Demand Forecasting in Omnichannel Retail

𝗛𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵: 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗮 𝘀𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. It’s a signals → decisions problem. Most teams chase a single number. Winners design a system that stays right when the world wiggles. Here’s my playbook for GenAI-driven demand + inventory, built for CIO/CTO and Ops leaders: 𝗦𝟯 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 — 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 → 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 → 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗹𝗲𝘃𝗲𝗹𝘀.  𝟭. 𝗦𝗶𝗴𝗻𝗮𝗹𝘀. Unify sell-through, returns, promos, weather, lead times, supplier risk. Use GenAI to convert messy text into structured features. Pull from sales notes and vendor emails.  𝟮. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀. Stop point forecasts. Run probabilistic demand curves with clear explanations. Ask: “What if lead time slips 10 days?” Then see SKU-level impact.  𝟯. 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗹𝗲𝘃𝗲𝗹𝘀. Optimize for cash and customer promise, not vanity accuracy. Respect constraints: MOQ, capacity, holding cost, spoilage. GenAI recommends reorder points; humans own overrides. 𝗤𝘂𝗶𝗰𝗸 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: A seasonal SKU with promo spikes. We fed signals and constraints. Weekly S&OP dropped from 8 hours to 20 minutes. Stockouts fell, dead stock shrank, and finance liked the cash delta. 𝗕𝘂𝗶𝗹𝗱 𝗶𝘁 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗼𝗿𝗱𝗲𝗿:  • Data contract for signals.  • GenAI reasoning layer for “why” and “what-if”.  • Optimizer for service levels and working capital.  • Feedback loop: accept or override, then learn. New rule for 2025: Don’t optimize forecasts. Optimize decisions. Your model can be “wrong” and your business still wins. Save this. 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗣𝗟𝗔𝗬𝗕𝗢𝗢𝗞” 𝗮𝗻𝗱 𝗜’𝗹𝗹 𝘀𝗵𝗮𝗿𝗲 𝘁𝗵𝗲 𝗦𝟯 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 𝗮𝗻𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘄𝗲 𝘂𝘀𝗲. #ThinkAI #SupplyChain #Inventory #AI

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?

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

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.

If you’re feeding raw sales data into your forecasts, you’re making a big mistake. Retail is full of noise: stockouts, listing suspensions, influencer campaigns, promotions — all of it distorts the true demand signal. The worst thing you can do is use that raw data to feed your forecasting algorithms — you'll just forecast a new stockout or end up overstocking again. As my friend Nicolas Vandeput often says (and I agree), the best forecasting models are the ones that can consume all the context data: sales units, revenue, price history, ad spend, influencer activity, inventory levels… With that context, models can actually make sense of the past and project the future. The problem? Most brands don’t have that data organized, accessible, or reliable. So what’s the next best thing? Algorithms that automatically detect and adjust for outliers — cleaning the past before predicting the future. That’s exactly what Flieber does. The moment you connect your data, we run it through anomaly detection and correction before it ever reaches the forecasting engine. And we feed our algorithms the adjusted sales, instead of the actual sales. That step alone improves forecast accuracy by up to 40%. For planners, that’s not just a nice-to-have — it’s life-changing.

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