Supply Chain Forecasting Best Practices

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

I’ve made mistakes in demand planning—mistakes that cost time, resources, and accuracy. If I could go back, here’s what I would tell my younger self: ↳ Don’t treat forecasting as a numbers game. Early in my career, I was obsessed with statistical models. I thought if I fine-tuned the algorithm enough, I’d get the perfect forecast. But I missed one thing—forecasting is as much about people and market insights as it is about data. Now, I ensure that numbers tell a story, not just a trend. ↳ Ignoring real-world disruptions is a dangerous game. Once, I confidently predicted demand for a product based on past trends. But then, a sudden regulatory change disrupted the entire market. My forecast? Completely irrelevant. Since then, I’ve learned that supply chain issues, economic shifts, and geopolitical events can break even the best predictions. ↳ Your best data source isn’t always in the system. I once dismissed a sales team’s warning about a shifting customer preference, thinking, "If the data doesn’t show it, it’s not real."" I was wrong. The sales team had firsthand insights from customers—insights that never made it into my spreadsheets. Now, I make sure to tap into sales, marketing, and customer service teams before finalizing forecasts. ↳ Gut feelings can be useful—but only if measured. I’ve seen leaders make brilliant intuitive calls—and also some that backfired spectacularly. The real lesson? Don’t discard intuition, but validate it. I now use Forecast Value Added (FVA) analysis to measure whether a human override improves or worsens accuracy. Data and experience must go hand in hand. Demand planning is a mix of art and science. What’s one forecasting lesson you learned the hard way? Let’s discuss.

Because with wrong demand forecasting everything else falls apart... This infographic shows 10 red flags in demand forecasting and how to turn them green: 🚩 # 1 - Over-reliance on historical data How to Turn Green: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 🚩 # 2 - Ignoring promotions and discounts How to Turn Green: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 🚩 # 3 - Forgetting cannibalization effects How to Turn Green: model cannibalization effects to adjust forecasts for existing products 🚩 # 4 - One-size-fits-all forecasting method How to Turn Green: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 🚩 # 5 - Not monitoring forecast accuracy How to Turn Green: track metrics like MAPE, WMAPE, bias, and forecast value-add (FVA) to improve over time 🚩 # 6 - High forecast error with no accountability How to Turn Green: tie accountability to S&OP (sales and operations) meetings 🚩 # 7 - Poor collaboration with sales and marketing How to Turn Green: hold regular cross-functional meetings to align forecasts with upcoming campaigns 🚩 # 8 - Over-reliance on intuition How to Turn Green: balance judgment-based inputs with statistical and AI-driven models 🚩 # 9 - Infrequent forecast updates How to Turn Green: move to a rolling forecast system that updates regularly based on the latest data 🚩 # 10 - Past sales (instead of demand) consideration How to Turn Green: 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? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement

When I worked as a demand and inventory planner in large multinational companies, I was often responsible for hundreds of SKUs across dozens of markets. With just one week each month to complete my plans during the S&OP cycle, I needed a way to manage the volume quickly and effectively. That’s when I started applying ABC–XYZ segmentation, not just for inventory, but for demand forecasting. It allowed me to focus on what mattered most and stop wasting time fine-tuning low-impact or erratic SKUs. Now, as a researcher in forecasting, I see how far academic progress has come, and yet how often it feels disconnected from the daily reality of planners. With so many forecasting models performing well in theory, the question remains: which ones should I actually use in practice? In this article, I revisit ABC–XYZ segmentation through a demand planner’s lens and offer concrete examples and recommendations for matching models to product behavior and business value. Quick Takeaways: • Segment SKUs by value (ABC) and variability (XYZ) to focus effort where it counts • Forecasting models should be matched to each segment, there’s no one-size-fits-all • Use machine learning or judgment only where they add real value • Segmenting at SKU level works best, but hybrid approaches are often necessary • Model choice depends on context: data quality, lifecycle stage, and available time #DemandPlanning #Forecasting #SupplyChainPlanning #InventoryManagement #MachineLearning