Strategic Scenario Analysis

Innovation isn’t just about new products. It’s about how you structure, deliver, and capture value—across your entire business model. In their book, "Ten Types of Innovation" (2013), Keeley et al. outline a powerful framework outlining no less then 10 types of innovation: Configuration 1. Profit Model – How you make money 2. Network – How you collaborate 3. Structure – How you organize 4. Process – How you operate Offering 5. Product Performance – What you offer 6. Product System – How offerings work together Experience 7. Service – How you support users 8. Channel – How you deliver value 9. Brand – How you're perceived 10. Customer Engagement – How you foster loyalty Most innovation efforts focus narrowly on the product. But real advantage comes from orchestrating multiple innovation types, often in combination. If you're looking for new strategic levers, this framework is a great place to start. Which of the ten are you already investing in?

Modeling something like time series goes past just throwing features in a model. In the world of time series data, each observation is associated with a specific time point, and part of our goal is to harness the power of temporal dependencies. Enter autoregression and lagging -  concepts that taps into the correlation between current and past observations to make forecasts.  At its core, autoregression involves modeling a time series as a function of its previous values. The current value relies on its historical counterparts. To dive a bit deeper, we use lagged values as features to predict the next data point. For instance, in a simple autoregressive model of order 1 (AR(1)), we predict the current value based on the previous value multiplied by a coefficient. The coefficient determines the impact of the past value on the present one only one time period previous. One popular approach that can be used in conjunction with autoregression is the ARIMA (AutoRegressive Integrated Moving Average) model. ARIMA is a powerful time series forecasting method that incorporates autoregression, differencing, and moving average components. It's particularly effective for data with trends and seasonality. ARIMA can be fine-tuned with parameters like the order of autoregression, differencing, and moving average to achieve accurate predictions. When I was building ARIMAs for econometric time series forecasting, in addition to autoregression where you're lagging the whole model, I was also taught to lag the individual economic variables. If I was building a model for energy consumption of residential homes, the number of housing permits each month would be a relevant variable. Although, if there’s a ton of housing permits given in January, you won’t see the actual effect of that until later when the houses are built and people are actually consuming energy! That variable needed to be lagged by several months. Another innovative strategy to enhance time series forecasting is the use of neural networks, particularly Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are designed to handle sequential data like time series. They can learn complex patterns and long-term dependencies within the data, making them powerful tools for autoregressive forecasting. Neural networks are fed with past time steps as inputs to predict future values effectively. In addition to autoregression in neural networks, I also used lagging there too! When I built an hourly model to forecast electric energy consumption, I actually built 24 individual models, one for each hour, and each hour lagged on the previous one. The energy consumption and weather of the previous hour was very important in predicting what would happen in the next forecasting period. (this model was actually used for determining where they should shift electricity during peak load times). Happy forecasting!

Ukraine’s success against the Russian navy is making the Pentagon nervous – and rightfully so. The US Navy is now actively training to counter the threat posed by autonomous, explosive-laden drone boats. During Baltic Operations 2025, Task Force 66 used uncrewed surface vessels (USVs) to simulate swarm-style attacks on ships like the USS Mount Whitney and USS Paul Ignatius. This training is a direct response to what Ukraine has pulled off against Russia since the start of the invasion. In the Black Sea, Ukraine’s drones have sunk dozens of Russian vessels, forcing Moscow to relocate its fleet to safer harbors. Fast, cheap, and lethal, these USVs have rendered legacy naval thinking obsolete almost overnight. The US military has been aware of this threat since the infamous Millennium Challenge 2002 wargame more than two decades ago, but here’s what planners are recognizing now: - Conventional defenses like manned gun stations and missiles struggle against agile, low-profile drones - Awareness must extend below radar to give service members enough time to detect and engage fast-moving threats - There’s no silver bullet: it will take a combination of sensors, kinetic weapons, and autonomy to effectively meet this moment Luckily, our Navy is leading the way. Task Force 66, formed last year, is integrating robotic systems into fleet operations and developing tactics for maritime theaters where speed, flexibility, and autonomy matter most. If aircraft carriers and destroyers remain the Navy’s most valuable naval assets, then they must also be protected with dynamic, intelligent counter-drone systems. The maritime battlefield is evolving – we need solutions that evolve with it.

🚨 New Article Alert! 🚨 The energy transition debate has been dominated by supply-side solutions—phasing out fossil fuels, scaling up renewables, and debating nuclear power. But are we overlooking a key part of the equation? In my latest piece, "Beyond Supply: The Case for Decarbonising Energy Demand", published in PLOS Climate, I argue that focusing only on energy supply is not enough. We need to address how much energy we use, how efficiently we use it, and when and where we use it. 🔑 Key takeaways from the article: ✅ Energy efficiency has delivered more emissions reductions historically than renewables. ✅ Demand-side flexibility (e.g., smart grids, dynamic pricing) can reduce fossil fuel reliance. ✅ Electrification must be paired with efficiency to avoid straining the energy system. ✅ Behavioural and structural changes—such as better urban planning and circular economy principles—must be part of the solution. 🌍 The cleanest energy is the energy we don’t need to produce. A demand-first approach can accelerate decarbonisation, lower costs, and enhance energy security. 🔗 Read the full article here: https://lnkd.in/ev-8cQ4u What are your thoughts on shifting the focus beyond supply? Let’s discuss! 👇

Trump has announced his intention to impose 25% tariffs on all EU goods. We immediately ran the numbers through the Kiel Institute for the World Economy's KITE model, and the results show a significant economic impact—not just for the EU, but also for the US. Our model estimates: 🇪🇺 EU real GDP would decline by -0.4% in the short run—a substantial hit. 🇺🇸 The US would see a -0.17% contraction, but if the EU responds with its own tariffs, the damage to the US economy would double. This is largely driven by a significant price impact in the US with up +1.5%. Importantly, this inflationary pressure is not only driven by pricier final products, but US production becomes more expensive through tariffs on imported intermediate inputs. The trade impact is also notable: EU exports to the US would drop by 15-17%, with Germany taking the hardest hit (-20%). However, this translates to only -1.5% of Germany’s total exports. At the sectoral level, manufacturing in Germany would bear the brunt. The German automotive industry could see a 4% decline in nominal production, with ripple effects across machinery, equipment, and supply chains. Caveat as always: The exact implementation of these tariffs remains unclear, as does the EU’s response. Past tariff threats have not always materialized as announced. However, uncertainty itself is already an economic factor, slowing investment, disrupting supply chains, and dampening growth. #TradePolicy #Tariffs #Economy #US #EU

‼️The huge importance of DEMAND‼️ The #energy debate tends always to focus on supply. It does so in normal times. It is even more nowadays!   However, the energy equation needs to look always to both supply AND demand. This is true especially in current crisis. Because if the core of the issue is in the supply disruptions linked to the war in Middle East, consumers have only limited options to look at while hoping for a rapid resolution of the conflict – the option is: saving energy.   The long wave of disruption is like a tsunami: it hits first the closest and the most exposed. In this case it is Asia both for proximity with epicentre of the crisis and because the region is historically more dependent on deliveries of #oilgas coming from the Middle East region.  But tsunamis travel also very far away, so better to not have false expectations….   The focus on demand is finally increasing – and rightly so! Of course the first wave implies emergency measures that sometimes are not popular among consumers but are made essential by the level of disruption. A detailed analysis is on IEA website But if the disruption becomes longer and more painful, the history shows that emergency conservation measures might turn into structural changes. After the 70s crisis, efficiency standards were finally introduced and car manufacturers in few years were able to halve the average consumption.   If this will be the case also for this crisis, it has to be seen, but for sure the crisis will have significant implications both in short and long term.

Phone calls have been buzzing. Laptops are glowing. Excel sheets are flying. This wasn’t a typical weekend for the C-suite. Across sectors, CEOs, CFOs, and CIOs have been on back-to-back calls, recalibrating their strategies as they enter what is one of the most significant policy shifts in modern geo-economic history. 💼 Emergency executive teams have been formed. 📈 Scenario models are multiplying. 🌐 Global subsidiaries are under the microscope. We are navigating a complex web of policy shifts—and these are not marginal adjustments. As leaders, we’re being pushed to ask questions that, until recently, seemed unfathomable: What happens to pricing in an era of shifting tariff and tax regimes and fractured supply lines? How will demand evolve as economies rebalance and national interests take center stage? Most critically, how resilient and responsive are our supply chains in a world where geopolitics now shapes logistics? This goes far beyond how we report earnings, manage compliance or mitigate risk. It’s a transformation in how the global economy functions—and we must treat it as such. Sector by Sector: What’s Being Redefined 🔸 Consumer Brands Witnessing a reevaluation of inventory strategies. Pricing models need to become more agile. Loyalty programs and e-commerce ecosystems are being re-modeled. 🔹 Manufacturing Supply chains are under pressure—this time from trade sensitivity - Transfer pricing and subsidiary-level planning are evolving fast. “Smart factories” in the US being evaluated are being looked into 🔸 Technology The location of R&D hubs and IP ownership is no longer just an efficiency play—it’s a governance priority. Technology Teams are developing new offerings with the highest return and lowest cost. 🔹 Investments & Funds Portfolio managers are engaged in intensive scenario planning as asset prices fluctuate rapidly on a daily basis. Asset managers have been the busiest lot! Research houses are backlogged. In moments like these, the role of leadership is clear: We must look ahead—not just at what’s changing but at what will be demanded of us in the next chapter. Are we prepared to act, restructure, and lead at the pace this new environment demands? The rules of the game are being Rewritten. Strategically. Permanently. C-suites are planning in layers—playing both defense and offense. Many businesses are stockpiling optionality. Some are building inventories. The savviest? Building entire alternate operating structures. What Few Are Saying Loudly—But Everyone's Acting On: 🔹 Investment houses are recalibrating long-term models 🔹 Export-heavy industries are rethinking FX and interest rate exposure 🔹 Defense, infrastructure, and energy assets are being repriced 🔹 The idea of “neutral geographies” is being redrawn in real time This isn’t just policy. It’s the geoeconomic restructuring of our time.

Most strategy work fails because it's blind to the outside world. Environmental Scanning solves that. It’s how smart companies build strategic foresight—by identifying the external and internal forces shaping their future. ☑ Start with the basics: the PESTLE Framework ↳ Political – Policy shifts, government intervention, geopolitical risk ↳ Economic – Inflation, recession signals, capital cost ↳ Social – Demographic changes, cultural shifts, behavior trends ↳ Technological – Disruption, automation, R&D investment ↳ Legal – Regulatory compliance, new mandates ↳ Environmental – Climate risk, ESG pressures, sustainability ☑ Add sharper tools to go deeper: ↳ Porter’s 5 Forces – Understand competitive pressure ↳ Materiality Assessment – Prioritize ESG realities ↳ Value Chain Analysis – Detect internal inefficiencies ↳ System Mapping – Reveal interdependencies ↳ SWOT – Only as a summary, never as the main tool Now you’re scanning with both eyes open. Here’s what you gain: ↳Anticipate threats before they show up in your KPIs ↳Spot emerging opportunities before your competitors ↳Align strategy with external realities, not internal fiction ↳Build scenarios, not just plans ↳Define where to play and how to win—with confidence. Strategy without scanning is guessing. P.S. If you like content like this, please follow me.

Forecasting in Banking: Managing Uncertain Economic Environments Forecasting in the realm of banking is far from a straightforward process. Although the ultimate objective is to arrive at the most plausible predictions possible, the ever-changing economic landscape often presents challenges that make absolute precision impossible. However, that does not mean financial institutions should shy away from attempting to create reliable forecasts. When making forecasts, it is crucial to base these predictions on prudent and conservative assumptions. Banks often rely on historical data to project future trends; although this method has its merits, especially in stable economic conditions, it may not be the most advantageous approach when the economy is in flux. It is essential to factor in the realistic possibility of economic changes, such as interest rate fluctuations or market volatility, to arrive at more robust forecasts. Scenario analysis serves as an invaluable tool for generating realistic expectations about future financial conditions. It allows treasury professionals to examine various outcomes, assessing each for its likelihood and potential impact on the bank’s finances. Scenario analysis provides the advantage of preparedness, offering a range of plausible outcomes rather than fixating on a single, ideal projection. Modern technology, e.g. data analytics and algorithms, can offer increasingly sophisticated ways to improve the accuracy of forecasting models. While technology can significantly aid in making more accurate projections, it's crucial to remember that these tools should complement, not replace, human expertise. A balanced approach, incorporating both technological solutions and skilled professional judgement, tends to yield the most beneficial results. Regulatory frameworks often require banks to maintain a certain level of forecasting accuracy to ensure stability and to protect the interests of stakeholders. Consequently, a bank should always be aware of these requirements and incorporate them into their forecasting methodologies. Regulatory compliance, although often time consuming, provides an additional layer of scrutiny that helps to improve the forecasting process. It is important to understand that forecasting is not a one-off activity. Economic conditions change, sometimes in unpredictable ways, necessitating a revisit of previous forecasts. A best practice is to schedule regular review periods where assumptions can be reassessed, and forecasts updated, to reflect the most current and accurate information available. Overall, the approach to forecasting in uncertainty should be one of cautious optimism. The goal is not necessarily to predict the future with any accuracy, but to understand a range of plausible scenarios and prepare accordingly. By doing so, banks can make more informed decisions, better manage risks, and contribute to the long-term stability and success of their financial institutions.

"Should we hire or should we cut?" is a question I'm hearing often from small business owners right now, which is fair given the mixed economic signals. Some clients are seeing their best quarters ever. Others are watching pipelines thin out. Everyone seems to be asking, "How do we plan for what we can't predict?" This is where scenario planning becomes your survival tool; not just hoping for the best, but modeling the reality of different futures. Here's what we walk our clients through: 🌳 The Growth Scenario: For example, if revenue is expected to be up, we’re looking at potential team expansion and higher overhead. Looking at what that does for cash flow given the changes to expected expense changes. 🌱 The Steady Scenario: Where flat growth is expected and we plan to maintain current team, we’ll want to optimize margins and prepare for inevitable per team member increases. There will likely be some percentage increase YOY but we expect the core costs to stay the same. 🍃 The Contraction Scenario: On the other hand, if revenue is expected to go down, we want to look at strategic cuts that allow the team to run efficiently while preserving cash. For our clients, this is usually a mix of team, professional services, and travel. We also want to ensure that the resources kept are used efficiently. Each scenario gets its own financial mode where we map out cash flow, runway, and break-even points for 3, 6, and 12 months ahead. The command center for this? Fathom. We've been using Fathom since the beginning of Little Fish Accounting and it lets us build the scenarios in real-time with clients, showing exactly how each decision ripples through their financials. No more spreadsheet gymnastics or gut-feeling guesses. Ultimately, the founders who survive uncertainty aren't the ones with crystal balls—they're the ones with clear models and decisive action plans. And we're glad to be the builders 🧱