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?

⚠️ 4 critical mistakes in your physical risk scenario analysis…  And how to avoid them    Having worked on scenario analysis with dozens of financial institutions and corporates, I see 4 common errors that vastly reduce the usefulness of the assessments and leave firms exposed to climate shocks. These are:  - Confusing Average Scenarios with Extremes  - Misalignment of Time Horizons  - Narrowing the Range of Relevant Hazards  - Ignoring Second-Order Effects and Correlations    Through our team’s experience supporting on climate scenario analysis, financial stress testing and risk modelling, we have developed strategies to help you avoid these pitfalls.     Below is our downloadable for identifying and addressing these 4 errors. It’s free for you to download and use as a resource with your risk and modelling teams. 👉 Newsletter subscribers get the earliest access to these new resources and also get a deep dive into overcoming these 4 errors.  Subscribe today and don’t miss out: https://lnkd.in/eAtjsNbA #ClimateRisk #PhysicalRisk #ClimateChange #RiskManagement #SustainableFinance #StressTesting #ScenarioAnalysis 

🛑 The Biggest Silent Killer of Mining Projects: Overconfidence in the Orebody 🛑 Every mine plan looks good... on paper. Production targets are met. Budgets approved. Equipment ordered. Everyone feels good until the mine starts underperforming. Month after month. Quarter after quarter. And the excuses pile up:   “Unexpected dilution” “Poor ground conditions” “Operational delays”   But here’s the truth nobody wants to say out loud:   The real failure happened years earlier, when we trusted the orebody model more than we should have. Mining is the only industry I know that builds billion-dollar businesses on statistical guesses... and then gets surprised when reality doesn't cooperate. Geological uncertainty is not a rounding error. It’s not a minor risk. It's shown to be the major contributor to project failures. It’s the foundation your entire operation stands on, or collapses on. And yet, companies build LOM plans assuming the estimated block model is the ground truth. Why? Because it's easier to assume certainty than to quantify uncertainty and plan for it. Because spreadsheets are cleaner when you don’t have multiple scenarios. Because no one wants to explain to the board that the “high-confidence” resource might still let them down. But pretending the orebody is perfect doesn't protect you. It just delays the realization.   🔍 Here’s what actually happens: Resource models, even “measured” ones, have built-in errors, including grade, volume, and continuity errors. Estimation methods like Kriging smooth out the grades, where high-grades (where we make money!) are underestimated, and low-grades are overestimated. Mine plans are optimized assuming every block behaves exactly as estimated. Operations find out the hard way that Mother Nature didn’t read the single 3D model.   🔴 And the cost? Missed production targets. Inability to control contaminants at the plant. Cash flow shortfalls. Poor reconciliation. Erosion of investor trust. Bad CAPEX decisions. Inability to fulfill contracts.   All because we decided to ignore the geological uncertainty!   ✅ What actually works? Quantify uncertainty, early and often. Simulate multiple orebody realizations that reproduce the local variability under the ground instead of relying on a single “best guess.” Optimize the strategic mine plan looking at all simulations. This will ensure you have integrated risk-management, prioritizing less risky, yet rich, areas early on till more information is available for later project stages. Report the production schedules probabilistically.   Mining doesn’t fail because it’s inefficient. It fails because it assumes the earth will behave the way a model says it should. And when that assumption breaks, everything else does too. Maybe it’s time we stop treating geological uncertainty as a technical inconvenience. It’s the core business risk, and facing it in advance is the only way we’ll stop falling short. #Uncertainty #Risk #ResourceModel #MinePlanning #Stochastic

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

Winter Wind Turbine Woes: Mitigating Risks for Unpowered Post-Installation Units As winter sets in, many wind projects face a critical challenge: newly installed turbines sitting unpowered (pre-grid connection) for weeks—exposed to harsh winds, freezing temps, and hidden risks like vortex-induced vibration (VIV). For unpowered turbines, VIV isn’t just a nuisance—it’s a silent threat to blades, towers, and drivetrains. When blades stay in a fixed position (no pitching) and align with dominant winds (3-8 m/s is high-risk), periodic vortex shedding triggers destructive resonance. Add winter’s gusty conditions (we saw 12 m/s winds last for hours!), and the stakes rise fast. 😰 Here’s how we’re mitigating risks in field operations: ✅ Manually adjust pitch angles to 5-15° (via backup power/hydraulics) to disrupt airflow patterns ✅ Yaw nacelles 30-60° off dominant winds to break vortex consistency ✅ Schedule hourly inspections during high-risk wind windows (3-8 m/s) ✅ Lock mechanical dampers (or use manual pitch cycling) to dissipate vibration energy Winter’s unique combo of low temps (affecting hydraulics/electronics) and unpredictable winds makes unpowered turbine care non-negotiable. Proactive adjustments beat costly repairs later—especially when grid connection timelines slip. Have you tackled similar challenges with post-installation turbines in cold climates? Share your go-to VIV mitigation hacks or lessons learned! #WindEnergy #WinterOperations #TurbineMaintenance #VIVMitigation #RenewableEnergy #WindPowerEngineering

Risk Assessment. Risk assessment is “The process of quantifying the probability of a risk occurring and its likely impact on the project”. It is often undertaken, at least initially, on a qualitative basis by which I mean the use of a subjective method of assessment rather than a numerical or stochastic (probablistic) method. Such methods seek to assess risk to determine severity or exposure, recording the results in a probability and impact grid or ‘risk assessment matrix'. The infographic provides one example which usefully visually communicates the assessment to the project team and interested parties. Probability may be assessed using labels such as: Rare, unlikely, possible, likely and almost certain; whilst impact considered using labels: Insignificant, minor, medium, major and severe. Each label is assigned a ‘scale value’ or score with the values chosen to align with the risk appetite of the project and sponsoring organisation. The product of the scale values (i.e. probability x impact) resulting in a ranking index for each risk. Thresholds should be established early in the life cycle of the project for risk acceptance and risk escalation to aid decision-making and establish effetive governance principles. Risk assessment matrices are useful in the initial assessment of risk, providing a quick prioritisation of the project’s risk environment. It does not, however, give a full analysis of risk exposure that would be accomplished by quantitative risk analysis methods. Quantitative risk analysis may be defined as: “The estimation of numerical values of the probability and impact of risks on a project usually using actual or estimated values, known relationships between values, modelling, arithmetical and/or statistical techniques”. Quantitative methods assign a numerical value (e.g. 60%) to the probability of the risk occurring, where possible based on a verifiable data source. Impact is considered by means of more than one deterministic value (using at least 3-point estimation techniques) applying a distribution (uniform, normal or skewed) across the impact values. Quantitative risk methods provide a means of understanding how risk and uncertainty affect a project’s objectives and a view of its full risk exposure. It can also provide an assessment of the probability of achieving the planned schedule and cost estimate as well as a range of possible out-turns, helping to inform the provision of contingency reserves and time buffers. #projectmanagement #businesschange #roadmap

🔍 What Is a Risk Assessment Methodology? A risk assessment methodology is the structured approach an organization uses to identify, analyze, evaluate, and prioritize risks. It ensures consistent, repeatable assessments across all business areas and is essential for risk-informed decision-making. ⸻ ✅ Core Components of a Risk Assessment Methodology: 1. Risk Identification • Pinpoint what could go wrong (risk events). • Sources: business processes, historical incidents, regulatory changes, third-party risks, IT systems, etc. • Tools: brainstorming, risk checklists, process walkthroughs, SWOT, interviews, PESTLE. 2. Risk Analysis • Determine the likelihood and impact of each risk. • Approaches: • Qualitative (e.g., High/Medium/Low or Heat Maps) • Semi-quantitative (e.g., scoring systems 1–5 for likelihood and impact) • Quantitative (e.g., Monte Carlo, VaR, financial modeling) 3. Risk Evaluation • Compare risk levels to your risk appetite and tolerance thresholds. • Decide which risks are acceptable, and which need treatment or escalation. 4. Risk Prioritization • Rank risks based on their score to allocate resources effectively. • Often visualized in a risk matrix or heat map. 5. Risk Treatment (Optional in Assessment Phase) • Recommend how to handle critical risks: • Avoid • Transfer • Mitigate (via controls) • Accept 📊 Common Methodologies Used: 1️⃣ISO 31000 Framework Emphasizes integration, structure, and continuous improvement in risk management. 2️⃣ COSO ERM Framework Aligns risk with strategy and performance across governance, culture, and objective-setting. 3️⃣ Basel II/III for Financial Risk Used in banking and finance, focusing on credit, market, and operational risk. 4️⃣ NIST Risk Assessment Applied in cybersecurity and federal agencies, emphasizing threats, vulnerabilities, and impacts. 🎯 Best Practices: • Use both inherent and residual risk ratings. • Involve first-line teams for accurate process-level risk input. • Align methodology with risk appetite and strategic objectives. • Document risk criteria (likelihood/impact definitions) clearly. • Update the risk assessment periodically or after significant events.

On March 5th, we witnessed the first negative spot prices of the year due to solar energy, and it is becoming increasingly evident that photovoltaic systems without battery energy storage systems (BESS) will not be feasible in the future.   Imagine you have a pay-as-produced power purchase agreement (PPA) for a stand-alone photovoltaic (PV) system, guaranteeing a fixed price for your volume. In that case, you’re not concerned about market prices since your cash flow is secure, right?   The more recent PPA agreements often have a negative price clause that exempts the buyer from paying the fixed price for the PV generation when the spot prices are negative. That creates another problem than the decreasing capture rate and capture price.   If you curtail your PV during those hours and lose part of your yearly production, it might be difficult to meet the volume obligations of the PPA. The PPA often covers/requires 70% of the expected generation on a yearly basis, so the PV park can afford only a very limited curtailment before risking default on the PPA.   The figure shows how a large share of the Danish PV production is produced in hours with negative prices. In the months with the most volume, the number reaches close to 25%! That volume has not been curtailed, so the actual share of production in negative price hours is even higher.   Apart from the decreasing capture rate and capture price, the growing number of negative prices is now reaching a level where they threaten existing solar with pay-as-produced PPAs. This problem will only worsen for stand-alone PV, while co-located BESS+PV will have an increasingly bigger advantage. Hybrid Greentech - Energy Storage Intelligence

The Hidden Risks in Construction Tenders: Are You Prepared? Every construction project begins with a tender, but too often, companies overlook the critical risks that can make or break a deal. The tendering process can be filled with hidden pitfalls, from ambiguous clauses to compliance issues that only surface after the bid is submitted. Here are some common risks that often go unnoticed in manual tendering: 1. Misinterpreting Contract Clauses: Complex legal language and poorly defined terms can lead to misunderstandings. Even a small misinterpretation could result in unfavorable terms that cost your company time and money. 2. Compliance Failures: Different tenders come with different compliance requirements, whether it's local regulations, industry standards, or international laws. Failing to fully understand these can lead to penalties, rejected bids, or even legal complications down the road. 3. Overlooking Financial Risks: Tenders often contain fine print about penalties, payment terms, and hidden costs. Without proper scrutiny, these financial risks can add up and erode your project’s profitability. 4. Inconsistent Evaluation Criteria: Manual tender reviews can lead to inconsistent evaluations, with some team members potentially missing key criteria or overlooking important risks that others flag. These risks can result in costly mistakes, delayed timelines, and ultimately, lost opportunities. In fast-paced, high-stakes markets like Mumbai, where every tender counts, even a single missed detail can have serious consequences. The key to success? Proactively identifying and mitigating these risks before you bid. That’s where AI-powered tools like ContraVault AI come in. With intelligent risk analysis, automated contract clause interpretation, and real-time compliance checks, ContraVault AI can identify and highlight these hidden risks, ensuring that you enter every tender with confidence.

World Health Organization updates #laboratory #biosecurity #guidance WHO recently issued updated guidance for national #authorities and #biomedical #laboratories to manage #biological #risks. Laboratories are essential components of #health #systems, critical for patient #diagnosis and rapid #clinical #care, #disease #surveillance, #pathogen characterization, and #research and #development for #treatments and #vaccines. Appropriately designed and equipped facilities, trained #staff, evidence-based risk mitigating measures, transparent reporting and layered oversight mechanisms will safeguard the #workforce and the community from pathogenic #microorganisms and #toxins. New updates in the guidance include the strengthening of #cybersecurity measures and handling of confidential information such as patient records; reducing risks from new #technologies, including those related to #genetic modification and #manipulation of pathogens, and #artificial #intelligence (AI); and advice on keeping laboratories #safe and #secure during #emergencies like #wars, civil unrest, and #disasters from natural #hazards. WHO’s updated laboratory biosecurity guidance helps all countries, especially those lacking #regulations, establish or strengthen frameworks for handling high-consequence pathogens. It highlights the importance of strong institutional governance through an Institutional Biosafety Committee with national oversight. The updated guidance provides best #practices and #recommendations, while encouraging Member States to adopt a risk-based approach, stipulated in the resolution on ‘Strengthening laboratory biological risk management’ adopted at the World Health Assembly this year. The guidance was developed in consultation with wide range of stakeholders including WHO collaborating centres and technical advisory groups, in particular, the WHO Technical Advisory Group on Biosafety (TAG-B).    By promoting engagement and #commitment from institutions and national authorities, the guidance mitigates risks associated with high-consequence pathogens and research work. These measures aim to safeguard communities from misuse and release of biological materials, be it intentional or inadvertent, all while allowing legitimate biomedical research to continue. https://lnkd.in/enwAEeFb