Design Efficiency Analysis

✅ How To Run Task Analysis In UX (https://lnkd.in/e_s_TG3a), a practical step-by-step guide on how to study user goals, map user’s workflows, understand top tasks and then use them to inform and shape design decisions. Neatly put together by Thomas Stokes. 🚫 Good UX isn’t just high completion rates for top tasks. 🤔 Better: high accuracy, low task on time, high completion rates. ✅ Task analysis breaks down user tasks to understand user goals. ✅ Tasks are goal-oriented user actions (start → end point → success). ✅ Usually presented as a tree (hierarchical task-analysis diagram, HTA). ✅ First, collect data: users, what they try to do and how they do it. ✅ Refine your task list with stakeholders, then get users to vote. ✅ Translate each top task into goals, starting point and end point. ✅ Break down: user’s goal → sub-goals; sub-goal → single steps. ✅ For non-linear/circular steps: mark alternate paths as branches. ✅ Scrutinize every single step for errors, efficiency, opportunities. ✅ Attach design improvements as sticky notes to each step. 🚫 Don’t lose track in small tasks: come back to the big picture. Personally, I've been relying on top task analysis for years now, kindly introduced by Gerry McGovern. Of all the techniques to capture the essence of user experience, it’s a reliable way to do so. Bring it together with task completion rates and task completion times, and you have a reliable metric to track your UX performance over time. Once you identify 10–12 representative tasks and get them approved by stakeholders, we can track how well a product is performing over time. Refine the task wording and recruit the right participants. Then give these tasks to 15–18 actual users and track success rates, time on task and accuracy of input. That gives you an objective measure of success for your design efforts. And you can repeat it every 4–8 months, depending on velocity of the team. It’s remarkably easy to establish and run, but also has high visibility and impact — especially if it tracks the heart of what the product is about. Useful resources: Task Analysis: Support Users in Achieving Their Goals (attached image), by Maria Rosala https://lnkd.in/ePmARap3 What Really Matters: Focusing on Top Tasks, by Gerry McGovern https://lnkd.in/eWBXpCQp How To Make Sense Of Any Mess (free book), by Abby Covert https://lnkd.in/enxMMhMe How We Did It: Task Analysis (Case Study), by Jacob Filipp https://lnkd.in/edKYU6xE How To Optimize UX and Improve Task Efficiency, by Ella Webber https://lnkd.in/eKdKNtsR How to Conduct a Top Task Analysis, by Jeff Sauro https://lnkd.in/eqWp_RNG [continues in the comments below ↓]

In IIT hostels, the worst insult was calling someone a 'maggu' - a studious plodder. Similarly, in power electronics, transformers are often the unglamorous workhorses that get minimal design attention. But what if transformers hold the key to both efficiency and EMI performance? I've been studying some fascinating work on flyback transformer design. When engineers tested several different transformer configurations - changing nothing else in the circuit - the results were eye-opening. Simply by optimizing the wire diameter and winding structure, efficiency jumped from 86.9% to 89.0%. This 2.1% improvement means 12% lower total system losses. And all from just one component. The secret? It's not about adding more copper. In fact, adding more copper (larger wire sizes or extra winding layers) can actually be counterproductive. The laws of physics are tricky here. At high frequencies, current doesn't flow uniformly through conductors. It concentrates near the surface - the famous "skin effect." When you place multiple wires near each other, things get even worse with "proximity effect." This creates a challenging balance: - Too-small wire diameter = high DC resistance and losses - Too-large wire diameter = high AC resistance and even greater losses The optimal solution isn't intuitive. For a 60 kHz flyback transformer, the sweet spot for primary windings was four strands of 0.25mm wire rather than a single thicker wire. Equally important was how the windings were arranged. Interleaving the primary and secondary windings reduced leakage inductance by 30%. This cuts energy losses in the snubber circuit considerably. For EMI, the engineers showed how built-in common-mode balancing reduced conducted emissions by up to 26 dB. That's enough to potentially shrink your EMI filter components or eliminate debugging nightmares later. I'm struck by how much performance was left on the table by conventional designs. The magnetizing energy lost through poorly designed transformers isn't just about efficiency - it directly impacts thermal management, reliability, and cost. Engineers often spend countless hours optimizing semiconductor components while neglecting transformer design. But without a well-designed transformer, the rest of the circuit can't reach its potential. What's the practical takeaway? Pay attention to: - Wire diameter relative to skin depth at your switching frequency - Interleaving techniques to reduce leakage inductance - Common-mode balancing for EMI reduction The transformer isn't just a component - it's the heart of your flyback power supply. Texas Instruments demonstrated this beautifully in their paper on flyback transformers, showing how seemingly small design choices can significantly impact overall performance. What component in your designs has delivered surprisingly significant improvements when you paid more attention to its design?

Ever wonder how the world’s most efficient manufacturers design their workcells for maximum flow? Designing an efficient production cell isn’t just about grouping machines together. It’s about crafting an environment where people, processes, and equipment align seamlessly to maximize flow and minimize waste. Here are the key elements you should focus on when designing your cell: 1. Layout & Flow Proximity: Arrange workstations so that materials move in a smooth, unidirectional flow. This minimizes unnecessary travel time and reduces transportation waste. Accessibility: Ensure that tools and materials are within arm’s reach. Well-planned storage and shadow boards support quick retrieval. Ergonomics: Design the cell with operator comfort in mind. A layout that reduces physical strain leads to fewer errors and higher productivity. 2. Standardization Consistent Processes: Establish clear standard operating procedures (SOPs) for each task in the cell. Standardization not only boosts quality but also makes training new operators faster. Visual Controls: Use visual cues like color-coded labels, signage, and displays to guide operators and ensure that processes are followed correctly. 3. Flexibility & Adaptability Modular Design: Create a cell that can be easily reconfigured as demand changes. Modular workstations allow you to quickly adjust the layout without major disruptions. Cross-Training: Equip operators with skills to handle multiple tasks. A flexible team can adapt to process changes more fluidly. 4. Communication & Collaboration Team Integration: Encourage teamwork by designing spaces that facilitate communication. Open areas and shared workstations foster collaboration and quick problem-solving. Feedback Mechanisms: Incorporate methods for continuous improvement—like daily huddles or visual performance boards—to keep everyone informed and engaged. 5. Waste Elimination Lean Principles: Identify and remove the 7 wastes (transport, inventory, motion, waiting, overproduction, overprocessing, and defects). Every design decision should aim to reduce these inefficiencies. Flow Efficiency: Focus on one-piece flow to reduce batch sizes and cut down on waiting time between steps. An effective cell design transforms chaotic, segmented workspaces into streamlined environments where every movement adds value. By carefully considering layout, standardization, flexibility, communication, and waste elimination, you can build a production cell that not only meets customer demands but also drives continuous improvement.

Before you spend on automation or expansion, audit your design debt first. Because in warehouses, design is leverage. It decides how efficiently your space, people, and capital perform, long before the first robot rolls in. Across sites, I’ve noticed one pattern: Design decisions that once made perfect sense quietly become constraints. That’s why I’ve started using a simple lens to assess warehouse health - a quick way to see where design amplifies performance, and where it quietly drains it. Here’s the 3-part audit I share with teams: 1. Utilization:- How much of your space drives value? Don’t stop at occupancy %. Ask what portion of your footprint moves revenue. A 90% full warehouse with 40% low-velocity SKUs isn’t efficient - it’s stagnant. 2. Flow Efficiency:- How many touches per order? Every U-turn, staging overflow, or backtrack adds cost and delay. Good design reduces touches, not just travel time. 3. Scalability:- Can the design absorb growth? Add 20% volume in your mind. If your answer is “we’ll add people,” the design is already past its shelf life. Scalable layouts have modular zones and pre-planned automation pathways. Bonus lens: Integration readiness:- if Wi-Fi, clear heights, or aisle widths can’t support tech, you’re not “automation-ready,” no matter what the brochure says. Design efficiency is capital efficiency. Fix flow before you fund expansion. Audit utilization before you automate. Because the building design is not a static cost - it’s a financial instrument that compounds (or erodes) ROI every day. If you’re evaluating a site or portfolio and want a structured way to uncover design debt, I help teams apply this lens to find where design adds leverage and where it leaks it. Reach out, happy to share what’s worked across networks. #WarehouseDesign #SupplyChain #WarehouseAutomation #PrivateEquity #OperatorFirst #Distribution

Our Simulated VRM Efficiency Curves Match Datasheet Measurements Exactly. Here's How. Just published a new article in How2Power Today on simulating DC-DC converter efficiency using state-space averaging VRM models in Keysight Technologies #ADS. The key finding: When we include proper DC and AC inductor losses in our Sandler State-Space Average models, the simulated efficiency curves correlate exactly with manufacturer-published measurement data across multiple input voltages for a 1.8V output. Not "close enough." Not "within tolerance." Exactly. Why this matters for your designs: • Every milliwatt saved extends battery life in IoT devices • Accurate efficiency predictions prevent thermal surprises • Cascaded power tree analysis catches compounding errors early • No more respins because your simulation said 92%, but reality delivered 85% What we cover in the article: • Why DC-DC converter efficiency is critical across applications • When efficiency simulation becomes a design imperative • How to set up accurate efficiency analysis in Keysight ADS • Modeling entire power systems with multiple cascaded regulators The method works because our models are built from actual bench measurements - not idealized equations that ignore switching losses and parasitic effects. Read the full article in How2Power Today: https://lnkd.in/eUyFx5CD Want to explore our SI/PI model library? We're adding new models every week to support higher-fidelity simulations: https://lnkd.in/eqNEyiu8 Because when thermal margins are razor-thin, "close enough" efficiency simulations lead to overheated products. 💪 #powerintegrity #keysightADS #VRM #simulation #signaledgesolutions #powerefficiency #electricalengineers #dcdc #powerelectronics #hardwareengineers #electricalengineers #pcbdesign #pcb #electricalengineering