Improving Engineering Outcomes by Optimizing Systems

As an advisor to tech scaleups, and a former CTO and SVP of Engineering,  I've often encountered a familiar CEO complaint: "Our engineering team is too slow!" However, focusing solely on increasing individual productivity is rarely the solution. Sometimes the answer is changing the organizational structure. 🔍 The Issue with Flat Structures: Time to market was a major problem in a scale-up I advised, even though they had a flat structure where 40+ engineers reported directly to the VP of engineering and all of them shared equal accountability to the delivery of the software. 🚧 The Consequences: Major overcommitment.  People raised their hands to take on work even if the group was super extended. There was nobody that fully understood the team’s capacity vs the actual workload they took on. This approach led to a lack of predictability, chronic delays, unhappy customers, and ultimately, a tarnished reputation. 🛠️ The Solution: Transitioning to a hierarchical structure with focused teams and accountable experienced leaders was the game-changer. This shift brought in clarity, accountability, and much-needed structure. 📈 The Results: Predictable schedules, improved customer satisfaction, and a thriving engineering culture. ✅ Takeaways for Your Organization: Examine your organization with critical eyes: Is your ownership and accountability structure clear? Are your teams sized and focused appropriately? Do your leaders have the authority to deliver effectively? For more on the case study and about building a sustainable, efficient, and customer-centric engineering team in the blog post. 💭 I'm curious to hear your thoughts: Have you faced similar challenges? How did you address them? Let's share insights and grow together! #EngineeringManagement #Leadership #Productivity  _______________ ➡️ I am Talila Millman, a fractional CTO,  a management advisor, and a leadership coach. I help CEOs and their C-suite grow profit and scale through optimal Product portfolio and an operating system for Product Management and Engineering excellence.  📘 My book The TRIUMPH Framework: 7 Steps to Leading Organizational Transformation will be published in Spring 2024 https://lnkd.in/eVYGkz-e

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

Want to Improve Everything? Stop Trying to Improve Everything... Most organizations struggle because they try to optimize cost, quality, speed, and efficiency all at once or in isolation. The result? Minimal or negative impact on system improvement. Dr. Eli Goldratt taught a powerful paradigm shift: "There are many things which are important. I know. Choose one. Become zealous on it. That's the way to get them all. Try to consider them all the time. You get nothing." 💡 If you focus on improving FLOW, everything else—quality, cost, lead time and even workplace harmony — will improve. The 4 Principles of FLOW 🚀 1)  Choose ONE Goal—FLOW—and Be Zealous About It If you try to focus on everything, you’ll improve nothing. Instead, for Operations, optimize Flow, and cost, quality, and speed will follow. 👉 Reality is deeply connected—you don’t need to fight on all fronts. 👉 The real constraint in any organization is leadership’s span of attention. Focus it on what matters most. 🚀2)  The Real Problem is Overproduction Too much work-in-progress slows everything down. Instead of asking “What should we produce?”, ask “What should we NOT produce?” 👉 Prevent overproduction, and Flow will improve dramatically. 👉 Employees aren’t lazy—the system needs better controls to prevent waste. 🚀 3️) Stop Chasing Local Optima and Efficiencies The sum of local efficiency is NOT equal to system efficiency. 👉 When you optimize Flow within Operations, by increasing flow rate and reducing flow time, local efficiencies improve naturally—often more than if you had focused on them. 🚀 4) Everything Can Be Improved—But Not Everything Should Be Continuous improvement without focusing on system constraints leads to wasted effort. The key question: Where should we improve? 👉 Without a mechanism to decide, you’ll work on what’s easy—not what’s impactful. Why This Matters ✅ If you focus on Flow, cost, quality, and lead time will improve. ✅ If you stop overproducing, you’ll not only eliminate waste and noise, but will unlock capacity and budget to focus on what matters most. ✅ If you prioritize system-wide or global optimization, you’ll outperform those chasing local optimizations. ✅ If you focus on improving what actually matters – removing constraints through better exploitation (improvement) or elevation (investment) - you’ll achieve continuous compounding improvement. This is the secret behind Henry Ford’s Flow Line, Taiichi Ohno’s Toyota Production System, and Goldratt’s Theory of Constraints. 💡 Stop trying to improve everything. Focus on Flow, and everything will improve. PS: This principle can also apply at a personal level. If you want to improve your Wealth, Health and Happiness, is there ONE that rules them all? One that if you can improve it, all the others will also improve? 👉 Looking forward to your comments/questions #TheoryOfConstraints #Goldratt #Flow #Lean #ContinuousImprovement #Leadership #ToyotaProductionSystem #HenryFord

💡 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

🚀 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 decades, engineering teams have been measured by lines of code, commit counts, and PRs merged—but does more code actually mean more productivity? 🚀 Some of the best developers write LESS code, not more. 🚀 The fastest-moving teams focus on outcomes, not just output. 🚀 High commit counts can mean inefficiency, not impact. Recent research from DORA, GitHub, and real-world case studies from IT Revolution debunk the myth that developer activity = developer productivity. Here’s why: 🔹 DORA Research: After studying thousands of engineering teams, DORA (DevOps Research & Assessment) found that the best teams optimize for four key engineering performance metrics: ✅ Deployment Frequency → How often do we ship value to users? ✅ Lead Time for Changes → How fast can an idea go from code to production? ✅ Change Failure Rate → Are we improving quality, or just shipping fast? ✅ MTTR (Mean Time to Restore) → Can we recover quickly when things go wrong? → Notice what’s missing? Not a single metric is based on lines of code, commits, or individual developer output. 🔹 GitHub’s Data: GitHub found that developers working remotely during 2020 pushed more code than ever—but many felt less productive. Why? Longer workdays masked inefficiencies. More commits ≠ meaningful work; some were just fighting bad tooling or slow reviews. Teams that automated workflows (CI/CD, code reviews) merged PRs faster and felt more productive. 🔹 IT Revolution case studies: High-performing engineering orgs measure outcomes, not just outputs. The best teams: Shift from tracking commit counts → to measuring customer value. Use DORA metrics to improve DevOps flow, not micromanage engineers. View engineering productivity as a team effort, not an individual scoreboard. If you want a high-performing engineering org, don’t just push developers to write more code. Instead, ask: ✅ Are we shipping value faster? ✅ Are we reducing friction in our workflows? ✅ Are our developers able to focus on meaningful work? 🚨 The takeaway? Great engineering teams don’t write the most code—they deliver the most impact. 📢 What’s the worst “productivity metric” you’ve ever seen? Drop a comment below 👇 #DeveloperProductivity #SoftwareDevelopment #DORA #GitHub #EngineeringLeadership

In every field, from supply chains to health systems, the key to enhanced performance lies in superior DECISION-MAKING. Warren Powell’s simple yet profound framework guides us to start with three fundamental questions: 1️⃣ What are your performance metrics? Define what success looks like in your system. Are you optimizing for cost, efficiency, speed, or reliability? Clear metrics guide focused decisions. 2️⃣ What types of decisions are being made, and who makes them? Identify the decision-makers and their roles. Whether it’s operational managers adjusting schedules or strategic leaders planning investments, understanding the hierarchy and process of decision-making can streamline operations and reduce bottlenecks. 3️⃣ What are the types of uncertainty that affect performance? With the seemingly endless amount of complexities in the world, identifying potential uncertainties, from market fluctuations to supply disruption, helps in crafting robust strategies that withstand volatility. 💡Applying this framework isn’t restricted to just large systems. Even smaller, modular components of a business can benefit significantly. 👉 For instance, in a manufacturing setting, applying these questions at the line level can uncover inefficiencies and lead to quicker, more effective responses to production challenges. 💭 How you have applied or plan to apply this decision-making framework in your domain? 💭 What challenges do you expect, and how do you plan to address them? #DecisionScience #DataScience #DataEngineering #Optimization #OperationsResearch #SupplyChainManagement #BusinessAnalytics #PredictiveAnalytics #StrategicDecisions #SystemImprovement #PerformanceMetrics #BusinessOptimization #ManagementScience #IndustrialEngineering #ContinuousImprovement

Here's a thought: Treat your mission-system-as-a-product-not-a-program. Don't just manage technology. Actually drive mission success. 1. 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐨𝐯𝐞𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬. Instead of defining systems by what they must do (requirements), focus on what they aim to achieve (outcomes). Solve problems effectively, don't build checklists. 2. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Products evolve through feedback loops. Feedback loops based on real-world data, user feedback, and changing needs (in the environment, in strategy, in execution). 3. 𝐔𝐬𝐞𝐫-𝐜𝐞𝐧𝐭𝐞𝐫𝐞𝐝. It's not just a UI problem--we know our interfaces are Y2K bad. Just fix it. What we really need is to ensure systems support the mission in the most intuitive, efficient way possible. 4. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭. Software is never done. We don't stop after hitting a "definition of done" criteria. We can always improve, optimize, and adapt. The mission depends on it. 5. 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭, 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩, 𝐚𝐧𝐝 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Everyone is responsible for delivering a mission system that actually improves outcomes for our end-users. This includes acquisitions, requirements, development teams, operations teams, etc. 6. 𝐌𝐨𝐯𝐞 𝐟𝐚𝐬𝐭 𝐚𝐧𝐝 𝐥𝐞𝐚𝐫𝐧 𝐭𝐡𝐢𝐧𝐠𝐬. That's it. 7. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. Cost, schedule, and performance isn't good enough. This also doesn't necessarily mean a program is successful. A program typically sacrifices or trades-off between the three. We don't have to do that if we focus on software delivery performance and the right performance--mission metrics. #mission #outcomes #productmindset