Project Risk Assessment Techniques

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

If I were starting a new PROJECT today and wanted to plan it with ZERO prior knowledge, I'd do this: Step 1: Define Your Objective • Clearly articulate what success looks like for the project. • Break down the high-level goal into smaller, manageable milestones. • Ensure the objective aligns with stakeholders' expectations to avoid misalignment later. Step 2: Build Your Plan Backwards and Leverage Historical Data Most people skip this step entirely. But this is a huge mistake—because you risk creating a plan that doesn’t align with deadlines, resources, or realistic expectations. Here’s how: • Start from the final deliverable and work backward to define the timeline. • Gather and review historical data or similar project examples to understand typical timelines and challenges. • Identify key dependencies and create a logical sequence for tasks. • Use project planning tools (like Gantt charts or Kanban boards) to visualize your plan. • Clearly define roles and responsibilities for each stage. Pro tip: Don’t forget to account for buffer time—projects rarely go 100% as planned. Step 3: Identify Risks and Create a Mitigation Plan This isn't easy. But if you can do this, you will get: • Clarity on potential roadblocks before they derail progress. • Stakeholder confidence in your ability to deliver. • A proactive, problem-solving mindset that boosts your credibility. Here's a quick way to do this: List out possible risks, evaluate their impact and likelihood, and create a plan to minimize or respond to them. Collaborate with your team to spot any blind spots. Don't skip this step. It took me months of trial and error (and some chaos) to crystallize these steps—hope this helps! 🚀

🚨 Too many Product Managers are shipping features into a financial black hole 🚨 Let me tell you a tale of two teams. Both shipped on time. Both nailed UX. Both hit NPS targets. Only one is still around. Why? Because the other had not a real product manager. They were obsessed with velocity - but ignored viability. They launched. They celebrated. They ignored product imbalances. And 12 months later? 💸 Burned budget 🧊 Stalled adoption 📉 Cancelled product Meanwhile, the survivor team? Their PM didn’t own the product P&L - but they damn well acted like it. ✅ Obsessively modeled product profitability ✅ Challenged scope creep that killed margins ✅ Aligned product decisions to value-based outcomes ✅ Partnered with Sales & CS - not just Design & Dev They used every product viability lever they had: 📈 Knew the pipeline better than Sales 💰 Balanced dev cost with services overhead 🧮 Services ROI 🎯 Growth + retention metrics: Tracked feature adoption by account tier 🚫 They didn’t just ask “Can we build it?” ✅ They asked, “Should we build it - and will it pay off?” Product viability is not a finance topic. It’s a product mindset. This isn’t a heroic story. This is table stakes. And it’s the difference between being a strategic asset or a feature factory. So here’s the challenge to every PM out there: 📌 Can you explain how your product generates revenue (or at least value) - or avoids unnecessary cost? 📌 Do you know what the top churn reasons are for your product? 📌 Have you seen your product’s margin trend over time? If not - why not? 🧨 If you don't work with sales, services, finance, and partners to shape a viable model, you are missing half the job. 🧨 If you aren’t asking “how does this feature move our pipeline?” you are just building ... not managing. "One ships features. The other ships outcomes." Which one are you? Let’s stop pretending Product ≠ Business. Thoughts? Agree? Disagree? 👇 Let’s debate this in the comments. #ProductManagement #ShipOutcomesNotFeatures #ProductLeadership #ValueDriven

Understanding Material Microstructures through Monte Carlo Simulation of Grain Growth In materials science, the microstructure of a material significantly influences its properties. One critical phenomenon that shapes these microstructures is grain growth—a process where grains within a polycrystalline material coarsen over time, driven by the reduction of surface energy at grain boundaries. What’s fascinating is how a simple set of rules in this simulation can accurately replicate grain boundary dynamics—similar to Conway’s Game of Life, where basic rules governing cell behavior give rise to surprisingly complex patterns. In grain growth simulations, the process is driven by calculating the free energy of each atom in a lattice based on its current crystallographic orientation and comparing it to a random alternative. If the new orientation results in lower or equal energy, it replaces the former, mimicking the natural tendency of materials to minimize surface energy. Despite its simplicity, this approach effectively captures the intricate process of grain coarsening, demonstrating how elegant mathematical models can unravel real-world complexities. One aspect I’m particularly excited about is how this implementation can simulate the evolution of a cold-worked structure during the annealing process. By initializing the simulation with elongated and oriented grains—representative of cold-worked materials—this model reveals how grains gradually recrystallize and coarsen over time, eventually leading to a more equiaxed microstructure. For those curious and intrigued by this simulation, you can try it yourself! Head over to https://lnkd.in/d2Qrd6yK, where I’ve shared the implementation along with detailed instructions. Dive in, experiment, and explore how grain structures evolve right on your own system!

ERP Projects Fail for Many Reasons. Ignoring Integrations is the Fastest Way to Doom One. Too often, ERP projects run over budget, take too long and fail to deliver. The culprit? Overlooked integrations. I see this mistake all the time. Companies focus on ERP functionality but forget that no system operates in isolation. Data flows, third-party systems, and automations must be planned from day one—not as an afterthought. That’s why I put together a no-nonsense whitepaper on how to make ERP integrations work instead of becoming a hidden pitfall. 5 Practical takeaways from the whitepaper: 1. Define all data flows at project kickoff – Document dependencies between systems early. Surprises later = delays & cost overruns. 2. Master data first, transactions second – Sync customers, vendors, and products first. If your master data is broken, transactions will fail. 3. Set a realistic integration timeline – Sync integration tasks with ERP rollout. If integrations are late, the entire project stalls. 4. Test with real data, not fake records – Your ERP test system should mirror production. Otherwise, the first real transaction is your actual test. 5. Make integrations visible – Use visual mapping tools to align teams, avoid assumptions, and ensure all critical systems stay connected. Get the full whitepaper here: https://lnkd.in/dfNHA9nN ERP success is not just about the ERP—it’s about how well everything connects. Integrations First. Always. #ERP #Automation #iPaaS #PMO #ProjectManagement

The most dangerous person on any engineering team isn't the one who knows nothing... It's the experienced engineer who just joined and wants to "fix everything." 🧨 I've seen this pattern countless times: Senior engineers join a new company, look at the codebase with horror, and immediately start planning the "great refactoring." They see messy code, strange architecture decisions, and technical debt everywhere. But here's what separates truly senior engineers from merely experienced ones: The ability to pause, observe, and understand before changing anything. 🛑 That "horrible" authentication system? It handles edge cases you haven't discovered yet. That "messy" database schema? It's optimized for specific queries that power critical features. That "outdated" framework? It might be the only thing preventing production outages. Junior engineers see a mess and want a revolution. Mid-level engineers see problems and want evolution. Senior engineers see context and make intentional decisions. The wisdom isn't in knowing how to rebuild everything—it's in understanding the difference between: • Technical debt worth addressing • Ugly code that works reliably • Actual system risks requiring intervention The highest form of engineering maturity isn't showing how much you can change—it's knowing precisely what to change, when to change it, and most importantly, what to leave alone. What's the most valuable legacy system you initially wanted to rewrite but later came to appreciate? #EmbeddedSystems #Firmware #SoftwareEngineering #LegacyCode #TechLead #Refactoring #Cprogramming #DeveloperLife

🚢 How To Launch Big Complex Projects. How to reduce costs and schedule overruns, manage risks and be prepared for an unlucky turn of events ↓ 🤔 99.5% of big projects overrun budgets and schedules. 🤔 These are big relaunches, legacy re-dos, big initiatives. 🚫 Adding 15–20% buffer time/costs rarely saves them. ✅ Complex projects often follow “fat-tailed” distribution. ✅ There, overruns of 60–500% turn into big disasters. 🚫 Beware of unchecked optimism → unrealistic forecasts. 🚫 Beware of “cutting-edge” → untested technology spirals risk. 🚫 Beware of “bespoke/unique” → high chance of exploding costs. 🚫 Beware of “brand new team” → rely on tested and reliable teams. 🚫 Beware of “most advanced” → build small things, then compose. 🤔 The only way to prevent big disasters is to plan more. ✅ Best strategy: Think Slow (designing) + Act Fast (delivery). ✅ Good planning includes experiments, tests, simulations. ✅ Reference-class forecast → study mean actual cost, time. ✅ Track and review past projects in your company (cost, time). Things almost never go according to the plan — and on complex projects, they don’t even come close. We often assume that if we just thoroughly collect all the costs needed and estimate complexity or efforts, we should get a decent estimate of where we will eventually land. Nothing could be further from the truth. Complex projects have plenty of unknown unknowns. No matter how many risks and dependencies and upstream challenges we identify, there are many more we can’t even imagine. The best way to be more accurate is to define a realistic anchor — for time, costs and benefits — from similar projects done in the past. That’s what Prof. Bent Flyvbjerg calls reference-class forecasting (RCF) — experience-based, real-world outcomes that shape our estimates. No project is a snowflake; it always shares similarities with other projects. And however meticulous our calculations are, they usually approximate best-case-scenarios. Complex projects start with a deep deficit of experience. To increase the chances of success, we need to minimize the chance of mistakes even happening. That means trying to make the process as repetitive as possible — with smaller “work modules”, repeated by teams over and over again. It also means relying on reliable: from well-tested technology to stable teams that have worked well together in the past. And: always spend a bit more time planning, experimenting, testing and refining the plan before drawing a single pixel on the screen. It will pay off big time — every single time. I can only wholeheartedly recommend a book on “How Big Things Get Done” by Prof. Bent Flyvbjerg and Dan Gardner which goes in all the fine detail of how big project fails and when they succeed. It's not a book about design, but a fantastic book for designers who want to plan and estimate better. #ux #design

Risk isn’t just about probability… it’s about impact. Some risks happen often, but they barely affect the outcome. Others are rare , but when they hit, they can completely derail a project. That’s why effective risk management is not about listing risks… It’s about prioritizing the right ones: 1- High probability / low impact → monitor & handle quickly 2- Low probability / low impact → document & watch 3- Low probability / high impact → plan mitigation & contingency 4- High probability / high impact → immediate action + escalation In projects (especially in IT & healthcare), the biggest mistakes happen when teams focus only on what is “likely”… and ignore what is “catastrophic”. Question: Which type of risk do you see most ignored in your organization ,high impact or high probability? #ProjectManagement #RiskManagement #PMO #HealthcareIT #Strategy #Governance #ProgramManagement

Ground conditions are the biggest project killer. So why plan site investigation without the input of the contractor? I constantly recommend getting contractors involved in planning site investigation. The pushback is always the same: "We have consultants for that." Here's my simple answer: Ground risk is the single largest unknown on any project. The contractor gets exposed to it immediately while you still carry the risk of any "unknowns." The ECI blueprint that actually works: 1. Joint objectives workshop. Get everyone around the table - client, designer, consultant, contractor. What risks must the site investigation answer? Dredgeability, rock, boulders, UXO? Make the questions explicit before selecting methodology. 2. Smart investigation selection Combine the investigation contractor's coverage plan with the contractor's equipment insights. Result? Targeted samples that produce results that reflect real equipment limits and capabilities. 3. Risk-priced options Translate findings into executable alternatives - different equipment, foundation types, extra passes, provisional sums - with time and cost implications. 4. Contract alignment with GBR Fix who owns residual ground risk. Use a Geotechnical Baseline Report to identify the real unknowns. What happens without ECI: → Misaligned investigation - boreholes where nothing matters, none where it does → Method mismatch - wrong equipment selected for the actual conditions → Late redesign - ground model changes post-tender → Inflated risk premiums - contractors price risk for the unknown between the sampled locations but still claim when conditions differ. $1 saved on investigation = $100 claim later. Early collaboration means a few extra meetings and perhaps a slight increase in ground investigation costs. Versus late discovery of the actual ground conditions which costs the entire project. Bottom line: Don't let ground conditions become tomorrow's headline claim. Open the door to your contractor before the drill rig shows up. Because "a problem aired is a problem shared." P.S. Planning a project with significant ground risk? Want to set up ECI that prevents claims rather than just delays them? Send me a DM and let's discuss the right approach.

#Safetytechtip for solo safety pros overwhelmed with risk register admin. As a solo safety professional, developing a comprehensive risk register can feel like a massive undertaking. But what if you could use a simple, tech-driven workflow to get it done faster and with better results? All while maintaining critical thinking & collaboration with teams. Here's a pro tip to streamline the process & tap into the collective knowledge of your organisation. Full disclosure: This entire post, from my core ideas to the final text, was generated using my voice—a workflow created entirely through my dictation & insights, then crafted into this narrative using LLMs. No keyboard used aside from pressing Ctrl + Windows key to activate my dictation tool*. Step 1: Brainstorm and Categorise with AI Start by physically walking through your planned risk scenarios, dictating your job steps, potential hazards, processes and areas of risk. Transcribe (there's loads of ways to do this) then use an AI tool like Claude, Gemini, ChatGPT etc to summarise these notes into risk assessment categories based on a company risk template which you can upload as context. This gives you a structured foundation for your register. Step 2: Host a Multidisciplinary Risk Conversation Schedule a session with key stakeholders to host a risk discussion - try to make it more conversational than line by line; nobody likes sitting through excel risk reviews. Use the risk categories you developed as a talking guide. Use an omnidirectional microphone to capture the conversation (with consent) & ask each person to state their name & role which with speaker identification during transcription. Step 3: Transcribe & Populate Your Register Upload the audio file to a transcription service (even Microsoft Word can do this) to get a written record of the discussion. Then use Claude to populate your risk register. Step 4: Develop Your Management Plan Once your register is populated, start a new chat with the same or alternate LLM** Upload a reference example of a risk management plan and prompt it to create a new one based on your newly populated risk register. This ensures your action plan aligns with your identified risks. Step 5: Turn Plans into Action Finally, turn your management plan into a clear, actionable list. Export these tasks directly into an electronic task manager like Microsoft Tasks or Asana; I used Google Tasks for my latest action register. This ensures accountability and helps you track progress toward mitigation. By leveraging AI and collaborative tools, you can evolve risk management into an efficient and effective process. *Hit me up if you'd like to learn more about how I overlay dictation into everything from excel cells to email replies. ** I like to use different LLMs for different tasks - they all perform differently depending on what you want to do; if you need coaching or guidance on this let me know. #Safetytech #Safetyinnovation