Forecasting and Inventory Synchronization

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

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

What if you could predict stockouts before they happen? When a customer sees “Out of Stock”, they don’t just lose patience. They lose confidence in your brand. And in the D2C world, stockouts don’t just mean lost revenue today—they mean: ❌ Higher customer churn – They buy elsewhere, and they don’t come back. ❌ Lost marketing dollars – You paid to acquire them, and now they can’t buy. ❌ Damaged brand reputation – Customers expect seamless inventory availability. Why Stockouts Happen: - Marketing & Ops Misalignment – Campaigns drive demand for products that supply chains weren’t ready for. - Lack of Predictive Analytics – Inventory forecasting is based on gut-feeling rather than data-driven signals. - Slow Reaction Time – By the time teams see a stockout, the damage is already done. - Supply chain disruption - Nobody is deliberately tracking how the inventory is moving and why is it moving the way it is. The real enemy of fulfilment isn’t inefficiency—it’s lag time. ❌ Marketing doesn’t know inventory constraints until it’s too late. ❌ Finance doesn’t realize rising fulfilment costs until margins shrink. ❌ Operations reacts to problems instead of preventing them. What Happens in a Data-obscure #D2C #SupplyChain: - Customers order products that are out of stock or delayed. - Warehouse teams are overwhelmed with orders they weren’t prepared for. - Your brand wastes ad spend on SKUs that won’t arrive on time. What Happens When You Fix It: ✅ Marketing campaigns adjust in real-time to inventory fluctuations. ✅ Finance tracks fulfilment costs live, avoiding margin erosion. ✅ Ops teams prevent bottlenecks instead of reacting to them. How Top COOs Prevent Stockouts: - Sales Velocity Tracking – Identify fast-moving SKUs before they go out of stock. - Fulfilment SLA Monitoring – Ensure 3PLs are keeping pace with demand. - Marketing-Inventory Syncing – Pause ad spend on products running low to avoid wasted clicks. #Stockouts aren’t just an ops problem. They’re a #data problem. When marketing, supply chain, and finance have a single source of truth, stockouts become preventable, not inevitable.

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 

Because wrong inventory replenishment destroys profit and cash... This infographics contains 7 ways for inventory replenishment and when to use each: ✅ Demand Forecasting 👉 Based on: demand ❓ When to Use: variable demand, long lead times, or seasonal trends to prevent stockouts or overstock ➡️ Replenishment Trigger: inventory required per demand plan ✅ Reorder Point 👉 Based on: stock level ❓ When to Use: consistent demand patterns, lead times and safety stock can be calculated reliably ➡️ Replenishment Trigger: inventory reaches a level that considers average daily sales, lead time, and safety stock ✅ Just-In-Time (JIT) 👉 Based on: demand, consumption ❓ When to Use: consistent, predictable production schedules and reliable suppliers ➡️ Replenishment Trigger: inventory required for production ✅ Min-Max 👉 Based on: stock level ❓ When to Use: stable demand, inventory is used consistently, but occasional fluctuations need buffer coverage ➡️ Replenishment Trigger: inventory reaches the minimum level set; the order is to get to the max level ✅ Periodic Ordering 👉 Based on: time period ❓ When to Use: predictable and relatively stable demand ➡️ Replenishment Trigger: regular intervals: weekly, monthly, etc ✅ Anticipation 👉 Based on: expectations about future outlook ❓ When to Use: high seasonality, promotional campaigns, or events requiring large, proactive stock buildup ➡️ Replenishment Trigger: seasonal inventory, expected demand peak, new system implementation ✅ Top-off 👉 Based on: production activity and stock levels ❓When to Use: ensuring storage or line-level inventory readiness before a surge in production or demand ➡️ Replenishment Trigger: in down time, bringing inventory forward to reach capacity levels Any others to add?

Effective inventory management is a must for profitability and happy customers. This guide outlines key techniques to optimize your inventory control: I. Demand Forecasting & Planning 🔮 Accurate Forecasting: Use historical data, market trends, and statistical models (or software) to predict demand. Key Metric: Forecast accuracy. 🎯 Setting Par Levels: Determine optimal stock levels based on demand, lead times, and safety stock. Key Metric: Stockout rate. 📊 ABC Analysis: Prioritize inventory based on value and consumption (A = high value/demand). II. Inventory Management Techniques 🔄 FIFO (First-In, First-Out): Rotate stock to minimize spoilage and obsolescence. Key Metric: Inventory turnover rate. 💨 JIT (Just-In-Time): Minimize inventory by receiving materials only when needed. Key Metric: Inventory turnover rate, lead time. III. Supply Chain Management 🤝 Strong Supplier Relationships: Ensure reliable deliveries and competitive pricing. Key Metrics: On-time delivery, supplier performance. 🛡️ Contingency Planning: Develop plans for supply chain disruptions. Key Metric: Resilience to disruptions. IV. Inventory Control & Auditing ✅ Regular Auditing: Conduct periodic physical counts to verify accuracy. Key Metric: Inventory accuracy rate. 💻 Warehouse Management Systems (WMS): Streamline tracking, improve accuracy, and optimize space. V. Advanced Techniques 🔮 Predictive Modelling: Use advanced analytics for more accurate demand forecasting. 💨 Agile Supply Chain: Adapt quickly to changing demand and disruptions. Key Metric: Time to adapt. 📦 Drop shipping: Outsource storage and fulfilment. 🧑💼 The Human Element Thorough staff training on inventory procedures is essential. Clear communication between departments is vital. Adherence to processes and regular system reviews are key. Implement these techniques, track your metrics, and watch your inventory become a strategic asset! ♻️ 𝙁𝙤𝙪𝙣𝙙 𝙩𝙝𝙞𝙨 𝙝𝙚𝙡𝙥𝙛𝙪𝙡? 𝙎𝙝𝙖𝙧𝙚 𝙞𝙩 𝙬𝙞𝙩𝙝 𝙮𝙤𝙪𝙧 𝙣𝙚𝙩𝙬𝙤𝙧𝙠 𝙩𝙤 𝙨𝙥𝙧𝙚𝙖𝙙 𝙩𝙝𝙚 𝙠𝙣𝙤𝙬𝙡𝙚𝙙𝙜𝙚! 𝗱𝗼𝗻'𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝗻 𝗲𝗹𝗲𝘃𝗮𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁'𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗺𝗽𝗮𝗰𝘁! #inventorymanagement #supplychain #procurement #forecasting #JIT #optimization #efficiency #riskmanagement

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.

Shipping doesn’t have to be a nightmare. Learn how to streamline your logistics and save big. This thread reveals the tools that work. 💡 Struggling with High Inventory Costs? Here's How to Optimize for Savings! Inventory management is one of the biggest balancing acts in business. Stock too much, and you tie up cash while risking obsolescence. Stock too little, and you risk losing sales and frustrating customers. The secret? Smart optimization. Here are 5 proven strategies to trim costs and boost efficiency: 1️⃣ Embrace Data-Driven Forecasting 👉 The Problem: Stocking based on guesswork leads to overstocking or stockouts. 💡 The Fix: Use historical sales data, market trends, and predictive analytics to forecast demand. Tools like ERP systems or inventory management software make this easier than ever. 2️⃣ Adopt Just-In-Time (JIT) Inventory 👉 The Problem: Holding large quantities of inventory drives up storage and carrying costs. 💡 The Fix: With JIT, you order stock only as needed. This reduces waste, but it requires strong supplier relationships and a reliable supply chain. 3️⃣ Categorize Inventory with ABC Analysis 👉 The Problem: Treating all inventory as equal drains resources on low-value items. 💡 The Fix: Prioritize high-value (A), medium-value (B), and low-value (C) items. Focus most of your attention and resources on A items—they drive the most revenue. 4️⃣ Monitor Inventory Turnover 👉 The Problem: Slow-moving inventory ties up capital and risks becoming unsellable. 💡 The Fix: Track your inventory turnover ratio (COGS ÷ average inventory) regularly. Aim to increase this number by running promotions or bundling slow-moving items. 5️⃣ Standardize Stock Replenishment 👉 The Problem: Erratic ordering patterns lead to inconsistent inventory levels and cash flow issues. 💡 The Fix: Establish reorder points and safety stock thresholds for every SKU. Automating replenishment through inventory systems reduces human error. ✨ Bonus Tip: Conduct regular inventory audits! Spotting inaccuracies early can save you thousands in unnecessary purchases or lost sales. Why It Matters: Optimizing inventory isn’t just about cutting costs—it’s about improving your cash flow, reducing waste, and staying competitive. The better your inventory processes, the more agile your business becomes. 💬 What’s your inventory management approach? Are you using any of these strategies today? What’s been your biggest challenge in keeping costs down? Share your thoughts below or tag someone in logistics or operations who might find these tips useful! Let’s keep this conversation going. 📦🚀