Engineering Product Development Stages

📚 Key Test Documentation Types 1. Test Plan Purpose: Outlines the overall strategy and scope of testing. Includes: Objectives Scope (in-scope and out-of-scope) Resources (testers, tools) Test environment Deliverables Risk and mitigation plan Example: "Regression testing will be performed on modules A and B by using manual TC" 2. Test Strategy Purpose: High-level document describing the overall test approach. Includes: Testing types (manual, automation, performance) Tools and technologies Entry/Exit criteria Defect management process 3. Test Scenario Purpose: Describes a high-level idea of what to test. Example: "Verify that a registered user can log in successfully." 4. Test Case Purpose: Detailed instructions for executing a test. Includes: Test Case ID Description Preconditions Test Steps Expected Results Actual Results Status (Pass/Fail) 5. Traceability Matrix (RTM) Purpose: Ensures every requirement is covered by test cases. Format: Requirement ID Requirement Description Test Case IDs REQ_001 Login functionality TC_001, TC_002 6. Test Data Purpose: Input data used for executing test cases. Example: Username: testuser, Password: Password123 7. Test Summary Report Purpose: Summary of all testing activities and outcomes. Includes: Total test cases executed Passed/Failed count Defects raised/resolved Testing coverage Final recommendation (Go/No-Go) 8. Defect/Bug Report Purpose: Details of defects found during testing. Includes: Bug ID Summary Severity / Priority Steps to Reproduce Status (Open, In Progress, Closed) Screenshots (optional) Here's a set of downloadable, editable templates for essential software testing documentation. These are useful for manual QA, automation testers, or even team leads preparing structured reports. 📄 1. Test Plan Template File Type: Excel / Word Key Sections: Project Overview Test Objectives Scope (In/Out) Resources & Roles Test Environment Schedule & Milestones Risks & Mitigation Entry/Exit Criteria 🔗 Download Test Plan Template (Google Docs) 📄 2. Test Case Template File Type: Excel Columns Included: Test Case ID Module Name Description Preconditions Test Steps Expected Result Actual Result Status (Pass/Fail) Comments 🔗 Download Test Case Template (Google Sheets) 📄 3. Requirement Traceability Matrix (RTM) File Type: Excel Key Fields: Requirement ID Requirement Description Test Case ID Status (Covered/Not Covered) 🔗 Download RTM Template (Google Sheets) 📄 4. Bug Report Template File Type: Excel Columns: Bug ID Summary Severity Priority Steps to Reproduce Actual vs. Expected Result Status Reported By 🔗 Download Bug Report Template (Google Sheets) 📄 5. Test Summary Report File Type: Word or Excel Includes: Project Name Total Test Cases Execution Status (Pass/Fail) Bug Summary Test Coverage Final Remarks / Sign-off 🔗 Download Test Summary Template (Google Docs) #QA

The Medical Device Iceberg: What’s hidden beneath your product is what matters most. Your technical documentation isn’t "surface work". It’s the foundation that the Notified Body look at first. Let’s break it down ⬇ 1/ What is TD really about? Your Technical Documentation is your device’s identity card. It proves conformity with MDR 2017/745. It’s not a binder of loose files. It’s a structured, coherent, evolving system. Annexes II & III of the MDR guide your structure. Use them. But make it your own. 2/ The 7 essential pillars of TD: → Device description & specification → Information to be supplied by the manufacturer → Design & manufacturing information → GSPR (General Safety & Performance Requirements) → Benefit-risk analysis & risk management → Product verification & validation (including clinical evaluation) → Post-market surveillance Each one matters. Each one connects to the rest. Your TD is not linear. It’s a living ecosystem. Change one thing → It impacts everything. That’s why consistency and traceability are key. 3/ Tips for compiling TD: → Use one “intended purpose” across all documents → Apply the 3Cs: ↳ Clarity (write for reviewers) ↳ Consistency (same terms, same logic) ↳ Connectivity (cross-reference clearly) → Manage it like a project: ↳ Involve all teams ↳ Follow MDR structure ↳ Trace everything → Use “one-sheet conclusions” ↳ Especially in risk, clinical, V&V docs ↳ Simple, precise summaries → Avoid infinite feedback loops: ↳ One doc, one checklist, one deadline ↳ Define “final” clearly 4/ Best practices to apply: → Add a summary doc for reviewers → Update documentation regularly → Create a V&V matrix → Maintain URS → FRS traceability → Hyperlink related docs → Provide objective evidence → Use searchable digital formats → Map design & mfg with flowcharts Clear TD = faster reviews = safer time to market. Save this for your next compilation session. You don't want to start from scratch? Use our templates to get started: → GSPR, which gives you a predefined list of standards, documents and methods. ( https://lnkd.in/eE2i43v7 ) → Technical Documentation, which gives you a solid structure and concrete examples for your writing. ( https://lnkd.in/eNcS4aMG )

AI Prototyping Tools Masterclass: If you've been bouncing between v0, Bolt, Replit, and Lovable wondering, "Which one should I actually be using?" You're not alone. They all look impressive. But if you don’t understand what each one actually does best, you're just spinning your wheels. So, let’s break it all down: — ONE - The 4 Major Players (and What They’re Built For) Let me remind you, these aren’t just "tools" anymore. They’re fast-evolving cloud development environments And each one has a clear edge. 1. v0 by Vercel This one’s all about beautiful front-end design - out of the box. Clean UIs, polished interactions, and a $3.25B valuation behind it. Perfect if you’re spinning up a demo for stakeholders... And want something that looks amazing fast. Just don’t expect deep backend stuff without plugging in extras like Supabase. 2. Bolt Built for speed. The CEO told us the whole thing runs in the browser, no VMs & no lag. That's the reason it went from $0 to $40M ARR in just 6 months. If you’re testing ideas fast (think 10-minute prototypes), this is your tool. It’s flexible, but you'll need to connect things like a database yourself. 3. Replit This one goes deep. Founded by Amjad Masad and now valued at $1.16B, Replit gives you full-stack power. Built-in auth, built-in database, built-in deployment. If your prototype needs to function like a real product, this is the play. It’s not as slick as v0 or as lightning-fast as Bolt... But when it comes to handling real logic, Replit is in a league of its own. 4. Lovable Lovable is becoming the most loved "vibe coding" tool. Founded by Antonin Osika, and it hit $17M ARR in just 3 months. Honestly? It’s the easiest tool in the game, especially if you don’t code. Drag, drop, sync with Supabase. That’s it. No setup headaches. No complex environment. Perfect for non-technical PMs or anyone who wants to go... From idea to live prototype without touching a line of code. — TWO - ADJACENT TOOLS But wait, there’s a twist. These tools aren’t where AI prototyping stops. There are adjacent tools you’ll want to layer in depending on your skill level: If you’re just looking to generate quick code or play around with ideas: → ChatGPT and Claude work great. But if you want to build something real (and you can code): → Tools like Cursor, Windsurf, Zed, and GitHub Copilot are insanely powerful. A great flow in my experience so far? Start in Bolt or Lovable → Sync to GitHub → Then build deeper in Cursor. — I broke all this down in my latest newsletter drop: "Ultimate Guide to AI Prototyping Tools (Lovable, Bolt, Replit, v0)" If you want to understand how to actually use these tools and which one fits your workflow best, go here: https://lnkd.in/eRypMZQ8 It’ll save you weeks of trial and error.

Are you aware of the hidden costs in your product's raw material? : : Accurately calculating raw material costs is a cornerstone of should-cost modeling. By effectively identifying the materials required, determining the cost per unit, and accounting for potential waste and additional costs like handling and transportation, you can develop a comprehensive and reliable cost model. Key Parameters for Should Cost Process in Material Calculation: # Raw Material Identification: ·  Material type and grade ·  Material source/origin # Material Quantity: · Required quantity (per unit or batch) · Packaging units # Material Cost per Unit: · Supplier quotes · Market prices · Historical data · Discounts and bulk pricing # Material Waste or Loss: · Scrap/waste factor ·  Defects and rejections # Handling and Storage Costs: ·  Material handling · Storage costs (rent, insurance, utilities) · Inventory management # Freight and Transportation: ·  Shipping costs · Delivery method (air, sea, road) ·  Customs and tariffs # Lead Time and Order Frequency: · Lead time variations · Order volume # Supplier Terms and Conditions: · Payment terms · Return and warranty policies · Exchange Rates (For Imported Materials) # Material Substitution and Alternatives: · Substitute materials ·  Material optimization # Environmental and Regulatory Factors: · Recycling or sustainability initiatives · Regulatory compliance # Operational Overheads Related to Materials: · Processing costs · Energy costs ------------------------------------------------------------------------------------- # Ask Yourself: -> Did you consider the net weight and gross weight calculation properly? -> Did you consider scrap weight and scrap cost in your estimation? -> Do you have access to the global raw material index and recent material price database? -> Have you asked your supplier about the raw material cost per kg as well as the scrap cost per kg? -> Do you consider Manufacturing overhead (MOH) and inventory cost (raw materials)? -> What about the scrap cost percentage based on different commodities? -> Did you optimize material through strip layout, nesting, cavity, and other techniques? -> What’s your strategy when the supplier asks for material cost increases due to market fluctuations? -> Did you consider the volume/batch/MOQ impact, as well as regional cost impact, in your calculations? -> Did you consider any coating and primary requirements in the raw material stage? -> Commodity-Specific Considerations, etc.

🌀 How To Stop Endless Stakeholder Reviews (https://lnkd.in/dQh9RaPc), a fantastic honest case study on how to deal with many stakeholders, conflicting priorities and overlapping timelines — and make design reviews more productive and more effective. By Parvaneh Toghiani from Uber. 🤔 Goal of design reviews isn’t to just get a final approval. ✅ Before review, identify its purpose and desired outcome. ✅ Troubles start with misalignment and showing work late. ✅ Frequent mistakes: too many directions + focus on UI. ✅ 3 distinct categories → Alignment, Evaluation, Sign-off. 🧭 Alignment → align on the PRD, discuss concept sketches. 📋 Evaluation → get actionable feedback on 2–3 proposals. 🚢 Sign-off → review design work, its priorities, feedback. ✅ Always reflect on previous reviews and what’s changed. ✅ Have a decision criteria + design recommendation ready. Of course different stakeholders view design through a different lens. For executives, we might need to focus on business impact or company priorities. For cross-functional leads, we better focus on problem space and how our work addresses it. And for the core team, focus on the execution and details. There is a variety of meetings when design is reviewed. For smaller touch points, set up an informal design critique or working session. One thing that has helped me is to always start by explaining our current state of work: 50%, 75%, 90% done — and explaining the desired goal or outcome of that meeting. One point from the article that I loved is to always show ideas on a spectrum: Most practical ↔ Blue sky — with the preferred concept in the middle. However, there is rarely a need to show all the fine detail for each concept — most design reviews are about finding a direction, not pixel-pushing on spot. Also, it's helpful to define a specific meeting type in email invitations, with a custom emoji or color coding for your calendar. I love the meeting format suggested by Rich Watkins: 📣 Broadcast Meetings for announcements and townhalls, 🥁 Rhythm Meetings for regular status updates, 🏗️ Planning Meetings to define timelines and provide estimates, 🛠 Problem-Solving Meetings for workshops and solution finding, 🏕️ Exploration Meetings for big questions and complex problems, 💚 Team Building Meetings for team spirit and collaboration, 🪴 Catch-Up Meetings for connecting moments without agenda, 🏆 Review Meetings for retros, 1:1, performance meetings. As Parvaneh writes, design reviews aren’t just for approvals. They are where influence, trust and alignment are built. They are incredible opportunities to grow as a designer — to test your storytelling, explain and defend your thinking and learn how people make their decisions. And *huge* thanks to Parvaneh Toghiani for the detailed case study! 👏🏼👏🏽👏🏾 I'd love to learn from you what helped you avoid endless loops of stakeholder reviews at your work in the comments below!

My 10 mistakes introducing PLM. 🚩  1. Lack of clear objectives PLM initiatives start without a precise definition of: - What exactly should be improved (e.g., change processes, data quality, time-to-market, …)? - How success will be measured? - How do I balance diverging targets: function, integration, technology? - ALM, PLM and ERP are the most important IT-Systems along the PLC. How are functions and processes distributed and integrated? ➡️ Consequence: The project loses focus, becomes bloated, or fails due to unrealistic expectations. 🚩  2. Treating PLM as an IT project PLM is fundamentally a process and organizational transformation, not just a software. ➡️ Consequence: Poor involvement of departments leads to low adoption and inefficient workflows. 🚩  3. Unclear or conflicting processes Companies often attempt to implement PLM while their underlying processes: - do not exist, - are poorly documented, - differ across organizational units. ➡️ Consequence: The tool ends up digitizing chaos instead of improving it. 🚩  4. Scope too large / Big-Bang implementation Trying to deploy a comprehensive PLM system all at once is one of the most common pitfalls. ➡️ Consequence: Delays, budget overruns, and user frustration. 🚩  5. Insufficient Change Management PLM affects roles, responsibilities, and daily work habits. Common oversights: - weak communication, - missing training, - lack of key-user involvement, - lack of C-level involvement. ➡️ Consequence: Resistance, workarounds, and low acceptance. 🚩  6. Poor master data and document quality - inconsistent or duplicated data, - no data cleanup before migration, - missing standards (naming, numbering, classification, ...). ➡️ Consequence: Bad data stays bad—only now inside an expensive system. 🚩  7. Over-customization Companies frequently try to model every exception and satisfy every request. ➡️ Consequence: Complex, costly, hard-to-maintain systems that hinder upgrades. 🚩  8. Underestimating integration PLM relies on clean interfaces to systems like: CRM, CAD, ALM, ERP, MES, SCM. ➡️ Consequence: Media breaks, duplicate data, and process gaps. ���  9. Insufficient resources or the wrong project team PLM is often done “on the side": - no dedicated project manager, - limited internal PLM expertise, - weak executive sponsorship. ➡️ Consequence: Delays and unsatisfied never ending stories 🚩  10. Focusing only on basic design features Many PLM deployments center solely on CAD and E-BOM. But PLM should cover: requirements management, variant management, change management, service, ... ➡️ Consequence: PLM becomes an expensive CAD data vault rather than an enterprise-wide product backbone or PLM functions are taken over by CAD (Onshape) or ERP ✅ Summary Most pitfalls arise not from technology or functional coverage, but from strategy, processes, and change management. Organizations often underestimate the cultural and organizational change—and overestimate what the software alone can fix.

Wow. I just built 3 mini-apps for PMs in under 10 minutes: an empathy mapper, a journey analyzer, and a competitive analysis tool with Opal (Google Labs). No PRD. No Figma. No tickets. Just an idea → an experience. Instead of debating documents, I’m now sharing working mini-apps with my team ask them "react to this, let’s refine it” I used Opal to prototype the vibe with an: -Empathy Mapper -User Journey Analyzer -Competitive Landscape Tool Each one took minutes. Each one was immediately shareable. Each one changed the conversation. Use Opal when: -You want to validate an idea before writing a PRD -You need a quick tool for a workshop or meeting -You want to make research or concepts visible -You want to better empathize about your user Think of Opal as your 10-minute lab. If it takes longer than that, move it to a full prototype — that’s where other AI prototyping tools come in. Tips for PMs adopting this workflow -Start tiny. Your first Opal app should take under ten minutes. That constraint keeps you focused on intent, not polish. -Think in verbs, not nouns. Prompts like “summarize feedback” or “visualize trends” produce far better prototypes than static descriptions. -Collaborate live. Invite designers, engineers, and stakeholders into the session. Watching the prototype evolve creates alignment faster than any meeting. -Reflect. After every prototype, note what worked. Each build sharpens your prompting instincts and your product intuition. 🔗 Guides + masterclass in the comments 👇

Piping & Fittings Material Grades in the Oil & Gas Industry 🌍 Selecting the right piping material is critical to the safety, reliability, and efficiency of oil & gas operations. From upstream platforms to downstream refineries, each application demands materials that meet specific service conditions. Here are key considerations in material selection: ✅ Service Conditions: Temperature, pressure, and corrosion levels ✅ Mechanical Properties: Yield/tensile strength, toughness ✅ Weldability: Especially for materials like P91 requiring controlled welding and PWHT ✅ Corrosion Resistance: Vital for offshore and sour service ✅ Cost vs. Performance: Balance lifecycle cost and durability ❶ Carbon Steel: 📌 Grades: ASTM A106 Gr. B/C, ASTM A53 Gr. B, API 5L Gr. B/X42–X70 📌 Applications: Oil, gas, and steam transport in moderate to high-temp environments 📌 Note: May require internal coatings or inhibitors in corrosive settings ❷ Low-Temperature Carbon Steel (LTCS): 📌 Grade: ASTM A333 Gr. 6 📌 Applications: Cryogenic and low-temp services (down to -45°C) 📌 Note: High impact toughness is essential to prevent brittle failure ❸ Stainless Steel: 📌 Grades: 304/304L, 316/316L, 321, 347 📌 Applications: Offshore, chemical injection, potable water 📌 Note: 316L offers excellent chloride resistance—ideal for marine settings ❹ Alloy Steel: 📌 Grades: ASTM A335 P11, P22, P91 📌 Applications: High-pressure/high-temperature systems in power & process industries 📌 Note: Demands precise PWHT to ensure integrity and performance 📌 Whether designing new systems or evaluating replacements, understanding material properties helps mitigate risks, optimize costs, and improve long-term reliability. 👉 What material grades are most common in your projects? Let’s connect and share insights! ====== 🔔 Consider following me at Govind Tiwari,PhD. #OilAndGas #PipingEngineering #MaterialsEngineering #QualityMatters #MechanicalIntegrity #StainlessSteel #CarbonSteel #CorrosionResistance #Weldability #PWHT #ProcessPiping #ReliabilityEngineering #AssetIntegrity #quality #iso9001

𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙘𝙝𝙤𝙤𝙨𝙚 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙥𝙞𝙥𝙞𝙣𝙜 𝙢𝙖𝙩𝙚𝙧𝙞𝙖𝙡? ✅ Fluid Characteristics - Type of fluid: water, steam, oil, gas, chemicals, corrosive media. - Corrosiveness: Is it acidic, alkaline, saline, or non-corrosive? - Toxicity & flammability: For hazardous fluids, material must be more robust and safe. - Cleanliness: For food, pharma, and semiconductor industries, hygienic stainless steel is a must. ✅Operating Conditions - Pressure (normal, medium, high, very high) → dictates wall thickness & material strength. - Temperature (cryogenic, ambient, high temp) → affects thermal expansion, creep resistance, and material selection. - Phase (gas, liquid, slurry, steam) → abrasive slurry requires erosion-resistant materials. ✅Mechanical Properties - Strength (yield, tensile, toughness). - Hardness (abrasion resistance). - Flexibility & ductility (ability to handle expansion/contraction). ✅Corrosion Resistance - Carbon steel for non-corrosive services. - Stainless steel (304, 316, 321, etc.) for corrosive, food, and pharma industries. - Special alloys (Duplex, Inconel, Hastelloy, Titanium) for highly aggressive environments. ✅Codes & Standards - ASME B31.3 (Process Piping). - ASME B31.1 (Power Piping). - API, ASTM, DIN, EN standards depending on industry & location. - Company specifications (PMS – Piping Material Specification). ✅Economics - Carbon steel is cheaper but needs corrosion allowance/lining. - Stainless & alloys are expensive but reduce maintenance & increase service life. - Balance between CAPEX (initial cost) and OPEX (lifetime maintenance). ✅Fabrication & Availability - Weldability, machinability, ease of forming. - Local availability of pipes, fittings, and spares. - Delivery time and vendor qualifications. ✅Special Considerations - Fire safety (e.g., non-combustible materials). - Regulatory requirements (FDA for food/pharma, NACE for sour service in oil & gas). - Thermal expansion (materials with high expansion coefficients may need special design considerations). ⚙️ Common Materials in Piping ➡️ Carbon Steel (CS): Cheap, widely used, but limited corrosion resistance. ➡️ Stainless Steel (SS): Corrosion & heat resistant (common grades: 304, 316, 321, Duplex). ➡️ Alloy Steels: For high temperature & pressure (e.g., Cr-Mo steels in refineries). ➡️ Non-metallics (PVC, CPVC, HDPE, PTFE, FRP): For corrosive, low-pressure, or water services. ➡️ Exotic Alloys (Inconel, Monel, Hastelloy, Titanium): For very harsh chemical or high-temperature service. ✅ In practice, companies prepare a Piping Material Specification (PMS) document that lists allowable materials for different services (fluid, pressure, temperature) based on the above factors. #piping #corrosion #pipingengineering #steel #mechanicalengineering #engineering

I've spent over 4,000 hours in stakeholder requirement-gathering meetings! Save hours of your life by asking these questions: 1. What do they plan to use the data for? 1. What initiative are they working on? 2. How will this initiative impact the business? 3. Is this for reporting or optimizing existing workflows? Understanding the purpose of the data helps you define its impact. 2. How do they plan to use the data? Will they access it via SQL, BI tools, APIs, or another method? 1. Do they have a workflow to pull data from your dataset? 2. Do they just do a `SELECT *` from your dataset? 3. Do they perform further computations on your dataset? This determines the schema, partitions, and data accessibility needs. 3. Is this data already present in another report/UI? 1. Is this data already available in another location? 2. Do they have parts of this data (e.g., a few required columns) elsewhere? Ensuring you're not recreating work saves time and avoids redundancy. 4. How frequently do they need this data? 1. How frequently does the data actually need to be refreshed? 2. Can it be monthly, weekly, daily, or hourly? 3. Is the upstream data changing fast enough to justify the required latency? Understanding frequency helps you determine the pipeline schedule. 5. What are the key metrics they monitor in this dataset? 1. Define variance checks for these metrics. 2. Do these metrics need to be 100% accurate (e.g., revenue) or directionally correct (e.g., impressions)? 3. How do these metrics tie into company-level KPIs? Memorize average values for these metrics; they’re invaluable during debugging and discussions. 6. What will each row in the dataset represent? 1. What should each row represent in the dataset? 2. Ensure one consistent grain per dataset, as applicable. 7. How much historical data will they need? 1. Does the stakeholder need data for the last few years? 2. Is the historical data available somewhere? Ask these questions upfront, and you'll save countless hours while delivering exactly what stakeholders need. - Like this post? Let me know your thoughts in the comments, and follow me for more actionable insights on data engineering and system design. #data #dataengineering #datastakeholder