Engineering Problem-Solving Techniques

6 engines. 168 cylinders. One aircraft. In this 1940's factory image (digitally enhanced) , the wing of the Convair B‑36 Peacemaker is being fitted with six Pratt & Whitney R‑4360 Wasp Major radial engines each producing 3,800 horsepower. That’s 28 cylinders per engine. 168 cylinders across the wing. Built by Pratt & Whitney, this was the most powerful piston aircraft engine ever mass-produced. But the real lesson isn’t horsepower. It’s systems engineering. Every one of those engines had to integrate with: • cooling airflow • fuel distribution • propeller dynamics • structural loads in the wing • vibration modes across a 70-meter wingspan • maintenance accessibility for ground crews One engine is a machine. Six engines become a system. And systems create problems you can’t see when you design components in isolation. That’s why early strategic aircraft like the B-36 forced engineers to think beyond parts , toward integration, redundancy, and failure tolerance. A single engine failure was expected. The aircraft had to keep flying anyway. The lesson still applies today , whether you're designing spacecraft, AI systems, or aircraft: Engineering breakthroughs rarely come from bigger components. They come from better integration of complex systems. The engineers at Convair building this aircraft understood something we often forget in modern engineering culture: Complexity isn’t solved by adding technology. It’s solved by designing systems that survive it. One aircraft designed to carry the weight of an entire strategic doctrine. Sometimes the most important engineering achievement… is making complexity fly. Pic Credit : Jets n Props

I’ve been waiting a long time to show an example of what gets me up in the morning… Because in this world, failure isn’t the end — it’s the start of real insight! 💥 During hot-fire testing, an additively manufactured GRCop-42 combustion chamber failed — and with it, offered a powerful #FailureAnalysis case study on the critical role of process rigor in additive manufacturing, especially when builds are interrupted. We conducted a full failure analysis: reviewing test day data, manufacturing records, post-processing steps, and metallurgical characteristics of both the failed chamber and adjacent components. 🔬 Key findings: •⁠ ⁠Failure occurred at a build interruption location, witness line, with metallographic analysis revealing higher porosity than expected. •⁠ ⁠This localized porosity reduced tensile strength and elongation, triggering the failure. •⁠ ⁠Interestingly, test bars with emulated build interruptions showed no performance degradation — confirming that proper restart procedures preserve part integrity. Additive manufacturing offers incredible promise, but as this work shows, it also demands discipline. Especially when the stakes are rocket engines. 🔗 Full article: https://lnkd.in/ekg-t4MH Ben Williams, Colton Katsarelis, Will Tilson, and Paul Gradl, thank you for the collaboration in making this fun analysis and article! #AdditiveManufacturing #RocketEngines #FailureAnalysis #MaterialsScience #GRCop42

AI answers start with reasoning. Before an AI agent produces a response, it often goes through structured thinking processes to analyze the problem, explore options, and determine the best path to a solution. Modern AI systems rely on different reasoning methods to handle complex tasks more reliably. - Chain-of-Thought The model breaks problems into step-by-step reasoning before producing the final answer. This method helps with math, coding, and structured analytical tasks. - ReAct (Reason + Act) ReAct combines reasoning with tool usage. The agent observes information, chooses tools, executes actions, and updates context before generating the final response. - nTree-of-Thought Instead of following a single reasoning path, the model explores multiple possible solution branches and evaluates which one produces the best outcome. - Self-Consistency The system generates multiple reasoning attempts for the same problem and selects the most consistent answer across those attempts. - Plan-and-Execute The agent first creates a structured plan and then executes each step sequentially to complete complex tasks. - Reflexion The model evaluates its own outputs, learns from mistakes, and adjusts its reasoning before retrying a solution. - MRKL (Modular Reasoning) This approach routes problems to specialized tools or models, combining outputs from different components to produce the final result. - Program-of-Thought Instead of only reasoning in text, the model generates code to solve logical or analytical problems and executes the program to derive the answer. AI is moving beyond simple text prediction. Modern systems combine reasoning strategies, tool usage, and iterative learning to solve increasingly complex problems. Which reasoning method do you think will become the standard for future AI agents?

AI is changing what problem-solving means in tech. Earlier, problem-solving often meant figuring out how to build something. Choosing the right algorithm. Optimizing performance. Writing clean code. Today, the “how” is no longer the hardest part. AI can generate code, suggest architectures, and fix syntax in seconds. What has become difficult is deciding what to build and why. Real problem-solving now starts much earlier. 🔸Understanding vague requirements. 🔸 Translating business needs into technical decisions. 🔸Choosing trade-offs that will age well. 🔸Knowing when a solution is good enough and when it is over-engineered. AI accelerates execution. It does not replace judgment. Strong engineers today are the ones who can 👉ask the right questions 👉narrow down the real problem 👉make decisions with incomplete information 👉and take responsibility for those decisions AI changed the surface of problem-solving. Not its core. The core is still thinking clearly in messy situations. And that skill is becoming more valuable, not less.

To become a top data analyst you need to be a strong problem solver! Follow this structure to find the real reasons behind business problems: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in the UK over the last few days.”   2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect relevant information such as order processing times, customer service interactions, inventory levels, and active marketing campaigns.   3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Use tools like SQL, Python, or Excel to analyze the data. Look for patterns, trends, and anomalies that could point to the root cause.   4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Brainstorm all possible reasons for the issue. Use methods like the 5 Whys technique to investigate each potential cause more deeply.   5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data to see if they are supported. If not, refine your hypotheses and test again.   6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Once you’ve identified the root cause, support the business by showing possible solutions to address it. Monitor the results to ensure the issue is resolved. 𝗔 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗽𝗮𝘀𝘁: We notice an increase in customer lead time and here’s how we tackle it. 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: We collected data on order processing, sales forecast deviation, and shipping times.     3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: We found that the actual sales were in line with the forecast, and shipping times had remained constant. However, order processing times had increased significantly.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: We checked factors such as outages in warehouses, staffing issues due to high sickness rates, and process inefficiencies resulting from operating close to maximum capacity.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data revealed that a spike in the sickness rate had reduced the available workforce.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: We proposed to increase capacity buffers by 5% to 10% during the winter and hiring additional temporary workers to address the situation in the short term.   Following this approach for your root-cause analysis, you will become a valued problem-solving partner for your stakeholders. How do you ensure you’re addressing the root cause of an issue and not just the symptoms? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #rootcauseanalysis #problemsolving #careergrowth

When it comes to problem-solving, rushing to find quick solutions overshadows the importance of deeply understanding the issue at hand. This can lead to superficial fixes that fail to address the root cause, resulting in recurring issues and stunted innovation. The challenge lies in shifting focus from a solution-first mentality to a problem-centered approach. Without a love for the messiness of process, organizations miss out on the depth of insight and innovation that comes from truly understanding an issue. Cultivating a passion for challenges involves embracing a mindset that sees challenges as opportunities for: ↳ Growth  ↳ Learning ↳ Innovation. This means slowing down, asking better and deeper questions, and encouraging a culture that values curiosity and exploration over immediate resolution. By creating an environment that sees the beauty and opportunity in challenges, your organization can unlock a richer, more innovative path to success. #ProblemSolving #Challenges #Curiosity #Solutions 📸 Photo Credit: Sahar Coston-Hardy

𝗦𝘁𝗼𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 𝗔𝗜 "𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗵𝗲𝗹𝗽 𝗺𝗲?" 𝗦𝘁𝗮𝗿𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 "𝗪𝗵𝗮𝘁 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗺 𝗜 𝘀𝗼𝗹𝘃𝗶𝗻𝗴?" Most people open ChatGPT and type vague requests like "help me with marketing" or "give me business ideas."  Then they wonder why the responses feel generic. The issue isn't the AI. It's your question. Problem definition beats prompt engineering every time. Instead of: "Help me grow my business" Try this: "My sales team is missing 30% of quarterly targets. Deals slowed from 60 to 90 days. Each missed quarter costs $2M in projected revenue." Now AI can actually help you. With a clear problem, you can ask targeted questions:  • Analyze patterns in top-performing deals  • Research what drives faster sales cycles in your industry • Generate hypotheses about pipeline bottlenecks 𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 1. Define the specific problem and its business impact 2. Quantify what success looks like 3. Use AI to research and validate solutions Six months of applying this approach will transform how you work.  Not because you become an AI expert, but because you master problem definition. The best AI users aren't prompt engineers. They're problem definers. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/eHDpy-fn Found this helpful?  𝗟𝗶𝗸𝗲 𝗮𝗻𝗱 𝗿𝗲𝗽𝗼𝘀𝘁 to share with your network. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for more insights on using AI strategically in business. Got a specific problem you're trying to solve? 𝗗𝗠 𝗺𝗲 - I'd love to hear about it.

The Case of the Costly Error Once upon a time, a critical bug in a bustling software company was reported in their flagship product just days before a major release. Panic spread through the team like wildfire. The bug was complex, and time was running out. At first, the team tried the usual approach—frantic debugging and patching. But the bug kept reappearing like a stubborn ghost. As deadlines loomed closer, frustration mounted. That's when Jane, one of the senior developers, stepped in. She suggested a different approach: structured problem-solving. She gathered the team in a meeting room and laid out a plan: 01. Define the Problem: They dissected the bug, identified its specific behaviors, and defined the problem statement clearly. 02. Collect Data: They gathered data on when the bug occurred, what actions triggered it, and the system conditions at that moment. 03. Generate Hypotheses: The team brainstormed potential causes, generating multiple hypotheses. 04. Test Hypotheses: They systematically tested each hypothesis individually, isolating variables and gathering more data. 05. Analyze Results: Based on the data collected, they analyzed the results of each test and eliminated hypotheses that didn't hold up. 06. Implement Solution: Finally, they identified the root cause and implemented a solution that fixed the bug once and for all. The bug was squashed, and the release went off without a hitch. What could have been a disaster turned into a valuable lesson. Structured problem-solving saved the day! → When faced with a daunting challenge, don't rush into solutions. Take a structured approach. #dhandhekafunda ps: Structured problem-solving approach acts as a compass when you are not emotionally trapped in the situation. If you are, have another competent individual take the lead. At least be that structured ;)

How software engineers solve problems vs how GRC teams solve problems 🔧 Software engineers: → Define the pain point clearly (Trello: "network partitioning causes message loss") → List specific requirements (failover capabilities, throughput needs, latency targets) → Evaluate multiple alternatives systematically (Kafka, SNS+SQS, Kinesis, Redis Streams) → Choose based on technical fit, not features (Kafka met requirements; Redis Streams was unstable) → Implement incrementally (shadow traffic, gradual rollout, measure results) → Build for future scale (anticipate growth, design for reliability) How traditional GRC solves problems: 📋 → Think in project cycles (audit deadlines drive everything) → Optimise for framework coverage over driving down risk → Ignore stakeholder toil ("just fill out this 50-question Google Forms") → Choose tools by feature count (does it have 200+ connectors?) → PoC based on demos not depth of what you actually need → Complain about your tool online → Reset after each audit instead of building iteratively The GRC Engineering difference? We apply software engineering problem-solving methodology to compliance challenges. ✅ Clear problem definition: "Automated evidence collection doesn't scale outside of public cloud specific controls" ✅ Requirements-driven evaluation: What do we actually need vs. what vendors sell vs. what we can build? ✅ Systematic comparison: Technical fit over feature lists ✅ Incremental implementation: Test with low-risk controls first but in production settings ✅ Future-proof architecture: Build systems that can be maintained by the team and can scale to production-grade environments Just like Trello didn't choose Kafka because it had the most features, they chose it because it solved their specific technical requirements. Your GRC program deserves the same engineering rigour. 🚀 #GRCEngineering #SystemsThinking #EngineeringMethodology

Problemeering: Engineering the Problem Before the Solution What is it? Problemeering (problem + engineering) is the art and science of identifying, defining, and framing problems so they can be solved more creatively and efficiently. Why it matters Many product launches, business strategies, and even personal projects flop because they target the wrong problem or never define one at all. Problemeering helps you: • Understand the real issue • Avoid premature “band‑aid” fixes • Uncover root causes and hidden opportunities • Frame challenges in a way that sparks breakthrough ideas Key steps Observe & Empathize – Listen to users and spot pain points. Define – State the core problem in one crisp sentence. Reframe – Challenge every assumption: “Is this really the problem?” Explore Context – Map the ecosystem, constraints, and stakeholders. Ask “How might we…?” – Turn the problem frame into innovation prompts. Quick example Late‑delivery complaints in a food‑delivery app. Instead of jumping straight to route optimization, a problemeering mindset asks: • Are customer expectations realistic? • Does the UI overpromise delivery times? • Are restaurants accepting orders they can’t fulfill? Addressing these upstream issues often fixes “late deliveries” more effectively than tweaking maps alone. Origin Not yet in the dictionary it just reminds us: engineer the problem first, then engineer the solution.