Automated Stock Replenishment

📦 Reorder Point Planning in FMCG (Explained Simply for SAP & Supply Chain Minds) Ever walked into a store and wondered: 👉 “Why is this shelf always stocked, while the other one is always empty?” That mystery is often solved by a concept called Reorder Point Planning (ROP). And trust me — once you get this, you’ll never see stock-outs the same way again. 🔑 The Core Idea Reorder Point Planning is like a traffic light for your inventory 🚦. Reorder Point (ROP): The exact stock level where you say, “time to order more!” Safety Stock: Your buffer zone, the just-in-case cushion. Lead Time: The waiting period between placing an order and receiving it. Lot Size: The quantity you usually order (like a truckload, pallet, or carton). 🧮 The Formula (Easy Memory Hook) Reorder Point = (Average Daily Demand × Lead Time) + Safety Stock 👉 Think of it as: D × L + S (Demand × Lead time + Safety stock) 🍾 Real-Life FMCG Example (Coca-Cola Bottles) Imagine you manage Coca-Cola 500ml bottles for a retail chain. Daily demand = 1000 bottles/day Supplier lead time = 5 days Safety stock = 2000 bottles ➡️ Reorder Point = (1000 × 5) + 2000 = 7000 bottles So, when stock hits 7000, your system should auto-trigger replenishment. 🛒 Why This Matters in FMCG Empty shelves = lost sales + disappointed customers Too much stock = expiry, wastage, and blocked cash Reorder Point = the sweet spot ⚖️ 👨💻 SAP Consultant Lens If you’re in SAP MM/PP/SD world, here’s how it translates: MM02/MM03 → Maintain Material Master MRP Type = VB (Reorder Point Planning) Safety Stock maintained in MRP2 Lot Size in MRP3 MD04 → Watch stock drop and hit reorder point MD03 → Run MRP (auto-creates purchase requisitions) ME21N → Convert to Purchase Order MB52 → Stock overview 📌 Cue to remember: VB + Safety Stock + MD04 = No stockouts 🥔 Another FMCG Example (Lays Chips) Daily demand = 500 packs/day Supplier lead time = 4 days Safety stock = 1000 packs ➡️ ROP = (500 × 4) + 1000 = 3000 packs When stock hits 3000, your SAP system says: ⚠️ “Hey, reorder before hungry customers go elsewhere!” 🧠 Genius Memory Hacks Traffic Light Analogy: Green = Safe (above reorder point) Yellow = Caution (at reorder point) Red = Danger (hitting safety stock) Formula Cue: D × L + S → call it “Daily Life Saver”. 💬 Final Thought In FMCG, success is not just about selling fast but also about replenishing smart. Reorder Point Planning ensures the shelves are full, customers are happy, and working capital isn’t tied up in slow-moving stock. For SAP consultants, linking theory + tcodes + real-life FMCG examples makes it click instantly. 👉 What about you? Have you ever faced a stock-out nightmare or had to explain ROP to a client? Drop your experience below — I’d love to hear how you tackled it! ---

Once, I assisted a fashion e-commerce brand that was facing issues with inventory turnover. Despite their large catalog of popular items, they were experiencing overstock on some products, while others went out of stock too quickly. The challenge was clear: they needed to optimize their inventory levels to meet customer demand without overstocking or understocking. Improving Inventory Turnover Using Data Analytics 1️⃣ Analyzing Sales Trends and Product Demand We started by analyzing past sales data to identify which products had high demand and which ones didn’t. By segmenting products by category, seasonality, and sales frequency, we were able to uncover patterns. SELECT product_id, SUM(sales_quantity) AS total_sales, AVG(sales_quantity) AS avg_sales_per_day, COUNT(DISTINCT order_id) AS total_orders FROM sales_data GROUP BY product_id HAVING avg_sales_per_day > 50; 🔹 Insight: Certain products had a high sales frequency, but others were consistently underperforming. This led to excess stock of the low-demand items. 2️⃣ Optimizing Stock Levels Based on Sales Velocity We then calculated the sales velocity for each product to determine the ideal stock levels. This data-driven approach helped us predict demand for each product more accurately. SELECT product_id, (total_sales / COUNT(DISTINCT month)) AS sales_velocity FROM sales_data GROUP BY product_id; 🔹 Insight: By calculating the sales velocity, we could forecast how quickly each product would sell, enabling us to optimize stock orders and avoid overstocking. 3️⃣ Implementing Replenishment Algorithms We used a replenishment algorithm that factored in sales velocity and historical demand patterns. The algorithm recommended restocking items that were selling quickly and scaling down orders for slower-moving products. # Pseudocode for Inventory Replenishment Algorithm def replenish_inventory(product_data): for product in product_data: if product['sales_velocity'] > threshold: reorder(product) else: reduce_order(product) return optimized_inventory 🔹 Insight: This allowed us to better balance stock levels, ensuring that popular items were replenished in time without holding excess inventory. Challenges Faced Demand forecasting was difficult due to rapidly changing fashion trends. Manual inventory tracking led to errors in stock levels, causing overstocking and stockouts. Seasonality made it harder to predict which items would be popular at any given time. Business Impact ✔ Inventory turnover improved by 30%, reducing excess stock and freeing up warehouse space. ✔ Stockouts decreased, leading to more sales and happier customers. ✔ Order fulfillment improved, as restocking decisions were more accurate and timely. Key Takeaway: Data-driven inventory optimization can balance stock levels, reduce overstocking and stockouts, and boost sales.

Replenishment isn’t a side feature, it’s a force multiplier. This is a big mistake. We’ve seen replenishment flows outperform promos and win-back emails combined. They convert better every time with the right timing and zero customer effort. Brands overspend on ads to win new customers, then forget to win them again. They need to predict exactly when a customer needs to repurchase and trigger the message at the perfect moment. Not too soon, not too late. Just right. ++ 𝗪𝗵𝘆 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗗𝗼𝗻’𝘁 𝗥𝗲𝗼𝗿𝗱𝗲𝗿 – 𝗔𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗙𝗶𝘅 𝗜𝘁 ++  𝗧𝗵𝗲𝘆 𝗙𝗼𝗿𝗴𝗲𝘁 ✅ Fix: Replenit’s AI triggers proactive reminders across channels exactly when customers are likely to run out, via the brand's own marketing automation vendors, without any migration. 𝗣𝗼𝗼𝗿 𝗧𝗶𝗺𝗶𝗻𝗴 𝗼𝗿 𝗖𝗵𝗮𝗻𝗻𝗲𝗹 ✅ Fix: Multichannel orchestration (SMS, push, email) with personalized timing based on consumption behavior. 𝗡𝗼 𝗖𝗹𝗲𝗮𝗿 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 ✅ Fix: Smart upsell bundles, urgency messages (“running low?”), and loyalty integration improve reorder ROI.   • Food & Beverage, pet food and treats, wellness & beauty products hold the highest repeat purchase potential, being very high due to frequent, perishable-driven consumption patterns. • Online groceries and FMCG rank high in habitual/impulsive behavior, presenting a strong fit for mobile push and SMS-driven replenishment campaigns. Brands like Glosel turned a leaky bucket into a revenue engine with Replenit’s AI-powered multichannel replenishment flows. 🚀 53.75% more automation revenue 🛒 +28% higher AOV 📲 100% of the Multichannel approach, email, SMS & Push channel revenue -12X Higher Engagement Rate Why does it work? Because Replenit activates timely, no-effort reorders across email, SMS, push, and more. Most brands forget to remind customers. ++ 𝟯 𝗧𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗥𝗲𝘁𝗮𝗶𝗹𝗲𝗿𝘀 ++ 1️⃣ Make Replenishment an Always-On Growth Engine Don’t treat it as a postscript. Integrate replenishment flows as a core revenue pillar in your retention strategy. 2️⃣ Automate Across Channels With Smart Triggers Use AI-powered solutions to trigger SMS, email, and push notifications based on usage cycles, not guesswork. 3️⃣ Track and Optimize With First-Party Data Loops Leverage Replenit’s dashboards to identify top retention products, run experiments on timing, and iterate continuously. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟮𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. #CPG #ecommerce #Replenishment #AI #FMCG

Artificial intelligence (AI) offers limitless possibilities, it seems. Not so long ago, we appeared to be moving rapidly towards a supply chain where smart machines would completely take care of planning. In practice, it seems we are still a long way from that. Especially in these uncertain and unpredictable times, human intervention is indispensable. For now, that smart machine appears to be nothing more than a handy assistant. Realco is a cooperative of supermarket owners with 180 stores and three warehouses in northern Italy. For determining stock levels and placing purchase orders, the company could only use its Enterprise Resource Planning system (ERP) and Warehouse Management System (WMS) for many years. “Planning was largely a manual process,” says Elena Bassoli, Logistics Manager at Realco. “To establish purchase orders, the planners used historical sales data, supplemented by information on upcoming promotions. But because information on, for example, delivery schedules and promotions was not always in the ERP system, they also had to extract information from spreadsheets, loose notes and memo sheets on their screens.” During the pandemic, this modus operandi no longer proved adequate. Realco saw some items running out of stock unexpectedly quickly, while at the same time distribution centres (DCs) were bulging as other stock levels were rising rapidly. Bassoli: “It was clear that we needed a more sophisticated and thought-out solution that would allow us to generate a more accurate forecast. We decided to immediately look for a state-of-the-art solution with AI. We found that at RELEX Solutions.” Realco deploys the Relex solution to replenish stock in its distribution centres. Using machine learning (ML), the tool generates a forecast, which uses external data on, for example, the weather in addition to internal data. This forecast is then automatically translated into a purchasing proposal. “We forward as many as 91% of the purchasing proposals directly to our suppliers without a single adjustment. As a result, stock availability has improved by 4 to 5%, while at the same time inventory has decreased by 10% in volume,” Bassoli states. Read the complete article in the latest edition of Supply Chain Movement quarterly magazine: https://lnkd.in/e9WEZ_8z #ai #machinelearning #supplychainplanning #genai #forecasting Mette Krogh Elliot Cartwright Marin Shipe Amélie NICOLAS Bas van Lith Jasper Van Rijn Cazijn Langeler Maarten Vaessen Marcel te Lindert Nicole Messink

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?