Engineering Product Development Stages

95% of companies are stuck in Chaos. Only 4% make it to Optimization.  Just 1% ever reach true Innovation. Why? Because most mistake activity for progress. I see it all the time. CEOs spinning their wheels in perpetual crisis mode. "We'll implement AI next quarter." "We need better dashboards." "Let's automate everything." Wrong. You can't automate chaos. The brutal truth? Most companies try to skip stages. They want the sexy stuff: ➟ AI ➟ Automation ➟ Predictive analytics But if your foundation is broken, none of that matters. You can’t build a skyscraper on quicksand. Here's what actually happens in each stage: Stage 1 - Chaos: ↳ You're firefighting daily. ↳ Every problem feels urgent. ↳ Your team is burned out from rework and constant pivots. Stage 2 - Standardization: ↳ You create repeatable processes. ↳ Document everything. ↳ Train your people. ↳ Build the foundation. Stage 3 - Optimization: ↳ Now you eliminate waste. ↳ Find bottlenecks. ↳ Perfect your systems through data and testing. Stage 4 - Automation: ↳ Technology does the heavy lifting. ↳ But only because you've standardized first. Stage 5 - Innovation: ↳ Operations become your competitive advantage. ↳ You're not just running a business. ↳ You're creating breakthroughs. The companies that reach Stage 5? They print money while their competitors drown in busywork. Think: ✅ Toyota vs. failed car startups ✅ Amazon vs. traditional retail ✅ Netflix vs. Blockbuster It’s not luck. It’s execution. Here’s the kicker: Skip a stage, and it all falls apart. I’ve watched brilliant founders fail because they couldn’t execute. I’ve seen average ideas dominate, because the operators were world-class. Your next breakthrough isn’t a feature. It’s mastering the stage you’re in. Stop chasing shiny objects. Start building operational excellence. The companies that survive the next decade won't be the ones with the best ideas. They'll be the ones that execute flawlessly. Where is your company right now? 🔖 Want a PDF of my Operations Funnel cheat sheet + 100 more leadership resources? Get them free here: https://lnkd.in/e4r7tA89 ♻ Repost to help a leader in your network. Follow Eric Partaker for more operational insights.

🚗 Imagine this: You launch a new car model after years of effort. Production is smooth, the assembly line is world-class… but six months later, the headlines scream “Massive Recall.” Billions lost. Reputation damaged. All because of a design flaw that was locked in during the product development phase. Takao Sakai once said: 👉 “95% of Toyota’s profits are determined in the product development phase, not production.” And it’s true across industries: In aerospace, material choices made at the design table decide 80% of lifecycle costs. In electronics, overengineering features adds cost but not value. In manufacturing, late design changes cause delays that no production efficiency can recover. ⚡ The real challenge? Most companies pour their energy into fixing problems on the shop floor instead of preventing them during development. 💡 The smarter way: Apply Design for Manufacturability (DFM) & Concurrent Engineering. Run early simulations & prototypes to detect risks. Involve quality, supply chain, and production teams at the concept stage. Use Voice of Customer (VOC) to cut out features no one wants but everyone pays for. The truth is simple: ✅ Every mistake caught in design costs a fraction of fixing it in production. ✅ Every smart decision in development compounds into long-term profit. 🔑 What’s one thing your team does during product development that safeguards future profitability? 👇 Share your experience—it might spark ideas for someone else! #Lean #ProductDevelopment #DesignThinking #Innovation #BusinessExcellence #Quality #TQM

So, you fancy launching a battery start-up? Splendid notion - but unless you’ve a big idea, a real product, and paying customers (all within three years, mind you), you’d be better off panning for gold in Wales, scaling Everest in flip-flops, or reinventing yourself as a war correspondent. Possibly all at once - far less stressful. Big Idea: If your battery does what today’s lithium-ion already does, don’t bother. You need something it can’t do – that someone will pay for. Ultra-cold performance? Extreme power? North of 600 Wh/kg? Now you have a chance. MVP (Minimum Viable Product): No, not a lonely coin cell on a lab bench. You need a real cell - the actual building- block your customer can drop straight into their drone, power tool, Arctic rover or infantry kit. It must solve an actual problem, and they must pay handsomely for it, even when your costs are eye-watering. Revenue - yesterday: PowerPoints don’t pay salaries. Letters of support won’t keep the lights on. A cooperative agreement is lovely but worthless if no money changes hands. Get paid for samples - better yet, get real cells into real kit for real market testing. First orders should fund the next ones. Thinking of building your own pilot line? Careful: PhD students are marvelous, but they’re not manufacturing engineers. You’ll need scarred and battle tested veterans who know how to run a production line. The CAPEX is painful and the timeline worse - you’ll be lucky to sample in three years. Investors have a strong dislike for long, expensive science projects these days. Better idea: piggyback on the pilot lines and foundries that now exist. Cross the scale-up valley of death with someone else’s bridge. Small-scale pilot line: @University of Michigan, @Battery Innovation Center, @Oak Ridge National Laboratory, @Argonne National Laboratory, @The Ohio State University, or @Binghamton University. Small to larger scale (MWh-level early production): @Nanoramic Laboratories, @EnPower, Inc., @Wildcat Discovery Technologies. Specialized customer runs: @Saft, @Navitas Systems, @EaglePicher Technologies. Back in the day, Duracell pulled this off - they built a low-speed line for a few MWh/year, shipping to customers in under two years, thanks to TDK acting as a toll coater. Foundries work. In summary: If you want to scale, think foundries. Think the ASIC model. It may be the only sensible path left. #batteries #foundries #energystorage #innovation #startup #scaleup

I’ve spent 15+ years writing Product Requirements Documents (PRDs). Give me 5 minutes, and I’ll show you how to write a great one: — SIX STAGES OF PRD LIFECYCLE Before we dive in, remember this: the PRD is not one and done. If your PRD is static, your team will build the wrong thing. A great PRD evolves over six stages to keep teams aligned and focused. Here’s how: — STAGE ONE - Aperture → Align leadership & team on direction This is the starting point where leadership and product teams decide what’s worth exploring. → It’s often just a speck of an idea — a pain point, market shift, or competitive insight. → The goal is not to finalize anything but to agree on where to dig deeper. — STAGE TWO - Discovery → Identify the right problem to solve Now, the team goes deep into problem exploration. → User research, data analysis, competitive benchmarking, everything to validate that the problem is real. → Often, this stage results in a one-pager that outlines why this problem matters. — STAGE THREE - Define → Shape and scope the problem This is the final convergence on the problem statement. → The team nails down key constraints, trade-offs, and non-goals. → You answer questions like: What’s in scope? What’s out? — STAGE FOUR - Design → Explore potential solutions Finally, we move from problem space to solution space. → Brainstorming, prototyping, and early technical feasibility checks happen here. → You don’t pick just one solution yet, you explore different options. — STAGE FIVE - Deliver → Finalize and commit to a single solution Now, the team is ready to lock in the approach. → Engineers define technical specs. → Designers finalize user flows and edge cases. → PMs ensure cross-functional alignment. — STAGE SIX - Live → Launch, track results, and iterate The product ships… but the PRD isn’t done. → Measure adoption, impact, and user feedback. → Analyze what worked, what didn’t, and what needs iteration. — TWO - PRD Process: Problem Space vs. Solution Space A great PRD is built in two phases: ONE - Problem Space → Define the ‘What’ Team Kickoff → What problem are we exploring? Planning Review → Leadership alignment on the problem’s scope. XFN Kickoff → Gather cross-functional input before locking in the problem statement. TWO - Solution Space → Define the ‘How’ Once the problem is crystal clear, this phase ensures you solve it the right way. Solution Review → Align on user experience, edge cases & tradeoffs. Launch Readiness → Lock in implementation details, success metrics & rollout plan. Impact Review → Iterate based on insights. — In a nutshell… A great PRD doesn’t just tell what to build, it also inspires teams to build the right thing. — And if you want to learn how to write the PRDs with examples, template, and step-by-step, go here: https://lnkd.in/e-nzkAwX

Top takeaways from my chat with Grant Lee (CEO of Gamma): 1. The first 30 seconds of using your product should be so good it earns the next 30 seconds. When Gamma wasn’t growing, they stopped everything and spent three months perfecting just the first 30 seconds of using their product. They made it so compelling that new users would immediately tell their friends. This single change transformed their growth trajectory. 2. Focus on one simple promise, not many features. Think of it like throwing eggs to someone: they can catch one, but if you throw five at once, they’ll drop them all. Your users are selfish, vain, and lazy—you have 30 seconds to show value before they leave. Gamma focused on “create a slide in seconds” rather than listing 10 features. 3. Don’t spend on ads until over half your growth comes from word of mouth. If you try to buy growth before your product spreads organically, you’re wasting money filling a leaky bucket. 4. Work with hundreds of small creators instead of a few big influencers. Rather than blowing your budget on five or six well-known influencers who treat it like just another ad read, find thousands of micro-influencers whose audiences genuinely care about tools like yours. Teachers sharing with teachers, consultants with consultants—these tight communities create authentic word of mouth that spreads fast. 5. Spend time personally onboarding each early creator like they’re joining your team. Don’t just send influencers a script. Jump on calls, walk them through the product, help them understand what makes it special, and let them tell your story in their own voice. This investment turns them into genuine advocates who post about you repeatedly instead of treating it like any other sponsorship. 6. Hire painfully slowly and only exceptional people. Gamma serves 50 million users with just 50 people and makes a profit. All 10 original employees are still there five years later. They never set headcount goals because that makes you hire to hit a number instead of hiring only when you find someone exceptional. When someone is exceptional, give them more responsibility, not less—top performers want harder challenges. 7. Test prototypes with 20 people on UserTesting before investing in a big project. Use platforms like Voicepanel or UserTesting to watch real people try your prototype. They’ll show you problems you never see because you’re too close to your product. Gamma goes from idea to results in a single day—morning idea, afternoon testing, evening results. This saves months of building things nobody wants. 8. Choose problems you’ll care about for 10 years. Before worrying about technology or tactics, ask if this problem matters enough to you personally that you’d dedicate a decade to solving it. Founders who are missionaries rather than mercenaries build better products because their authentic commitment shows through to customers and attracts people who want to build alongside you for the long term.

Work Breakdown Structure (WBS) for an Airport Construction Project 📁 1.0 Project Milestones 1.1 Project Award 1.2 Design Completion 1.3 Mobilization Start 1.4 Major Procurement Orders Issued 1.5 Construction Start 1.6 Substantial Completion 1.7 Testing & Commissioning Complete 1.8 Final Handover 1.9 Project Closeout 📁 2.0 General & Preliminaries 2.1 Site Mobilization 2.1.1 Mobilization of Staff 2.1.2 Temporary Facilities Setup 2.1.3 Site Hoarding & Access Roads 2.2 Project Management 2.2.1 Project Controls (Planning, Cost, Risk) 2.2.2 Safety & Quality Management 2.3 Permits & Approvals (Initial) 2.4 Insurance, Bonds & Legal Compliance 📁 3.0 Engineering / Design 3.1 Master Planning & Concept Design 3.2 Architectural Design 3.3 Structural Design 3.4 MEP Design (HVAC, Electrical, Plumbing) 3.5 Special Systems Design (BHS, FIDS, Security, etc.) 3.6 Runway & Airside Engineering 3.7 Civil & Infrastructure Design (Utilities, Roads) 3.8 IFC Drawings & Shop Drawings 3.9 Design Approvals & Authority Submission 📁 4.0 Procurement 4.1 Long Lead Items Identification 4.2 Vendor Prequalification & Selection 4.3 Procurement of MEP Equipment 4.4 Procurement of Finishes & Special Systems 4.5 Procurement of Airfield Equipment (Lighting, Signage) 4.6 Factory Acceptance Testing (FAT) 4.7 Logistics & Delivery 📁 5.0 Construction 🧱 5.1 Civil & Structural Works 5.1.1 Excavation & Earthworks 5.1.2 Foundation Works 5.1.3 Superstructure (Columns, Slabs, Roof) 5.1.4 Internal & External Blockwork 5.1.5 Concrete Aprons, Taxiways & Runways 🏗️ 5.2 Architectural Works 5.2.1 Internal Finishes 5.2.2 Facade & Cladding 5.2.3 False Ceilings, Partitions, Doors, etc. 🔌 5.3 MEP Works 5.3.1 HVAC Installation 5.3.2 Electrical Works 5.3.3 Plumbing & Drainage 5.3.4 Fire Fighting & Fire Alarm 5.3.5 BMS & Control Systems 🖥️ 5.4 Special Systems 5.4.1 Baggage Handling System (BHS) 5.4.2 Flight Information Display System (FIDS) 5.4.3 Security & Surveillance Systems 5.4.4 Public Address & Access Control 🌐 5.5 External Works 5.5.1 Roads & Parking 5.5.2 Landscaping 5.5.3 Perimeter Fencing & Gates 5.5.4 Utility Tie-Ins 📁 6.0 Testing & Commissioning 6.1 Pre-Commissioning Checks 6.2 MEP Commissioning 6.3 ELV & Special Systems Commissioning 6.4 Integrated Testing (Fire & Life Safety) 6.5 System Integration Tests (SIT) 6.6 Final Performance Testing & Signoff 6.7 Training to Operators/Authorities 📁 7.0 Authority Approvals & Inspections 7.1 Civil Defense Approvals 7.2 Airport Authority Inspections 7.3 Municipality & Infrastructure Approvals 7.4 Final NOC Collection 📁 8.0 Project Closeout 8.1 Punch List Clearance 8.2 Final As-Built Drawings 8.3 Operation & Maintenance Manuals 8.4 Warranties & Guarantees Submission 8.5 Demobilization 8.6 Project Documentation Archive 📁 9.0 Final Handover 9.1 Provisional Acceptance Certificate (PAC) 9.2 Final Acceptance Certificate (FAC) 9.3 Final Handover to Client/Operator 9.4 Client Feedback & Lessons Learned

The venture model for hardware is broken. Let’s fix it. After nine years as a vc-backed founder, I’ve seen many promising hardware startups gain early traction, raise large funding rounds, then struggle with their go-to-market. Why? Hardware has a different path to success than software, but most people still treat them the same. The playbook for hardware founders to succeed at scale looks more like this: 1. Largest Pain, not Largest Market There’s less competition in hardware because it is so technically challenging. Don’t make it harder by building an umbrella product that inflates your market's size for investors. Align the product with your customer’s biggest pain first, and they’ll be 10x more willing to test and buy your product - even if it’s not perfect. Then expand. 2. Partners First, not Customers The primary goal of deployments with customers should be proof, not revenue. Partner early with customers to clearly define what success means to them. If the deployment doesn’t meet that criteria, iterate. If it works they will happily pay you knowing exactly what your product can do. 3. Scale when You're Ready VC-backed founders are under pressure to chase revenue quickly, forcing many to try to scale incomplete test versions as production products. This mistake will burn your early adopters and ultimately be slower. Take the most direct route to a truly market ready product. Scale once you have it. The old & broken model: Chase early revenue → Rush production → Iterate on vanity metrics The new model: Validate problem deeply → Partner with clear success criteria → Build for scale Hardware takes 3x longer to reach the market, but can become 100x more defensible than SaaS once you do. Do it right and your investors will later thank you for it!

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

A CFD Story: The Brief History of Formula One Aerodynamics! The author Christopher Beves [from SIEMENS] examines the aerodynamic evolution of Formula One cars over a span of 50 years, focusing on 11 significant models up to the late 1990s. The piece highlights the transition from the early days of F1, where aerodynamics was rudimentary, to the modern era, where Computational Fluid Dynamics (CFD) plays a pivotal role in vehicle design. The technical analysis in the blog encompasses several key aerodynamic concepts: Front Wing Vortices: The study illustrates how the design and width of front wings influence vortices that interact with the front tires, affecting the car's aerodynamic stability. Ground Effect: The article discusses the transition from flat floors in early 1970s cars to the implementation of airfoil-shaped tunnels that accelerated airflow beneath the car, significantly increasing downforce. Evolution of Car Shapes: By aligning various car models, the author highlights how airflow patterns have shifted over time, with modern designs generating stronger vortices and directing airflow upwards to enhance aerodynamic performance. The integration of CFD into the design process not only enhances vehicle performance but also streamlines development, reducing the reliance on physical prototypes. Blog link available in the comment section! #mechanical #aerospace #aerodynamics #automotive #formulaone #cfd #motorsport

Let's build a Real Time ML System to fraud. Step by step 🧵↓ 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 💼 Every time your credit card is used online by someone (hopefully you), your card issuer (for example Visa, Mastercard or PayPal) has to verify if it is you the person trying to pay with the card. Otherwise, the transaction is blocked. Now the question is: ““𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗩𝗶𝘀𝗮 𝗱𝗼 𝘁𝗵𝗮𝘁?”” And the answer is… a real time ML system! 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 📐 As any ML system that has existed, exists and will exist, this one can be broken down into 3 types pipelines 1️⃣ Feature pipelines 2️⃣ Training pipeline 3️⃣ Inference pipeline Let's go one by one 1️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 💾  The feature pipelines are the Python services that produce the inputs (aka features) our ML model needs to generate its predictions. In our case, we have (and I bet Visa has) at least 3 feature pipelines: ▣ ��𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 feature pipeline from recent transactional data. - runs 24/7 - consumes incoming data from an internal message bus (like Kafka, Redpanda) - transforms this data on-the-fly using a real-time data processing engine - saves the the final features in a feature store, like Hopsworks. ▣ 𝗕𝗮𝘁𝗰𝗵 pipeline from historical features in the data warehouse. - runs daily - reads data from the data warehouse/lake, and - saves it into another feature group in our feature store, so it can be consumed by our ML model really fast. ▣ 𝗟𝗮𝗯𝗲𝗹𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, so the ML model can be trained with supervised ML. Each completed transaction that is not claimed by the card owner within 6 months can be safely called non-fraudulent (class=0). We call it fraudulent (class=1) otherwise. Once we have these 3 feature pipelines up and running, we will start collecting valuable data, that we can use to train ML models. 2️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🏋🏽 We can use a supervised ML model (a boosting tree model like XGBoost does the job in most cases) to uncover any patterns between > the features available in your Feature Store, and > the transaction class: 0 = non-fraudulent, 1 = fraudulent. The final model is pushed to the model registry (like MLflow, Comet or Weights & Biases), so it can be loaded and used by our deployed model. And this is precisely what the last pipeline in our design does. 3️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🔮 The inference pipeline is a Python streaming application, that at start up loads the model from the registry into memory and for every incoming transaction > loads the freshest features from the store for that card_id, > feeds them to the model, and > outputs the predictions to another Kafka topic. These fraud scores can be then consumed by downstream services, to > Block the card, and > Send an SMS alert to the card owner, for example. BOOM! No dark magic. Just Real World ML. Follow Pau Labarta Bajo for more Real World ML