Demand Planning In Retail

🍦 𝗔𝗜 𝗖𝗮𝘀𝗲: 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿 𝗜𝗰𝗲 𝗖𝗿𝗲𝗮𝗺 — 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗧𝗵𝗮𝘁 𝗥𝗲𝗮𝗰𝘁𝘀 𝘁𝗼 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 & 𝗦𝘁𝗼𝗿𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 🤔 AI in supply chains isn’t just a promise — it’s already delivering measurable results. 🌡️ 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿’𝘀 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗶𝗰𝗲 𝗰𝗿𝗲𝗮𝗺 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 faces rapid, weather-driven demand swings. Seasonal volatility often outpaces traditional forecasts, leading to lost sales and waste. 📣 𝗛𝗼𝘄 𝗔𝗜 𝗵𝗲𝗹𝗽𝗲𝗱 𝗨𝗻𝗶𝗹𝗲𝘃𝗲𝗿’𝘀 𝗗𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 & 𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 ▪️ Uses daily weather updates from hyperlocal data (temperature, rainfall by city). ▪️ Pulls live data from AI-enabled freezers with IoT sensors tracking SKU presence and quantities. ▪️ Combines POS and distributor sales to reconcile forecasts in near-real-time. ▪️ Adds event and promotion data to refine demand signals. 𝗧𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘂𝘀𝗲𝘀 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿: 🔹 Weekly rolling forecasts that adjust monthly plans. 🔹 Daily alerts so teams can replenish high-demand SKUs fast (e.g., +5°C triggers orders within 48 hrs). 🔹 Inventory reallocation from low- to high-demand areas before expiry. 📈 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✔️ 10% higher forecast accuracy, reducing waste and missed sales. ✔️ 30% higher retail orders due to proactive replenishment and SKU mix optimisation. ✔️ Lower waste through stock reallocation in cooler periods. ✔️ Faster decisions — from a week to hours. 📍 𝗧𝗵𝗶𝘀 𝘀𝗵𝗼𝘄𝘀 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝘁𝘂𝗿𝗻 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝘀𝗮𝗹𝗲𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝘂𝘁 𝘄𝗮𝘀𝘁𝗲, 𝗯𝗼𝗼𝘀𝘁 𝘀𝗮𝗹𝗲𝘀, 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲. 👇 𝘞𝘩𝘢𝘵 is 𝘩𝘰𝘭𝘥𝘪𝘯𝘨 𝘭𝘰𝘤𝘢𝘭 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘧𝘳𝘰𝘮 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘪𝘯𝘨 𝘈𝘐 𝘪𝘯 𝘴𝘶𝘱𝘱𝘭𝘺 𝘤𝘩𝘢𝘪𝘯𝘴?

3 to 5 SKUs in your catalog are eating your contribution margin. You just don't know which ones yet. Here's how to find them, and what to do about it. Step 1: Build your fully loaded contribution margin per SKU. Not just COGS. Fully loaded means: - Cost of goods - Fulfillment + 3PL fees - Returns and restocking costs - Merchant processing fees - Allocated ad spend (if product-specific) Step 2: Sort your SKUs into 4 buckets. - Keep & Scale: positive CM, strong velocity - Fix or Reposition: positive CM, weak velocity - Cut: negative CM after fully loaded costs - Watch: borderline, not enough data yet Step 3: Make the call without gut-feel bias. A SKU with a loyal following, a great story, or a personal attachment is hard to kill. A product with negative contribution margin doesn't just break even. It actively subsidizes losses with revenue from your winners. Every unit you sell is costing you money. - - - SKU rationalization feels like giving up on a product. It's not. It's protecting the ones that are actually working. Your Q4 inventory budget is finite. Every dollar you allocate to a money-losing SKU is a dollar you can't put behind your winners during the highest-revenue quarter of the year. Run the numbers now, while you still have time to act on them. - - - Which SKU in your catalog do you suspect is margin-negative, but you've been avoiding the analysis? ♻️ Repost this if you know a DTC founder heading into Q4 with an unaudited product lineup. P.S. If you want to run this analysis together before Q4 inventory decisions lock in, grab a time with me here ➜ https://lnkd.in/eZ9cu5vR #DTCBrands #CashFlowTips #EcommerceStrategy #FinanceTips

Demand Sensing: Using Real-Time Signals Before It’s Too Late!! Most forecast issues don’t happen because planners ignored data. They happen because the business reacted too late. Traditional forecasting often asks: “What happened last month?” Demand sensing asks: “What is demand starting to do now?” That difference matters. Because markets move faster than monthly planning cycles. Demand can shift when: 🔹 Customers change buying behavior 🔹 Competitors launch promotions 🔹 Online demand suddenly spikes 🔹 Supplier delays affect availability 🔹 Sales teams notice early market signals 🔹 Warehouse dispatches increase before reports are reviewed 📊 A Simple Example Forecast: 20,000 units But early in the month: ⚠️ Competitor goes out of stock ⚠️ Online searches increase ⚠️ Retailers order more ⚠️ Sales reports faster movement ⚠️ Warehouse dispatches rise Potential demand moves toward: 28,000 units If the team waits until month-end, the result may be: ❌ Stockouts ❌ Lost sales ❌ Expediting cost ❌ Emergency procurement ❌ Poor customer service The issue is not only forecast accuracy. The issue is weak demand sensing. 🔍 What to Monitor Demand sensing works when planners connect signals such as: 📦 Sales orders 🛒 POS data 💻 E-commerce demand ⏳ Backorders 📞 Customer inquiries 📢 Promotion plans 🏁 Competitor activity 💰 Market pricing 🚚 Supplier delays 🌍 Logistics disruptions The goal is not to chase every signal. The goal is to identify which signals matter for each product category. ✅ Better Approach Use demand sensing practically: 🔹 Segment SKUs using ABC/XYZ 🔹 Track early demand deviation 🔹 Connect sales, inventory, procurement, and logistics data 🔹 Use exception-based planning 🔹 Validate changes through S&OP 🔹 Apply DMAIC when the same signals are repeatedly missed Demand sensing is not only about technology. 📊 Dashboards can detect signals. 🤝 Alignment turns signals into decisions. Demand sensing does not replace forecasting. It improves the forecast before it becomes outdated. The best planners do not only explain forecast error after it happens. They detect demand change early enough to act. #DemandPlanning #Forecasting #DemandSensing #SupplyChainPlanning #SOP #SupplyChainAnalytics #InventoryManagement #LeanSixSigma #OperationsManagement

Forecasts are worthless if they don’t drive action. This document shows how to turn forecast errors into insights: # 1 - Compare Forecast vs Actual Pattern, Not Just Values Look for trend breaks: promotions, seasonality shifts, competitive actions Insight: shows whether the model or the business behavior changed # 2 - Separate Volume Error from Mix Error Your total forecast may be right but SKU mix is wrong Insight: points to cannibalization, launches, or customer preference shifts # 3 - Slice the MAPE (forecast error) MAPE at total level hides the real problem; slice by SKU, region, channel, and planner Insight: find where the system is breaking, not the average # 4 - Track Bias Consistently MAPE shows how much you miss; bias shows how you think Insight: positive bias = optimism; negative bias = fear of stockouts # 5 - Connect Error Spikes to Events Overlay error trend with business events; launches, stockouts, price changes and map everything Insight: turns disconnected numbers into cause-and-effect stories # 6 - Use FVA (forecast value added) to Check If Adjustments Helped or Hurt Measure whether human overrides improved or worsened accuracy Insight: helps remove emotional adjustments from the process # 7 - Build an Error Heatmap One view showing where the biggest misses are by SKU, month, region Insight: quickly identifies where planning attention is needed # 8 - Weekly Error Deep Dive Pick the top 5 SKUs with the biggest misses; ask: “what changed?” and “who owns the correction?” Insight: makes forecasting a feedback loop, not a ritual Any others to add?

SKU- Benefis and Managment In FMCG SKU (Stock Keeping Unit) is a unique identifier for a product in inventory, commonly used in the retail and FMCG (Fast-Moving Consumer Goods) sectors. Each SKU represents a distinct item with specific characteristics, such as brand, size, color, and packaging. Here's an overview of the benefits and management of SKUs in the FMCG context: Benefits of SKUs in FMCG 1. Inventory Management - Accuracy: SKUs help in accurately tracking inventory levels, reducing the chances of overstocking or stockouts. - Efficiency: Facilitates faster and more accurate order processing, restocking, and inventory audits. - Demand Forecasting: Historical sales data associated with SKUs can help in predicting future demand and optimizing stock levels. 2. Sales Analysis - Product Performance: SKUs enable detailed sales analysis by product, helping in identifying bestsellers and underperforming items. - Promotional Effectiveness: Tracking sales by SKU allows for assessing the impact of promotions on specific products. 3. Customer Experience - Product Availability: Proper SKU management ensures that popular products are always available, enhancing customer satisfaction. - Variety: SKUs allow for offering a wide range of product variants, catering to different customer preferences. 4. Pricing Strategy - Dynamic Pricing: SKUs support targeted pricing strategies, enabling the adjustment of prices for specific items based on demand, competition, or seasonality. - Profit Margin Analysis: Detailed SKU-level analysis helps in understanding profit margins and optimizing pricing strategies. SKU Management in FMCG 1. SKU Rationalization - Product Portfolio Optimization: Regularly review and reduce the number of SKUs to eliminate low-performing or redundant products, focusing on high-demand items. - Cost Reduction: Fewer SKUs can lead to lower inventory holding costs and simplified supply chain processes. 2. SKU Creation and Maintenance - Standardization: Develop a consistent system for creating and maintaining SKUs, including naming conventions and categorization. - Data Accuracy: Ensure that all SKU-related data (e.g., product description, pricing, and stock levels) is accurate and up-to-date. 3. Technology Utilization - Inventory Management Systems (IMS) Use advanced ERP systems to manage SKUs, track inventory in real-time, and integrate with sales and supply chain operations. - Barcoding: Implement barcode to automate SKU tracking and reduce manual errors. 4. Analysis and Reporting - Regular Reviews: Conduct regular SKU performance reviews, analyzing sales trends, inventory turnover, and profitability. - Dashboard Integration: Utilize dashboards for real-time monitoring of SKU performance metrics to make informed decisions quickly. Effective SKU management is critical for optimizing operations, reducing costs, and enhancing customer satisfaction in the FMCG sector.

Retailers are no longer asking. They’re demanding. Cut your range. Rationalise your portfolio or be squeezed out. This isn’t nuance. It’s a survival fight. The giants have the shelf power. The rest? We fight for space. And in that fight, retailers are quietly rewriting the rules of engagement. You either slim down and play clever, or you collapse under the weight. Let me peel back what’s happening in the trenches. This isn’t about the retailers being evil. It’s about leverage. Scale. Efficiency. Take Coles. They’re directive: slash at least 10% of products from their shelves. They’re saying: “We’ve got too much duplication, too many options that don’t matter, and too much noise for shoppers.” Meanwhile, Woolworths is rearranging the battlefield. Their internal restructure is consolidating divisions (Supermarkets, Metro, Greenstock, Private Label) under a unified “Retail” banner. This isn’t cosmetic. It’s streamlining decision-making, simplifying back-end systems, and making it easier to spot wasted SKUs. This means brands with marginal lines, slow movers, or “nice-to-haves” are under pressure. Retailers want “must-haves.” They’ll back you, but only if you clear the clutter first. So what’s going on beneath the surface? Margin pressure & efficiency: Fewer SKUs → leaner supply chains, less waste, easier forecasting. Shelf space is gold: Every half-bay has an opportunity cost. Retailers want brands that fight for space, with fewer lines. Customer confusion & decision fatigue: Retailers know shoppers hate choice overload. 🛠 What brands can do (if you want to stay alive) 1. Audit your under-performers ruthlessly. If a SKU hasn’t earned its keep in 12 months, let it go. 2. Consolidate adjacent lines. Merge variants, eliminate duplicated flavours, and simplify pack sizes. 3. Make your core SKUs indispensable. Invest in distribution, marketing, retailer exclusives. 4. Use data arguments, not emotion. Show how streamlining increases fill rates, simplifies ordering, reduces waste. 5. Be proactive in the rationalisation conversation. Don’t wait to be asked. Present your own rationalisation roadmap. 6. Negotiate for specialty space. If a line is marginal but strategic, ask for promo bays, limited-time placements, or pilot zones. 7. Lean into your agility.  Experiment with limited editions, short-run SKUs, but don’t let them clutter forever. 8. Tell your story. Make the shift part of your brand narrative (focus, clarity, discipline). If you’re being rationalised, don’t go down whining, go down proving you’re smarter. If you’re a brand: has a major retailer already pushed you to rationalise? How did you navigate it? If you work on the retail side: how do you balance culling SKUs vs. preserving diversity? Drop your thoughts below 👇 If you’re a brand feeling the squeeze and want help carving a rationalisation playbook,  let’s talk. Real strategy doesn’t come from hiding. It comes from surviving the pressure, then pushing back smarter.

Post #23 — Demand Sensing in SAP IBP: Improving Short-Term Forecast Accuracy Traditional forecasting looks at history. Demand Sensing looks at what is happening now. In volatile markets, relying only on historical patterns is no longer enough. This is where SAP IBP Demand Sensing becomes critical. ⸻ 1️⃣ What Is Demand Sensing? Demand Sensing is a short-term forecasting approach that adjusts the forecast using near-real-time signals instead of long historical trends. It focuses on: • The immediate future (days or weeks ahead) • Actual customer behavior • Rapid reaction to demand changes The goal is not to replace statistical forecasting — but to enhance it close to execution. ⸻ 2️⃣ How Demand Sensing Works in IBP SAP IBP Demand Sensing uses advanced algorithms to combine: • Historical demand • Recent sales orders • Shipments • Open orders • Short-term demand signals These signals are weighted dynamically to correct the near-term forecast. The result is a more responsive forecast that reflects what customers are actually buying right now. ⸻ 3️⃣ Demand Sensing vs Traditional Forecasting Traditional Forecasting: • Long-term focus • Pattern-based • Slower reaction to changes Demand Sensing: • Short-term focus • Signal-driven • Fast reaction to spikes and drops Demand sensing does not try to predict the future months — it improves today, tomorrow, and next week. ⸻ 4️⃣ When Demand Sensing Adds Value Demand sensing works best when: • Demand is volatile • Promotions impact sales heavily • Customer orders change frequently • Short-term accuracy matters for execution • Supply decisions are made weekly or daily Typical industries: FMCG, Retail, Pharma, Consumer Electronics. ⸻ 5️⃣ Key Benefits SAP IBP Demand Sensing helps organizations: • Reduce short-term forecast error • React faster to demand changes • Improve service levels • Reduce expediting and firefighting • Align demand and supply closer to execution It bridges the gap between planning and reality. ⸻ 6️⃣ Important Design Considerations Demand sensing requires: • Clean and timely input signals • Proper time buckets (daily / weekly) • Alignment with supply planning horizons • Clear separation between sensing and base forecast Bad input data = noisy results. ⸻ Final Thought Demand sensing is not about being perfect. It’s about being less wrong, faster. In SAP IBP, demand sensing turns demand planning from a static exercise into a living, responsive process.

Everyone is asking whether AI is worth the cost. The sharper question is whether it can make a decision siloed teams never could, and cutting the products that quietly lose money is exactly that kind of decision. Every new SKU passes an easy test: at expected variable cost, it adds margin. So the portfolio only ever grows. A variant for one retailer, a seasonal pack, a slightly different size, each clears the bar on its own, and none is ever the one to cut. But that is not the whole cost. The true cost of a variant is what it does to the plant and the balance sheet: another changeover, a shorter run, more safety stock, the risk it ends up obsolete. Its true value is not its own margin but its margin net of the demand that would simply move to the product beside it on the shelf if it were gone. A SKU can be profitable on its own line and destroy value for the firm once you net out the manufacturing complexity it adds and the sales it takes from its siblings. And that's a complex calculation. The cost is absorbed into aggregate plant and inventory costs and never charged back to the SKU that caused it, and the substitution question needs a demand model, not a sales report. So the number that would justify cutting a variant remains invisible, and margin-negative SKUs continue to sit in the line, quietly eroding margin. What is new is that the true price is now computable: an agentic team that scores each variant against its full cost to make and serve, and against the demand that would substitute to its siblings if it were dropped, ranks the portfolio, and sizes the margin and working capital that rationalizing it would release. The portfolio decision that never got made becomes one you can run on demand. That's Auger. Next: the warehouse. #EfficiencyRedefined #SupplyChain #Manufacturing #AI #Economics  #Auger

🔄 From Forecast-Push to Demand-Pull Planning “Forecast-Push” planning relies on predictions to produce and push products into the market. Decisions are driven by historical data and long-term forecasts. “Demand-Pull” planning, however, responds to actual customer demand signals in real time. Production, replenishment, and procurement are triggered by consumption — reducing waste, excess inventory, and obsolescence. In today’s volatile markets, organizations are shifting from forecast dependency to demand-driven agility. 1️⃣ 📊 Real-Time Demand Sensing Uses POS data, customer orders, and market signals to adjust plans dynamically. 🔹 Reduces forecast error 🔹 Improves service levels 🔹 Enables faster response to demand shifts 2️⃣ 🏭 Pull-Based Replenishment (Kanban / JIT) Production and replenishment are triggered by actual consumption. 🔹 Lower inventory carrying costs 🔹 Reduced overproduction 🔹 Lean flow across supply chain 3️⃣ 🤝 Collaborative Planning (CPFR) Joint demand visibility between suppliers, distributors, and retailers. 🔹 Shared forecasts & sales data 🔹 Reduced bullwhip effect 🔹 Better alignment across partners 4️⃣ 📦 Demand-Driven MRP (DDMRP) Strategic inventory buffers placed at decoupling points. 🔹 Protects flow from variability 🔹 Improves lead-time reliability 🔹 Balances service & working capital 🚀 The Shift Means: From prediction-based planning ➝ to consumption-driven execution From inventory buffers ➝ to information buffers From silos ➝ to synchronized supply networks #SupplyChain #DemandPlanning #DDMRP #Lean #S&OP #CPFR #OperationsExcellence #Logistics #DigitalTransformation