Engineering Problem-Solving Techniques

VSM isn't just a Lean tool.     It's the key to your business success.    The main goal of a business is to provide outstanding value at the lowest price.    In line with this goal, Value Stream Mapping aims to show you where you can reduce waste.    VSM visualises your operations from material inputs to customer delivery.     Here are 5 simple steps to create your Value Stream Map:    Step 1. Pick a Product Family    Recognize the value stream from your customer perspective.    Select a large-quantity and high-revenue product family.  Focus on reducing cycle times for your best sellers.    It's crucial to maximize the gains.  __    Step 2. Build your team    Assign a dedicated team leader.  Pick subject matter experts from key departments.     Make sure they know enough about the product and VSM.  __    Step 3. Figure out your customer demand    Check your production history.    Figuring out your customer demand is critical to calculate your takt times.  __    Step 4. Map the material flow    Map the journey that materials embark on, from receive to customer delivery.    It helps identify waste by analyzing each step of material handling.     Look at how the material flow is secured by operators.    At this point, key questions can be:    -What systems are in place to replenish your inventory levels?    -Do your operators have the materials they need to hand?      Then, add the collected data to this flow.  __    Step 5. Map the information flow    How does information move through the value stream from the initial request to order delivery?    Capture how the information goes through your organization.    Pinpoint communication gaps, and enhance data flow.  Video Credits: Krish Sengottaiyan __    TL;TR    5-step framework for Value Stream Mapping:    1. Pick a product family  2. Build the VSM team  3. Figure out your customer demand  4. Map the material flow  5. Map the information flow

5-WHY ROOT CAUSE ANALYSIS (RCA) Problem Statement: A batch of parts was rejected due to an oversized hole diameter. 5-Why Analysis: 1.Why was the batch rejected?→ Because the hole diameter was larger than the specified tolerance. 2.Why was the hole diameter too large?→ Because the drilling machine was not properly adjusted. 3.Why was the machine not properly adjusted?→ Because the operator used an outdated setup sheet. 4.Why did the operator use an outdated setup sheet?→ Because the latest revision was not available at the machine. 5.Why was the latest revision not available at the machine?→ Because there is no system in place to ensure controlled document distribution. Root Cause: No document control system for distributing updated setup sheets. Corrective Actions: •Introduce a document control procedure to issue and display the latest revision only. •Restrict access to outdated setup sheets by removing old versions from machines. •Train machine operators and line leaders on verifying document revision before setup. Preventive Measures: •Digitize all setup sheets with access through a centralized network folder or MES (Manufacturing Execution System). •Implement revision control logs with sign-off for updates and acknowledgments by operators. •Conduct regular audits on setup documents at workstations. •Establish standard work that includes a revision check step before every job setup. •Integrate barcode or QR code scanning to verify correct document versions at machines.

One of the biggest challenges in understanding modern software development is seeing how all the pieces fit together. That's why I've captured the entire process in a single, comprehensive diagram. It's a bird's-eye view that many miss, but it's crucial for grasping the big picture. Let's walk through this end-to-end journey: 1. Plan: It all starts with the Product Owner creating user stories, setting the development direction. 2. Code: Developers collaborate, code, and push changes to version control. 3. Build: Automated CI servers spring into action, compiling code and managing dependencies. 4. Quality Assurance: A suite of tools runs unit tests, checks code coverage, scans for security issues, and performs static analysis. 5. Package: Artifacts are versioned and stored, ready for deployment. 6. Deploy: The code progresses through multiple environments (DEV, QA, UAT, PROD), each with its own deployment gates. 7. Test: Automated regression and UAT tests ensure software integrity at each stage. 8. Release: The final step, delivering value to end-users. 9. Infrastructure as Code: Showcasing how infrastructure provisioning is integrated into the pipeline. 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ✅ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀: Seeing the big picture helps identify bottlenecks and improvement areas. ✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆: Frequent code integration reduces conflicts and catches issues early. ✅ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗿𝗲𝗹𝗲𝗻𝘁𝗹𝗲𝘀𝘀𝗹𝘆: Every manual step is an opportunity for automation. ✅ 𝗦𝗵𝗶𝗳𝘁 𝗹𝗲𝗳𝘁 𝗼𝗻 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴: Integrate these earlier in the process for better outcomes. ✅ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: Use feedback loops to constantly refine your process. By visualizing the entire flow, from planning to production, including feedback loops and tool integrations, we can better understand how Agile and DevOps practices intertwine to create a seamless, efficient development process. What part of this flow presents the biggest opportunity for improvement ? Where would you start?

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

4 months into my first sustainability job, and I can't believe more companies aren't doing life cycle analysis. I regularly analyse products from start to finish - raw materials, manufacturing, transport, use, and disposal. The insights are eye-opening. Here's what I've learned: Life cycle analysis reveals hidden environmental impacts that companies frequently miss. It can identify cost-saving opportunities and carbon emission hotspots that often go unnoticed. It helps create better products that customers actually want! On the surface, the process isn't complicated: 1. Collect data from supplier/manufacturer 2. Input materials, processes and waste management 3. Analyse and understand impact categories 4. Find improvement opportunities 5. Implement changes Small changes make big differences. One of our recent projects reduced water usage by over 90% AND decreased CO2 by over 50% just by changing the material (yes, I couldn’t believe it either). Don't wait for regulations to force your hand. Start analysing your products now. The data is there, the methods are proven, and the benefits are clear.

I love good systems engineering. It isn’t about solving individual problems — it’s about architecting coherence across complexity. At its core, systems engineering ensures that every subsystem, every interface, and every requirement aligns to achieve the mission. It’s not about chasing isolated performance metrics; it’s about designing solutions that thrive under real-world constraints. 1 / Defining the Mission. The starting point isn’t the technology—it’s the purpose. Systems engineering begins by asking: What is the system intended to accomplish? Every design choice flows from this mission, ensuring all components serve the larger objective. 2 / Managing Complexity. Modern systems—whether in aerospace, software, or manufacturing—operate at scales that defy intuition. Systems engineers impose order on this complexity by defining interfaces, ensuring compatibility, and anticipating cascading failures. It’s the discipline of integrating the parts into a functional whole. 3 / Designing for Resilience. Systems don’t operate in ideal environments; they face uncertainty, failures, and external disruptions. A good system works when conditions are perfect. A great system works when they’re not. Systems engineers design for these edge cases, ensuring robustness and adaptability. 4 / Iterating with Precision. The process is iterative, but not aimless. Each phase—requirements definition, design, validation—is rigorously structured. Feedback loops aren’t just encouraged; they’re built into the system. Systems engineering is the invisible backbone of modern innovation. It’s what makes rockets fly, networks connect, and critical infrastructure endure. Without it, complexity devolves into chaos. Thoughts????

In conversations with engineering leaders, I'm noticing an emerging theme: smart, capable managers who "grew up" in the 2010s and early 2020s are struggling to adjust to a new reality in tech leadership. For over a decade, the rule of the game was simple: hire, grow, and retain. Leadership meetings were dominated by conversations about headcount, hiring progress, and ambitious growth targets. There was grilled venison tapas at lunch, and we talked a lot about psychological safety and inclusion. These were important topics (and tapas), but they existed in an environment of abundance. Sure, we wanted things to be more efficient — but the solution was often to spend more money to make it so. We had no choice — headcount was growing by the day, and the focus was on scaling rapidly to meet demand and capture market share. Fast forward to today, and the landscape has shifted dramatically. I spoke with a VP of Engineering recently: smart, capable, and struggling with how to report upwards effectively while still maintaining empathy for the realities of software engineering and the people in their organization. They were visibly relieved to hear me say that others are grappling with these same challenges. Engineering leaders at all levels are living in a new world of intense scrutiny and accountability. The instincts and strategies they honed over years of rapid growth aren't serving them well in this new environment. Under pressure, toxic approaches that would have been quickly dismissed in the past are now getting airtime they never would have deserved before. We're seeing a fundamental shift in what it means to be an effective engineering leader: 1. Financial Acumen: Leaders now need a deep understanding of financial metrics and how engineering decisions impact the bottom line. 2. Operational efficiency: There's a renewed focus on doing more with less, optimizing processes, and identifying areas of waste. 3. Strategic prioritization: With limited resources, the ability to ruthlessly prioritize and communicate trade-offs has become crucial. 4. Change Management: Leaders must guide their teams through organizational changes and shifts in company strategy with transparency and empathy. 5. Metrics-driven decision-making: There's increased pressure to justify decisions with data and demonstrate tangible value. 6. Stakeholder management: Navigating complex relationships across the organization and managing expectations has become more critical than ever. The challenge lies in balancing these new demands with the core principles of effective engineering leadership: fostering innovation, maintaining team morale, and delivering high-quality products. How has your role changed in the past 12-18 months?

I could watch this 19th-century water-powered sash sawmill run all day. No electronics. No PLCs. No hydraulics. Just gravity, flowing water, wood, iron, and some very clever mechanical engineering. What impresses me most is not just that it works—it’s that it still works. The cams, linkages, and wooden frame are all doing exactly what they were designed to do over a century ago: convert the steady force of moving water into a precise, repeatable cutting motion. Designed without CAD, built without CNC, and yet the tolerances, alignments, and load paths are good enough to survive generations of real-world use. It is a masterclass in: -Simplicity of design -Durability and maintainability -Using local materials and available energy -Engineering that respects both physics and craftsmanship In modern projects we talk a lot about “sustainability,” “resilience,” and “design for maintenance.” This old mill is a reminder that those ideas are not new. The millwrights, carpenters, and blacksmiths who built systems like this were solving the same problems we face today—just with different tools. As engineers, inspectors, and builders, there is a lot we can still learn from these legacy systems about robustness, clarity of design, and respect for the trades that bring our drawings to life. #engineering #manufacturing #mechanicalengineering #craftsmanship #industrialhistory #design #quality

Last month, I shared my observations about lawyers who successfully transitioned to business roles. Many of you resonated with one of the points that I made: that these lawyers tend to be great at issue spotting, but "with an eye for opportunity—not risk." Today I’ll share four ways that have helped me calibrate my issue spotting skill for the business world: 1. Be aware of the bigger goals. Without knowing what my company’s goals were, I would always default to issue-spotting for risk. It’s just how lawyers are trained, I guess. Understanding our top priorities helped me also figure out what our CEO/execs needed, and where our company could find unexpected ways to achieve them. Seeing the bigger picture gave me a framework for understanding how to make tactical, day-to-day type of decisions. 2. Focus on how to “get lucky” instead of “being correct.” There was something comforting about pointing out risks because even if bad things didn’t end up happening, I felt like I was “correct” in warning my teammates about them. Eventually I started using my ability to process fact patterns to quickly visualize multiple unexpected paths to achieving company objectives. Leadership appreciates when you come up with new ways to help them hit their goals. 3. Recognize the hidden costs of the status quo. Your lawyer brain may scream “don’t sign that contract” but what are the consequences of not bringing on that customer? As a startup person, I eventually realized that if we didn’t hit certain revenue milestones in time, it would put fundraising at risk—which would lead to the company running out of money. It dawned upon me why the status quo was unacceptable—even if that path was safer from a legal perspective. 4. Get comfortable acting with incomplete information. Lawyers, especially those of us who come from the law firm world, are used to researching thoroughly before recommending a course of action. However in the business world, speed often matters just as much as accuracy. Which means you have to move quickly and adjust/iterate over time as you learn new information. This can be very uncomfortable for lawyers—but the good news is that it gets easier over time. Would love to hear your thoughts, especially if you’re a business person who works with ex-lawyers, or if you’re a lawyer who’s successfully made the transition! 

How do engineers simulate turbulence? Let’s explain it like you’re 5. Imagine you’re watching a big football match. The players are running everywhere, passing the ball, the crowd is cheering. It's total chaos sometimes. Now, you have three ways to understand what’s happening on the field: 1. DNS (Direct Numerical Simulation): You follow every single player, every step, every pass, every move of the ball, every bounce. You know exactly what’s happening. But it's exhausting and takes a super powerful camera and endless storage! ➡️ In fluid flow, DNS resolves every little swirl and eddy (smallest to largest). Super accurate but takes huge computing power and storage. 2. LES (Large Eddy Simulation): You follow only the star and big plays, like goals and key passes. For the small moves and background players, you just make a smart guess. You still understand most of the game, and it's easier to watch. You might miss some tiny details. ➡️ In fluid flow, LES resolves the big turbulent eddies and models the small ones. A balance between detail and effort. 3. RANS (Reynolds-Averaged Navier-Stokes): You don’t watch every move. You just say, “On average, this team had more possession and scored more goals.” Fast and easy to understand the overall result. But you miss all the exciting plays and details. ➡️ In flow, RANS gives the average effect of turbulence on the mean flow, without tracking all the chaos. It all depends on what you need. The full match analysis? (DNS) The highlights? (LES) Or just the final score? (RANS) That’s how engineers balance accuracy and computing power in turbulence modeling. #mechanical #aerospace #turbulence #automotive #cfd #aerodynamics