Improving Engineering Outcomes by Optimizing Systems

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

Leveraging the Pareto Principle to Optimize Quality Outcomes: 1. Identifying Core Issues: Conduct a thorough analysis of defect trends and recurring quality challenges. Prioritize the 20% of issues that account for 80% of quality failures, focusing efforts on resolving the most impactful problems. 2. Root Cause Analysis: Go beyond mere symptomatic observation and delve deeper into underlying causes using advanced tools such as the "Five Whys" and Fishbone Diagrams. Target the critical few root causes rather than dispersing resources on peripheral issues, ensuring a concentrated approach to problem resolution. 3. Process Optimization: Streamline operational workflows by pinpointing and addressing the most significant process inefficiencies. Apply Lean and Six Sigma methodologies to systematically eliminate waste and optimize processes, ensuring a more effective production cycle. 4. Supplier Performance Management: Identify the 20% of suppliers responsible for the majority of defects and operational disruptions. Enhance supplier oversight through rigorous audits, stricter compliance checks, and fostering closer collaboration to elevate overall product quality. 5. Targeted Training & Development: Tailor training programs to address the most prevalent quality challenges faced by frontline workers and engineers. Ensure that skill development efforts are focused on equipping teams to handle the most critical aspects of quality control, thus driving tangible improvements. 6. Robust Monitoring & Control Mechanisms: Utilize real-time data dashboards to closely monitor key performance indicators (KPIs) that have the highest impact on quality. Implement automated alert systems to detect and address critical deviations promptly, reducing response time and maintaining high standards of quality. 7. Commitment to Continuous Improvement: Cultivate a Kaizen mindset within the organization, where small, incremental improvements, focused on key areas, result in significant long-term gains. Leverage the Plan-Do-Check-Act (PDCA) cycle to facilitate ongoing, iterative process enhancements, driving continuous refinement of operations. 8. Integration of Customer Feedback: Systematically analyze customer feedback and complaints to identify recurring issues that significantly affect satisfaction. Prioritize improvements that directly address the most frequent customer concerns, ensuring that product enhancements align with consumer expectations. Maximizing Results through Focused Effort: By concentrating efforts on the critical 20% of factors that drive 80% of outcomes, organizations can significantly improve efficiency, reduce defect rates, and elevate customer satisfaction. This targeted approach allows for the optimal allocation of resources, fostering sustainable improvements across the quality process. Reflection and Engagement: Have you successfully applied the Pareto Principle in your quality management systems?

🔒 Local optimisation is the silent killer of GRC careers. You can write flawless controls language and policies. You never got a qualified report in your career. You can crush your Jira queue and manage issues like a boss. And still be irrelevant to the actual risk posture of your company. Why? Because you optimised for your domain, not the system. Most GRC roles reward local wins: ✅ Implement a new policy ✅ Collect 100 pieces of evidence ✅ Close the audit findings in time But none of that matters if: - Those policies don't change behaviour - The evidence isn't used to drive assurance - The audit scope misses critical risk exposure This is local optimisation. You solve for your team, your sprint, your metrics. But real leverage lives in global optimisation: ⚙️ Did this control reduce attack surface? ⚙️ Did we unblock a revenue-critical customer? ⚙️ Did this exception inform our roadmap? GRC Engineering isn't about ticking faster. It's about aligning your part with the whole. Being "high-performing" in an irrelevant system is just... wasted throughput. Zoom out. Map your work to systemic outcomes. Because the future belongs to GRC professionals who understand that GRC engineering is about systems, not just about static deliverables. Any examples where you've caught yourself optimising locally instead of globally? #GRCEngineering #SystemsThinking

As 2025 comes to a close, I am starting a short personal series. Each day, I will share one technical paper that, in my view, had a meaningful impact on how AI systems are being built this year. Day 1 starts with a paper that helped clarify a shift many teams were already experiencing in practice. The paper is “Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions.” While it is presented as a survey, its real contribution is giving structure to an area of work that had become fragmented. The central argument of the paper is straightforward. The main challenge in AI today is no longer improving individual models. It is optimizing systems composed of many interacting components such as language models, retrieval modules, tools, code, and agents. These systems are dynamic and context dependent, which makes traditional optimization approaches harder to apply. The authors offer a simple way to think about these AI systems. Instead of looking at each model or tool in isolation, they look at the whole system as a set of steps that can change based on the situation. They then show how the system can improve over time by learning from outcomes, either through clear success and failure signals or through written feedback generated by AI itself. This helps connect ideas like agents, retrieval-based systems, and tool-driven workflows into one coherent way of thinking about improvement. One part I found especially useful is the 2×2 taxonomy based on structural flexibility and learning signals. It makes it easier to understand why different agent frameworks behave the way they do and what trade-offs they make around cost, reliability, and control. This is directly relevant when designing systems meant to run in production. The paper also stays close to real engineering constraints. It discusses compute cost, token usage, lack of benchmarks for system-level behavior, safety risks that emerge from combining multiple components, and the absence of mature tooling. These are the same issues many teams are dealing with today. If earlier phases of AI focused on what models can do, this paper reflects a shift toward how complete systems should be designed, optimized, and evaluated. I will share the next paper in this series tomorrow. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal

The journey from 5 prototypes to 20,000 production units was nothing short of a masterclass in Electronics manufacturing! Here are some key takeaways that significantly boosted our efficiency and quality: Testing Process Optimization: We drastically improved our testing workflow by implementing automated test sequences and in-line diagnostics. This not only reduced testing time but also ensured higher accuracy and reliability. Customized Elecbits Protocol-Based Jigs: We designed and built custom jigs tailored to our EBC protocol to streamline code uploading. This eliminated manual errors and significantly sped up the programming process, ensuring smooth and consistent code deployment across all units. 3D-Printed Stencils for Conformal Coating: Recognizing the bottleneck in our conformal coating process, we developed 3D-printed stencils. These stencils allowed for precise and consistent coating application, dramatically reducing application time and minimizing waste. This resulted in a much faster and more reliable conformal coating process. Robust Counterfeit Component Detection: We implemented a rigorous process to identify and prevent the use of counterfeit components and ICs. This involved strong control and monitoring of our supply chain, ensuring the integrity and reliability of our final product. It's amazing to see how targeted engineering solutions can make such a significant impact on large-scale manufacturing. We at Elecbits are now better equipped than ever to go from an idea to mass manufacturing in the most simpler, faster, and scalable manner. #ElectronicsManufacturing #ProductionOptimization #TestingAutomation #EmbeddedSystems #Engineering

💡 𝟯 𝗙𝗶𝗿𝗺𝘄𝗮𝗿𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 After years of working with embedded systems, I've learned that optimization isn't about making everything faster—it's about making the right things better. Here are three techniques that deliver real impact: 𝟭. 𝗗𝗠𝗔 𝗢𝘃𝗲𝗿 𝗣𝗼𝗹𝗹𝗶𝗻𝗴 Stop burning CPU cycles waiting for data transfers. Direct Memory Access frees your processor to handle critical tasks while peripherals move data independently. → Real impact: CPU load reduction of 40-60% in data-intensive applications → When to use: SPI/I2C sensors, UART communication, ADC sampling 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗿𝘂𝗽𝘁 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Not all interrupts are created equal. Strategic priority assignment prevents critical tasks from being starved by less important ones. → Real impact: Eliminates timing issues and missed events → The key: Safety-critical > Time-sensitive > Background tasks 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗣𝗮𝗱𝗱𝗶𝗻𝗴 Understanding how your microcontroller accesses memory can dramatically improve performance. Proper alignment reduces memory access cycles. → Real impact: 20-30% speed improvement in struct-heavy code → Bonus: Reduces power consumption on memory-constrained devices The Bottom Line: Optimization is a tool, not a goal. Profile first, optimize second. Focus on bottlenecks that actually impact your system's performance, reliability, or power consumption. What's your go-to optimization technique in embedded systems? #EmbeddedSystems #Firmware #Optimization #Microcontrollers #Engineering #EmbeddedProgramming #IoT #TechTips

I’ve spent most of my career leading design in growth orgs where performance pressure is constant and quality is often the first thing on the chopping block. One of the most persistent beliefs in product organizations is that you can optimize for performance or for design quality — but not both. I’ve heard the rationale many times. The work is experimental. The pace is different. The culture rewards iteration. Quality, in this framing, becomes something you protect when you can — and compromise when you can’t. That framing is wrong. Time and quality are not opposing forces. Tools are faster, ideas are cheaper, and production is more fluid than ever. When measurable outcomes and experience quality diverge, it’s rarely because the goals are incompatible. It’s because optimization is happening without a clear product point of view — and without anyone accountable for how the experience holds together end to end. Experimentation doesn’t degrade products. Fragmented decision-making does. When each step of a journey is tuned in isolation, you can improve local metrics while making the overall experience harder to understand. A surface might “win” a test while introducing inconsistency, extra cognitive load, or downstream friction elsewhere. These effects compound, and over time the product becomes harder to use and harder to evolve. Well-designed systems don’t fight performance — they enable it. Not “systems” in the narrow sense of UI components, but systems in the experiential sense: how flows connect, how patterns behave consistently, how decisions in one area support understanding in another. This kind of coherence doesn’t limit bold moves; it makes them legible and sustainable. Clarity reduces hesitation. Coherence reduces cognitive load. Thoughtful defaults reduce error and support better decisions. These are experience qualities, and they are also performance drivers. The work of design leadership in these environments isn’t to defend craft as a special interest. It’s to make quality structural — to ensure that performance work happens inside a strong experiential frame, not outside of it. To reject the idea that “different” ways of working excuse shallow thinking or poor execution. And to hold the line on coherence across the user journey, especially when organizations are structured in ways that naturally fragment it. The tradeoff is common. It isn’t inevitable. #DesignLeadership #ProductDesign

Every organization has one primary constraint Most performance problems are framed as multi-factor issues. Research shows they usually are not. In complex systems, outcomes are limited by a single dominant constraint. Improving areas outside that constraint produces minimal impact. What research shows Studies in operations and organizational performance consistently find that system output is governed by the weakest link. Effort spent optimizing non-constraints creates local improvements without changing overall results. Research also shows that organizations routinely misidentify constraints, spreading resources across many initiatives instead of addressing the limiting factor. Study-based situations Situation 1: Revenue growth stalls Research found that teams increased marketing, sales activity, and features without impact because the real constraint was onboarding friction. Once onboarding was fixed, growth resumed without additional spend. Situation 2: Execution slows Studies on execution delays showed that adding staff did not improve speed when decision approval remained centralized. The constraint was decision latency, not capacity. Situation 3: Quality issues Research on operational quality found that defects were driven by one process step, not overall workload. Fixing that step reduced errors system-wide. How effective leaders manage constraints They identify the single limiting factor They focus resources on that constraint only They avoid optimizing non-constraints They reassess constraints as conditions change Improvement is sequential, not simultaneous.

🚀 Everything is “just optimization” - and that's why science matters Any discovery task can be phrased as an optimization in a sufficiently high-dimensional space. However, that framing is often useless in practice because the search becomes intractable once you include all steps and time costs. Think about a pipeline A → B → C → D, where D is the final outcome we truly care about (e.g., device performance). Each stage has its controls and latencies. If you “optimize D,” every evaluation means running the entire pipeline - slow, expensive, and exponentially hard as dimensions grow. “Fine, then optimize locally,” you say: tune A → B, B → C, and C → D separately because those spaces are smaller and faster. Here’s the problem: we usually know the objective at D, but we don’t know the right local objective for A or B. Simple example from batteries: A = X-ray (XRD), B = coin-cell test, C = long-term fade, D = lifetime/cost target. Optimizing XRD patterns alone is easy - and misleading - because translating diffraction features into long-term electrochemistry is non-trivial. Local gains at A can be neutral (or negative) for D. The way forward is a science of connecting loops: make early steps optimize shareable targets that predict late outcomes with quantified uncertainty. In practice that means bridging models (surrogates) that map A-signals to D-rewards, multi-fidelity BO that mixes cheap early readouts with sparse late measurements, reward-shaping that encodes physics and constraints, and causal checks so proxies don’t drift. You need contracts between steps. Done right, you don’t “optimize everything”—you align local loops so the fast things you can optimize today actually move the slow thing you care about tomorrow. This connects directly to early proxies, process monitoring, and accelerated testing—but it’s not the same thing. Early proxies (e.g., features from XRD, STEM, or a short galvanostatic pulse) are candidates for shareable targets; they become useful only after you calibrate them to D with uncertainty (otherwise: Goodhart’s law). Process monitoring (in-line sensors, drift/fault detection, control charts, digital twins) supplies dense, low-latency signals that keep local loops on track and detect regime shifts - perfect inputs for adaptive acquisition but still requiring a validated bridge to D. Accelerated tests (elevated T/C-rates, stressors) compress time to approximate D; they’re powerful mid-loop targets when their acceleration model is trustworthy and stable under domain shift. The unifying move is to treat all three as multi-fidelity readouts: quantify how each proxy/monitor/accelerated metric predicts the end goal, propagate that uncertainty through the optimizer, and continuously recalibrate with sparse ground truth from full-length D. Do this, and your fast signals stop being “nice-to-have plots” and start acting as reliable currencies that align local optimization with the outcome you actually care about.

For years, I kept returning to two books: Thinking in Systems by Donella Meadows and Principles of Systems by Jay Forrester. Not because they were trendy. But because every time I read them, something clicked. They didn’t just teach me how to model systems. 1. They taught me to stop blaming people for structural outcomes. 2. They gave me a lens that permanently changed how I see work, teams, and even personal behavior. Most engineers are trained to optimize outputs. Few are trained to examine the system that produces them. This week, I launched a new series: Systems at Work. It’s not a list of habits or tactical fixes. It’s a way to understand why problems repeat—no matter how much effort, intelligence, or goodwill is applied. In the first post, I cover: • Burnout, misalignment, and layoffs aren’t just “unfortunate outcomes”—they’re the natural behaviors of a system doing exactly what it was designed to do. • What looks like chaos—missed deadlines, buggy releases, last-minute scrambles—is often the result of flow design: how information, decisions, and requests move through your team. • Even small structural choices—like where tickets land or how QA is set up—can quietly shape morale, speed, and failure rates over time. • We stay stuck because we focus on events. But until you redesign the structure, you’re just reacting to symptoms. This isn’t about productivity hacks. It’s about leverage. If you’ve ever felt like you’re solving the same problem over and over, this post is for you. Read Part 1: https://lnkd.in/gJTxeVkS