of developer account – verification loops and onboarding flows – hidden settings you must toggle for the API to work – docs scattered across multiple pages – rate limits you discover by accidentally hitting them – sandboxes that behave nothing like production And this is before writing a single line of integration code. Then comes the real work: mapping endpoints, generating test data, validating schemas, handling inconsistent responses, debugging errors that surface only under specific user states, reproducing failures you didn’t even know were possible. And after shipping, the universes keep shifting: API deprecations, permission rewrites, breaking changes with zero notice, silently introduced fields, dashboard settings that suddenly matter, partner switches flipped without warning. This is what makes multi-integration systems brutal. The volatility of living inside dozens of external realities at once. Integrations are ecosystems. And building across ecosystems is never simple. That’s the world we’re trying to tame at Composio - turning these chaotic universes into something consistent, testable, and manageable so teams don’t drown in the complexity behind the scenes.

Most integration problems in enterprises today are not technical gaps. They are architectural misalignments that only show up when systems are pushed to operate in real time. What I continue to see across environments is this: Organizations invest in AI, automation, and cloud platforms, but the integration layer underneath still assumes a slower, human-orchestrated world. Workflows are predefined. Connections are tightly coupled. Change is expensive. That model holds until it doesn’t. The moment AI systems start interacting dynamically, calling services, responding to events, and orchestrating workflows on their own, the friction becomes visible. Latency increases. Dependencies break. Teams start compensating with workarounds that add more complexity instead of reducing it. This is why integration is quietly becoming the most critical architectural layer in the AI enterprise. Not as a platform you manage. But as a capability fabric embedded into how systems operate. The shift I find most important is this: from connecting applications to exposing capabilities. When business functions are available as reusable services, AI systems and applications can compose workflows dynamically. That changes not just system design, but how organizations operate at scale. In practice, getting there is not trivial. It involves unwinding years of tightly coupled integrations, dealing with hybrid environments, aligning API strategies across teams, and managing production risk while systems are still running. Most of the effort is not in building new integrations, but in rethinking how existing ones are structured and governed. This is not a one-off transformation. It is a repeatable pattern we are seeing across enterprises trying to move from digital systems to intelligent operations. We explored this in more depth in our latest blog: If you are investing in AI and automation, it may be worth stepping back and asking: Is our integration layer designed for systems that execute workflows, or for systems that define them? #EnterpriseArchitecture #AITransformation #CloudNative #SystemIntegration #DigitalTransformation #APIArchitecture #SageIT #ThoughtLeadership

Standalone CTMS platforms are useful. Integrated CTMS platforms are transformational. After implementing hundreds of eClinical systems, I've learned that three integrations create exponentially more value than any single system alone. Here's the integration trinity that matters most: 1. CTMS + eTMF integration eliminates document management chaos. When your CTMS tracks site activation milestones, it should automatically pull document status from your Study Start Up and eTMF. You see immediately if regulatory documents are complete, which approvals are pending, and what's blocking site activation. Study managers don't toggle between systems or reconcile conflicting data. Site activation status updates flow automatically from Study Start Up to CTMS dashboards. 2. CTMS + EDC integration provides real-time enrollment intelligence. Manual enrollment tracking means study managers email sites weekly asking for updates. Integrated systems pull enrollment data directly from EDC. You see screening, randomization, and enrollment in real-time. Underperforming sites become visible within days, not weeks. You can reallocate resources, intensify recruitment efforts, or add backup sites before enrollment timelines crater. 3. CTMS + Safety systems integration enables proactive risk management. When your safety database captures adverse events, that data should flow into CTMS dashboards. You see AE reporting patterns by site and investigator. Sites with unusually high or low AE reporting rates warrant investigation. This integration has helped clients identify under-reporting problems and protocol safety signals earlier than traditional safety reviews would catch them. Why these three integrations specifically? They connect the three core operational workflows: study management, documentation, and patient data. Everything else in clinical operations touches one of these areas. Get these integrations right and you've connected 80% of your critical data flows. The implementation reality: Integration requires APIs, data mapping, and careful planning. Budget 30-40% more time than standalone implementations. But the ROI is massive: elimination of duplicate data entry, real-time visibility, and automated workflows that would be impossible with siloed systems. Which integrations have created the most value in your eClinical ecosystem?

MES-ERP integration creates tech debt. There's a better way. Most manufacturers know they need MES and ERP talking to each other. The value is obvious — real-time plant visibility, accurate inventory, production actuals vs. plan, root cause data across the enterprise. So why do so many integrations fail or stall? Point-to-point integrations. Every time you connect two systems directly, you create a dependency. Add a few more and you have a web of brittle connections — each one a liability when a system upgrades, a vendor changes an API, or you add a new plant. We've seen manufacturers with 5-15 point-to-point integrations grinding to a halt. The data syncs but isn't accurate. As a result no one knows which system is the source of truth. Lastly, IT is struggling to get out from under this massive tech debt and instead get to driving value. There's a better architectural approach — Event-Driven Architecture (EDA) with pub/sub and message queuing. Instead of connecting systems directly to each other, every system publishes and subscribes to a central data broker. MES publishes production events. ERP subscribes to what it needs. Add a new system — connect it once to the data hub and not to every other system. The result: • No point-to-point debt — systems are decoupled; one change doesn't break everything • Real-time data flow — events publish the moment they happen on the floor • Scale without chaos — add plants, systems, or consumers without rewiring integrations We're starting a MES-to-ERP integration project using exactly this approach. First phase: real-time visibility from a Level 2/3 plant system up to Level 4 corporate ERP — WIP value, utilization, production actuals. Future projects will include, among others, enterprise-wide root cause analysis across multiple plants that are vertically integrated. Why will it succeed where others have failed? Leadership defined the business outcomes first, built an internal transformation team (and in IT no less), and that team is using good strategy and principles we're bringing to the plate to chose an architecture designed to scale — not just solve today's problem. Are you stacking up point-to-point integrations and wondering why your data still isn't trustworthy? There's a better way to build this. Let's talk.

I’m Ben, and I’ve led 16 acquisition-driven system integrations in the last 3 years. Here’s the pattern: integrations don’t collapse in production. They collapse in governance — when no one knows who actually owns the decision. Too many integrations run like a bad sitcom: Finance says, “That’s IT’s call.” IT says, “That’s Ops’ problem.” Ops says, “We’ll need Finance to weigh in.” Meanwhile, the clock is ticking and the deal thesis is slipping. Here are 3 things that keep integrations from turning into a rerun: Decision rights that aren’t a mystery novel If no one knows who approves revenue recognition rules, expect every meeting to end with “let’s circle back.” A steering function that actually steers Call it a PMO, call it a committee — just make sure it exists. Without one, you’re basically herding cats with a whiteboard. Escalation paths that work Problems will surface. What matters is whether you solve them in a week… or watch them resurface in an audit nine months later. Takeaway: Governance isn’t paperwork. It’s the glue that keeps people aligned and the deal thesis intact. Integration planning starts in diligence, not on the close date. If diligence is about buying a business, integration is about proving it works. Contracts, people, process, governance — in that order.

Revisiting the System-of-Systems Approach: Resilient and Sustainable Infrastructure As I revisit material I’ve written over the years, including my earlier work on the system-of-systems approach, its relevance today is clearer than ever: Our critical infrastructures — power & energy, telecommunications, transportation, finance, and many more — are deeply interconnected, making them vital yet vulnerable. Failures in one system often cascade across others, disrupting economies and lives. Building resilience requires integrating technological innovation with ethical leadership and strategic investments. > Interconnected Challenges, Shared Solutions — Critical systems rely on each other. For instance: • Power plants use 40% of national water withdrawals for cooling, while water systems depend on electricity. • The 2003 Northeast Blackout disrupted rail, traffic, and fuel networks, illustrating cascading failures. • Hurricane Katrina’s levee breaches paralyzed emergency services and communication networks. A system-of-systems approach evaluates these interdependencies to prioritize risks and investments, ensuring reliability across sectors. > Real-World Examples of Resilience: • Minnesota’s Renewable Energy Leadership: Despite limited sunlight (4.5 hours/day), Minnesota leads in solar and microgrid projects. Renewable Energy Partners Inc (REP), where I serve as CTO, combines clean energy innovation with job training, creating opportunities in underserved communities while supporting local economies. • Stockholm’s Waste-to-Energy System: Converts 99% of municipal waste into energy, powering 60,000 homes. • Singapore’s Smart Nation Initiative: AI-driven monitoring protects energy grids, transit, and healthcare systems, reducing downtime. > Integrating Technology and Leadership: While technology drives solutions, human leadership ensures ethical, forward-looking decisions. REP exemplifies how clean energy programs can align sustainability with economic growth. Workforce initiatives like IBM’s SkillsBuild, which trained over 2 million people globally, close skill gaps in cybersecurity and energy. > Proactive Investments for Resilience — A system-of-systems approach provides tools for smarter investments: • Standardized Metrics: Compare and prioritize projects based on cost, impact, and resilience. • Sustainability Integration: Center renewable energy and waste reduction in infrastructure planning. • Global Collaboration: Frameworks like the EU’s Circular Economy Plan and U.S. cybersecurity efforts align responses to shared challenges. Critical infrastructures are the backbone of modern life. Strengthening them through a system-of-systems approach will prevent cascading failures, drive equitable growth, and ensure long-term sustainability. This demands integrating technology, ethical leadership, and inclusive workforce development to build a secure, resilient future. #Infrastructure #Cyber #Energy #Leadership #Minnesota #Resilience #USA