Inventory Forecasting Approaches

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

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.

Deterministic vs “stochastic” forecasting Talking #supplychainmanagement I am sometimes stunned by the confusion surrounding the use of “point” (or deterministic) forecasts, versus “probabilistic” (or stochastic) forecasting.   Let me use the context of inventory planning in a setting with long lead times (say several months). Uncertainty can be traced to the manufacture of the product, shipping delays, and the weekly demands for a product which can vary from the randomness of customer choice, seasonal and holiday variations, competitor behavior and corporate decisions (pricing, marketing).   Despite all these uncertainties, industry continues to equate “forecast” with “point forecast” where forecast errors are given by the difference between the actual demand and the (point) forecast.   There are two ways to meet service requirements such as meeting 95 percent of demand:   1)   Inventory decisions are made using a stochastic (probabilistic) lookahead model, the heart of which would be a probabilistic (or stochastic) forecast. This is the most familiar approach in inventory textbooks (e.g. using the 95th percentile of the lead time demand). 2)   We can use a parameterized policy (possibly based on a point forecast) that is tuned using a stochastic simulator which captures all the forms of uncertainties.   There are two ways of performing a stochastic simulation:   1)   Development of a computer program to simulate the system (often called a “digital twin”). 2)   Evaluate the policy in the real world.   The use of a simulator to tune (optimize) a policy seems to be completely overlooked in the standard textbooks on inventory problems. It is easy to overlook that *any* policy will eventually be tested in the field, which is a form of simulation which is more realistic, but very slow. 

Inventory is NOT one-size-fits-all. This document contains how to do and actually use ABC-XYZ inventory segmentation step-by-step: Step # 1 - Run ABC Analysis – Based on value contribution ↳ use annual consumption × unit cost to rank SKUs ↳ A items = top 70-80%; B items = next 15-25%; bottom 5-10% of value Step # 2 - Run XYZ Analysis – Based on demand predictability ↳ use demand data over 12–18 months ↳ X Items = Very predictable (low Coefficient of Variation <25%); Y items = Moderate variability (25–50%); Z Items = Highly erratic demand (>50%) Step # 3 - Combine ABC with XYZ to form 9 Inventory Buckets ↳ include the items in a matrix with ABC and XYZ ↳ plot the x-axis with XYZ and the y-axis with ABC Step # 4 - Use the Output to Make Smarter Inventory Decisions ↳ AX / BX → Frequent review, tight controls, lean inventory ↳ AZ / CZ → Keep minimal stock or make-to-order ↳ AY / BY → Forecast cautiously, build buffers ↳ CY / CZ → Consider phasing out, review periodically Step # 5 - Segment Customers Based on Strategic Importance and Profitability ↳ Use metrics like: ↳ Revenue contribution ↳ Gross margin ↳ Strategic value Step # 6 - Cross-reference Inventory Buckets with Customer Segments ↳ Use metrics like: ↳ Avoid blunt phase-out decisions ↳ Consider strategic priorities, service levels Step # 7 - Refresh the Segmentation on a Regular Cadence ↳ Run ABC-XYZ analysis twice a year or quarterly  ↳ Use it for better decision making Any others to add?

🚨 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐦𝐚𝐤𝐢𝐧𝐠 𝐢𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐠𝐮𝐭 𝐟𝐞𝐞𝐥𝐢𝐧𝐠? It is time to level up with 𝐀𝐁𝐂 𝐗𝐘𝐙 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 — the ultimate duo for smarter stock management. 🧠📦 Let us break it down so you can optimize inventory, reduce waste, and keep customers happy. 🔍 What is ABC XYZ Analysis? It is a combined inventory classification method used in supply chain and inventory management. It merges two powerful frameworks: 𝐀𝐁𝐂 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Categorizes inventory based on 𝐯𝐚𝐥𝐮𝐞 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 (e.g. revenue or cost). ↳ A-items: High value, low quantity ↳ B-items: Moderate value and quantity ↳ C-items: Low value, high quantity 𝐗𝐘𝐙 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Classifies items based on 𝐝𝐞𝐦𝐚𝐧𝐝 𝐯𝐚𝐫𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲. ↳ X: Predictable demand ↳ Y: Moderate demand variability ↳ Z: Highly irregular demand By combining them, you get a 3x3 matrix (like AX, BY, CZ...) to identify what really matters in your inventory. ⚙️ How does it work? 1️⃣ Run ABC classification by analyzing cumulative consumption value (Pareto principle). 2️⃣ Run XYZ classification by calculating demand variability (coefficient of variation). 3️⃣ Cross-tabulate the results to assign inventory strategies: ↳ 🔺 AX: High-value & stable → tight control, frequent review ↳ 🔻 CZ: Low-value & erratic → minimal investment, possibly phase out 📦 Real-Life Example Imagine a retailer with 1,000 SKUs: ↳ iPhones: High value, stable sales → AX ↳ Phone cases: Low value, steady demand → CX ↳ Christmas lights: Low value, unpredictable sales → CZ Now the retailer can: ✅ Prioritize planning and forecasting for iPhones ✅ Bulk order phone cases less frequently ✅ Avoid overstocking seasonal items 🎯 Benefits ↳ Improved forecasting and procurement ↳ Reduced holding and obsolete inventory costs ↳ More focused inventory strategy ⚠️ Challenges ↳ Requires accurate data and analysis ↳ Demand patterns may shift (e.g. due to market trends or seasonality) ↳ Risk of oversimplifying complex SKUs ✅ Conclusion ABC XYZ Analysis is not just a tool — it is a strategy. By classifying items based on value and predictability, you can drive efficiency, cut costs, and boost customer satisfaction. 📈 ✨ Whether you are in retail, manufacturing, or logistics — this technique can transform how you manage stock. 𝐒𝐭𝐚𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠, 𝐬𝐭𝐚𝐫𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠.