Analyzing Current Conditions for Accurate Forecasts

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.

I was interviewing a bright candidate for a demand planner role a while back. To gauge his practical thinking, I posed a scenario. "Imagine your system generates a baseline forecast of 10,000 units for a key product next month. The statistical model is sound. What's your next action?" He gave a textbook-perfect answer about reviewing historical trends, model accuracy, and checking for outliers. All crucial steps. I paused. "That's an excellent start. But what if that 10,000, as precise as it looks, is missing the most critical piece of information?" He seemed curious, waiting for the answer. This is a scenario I see play out in many organizations. We invest heavily in sophisticated forecasting systems that are brilliant at analyzing the past. But they often lack forward looking context. A forecast is just a number until we enrich it. Many industry studies highlight that forecasts relying purely on historical data can often miss the mark significantly, sometimes by as much as 30-40% for more volatile items. The plan becomes a mathematical exercise, disconnected from the commercial realities on the ground. This is where the concept of Demand Enrichment becomes invaluable. It is the structured process of layering qualitative intelligence on top of the quantitative baseline. In a previous role, we transformed our forecast accuracy not by buying new software, but by changing our process. Our statistically generated forecast was our starting point, not our destination. We built a simple but disciplined enrichment framework: - Collaborative Input: We worked with the sales team to capture insights on key account promotions, new listings, or potential risks. This wasn't a casual chat; it was a structured input into the plan. - Marketing Integration: The marketing team’s activity calendar was overlaid onto the demand plan. We could now quantify the expected uplift from a specific campaign instead of just hoping for the best. - Market Intelligence: We dedicated a small part of our demand review meeting to discussing competitor activity and market trends, translating these discussions into tangible assumptions in our plan. Suddenly, the number had a narrative. 10,000 units was no longer just a point on a graph. It became "10,000 units, which is composed of 8,500 baseline sales, an anticipated lift of 2,000 units from the 'Summer Sale' campaign, offset by a potential 500 unit loss due to a competitor launching a similar product." This enriched number is something the entire organization can understand, align on, and execute against. It transforms the forecast from a passive prediction into an active planning tool. ----- If you have any questions about Demand and Supply planning, feel free to ask using the links in the Bio. P.S. How does your organization go beyond the numbers to tell the full story of your demand?

Google DeepMind and Google Research have developed a new experimental AI model to predict tropical cyclones, and the results on recent hurricanes like Hurricane Erin are really exciting. Watch Olivia Graham from our team explain it below. 🌀Tropical cyclones cause immense destruction and seriously impact communities. Improving the accuracy and timeliness of our forecasts is critical for protecting property and saving lives. 💧Traditional models💧 Physics-based models struggle to accurately predict both a cyclone's path and its intensity. This is because a cyclone's path is influenced by vast atmospheric currents, while its intensity depends on complex turbulent processes within and around its core. ✨Our new model✨ Our new experimental model is a single system that overcomes the traditional tradeoff between track and intensity. It's trained on two distinct types of data 1. A vast reanalysis dataset that reconstructs global weather patterns from millions of observations. 2. A specialized database containing key information about the track, intensity, size, and wind radii of nearly 5,000 observed cyclones from the past 45 years. This allows the model to learn from historical events in a way that traditional models cannot. We’re working with the National Hurricane Center to test this experimental model out this season.  It was really gratifying to see this writeup from the former chief of the hurricane specialist unit there on Hurricane Erin, the strongest Atlantic storm this year. According to his analysis, our model (GDMI in the graphs) had the most accurate forecasts for both track and intensity for the first 72 hours, outperforming a number of the best physics-based models and even the consensus models used by forecasters. This model is now live on Weather Lab, where it's generating 50 possible scenarios for potential future outcomes. If you'd like to explore the model yourself, check it out on Weather Lab: https://lnkd.in/gY9z5wCK Analysis on Hurricane Erin from the former chief of the hurricane specialist unit at the National Hurricane Center: https://lnkd.in/gTHHXdup

The USDA's recent US corn yield forecast of 188.8 bu/ac has stirred the US grain markets, with CropProphet customers notably unsurprised. Over the past four Septembers, the Midwest US crop growing regions have experienced generally drier conditions than usual. Given these factors, the US #graintrading market is closely monitoring the potential return of drier conditions in the Fall of 2025, which could impact corn yields and corn futures prices. Keeping a close eye on weather patterns during this period will be crucial to capturing any remaining upside in corn futures prices before harvest, while also maintaining a contextual understanding of the information. Recent data indicate a shift towards drier conditions in the 15-day US corn production weighted accumulated precipitation forecast. This change represents one of the most significant shifts towards drier conditions seen at this time of year in recent history. The change is in at 3.4% relative to historical distribution of ECMWF IFS forecast changes. To achieve this analysis, we have compiled an eight-year archive of weather forecasts from various sources like ECMWF, ECMWF #AIFS Ensemble, GEFS, and GFS. These forecasts have been transformed into commodity market indices, such as the US corn production weighted accumulated precipitation, within a 30-day window centered on today's date. By comparing real-time forecast precipitation changes with historical data, we can gauge the current forecast's position within the distribution of past changes. This methodology provides us with valuable insights into the current forecast's deviation from historical patterns, offering a deeper understanding of the forecast's implications in the context of long-term climatology and prior grain market responses.

Because with a bad forecast everything else will fail... This infographic contains 7 steps to create and improve a forecast: ✅ Step 1 - Start with Historical Data Collection & Cleaning 👉 gather and clean past sales data (ideally 3 years) 👉 remove outliers, fill in gaps, and ensure data accuracy before analysis ✅ Step 2 - Segment Your Demand 👉 break down your demand into segments to create more granular forecasts 👉 examples: volume, value, product categories, customer types, regions ✅ Step 3 - Generate a Baseline Statistical Forecast 👉 as starting point, generate a baseline forecast using statistical methods like time series analysis ✅ Step 4 - Apply Seasonality and Trend Adjustments 👉 use historical seasonal patterns and emerging trends to fine-tune your forecast for upcoming periods ✅ Step 5 - Collaborate & Fine-tune in S&OP Meetings 👉 collaborate with sales, marketing, finance, and operations to align on one consensus forecast ✅ Step 6 - Adjust for Market Intelligence 👉 incorporate insights from sales teams, marketing campaigns, external research, and product launches to adjust your baseline forecast ✅ Step 7 - Incorporate Forecasts into S&OE (Sales & Operations Execution) 👉 drive actionability in the short term based on this aligned forecast, helping the team respond quickly to deviations 💥 Bonus Step: Build a Continuous Feedback Loop 👉 track forecast accuracy by comparing actual sales to forecasted figures, and regularly update your model based on this feedback Any other steps to consider? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement