Improving Predictive Accuracy

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?

When L'Oréal uses AI to create new hair colors based on social media trends, they're in salons within weeks. Kraft Heinz—dead last in our study—still takes months to tweak a formula. After analyzing 26 major CPG companies at IMD's Center for Future Readiness, I discovered what separates winners from losers: The most future-ready companies treat consumer data like insider trading information. BACKGROUND: CPG in 2025 is brutal. Inflation persists. Gen-Z demands sustainability without premiums. Tariffs reshape supply chains daily. McKinsey & Company identified 150+ AI use cases for CPG transformation. Only 5 of 26 companies actually execute them. THE REVELATION: Coca-Cola didn't randomly launch Topo Chico Hard Seltzer. Their AI spotted the trend through social listening while competitors debated in boardrooms. By launch, they'd secured distribution nationwide. That's not innovation. That's prediction. What separates the top 5: L'Oréal (#1): 3.5% of sales to R&D. AI analyzes preferences real-time. Virtual try-on apps. Creates products from social trends. A 110-year company with startup velocity. The Coca-Cola Company (#2): Democratized AI internally. Every manager accesses demand forecasting. They analyze weather + social sentiment + sales simultaneously. These aren't tech companies selling beauty and beverages. They're prediction machines that happen to make products. THE WINNER'S FRAMEWORK: 1. AI at scale, not in pilots Winners integrate into workflows. Losers run demos. 2. Supply chains that anticipate Real-time visibility + AI forecasting = competitive firepower 3. D2C as intelligence goldmine 73% use multiple channels. Mine every interaction. 4. Disrupt yourself first Coca-Cola launched Costa Coffee, hard seltzers. Grew. Kraft Heinz protected legacy brands. Shrank. 5. Sustainable without premium Gen-Z spending hits $12T by 2030. They demand action at everyday prices. —— The inconvenient truth: Most CPG companies treat data like reporting instead of radar. Winners don't predict trends—they're already shipping products while competitors debate. Technological patience (knowing when to scale) + organizational agility (pivoting fast) = market domination. Three years from now, every CPG company operates like L'Oréal. Or they don't operate at all. P.S. Full Future Readiness Indicator here: https://bit.ly/3YTBzbX

Inflation isn't just about rising prices; it's a catalyst for changing consumer behaviors. As purchasing power shifts, businesses must adapt swiftly to meet evolving demands. Hindustan Unilever Limited (HUL), a leader in the FMCG sector, showcases how embracing AI can turn these challenges into opportunities. 📌 The Challenge #HUL observed significant fluctuations in demand across its diverse product portfolio during inflationary periods. Premium products experienced slower sales, leading to overstock situations, while budget-friendly items frequently faced stockouts. Traditional forecasting methods, relying heavily on historical sales data, struggled to keep pace with these rapid changes in consumer preferences. 📊 The Solution: AI-Driven Demand Forecasting To address this, HUL integrated AI-powered analytics into its demand forecasting processes. This advanced system enabled the company to: Analyze Real-Time Consumer Behavior: By examining current purchasing patterns and consumer sentiment, HUL could detect emerging trends and shifts in preferences. Incorporate External Economic Indicators: The AI model factored in various economic indicators, such as inflation rates and consumer confidence indices, to predict their impact on product demand. Optimize Inventory Management: With precise demand forecasts, HUL adjusted its inventory levels accordingly, ensuring optimal stock across all product categories. 🔹 Key Insight: The AI-driven approach revealed that demand for budget-friendly products was increasing at a rate three times higher than traditional models had predicted, while premium product sales were declining in specific regions. 📈 The Impact 20% Reduction in Unsold Premium Stock: By aligning inventory with actual demand, HUL minimized excess stock of premium items. 35% Improvement in Stock Availability for Budget-Friendly Products: Ensuring that high-demand, cost-effective products were readily available led to increased customer satisfaction. Enhanced Revenue and Profit Margins: Optimized inventory management reduced holding costs and prevented lost sales, positively impacting the bottom line. 💡 The Lesson In times of economic uncertainty, relying solely on historical data can be a pitfall. HUL's proactive adoption of AI-driven demand forecasting exemplifies how leveraging advanced analytics allows businesses to stay agile and responsive to market dynamics, ensuring they meet consumer needs effectively How is your organization utilizing data analytics to navigate market fluctuations? #datadrivendecisionmaking #businessstrategies #dataanalytics #demandforecasting

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Building AI models for forecasting? Prepare to rethink everything—because a general-purpose tabular model just rewrote the rules. A new study shows that TabPFN, a tabular foundation model, can outperform state-of-the-art time series forecasting models—without ever being trained on real-world time series data. The approach, TabPFN-TS, is strikingly simple: (1) Extract features from timestamps, like day of the week, month, and running index (2) Frame time series forecasting as a tabular regression problem (3) Let TabPFN predict future values without auto-regressive dependencies Despite having only 11M parameters, TabPFN-TS outperforms larger forecasting models such as Chronos-Mini (20M) and even slightly surpasses Chronos-Large (710M), which has 65x more parameters. Unlike Chronos, it doesn’t rely on pre-training with real-world time series data. What does this mean? Foundation models might be more general than we thought -TabPFN wasn’t designed for time series, yet it excels. Feature engineering still matters - carefully selected timestamp features make all the difference. Smaller models can be competitive - when combined with smart feature selection, even lightweight models can challenge much larger architectures. Is tabular modeling the future of time series forecasting? Paper https://lnkd.in/gcc5D5QX — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

Most forecasts fail not because of the model… ...but because of the mindset. Do you agree? 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟱 𝘁𝗶𝗽𝘀 𝗖𝗙𝗢𝘀 𝗰𝗮𝗻 𝘂𝘀𝗲 𝘁𝗼 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗯𝗲𝘁𝘁𝗲𝗿: • 𝗔𝗻𝗰𝗵𝗼𝗿 𝗶𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 → Base assumptions on both history and external signals like inflation, interest rates, and market shifts. • 𝗠𝗼𝗱𝗲𝗹 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀, 𝗻𝗼𝘁 𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝗶𝗲𝘀 → Build base, upside, and downside cases with clear triggers for each. • 𝗦𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁 𝘁𝗵𝗲 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 → Run sensitivity analysis on the few variables that matter most. • 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗿𝗲𝗹𝗲𝗻𝘁𝗹𝗲𝘀𝘀𝗹𝘆 → Compare your projections against peers and industry data to avoid wishful thinking. • 𝗖𝗹𝗼𝘀𝗲 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 → After every cycle, measure forecast vs. actual, then refine. Forecasting is a muscle; it gets stronger with feedback. Better forecasting isn’t about predicting the future perfectly. It’s about preparing the business to act with confidence when it arrives. P.S. If you had to cut forecasting time in half, which of these 5 would you double down on?

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

As a Buyer, most decks I saw in supplier meetings were dominated by the wrong type of data... ‘Our brand sold X, or has Y% share’ ‘Our last promotion did X’ ‘Our availability went up by X or returns down by Y’ Now obviously this data is needed somewhere, particularly as Buyers are often risk-averse, want reassurance about rate of sale & are not normally shy of a performance review. But what I’m saying is there was not enough BALANCE. Historical (or lagging) data only looks backwards, & as they say in the ads: "Past performance is no guarantee of future results”.  Categories change ALL the time – one brand (or retailer) doing one thing can set off a chain of events or a new direction for category evolution which affects everything. What I was more interested in was what was going to happen in the FUTURE That’s where my targets are & that’s why one of the best ways to enhance the persuasiveness of your pitch is by using forward-looking, or LEADING data – data which provides crucial insights into future trends, consumer behaviour & market dynamics.  Painting a picture of future category demand (with data to back it up) can create a very compelling narrative, transforming your pitch & therefore your brand’s prospects in the retailer’s Stores.  Here’s 5 examples of Leading data that you can use to give your pitch the edge: 1. Consumer Trends & Preferences - Consumer preferences identified in surveys & market research - Social Media listening insights - Search engine trends 2. Environmental & Social Trends - Growing Consumer adoption of more sustainable products - Regulatory changes (both announced & potential) 3. Economic Indicators:  - Consumer Confidence Index - Disposable Income trends - Employment rates 4. Market Trends & Innovations:  - Industry trends & technological advancements  - Competitive analysis (other brands, retailers, markets) 5. Underlying Category KPIs - Although not yet visible in headline performance, underlying KPIs may not be healthy e.g. declining category penetration or frequency. - Detailed analysis of the causes of these factors combined with predictive modelling can identify issues & actions to course correct which the Buyer will highly value Summary - Buyers know leading data & insights can provide the edge they need to compete with their competitors - Talking about what consumers will want NEXT gives you the opportunity to become a thought-leader in the category, helping to unlock your growth & strengthen your longer term relationship with your Buyer too At Optima Retail we are Category Management experts who specialise in category story development & sales presentations.  If you need any advice, or just want a quick chat to explore options for how to address a particular challenge please do get in touch. Any questions?  Please DM me or ask me in the comments… ♻️ If you found this post useful, please give it a like & consider sharing it to your network too. #categorymanagement #sales #growth