Real-Time Inventory Visibility

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

Ever stocked up on a product that turned into a dust-gathering flop? Or worse, missed out on a sales surge because your shelves were empty? That's the pain of bad demand forecasting, and it's felt across the manufacturing world. Get this: businesses with accurate demand forecasts enjoy a whopping 70%-90% reduction in inventory holding costs AND a 98% service-level rate.  Those numbers aren't magic; they're the result of ditching guesswork and embracing data analytics. Why Demand Forecasting Matters? 👉 Optimized Production: Produce what you'll actually sell. No more overstocking or frustrating shortages. 👉 Smoother Operations: Match your resources to real demand. Plan staffing, material procurement, and production schedules with confidence. 👉 Happy Customers = Happy Bottom Line: Have the right products available at the right time. Boost customer satisfaction and sales. Accurate demand forecasting has a ripple effect: 👉 Reduced Waste: Overproduction leads to wastage at every level. Forecast accurately, and minimize your environmental impact. 💪 Better Pricing Strategy: Understand demand peaks and valleys to make smarter, data-backed pricing choices. 👊 Boost in Competitiveness: Stay ahead of the game by anticipating market trends before your competitors even see them coming. Demand forecasting isn't about staring into a crystal ball. It's about using data analytics to uncover hidden patterns and build smart predictive models: 👁️🗨️ Historical Sales Data: The foundation of any good forecast. 👀 Market Trends: Watch for economic indicators, competitor moves, and changes in consumer preferences. 🙌 External Factors: Seasonality, promotions, even the weather can influence demand. 💥 Advanced Analytics: Machine learning algorithms can spot patterns humans miss, leading to supercharged forecasting accuracy. Here's what to analyze to up your demand forecasting game: 👉 Product-Level Specificity: Don't forecast in broad strokes. Break it down by SKU, location, and timeframe for granular insights. 👉 Time Horizons: Need both short-term (production planning) and long-term (strategic decisions) forecasts. 👉 Forecast Accuracy Tracking: Measure how your predictions stack up against reality, and keep refining those models. Wrangling complex demand data and building those super-smart forecasts can be tough. That's where Google's magic comes in. We can help you make sense of the numbers and get the insights you need to make confident, profit-driving decisions. Ready to conquer your demand forecasting challenges? Let's chat! Follow Omkar Sawant for more information! #demandforecasting #dataanalytics #manufacturing #supplychain #AI

Shopify made it a lot easier to monitor what’s really going on with your stock right inside your admin 👉 With the new Inventory Adjustment History reports in Analytics > Reports, merchants can now: ✔ Track every stock movement Every increase or decrease in quantity is logged across all SKUs and locations ✔ Get full transparency on changes Know exactly when, why, and who made a change. Yes, even that mystery inventory dip last Thursday ✔ See what’s on the way Incoming shipment data now shows expected arrivals by location ✔ Follow your internal transfers Whether it’s between stores or from your warehouse to retail, you’ll see pending transfer orders and timing ✔ Access deeper insights Historical inventory data now goes beyond the old 180-day limit, helping you spot long-term trends, not just short-term noise These reports are available if you're using the new Shopify Analytics You’ll find them in Analytics > Reports > Inventory 💡 Why this matters: If you’ve ever had to answer: > “Why does this SKU show negative stock?” > “When is the new stock arriving at this location?” > “Where did 20 units just disappear to?” …you now have the audit trail to answer confidently, no more digging through emails or Slack

Hawke’s Bay to Singapore with IoT eyes on every apple. Welcome to the future of export visibility. In the past, exporters would ship crates of premium produce and hope for the best. Today, they're shipping real-time data. A Hawke’s Bay apple grower is now using IoT-enabled containers to track every shipment to Asia: Monitoring temperature, humidity, and even impact shocks along the way. Why does it matter? Because one overheated container can ruin an entire deal. And one spoiled shipment can cost next season’s shelf space. With IoT, that same grower can: → Guarantee cold chain quality → Send alerts to overseas buyers → Back deliveries with hard data This isn’t a “nice-to-have.” It’s how Kiwi exporters stay competitive. Real-time tracking means fewer risks, better margins, and more trust. NZ’s green brand is strong. But now, it’s smarter too. Resilience starts with knowing what’s happening inside the box.

🚀 Achieving Real-Time Power BI Dashboard Updates Recently, I worked on a request to develop a real-time Power BI dashboard. Here’s how I tackled the challenge and what I learned along the way: 1️⃣ Live Data in Data Warehouse: I needed a live database in my DWH. Achieved this through SQL replication from OLTP or by scheduling an SSIS package to run every minute. 2️⃣ Power BI Dataset Refresh Limitation: After publishing the dashboard, I faced a limitation: Power BI Service allows a minimum refresh interval of 15 minutes, which wasn’t sufficient for the business. 3️⃣ Forcing Dataset Refresh Every Minute: After some research, I found a solution using Power Automate to trigger dataset refresh every minute. This workaround helped achieve near real-time updates. 📌 The screenshot below demonstrates this approach. 💡 If you have any alternative solutions or need more details, feel free to drop a comment! Note: This solution is perfect for pro licenses and you have to select import mode in that case, if you have a premium license it's much easier to get a live dashboard (will be shared on another post) #PowerBI #DataAnalytics #RealTimeDashboard #PowerAutomate #SQL #BusinessIntelligence

Because inventory causes exponential pain with multiple warehouses... This infographics shows how to manage inventory in this context: ➡️ Centralize Inventory Visibility ↳ Issue: not knowing inventory levels across locations can lead to overstock in one warehouse and stockouts in another ↳ Action: Implement an inventory management system/ ERP that shows real-time inventory positions for all warehouses in one snapshot ➡️ Classify Products and Prioritize ↳ Why: Not all SKUs deserve the same treatment; some are high-value, others are seasonal ↳ Action: Use ABC analysis to rank products by focusing on A-items for tighter control ➡️ Define Replenishment Rules by Warehouse ↳ Why: Different warehouses cater to different regions or demand patterns. One-size-fits-all reorder points (ROP) won’t cut it ↳ Action: Tailor ROP, safety stock, and min-max levels by location. Consider lead times from central distribution centers or suppliers for each site ➡️ Breakdown Forecast by Warehouse ↳ Why: Each warehouse faces unique market dynamics ↳ Action: Generate warehouse-level forecasts, combining local sales trends with broader S&OP inputs ➡️ Plan Transfers Strategically ↳ Why: Sometimes it’s of lower cost or faster to transfer stock than reordering from suppliers ↳ Action: Set up a transfer framework; regularly review surplus vs. deficit at each location. Automate triggers for transfer orders when it’s cost-effective. ➡️ Monitor KPIs Proactively ↳ Why: Multi-warehouse complexity can hide inefficiencies when not tracking the right metrics ↳ Action: Track fill rate, inventory turnover, stock aging, and transfer costs at each site. ➡️ Plan Direct Dispatches & Save Costs ↳ Why: Dispatch directly from the plant to save logistics costs ↳ Action: Prepare daily dispatch plans targeting direct replenishment from the plant and use these warehouses for milk runs for distributors 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.

📌 Real-Time Data Alerts in Power BI Many businesses still rely on static dashboards that require manual monitoring. But what happens when something critical, like a sudden sales dip or inventory shortage, occurs while you're not actively watching? The truth is… Most businesses discover critical changes hours or even days after they occur—when it's often too late to take immediate action. That’s why you need to set up real-time alerts. For example: ⤷ Get an alert if daily website traffic drops below 5,000 visitors. ⤷ Notify the team if sales spike above $50,000 during a campaign. 👉 Let's see how you can set this up in Power BI 1️⃣ Select Your Main KPIs Identify the metrics critical to your business. For example, daily sales, customer complaints, or web traffic. Focusing on the most important KPIs ensures that the alerts you receive are actionable and not overwhelming. 2️⃣ Design Your Dashboard Ensure that the key metrics you want to monitor in real-time are displayed on a Power BI dashboard. The metrics should already be set up using streaming data sources or a direct database connection with a high-frequency refresh rate (e.g., hourly). 3️⃣ Configure Alerts In Power BI, select the tile displaying your KPI and click the bell icon to configure your alert. Set the condition that will trigger the alert. For example: ⤷ If CPL rises above $50, or sales drop below $10,000. Choose how you'd like to receive the alert—via email, in the Power BI app, or as a push notification to tools like Microsoft Teams or Slack. 4️⃣ Integrate with Workflows To take your alerts to the next level, integrate them with automation tools like Microsoft Power Automate. For example, you can set up a workflow to: ⤷ Escalate the alert to the appropriate team member. ⤷ Log it in a task management tool. ⤷ Trigger an automatic process, such as pausing an ad campaign. Remember: The goal isn’t just to get notifications—it’s to enable quick, informed responses to business-critical changes. 👉 What alerts have you found most valuable in your Power BI dashboards? Share your experiences below! #DataAnalytics #BusinessIntelligence

Predictive analytics isn't magic. It's the difference between guessing and knowing what your customers need next. I recently spent time with Scott Schmitz of Michigan Software Labs to discuss what we're hearing from clients across industries. One question keeps coming up: "How can we better predict future sales to improve inventory management?" It's a simple question with profound implications. Most businesses approach inventory as a necessary evil: • Too much inventory = wasted capital • Too little inventory = lost sales • Just right = seemingly impossible But here's what forward-thinking companies understand: Predictive analytics isn't just about forecasting - it's about fundamentally changing how you operate. When implemented correctly, AI-powered predictive models can: 1. Identify seasonal patterns human analysts miss 2. Detect micro-trends before they become obvious 3. Account for external factors (e.g. - weather, events, and economic indicators) 4. Continuously learn and improve with each sales cycle The real breakthrough comes when you stop treating AI as a bolt-on solution. Instead, integrate it into your core business processes. This means: → Connecting your sales, marketing, and supply chain data → Building models that explain, not just predict → Creating feedback loops for continuous improvement → Empowering teams to act on insights, not just receive them The companies seeing the greatest ROI aren't just predicting better. They're fundamentally transforming how they make decisions. My takeaway: The question isn't whether AI can help predict sales - it absolutely can. The real question is whether your organization is ready to transform how it operates based on those predictions. We are excited to start a new (but familiar) series again to offer you value through 1 minute videos. Follow the "Minute with Mark" series at Mark Johnson. Click the bell. 🔔