Engineering Product Development Stages

🚗 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

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.

Delivering impactful data projects starts with understanding the requirements! Here’s how to become better at gathering requirements: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀: Begin by having detailed conversations with your stakeholders. Understand their goals, challenges, and what they hope to achieve with the data project. This sets a clear direction from the start.     2. 𝗔𝘀𝗸 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Find answers to questions like, “What problem are we trying to solve?”, “What decisions will this data influence?”, and “What are the main metrics to track?”. These questions help to clarify the scope and objectives.     3. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: Keep detailed notes from your stakeholder meetings. Document requirements, assumptions, constraints, and expected outcomes. A well-documented requirement list ensures everyone is on the same page. Develop a requirements template to standardize your approach, making sure you cover all bases and don’t miss any critical details.     4. 𝗖𝗹𝗮𝗿𝗶𝗳𝘆 𝗮𝗻𝗱 𝗖𝗼𝗻𝗳𝗶𝗿𝗺: Regularly check in with stakeholders to confirm your understanding. Use techniques like paraphrasing their requirements and asking for confirmation to ensure accuracy.     5. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀: Determine where the necessary data will come from. Understand the data sources, availability, and quality to ensure you have the right data to meet project requirements.     6. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Clearly outline what success looks like. Establish KPIs and benchmarks that will measure the effectiveness of your analysis and the impact of your project.     7. 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Build in regular feedback loops throughout the project. Present interim findings to stakeholders and adjust requirements as necessary. This iterative process helps refine the project and keeps it aligned with stakeholder expectations. By becoming great at gathering requirements, you’ll ensure your data projects are more focused, aligned with business goals, and in the end more impactful. What’s your top tip for gathering requirements effectively? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #requirementsengineering #projectmanagement #careergrowth

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

Product development entails inherent risks where hasty decisions can lead to losses, while overly cautious changes may result in missed opportunities. To manage these risks, proposed changes undergo randomized experiments, guiding informed product decisions. This article, written by Data Scientists from Spotify, outlines the team’s decision-making process and discusses how results from multiple metrics in A/B tests can inform cohesive product decisions. A few key insights include:  - Defining key metrics: It is crucial to establish success, guardrail, deterioration, and quality metrics tailored to the product. Each type serves a distinct purpose—whether to enhance, ensure non-deterioration, or validate experiment quality—playing a pivotal role in decision-making.  - Setting explicit rules: Clear guidelines mapping test outcomes to product decisions are essential to mitigate metric conflicts. Given metrics may show desired movements in different directions, establishing rules beforehand prevents subjective interpretations during scientific hypothesis testing.  - Handling technical considerations: Experiments involving multiple metrics raise concerns about false positive corrections. The team advises applying multiple testing corrections for success metrics but emphasizes that this isn't necessary for guardrail metrics. This approach ensures the treatment remains significantly non-inferior to the control across all guardrail metrics. Additionally, the team proposes comprehensive guidelines for decision-making, incorporating advanced statistical concepts. This resource is invaluable for anyone conducting experiments, particularly those dealing with multiple metrics. #datascience #experimentation #analytics #decisionmaking #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gewaB9qC

Most defence innovation discussions still treat sustainment as a secondary issue. That is the problem. We talk about what systems can do, how fast they can be acquired, how cheaply they can be produced, and how impressive they look in demonstrations. But we rarely stay with the question long enough to ask what happens after those systems are actually used. Because that is where the real test begins. This piece looks at sustainment not as a support function, but as the condition that defines whether capability exists at all. It breaks down what happens after day one, when systems start to degrade, fail, and diverge from their initial state. When logistics are no longer predictable. When energy becomes a constraint. When software starts to drift. When recovery and repair determine whether something is lost or returned to use. The uncomfortable reality is that modern systems, especially autonomous and unmanned ones, do not reduce the sustainment burden. They increase it. More systems means more batteries, more updates, more configuration states, more spare parts, more logistics pressure, and more exposure to disruption. The idea that lower-cost systems can simply be replaced at scale ignores the practical constraints of moving, integrating, and sustaining them under contested conditions. And this is where most capability conversations still fall short. Attrition is treated as a problem to minimise rather than a baseline to design for. Repair is treated as secondary to replacement. Protection is focused on platforms rather than on the infrastructure that keeps them operational. Procurement evaluates entry, not endurance. But what decides outcomes is not what works once. It is what continues to function when conditions are no longer controlled, when losses are constant, and when the system is under pressure across every layer at the same time. Sustainment is not about keeping everything alive. It is about keeping enough of it working, trusted, and integrated to still matter. And that is not a technical problem. It is a system design problem. If we keep optimising for introduction instead of endurance, we will continue to mistake initial capability for real capability. And we will keep being surprised when it fades. #defenceinnovation #militarylogistics #autonomy #sustainment

#APQP in IATF 16949 (Automotive Quality Management System) APQP (Advanced Product Quality Planning) is a structured, preventive quality planning methodology required by IATF 16949 to ensure that products meet customer requirements, are robust at launch, and are capable in mass production. 🔹 Why APQP is Important in IATF 16949 IATF 16949 focuses on risk prevention, defect avoidance, and process robustness—APQP is the core tool to achieve this. APQP helps to: Prevent defects before production Reduce launch issues & customer complaints Ensure cross-functional coordination Meet customer-specific requirements (CSR) Demonstrate compliance during IATF audits 📌 APQP is mandatory for automotive suppliers (#Tier-1, #Tier-2, etc.) 🔄 APQP – 5 Phases (As per AIAG & IATF) #Phase 1: Plan & Define Program Goal: Understand customer needs and risks Key Outputs: Voice of Customer (VOC) Feasibility study Risk assessment Product quality goals Project timing plan 📌 IATF Clause Link: 8.2, 6.1 (Risk-based thinking) #Phase 2: Product Design & Development Goal: Design product that meets functional & quality requirements Key Outputs: DFMEA Design Reviews Design Verification & Validation Special Characteristics identification 📌 If design responsibility exists #Phase 3: Process Design & Development Goal: Develop a stable & capable manufacturing process Key Outputs: Process Flow Diagram PFMEA Control Plan (Prototype / Pre-launch / Production) Work Instructions Layout & capacity planning 📌 Very critical for Gear Manufacturing #Phase 4: Product & Process Validation Goal: Validate product and process before SOP Key Outputs: PPAP submission MSA (Gauge R&R) SPC / Process capability (Cp, Cpk) Run @ Rate Initial sample inspection report (SIR) 📌 IATF Clause Link: 8.5.1.1, 9.1 #Phase 5: Feedback, Assessment & Corrective Action Goal: Continuous improvement after SOP Key Outputs: Customer feedback & PPM monitoring Lessons learned Corrective actions Process audits & LPA 📌 IATF Clause Link: 10.2, 9.2 📄 Key APQP Documents (Audit Focus) #APQP Timing Plan #DFMEA / PFMEA (linked) #Control Plan (linked with PFMEA) #MSA & SPC records #PPAP approval #Change management (4M) #Customer approvals ⚠️ Common audit gaps ❌ APQP treated as paperwork ❌ Weak linkage between PFMEA & Control Plan ❌ 4M changes without APQP review ❌ Lessons learned not captured Lessons learned not capture ⚙️ APQP in Gear Manufacturing (Practical Focus) Tooth profile, lead & runout → Special Characteristics Heat treatment risks → PFMEA focus Fixture & gauge capability → MSA critical Tool wear & setup change → Control Plan updates Noise & durability → Validation testing #APQP #IATF16949 #AutomotiveQuality #QualityEngineering #PFMEA #DFMEA #PPAP #MSA #SPC #GearManufacturing #RiskBasedThinking #ContinuousImprovement

How I Elicit requirements as a Business Analyst (for a brand new project in the planning phase) When a new project kicks off, I’m not diving straight into the details. Instead I start by setting the right foundation. Get clarity and structure first, and then move into documentation. Here’s my approach → Understand the business context. What are we trying to solve? Why now? If you skip this, you’ll end up capturing requirements with no real direction. → Map out key stakeholders. And don’t just talk to the usual suspects… bring in Legal, Compliance, Security, etc., early. Cross-functional input now = less rework later. → Break the project into logical categories. Before jumping into process maps or detail, I define the bones - the high-level steps or categories of the process or journey that your project is implementing. This structure helps guide future workshops and gives everyone a clear mental model of the scope. → Capture high-level requirements. Meet with stakeholders and start gathering their inputs. Use the categories above to guide discussions… and where it helps, co-design the high-level process to elicit requirements. I then use user stories at this stage; it keeps things outcome-focused, even when we’re still defining the “what.” → Document just enough. No 50-page BRDs here. I use Jira, Confluence and lightweight templates that the whole team can actually engage with. → Engage stakeholders to validate. Don’t assume your documentation speaks for itself. Walk stakeholders through what’s been captured… clarify assumptions, confirm priorities, and bring them along the journey. It’s the easiest way to spot gaps early and avoid those “wait… that’s not what I meant” moments later. → Baseline and prepare for the next phase. Once validated, baseline the high-level requirements and identify any dependencies or open items. This creates a clear handover point; setting you up for detailed requirements, solution design, or sprint planning in the next phase. → The goal at this stage? Clarity, alignment, and momentum - not perfection. If you follow these steps, I promise: → Your stakeholders are going to be happy → Requirements higher quality → Reduce risks of missed requirements → Much higher chance of project success If this resonated with you → that’s exactly what we teach in our BA Mentoring sessions in BA Bootcamp. We focus on practical skills that make your work clearer, your stakeholders happier, and your value stand out. As always, like, repost and add a comment if you found this interesting… How do you approach your requirements when you’re starting a brand new project?