How to use a fast feedback loop for replenishment decisions

What if our KPIs are fighting each other? In retail, store teams are often told: “Keep availability high.” But they’re also measured on shrink, waste, and labour. So every day, they make silent trade-offs. Overfill the chiller to avoid an OOS? Or play it safe to avoid markdowns? The result? Margins lost on both ends — and performance tension between floor and forecast. I believe we need a smarter metric. Something like Net Availability Efficiency — a blended score that values efficient availability, not just full shelves. Because true retail performance isn’t availability at any cost. It’s getting the right product, in the right space, at the right time — profitably. #RetailStrategy #SupplyChainThinking #StoreOps #ShrinkControl #Availability #RetailMetrics #RetailAnalytics #CTMToAnalyst

Issue 112: Harness the power of data Our latest case study shows how we helped a retail company leverage big data to optimize their inventory management, resulting in a 30% reduction in overstock and a 15% increase in sales. #DataAnalytics #Retail #Optimization

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝘀𝘁𝗼𝗿𝗲-𝗹𝗲𝘃𝗲𝗹 𝗱𝗲𝗺𝗮𝗻𝗱 𝟰𝟱 𝗱𝗮𝘆𝘀 𝗮𝗵𝗲𝗮𝗱? Imagine knowing exactly which products will fly off the shelves, and how many, weeks before the rush hits.  𝗡𝗼 𝗺𝗼𝗿𝗲 𝗹𝗮𝘀𝘁-𝗺𝗶𝗻𝘂𝘁𝗲 𝗿𝗲𝘀𝘁𝗼𝗰𝗸𝘀.  𝗡𝗼 𝗼𝘃𝗲𝗿𝘀𝘁𝘂𝗳𝗳𝗲𝗱 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆.  𝗡𝗼 𝗰𝗮𝘀𝗵 𝗳𝗿𝗼𝘇𝗲𝗻 𝗶𝗻 𝘀𝗹𝗼𝘄-𝗺𝗼𝘃𝗶𝗻𝗴 𝘀𝘁𝗼𝗰𝗸. Let’s be real, most retailers still rely on a mix of spreadsheets, gut instinct, and guesswork.  But 𝗔𝗜 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁. With AI-driven demand prediction, you can get 𝟰𝟱-𝗱𝗮𝘆 𝘀𝘁𝗼𝗿𝗲-𝗹𝗲𝘃𝗲𝗹 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 into what’s coming next.  That means:  𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗽𝘂𝗿𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝗰𝗮𝗹𝗹𝘀  𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 + 𝘀𝗵𝗲𝗹𝗳 𝘀𝗽𝗮𝗰𝗲  𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗰𝗮𝘀𝗵 𝗳𝗹𝗼𝘄  𝗙𝗲𝘄𝗲𝗿 𝗹𝗼𝘀𝘁 𝘀𝗮𝗹𝗲𝘀 𝗳𝗿𝗼𝗺 𝘀𝘁𝗼𝗰𝗸𝗼𝘂𝘁𝘀 And the best part?  𝗜𝘁 𝗸𝗲𝗲𝗽𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿. Every cycle learns from your data, making predictions sharper and more aligned with how your customers actually buy. So here’s my question for you,  𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘀𝗲𝗲 𝟰𝟱 𝗱𝗮𝘆𝘀 𝗮𝗵𝗲𝗮𝗱, 𝘄𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂’𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀? Drop your thoughts below. #AIPoweredRetail #DemandForecasting #RetailInnovation #InventoryOptimization #FutureOfRetail #PredictiveAnalytics

If you could predict store-level demand 45 days out, how would that change your ops? Imagine knowing exactly which products will sell (and in what quantities) well before the rush hits. No more last-minute scramble to restock, no piles of unsold items, no cash tied up in overstock. For many retailers, demand forecasting feels like guesswork, spreadsheets, hunches, and reacting too late. But with AI-driven demand prediction, it’s possible to get 45-day store-level visibility into what’s coming. That means: Smarter purchasing decisions Optimized warehouse and shelf space Better cash flow management Fewer lost sales due to stockouts And here’s the real kicker: the system keeps learning. Each cycle makes predictions sharper and more aligned with your unique customer behavior. So here’s the question for you: If you could see 45 days ahead, what would you do differently in your operations? We’d love to hear your thoughts.

About 65% of retail sales are shaped by seasonal trends and changing category cycles! 🛍️ But here is the thing: many retailers still miss the mark when it comes to predicting what, when, and how much their customers will buy. 🛒 That is where understanding seasonality comes in. It is not just about spotting the big spikes around holidays. It is about reading about the subtle shifts in demand that happen every day. 🎯 Retail intelligence digs deep into your past sales, external factors like weather and events, and new market signals to uncover those hidden patterns. The payoff? Less waste from overstock, fewer lost sales from empty shelves, smarter promotions, and happier customers who find what they want, right when they want it. ☑️ In a world where customer habits constantly change, having your finger on the pulse with data-driven insights is the difference between keeping up and leading the pack. 🔎 #RetailIntelligence #DemandForecasting #Seasonality #RetailData #InventoryManagement #Merit #MeritDataTech 

🟣 In today’s fast-paced retail landscape, guessing demand is no longer enough and  accurately predicting customer demand is becoming more critical than ever. 🟣 Retailers must strike the perfect balance between stock availability and cost efficiency — and that starts with smart forecasting. Discover the real benefits of demand forecasting and the techniques that help retailers stay agile, reduce waste, and boost profitability in this article 👉 📈 https://lnkd.in/dgZk2jbx #reinnovation #demandplanning #forecasting #retail

We've all been there - you see a great deal advertised, rush to the store, and find...an empty shelf with a sale tag mocking you. The other day I was shopping at my favorite grocery retailer and I had this exact situation. This retailer arguably has some of the best POS data available. And the OOS product was from arguably one of the most sophisticated CPGs. It is 2025 - why are promotional out-of-stocks STILL a problem? It's easy to blame out-of-stocks on a bad promotional plan or a faulty demand plan. But what if the plans were solid...and the failure came somewhere else? Maybe the product is sitting in the backroom on a pallet. Maybe it never made it to the store floor. Maybe the data that informed the plan was flawed to begin with - the ghost of a past out-of-stock dragging down the forecast. And here's the kicker: this single OOS could represent hundreds of lost units and disappointed consumers who may switch brands. Studies show promotional out-of-stocks can be 2-3x more damaging than regular stockouts because consumer expectations are heightened. Now this out-of-stock will further depress the sales signal, distorting the next cycle's plan. The ghost grows stronger. We chase optimization. We layer on AI. But how often do we stop and ask: - Was this a planning miss or an execution breakdown? - Is our demand signal clean, or clouded by past failures? - Are we fixing the forecast, when we should be fixing the shelf? This is where connected planning is supposed to shine. But unless we connect the full chain - not just Finance and Sales, but Brand, Demand Planning, and Retail Execution - we're just automating dysfunction. What's the most frustrating promotional out-of-stock you've encountered? Was it a planning problem or execution failure? Thanks to Daniel Vanden Brink for his input and insights. #TradePromotionManagement #SupplyChain #ConnectedPlanning

For store-based retailers, speed is no longer optional - it’s expected. The real challenge is doing it without breaking the cost model. ⚡Faster fulfilment means higher labor and transportation demands 🔄Easy returns keep customers loyal, but strain inventory and operations 🌍Sustainability priorities build brand trust, but add cost pressure These aren’t isolated problems, they’re connected forces reshaping retail supply chains. Success comes from designing supply chains that adapt in real time: flexing with demand spikes, balancing cost with service, and meeting sustainability goals without slowing down. In today’s market, resilience doesn’t come from choosing one priority over another. It comes from building supply chains that can deliver on all fronts at once. #UPSCustomerSolutions #SupplyChainConsulting #UPSinsights #Retail #CustomerExperience #Archetypes

How #KrogerGroceryDataScraping Transforms Inventory & Pricing Strategies In today's fast-moving grocery market, having up-to-date data from leaders like Kroger isn't a nice-to-have — it’s essential. When retailers #scrape product, price, promotion, and stock data from Kroger, they unlock strategic insights that directly improve efficiency, profitability, and competitiveness. Here’s what this kind of intelligence enables: #DynamicPricingOptimisation : Monitor Kroger’s real-time pricing, discounts, and promotions to adjust your prices proactively. This helps you stay competitive while protecting margins. #InventoryManagement & #StockVisibility : Track stock levels and restocking trends across Kroger’s network to anticipate shortages or overstock situations. Enables smarter ordering, reduced waste, and improved product availability. Competitive Benchmarking: See how your assortments and pricing stack up against Kroger’s, especially for key SKUs. Identify categories where Kroger is gaining, or where there are opportunities for differentiation. Promotion & Assortment Insights: Understand which promotions work well, which products are moving fastest, and how packaging, layout, or bundle offers from Kroger affect demand. Enhanced Forecasting & Demand Prediction: Using historical Kroger data on sales, promotions, and price shifts helps in modelling demand better — reducing missed sales and minimizing spoilage. Retailers using such data aren’t just reacting to trends — they’re anticipating them. Through Kroger data scraping, you get ahead on pricing changes, optimize your inventory, and craft strategies grounded in what the market is actually doing. Learn more: https://lnkd.in/dV7Zi73d #KrogerData #GroceryAnalytics #InventoryStrategy #PricingOptimization #CompetitiveIntelligence #RetailTech #DataDrivenDecisions #DynamicPricing #DemandForecasting #FMCGInsights #WebScraping #SmartRetail #AssortmentPlanning #RetailGrowth

🛒 Amplify Your Retail Edge with #WalmartPriceStockMonitoring 📊 In the fast-paced world of eCommerce, real-time insights into Walmart’s pricing, inventory & competitor behavior can make all the difference. WebDataCrawler’s Walmart Data Scraping & Price Monitoring gives #Brands, #Retailers, and #Analysts the intelligence to stay ahead. 🚀 🔍 What You Can Extract: • Product listings with live prices, promotions & discounts. • Stock availability & SKU-level inventory across regions. • Price change patterns — fluctuations between online vs in-store, competitor adjustments. • Insights into grocery & fast-moving category trends. • Historical data to analyze demand, overstock & stockout incidents. 💡 “Data from Walmart isn’t just about prices — it’s about ensuring you always have what your customers want, in the right place, at the right price.” Use Cases: ✔️ Retailers optimizing pricing & promotions in near real-time. ✔️ Brands ensuring their products are stocked & visible where demand is high. ✔️ Operations & supply chain teams reducing overstock and avoiding stockouts. ✔️ Market intelligence teams monitoring rivals and shifting category trends. 📈 With Walmart data scraping, businesses see measurable gains in efficiency, margin protection, and competitive agility. 🔗 Explore More>> https://lnkd.in/d6wUvy-d 📩 Contact Us: sales@webdatacrawler.com #Walmart #RetailData #PriceMonitoring #StockInsights #DataScraping #CompetitiveIntelligence #EcommerceAnalytics #InventoryManagement #RetailGrowth #DataDrivenDecisions