Demand Forecasting in Financial Planning

Demand forecasting errors silently bleed profits and cash. This document shows 7 red flags in demand forecasting and how to fix them: 1️⃣ Over-reliance on historical data ↳ How to fix: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 2️⃣ Ignoring promotions and discounts ↳ How to fix: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 3️⃣ Forgetting cannibalization effects ↳ How to fix: model cannibalization effects to adjust forecasts for existing products 4️⃣ One-size-fits-all forecasting method ↳ How to fix: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 5️⃣ Not monitoring forecast accuracy ↳ How to Fix: track metrics like MAPE, WMAPE, bias, to improve over time 6️⃣ High forecast error with no accountability ↳ How to fix: tie accountability to S&OP (sales and operations) meetings 7️⃣ Past sales (instead of demand) consideration ↳ How to fix: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations Any others to add?

A few months back, I interviewed a senior demand planner from a global skincare brand. I asked a simple question: "How do you improve your forecast when the system gives you a number that feels... off?" She replied, "We talk to the right people before we talk to the system." That line stayed with me. In Demand Planning, we often focus heavily on historical data, statistical models, and software outputs. But what truly differentiates an average forecast from a high-confidence, actionable one - is the process of Demand Enrichment. And no, it’s not just a buzzword. It’s a discipline - a method of adding intelligence beyond what the system predicts. In fact, according to a McKinsey study, companies that effectively integrate enriched demand signals (like promotions, competitor moves, distribution expansion, influencer campaigns, and even climate effects) can improve forecast accuracy by up to 25%. When I worked for a consumer brand in North India, we noticed our system forecast underestimated demand by 18% during Q4. Why? Because it didn’t factor in the impact of a regional festival that doubled store footfall across 3 key states. Our statistical model was flawless. But our insights were incomplete. That’s when we built a cross-functional "Demand Intelligence Loop" - gathering inputs from marketing, sales, trade partners, and retailers - and feeding it back into planning. The result? Forecast accuracy jumped. Inventory positioning improved. And stockouts during peak weeks were cut in half. If you're a planner reading this: Don't just accept the forecast. Enrich it. Challenge it. Elevate it. That’s how Demand Planning transforms from reactive to strategic.

SAP Demand Planning SAP Demand Planning is a critical component of the SAP Integrated Business Planning (IBP) suite, designed to help organizations anticipate and meet customer demand more accurately and efficiently. Here are the key elements and features of SAP Demand Planning: Key Features: 1. Statistical Forecasting: • Utilizes advanced algorithms to analyze historical data and predict future demand. • Offers various forecasting models such as time-series, causal analysis, and regression models. 2. Demand Sensing: • Provides near-term demand visibility using real-time data. • Adjusts forecasts based on the latest market signals, such as point-of-sale data or customer orders. 3. Collaboration Tools: • Facilitates collaboration across departments and with external partners to align demand forecasts with business objectives. • Allows for consensus forecasting by integrating inputs from sales, marketing, and supply chain teams. 4. What-if Analysis: • Supports scenario planning to evaluate the impact of different business strategies or external factors on demand. • Helps in risk assessment and decision-making by visualizing potential outcomes. 5. Integration with Supply Planning: • Seamlessly integrates with supply planning processes to ensure that production and procurement plans are aligned with demand forecasts. • Helps in balancing supply and demand across the entire supply chain. 6. Machine Learning and AI: • Leverages machine learning algorithms to improve forecast accuracy by continuously learning from new data and trends. • Identifies patterns and anomalies that may affect demand. 7. User-Friendly Interface: • Provides a customizable and intuitive user interface for planners to easily access and analyze demand data. • Offers dashboards and reports for real-time visibility into demand trends and KPIs. Benefits: • Improved Forecast Accuracy: Reduces forecasting errors, leading to better inventory management and customer satisfaction. • Enhanced Responsiveness: Enables organizations to quickly adapt to changes in demand and market conditions. • Cost Reduction: Optimizes inventory levels, reducing excess stock and carrying costs. • Strategic Alignment: Ensures that demand plans are aligned with business goals and operational capacities. Implementation Considerations: • Data Quality: Accurate demand planning relies heavily on high-quality data from various sources. • Change Management: Successful implementation requires stakeholder buy-in and training to adapt to new processes and tools. • Integration: Ensuring seamless integration with existing ERP and supply chain systems is crucial for a comprehensive view of demand and supply. SAP Demand Planning is a powerful tool that helps organizations improve their demand forecasting capabilities, leading to more efficient and responsive supply chain operations.

Here are 5 machine learning algorithms used for FP&A and #finance time series analysis: ✅ ARIMA/SARIMA: Forecast future revenues and expenses by identifying trends and seasonality. ✅ LSTM: Analyze complex patterns in cash flow or sales data to improve financial planning. ✅ Prophet: Handle unpredictable markets and still make reliable forecasts. ✅ GARCH: Assess and predict market volatility to make more informed investment or budgeting decisions. More detail below ↓ 1. ARIMA (Auto-Regressive Integrated Moving Average) ARIMA helps predict future values by analyzing past data to identify patterns like trends or seasonality. For example, you can use ARIMA to forecast next year’s monthly revenue by recognizing historical trends and seasonal variations, such as higher sales during holiday seasons. 2. LSTM (Long Short-Term Memory) Networks LSTM is an artificial intelligence technique that learns from past data and remembers long-term patterns. It can be used in FP&A to forecast cash flow by identifying recurring inflows and outflows over time, like specific project payments or seasonal cash patterns. 3. SARIMA (Seasonal ARIMA) SARIMA extends ARIMA by incorporating seasonality, making it ideal for forecasting data with regular patterns. For example, you can predict quarterly expenses more accurately if certain quarters have consistently higher costs due to contracts or seasonal demand. 4. Prophet Prophet, developed by Facebook, handles missing data and outliers well, making it useful for complex datasets. To get the code and example for implement it, go here: https://lnkd.in/eJKcHzqU You could use Prophet to forecast annual sales even when your data is incomplete or affected by irregular events like economic shifts. 5. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models volatility and is great for predicting how much financial data varies over time. You can apply it in FP&A to assess and predict the volatility of stock prices in your investment portfolio, helping in risk management and budgeting.

Ever stocked up on a product that turned into a dust-gathering flop? Or worse, missed out on a sales surge because your shelves were empty? That's the pain of bad demand forecasting, and it's felt across the manufacturing world. Get this: businesses with accurate demand forecasts enjoy a whopping 70%-90% reduction in inventory holding costs AND a 98% service-level rate.  Those numbers aren't magic; they're the result of ditching guesswork and embracing data analytics. Why Demand Forecasting Matters? 👉 Optimized Production: Produce what you'll actually sell. No more overstocking or frustrating shortages. 👉 Smoother Operations: Match your resources to real demand. Plan staffing, material procurement, and production schedules with confidence. 👉 Happy Customers = Happy Bottom Line: Have the right products available at the right time. Boost customer satisfaction and sales. Accurate demand forecasting has a ripple effect: 👉 Reduced Waste: Overproduction leads to wastage at every level. Forecast accurately, and minimize your environmental impact. 💪 Better Pricing Strategy: Understand demand peaks and valleys to make smarter, data-backed pricing choices. 👊 Boost in Competitiveness: Stay ahead of the game by anticipating market trends before your competitors even see them coming. Demand forecasting isn't about staring into a crystal ball. It's about using data analytics to uncover hidden patterns and build smart predictive models: 👁️🗨️ Historical Sales Data: The foundation of any good forecast. 👀 Market Trends: Watch for economic indicators, competitor moves, and changes in consumer preferences. 🙌 External Factors: Seasonality, promotions, even the weather can influence demand. 💥 Advanced Analytics: Machine learning algorithms can spot patterns humans miss, leading to supercharged forecasting accuracy. Here's what to analyze to up your demand forecasting game: 👉 Product-Level Specificity: Don't forecast in broad strokes. Break it down by SKU, location, and timeframe for granular insights. 👉 Time Horizons: Need both short-term (production planning) and long-term (strategic decisions) forecasts. 👉 Forecast Accuracy Tracking: Measure how your predictions stack up against reality, and keep refining those models. Wrangling complex demand data and building those super-smart forecasts can be tough. That's where Google's magic comes in. We can help you make sense of the numbers and get the insights you need to make confident, profit-driving decisions. Ready to conquer your demand forecasting challenges? Let's chat! Follow Omkar Sawant for more information! #demandforecasting #dataanalytics #manufacturing #supplychain #AI

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.