Demand Planning Software

Google just open-sourced a time series foundation model. It works with any data without training. Traditional forecasting models need to be trained on your specific dataset before they can predict anything. Google's TimesFM works differently. you give it historical data, and it generates forecasts out of the box. This is possible because they trained the model on 100 billion real-world time-points across domains like traffic, weather, and demand forecasting. Key features: - supports up to 16K context length for deeper historical coverage - built-in probabilistic forecasting with quantile predictions - works with both PyTorch and JAX It currently sits at the top of GIFT-Eval, the standard benchmark for time-series forecasting. If you work with demand forecasting, financial data, or any time-series problem, this is worth exploring. I've shared the link to the GitHub repo in the first comment. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!

Inventory is the silent killer of consumer brands. Too much stock? Your cash is stuck. Too little? Customers walk away. There’s no perfect forecast — you’ll either overstock or run out of something critical. Last year we had a horrid quarter with overstocking on all the slow moving and OOS on all fast moving walking into festive with very less fuel.  We have been building this first off excel sheets and now in what looks like a system (built off Replit). Here’s what worked for us at Koparo: 1. Move Beyond Gut Feel For a long time, reorder decisions were instinct-based or working off plain averages. That stopped working as we scaled. We introduced formulas: ReorderPoint=(AverageDailyDemand×LeadTime)+SafetyStockReorder Point = (Average Daily Demand × Lead Time) + Safety StockReorderPoint=(AverageDailyDemand×LeadTime)+SafetyStock This one change helped us avoid both empty shelves and excess stock. 2. Get the Order Size Right Knowing when to reorder isn’t enough. You need to know how much: To be honest this is still hard but if your unit costs don’t fall too much based on order volume then just be conservative on this with a very accurate handle on actual vendor lead times and not just average but in season time. This helped us strike a balance between ordering frequently and locking cash in inventory. 3. Safety Stock That Makes Sense Earlier, we’d just add 20% “for safety.” Now, buffers are calculated based on actual demand variability and service levels. No more guesswork. 4. Lead Times Aren’t Assumptions We learned the hard way that vendor timelines on paper don’t match reality. Our system now tracks actual lead times — which changed planning dramatically and yes also our vendors. 5. Automate the Triggers We built an in-house system (on Replit) with auto-replenishment triggers. When stock hits ROP, it suggests orders. No manual chasing, no panic buying. What’s the impact? ✔ Fewer stock-outs ✔ Lower working capital ✔ Predictable operations We’re still evolving this — and have built a simple system on Replit. It’s far from sophisticated, but it has improved our decision-making, forced us to make assumptions real, and saved at least 10 hours per week. Curious: How are you managing inventory? DIY system, off-the-shelf software, or still spreadsheets? #InventoryManagement #SupplyChain #D2C #Koparo Kshitij Ranjan Vishal Singh Saurabh Nidar Abhishek Sharma Rahul Gaur

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

 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 : 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐢𝐧𝐠 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 The Challenge: Our inventory management system was struggling to keep up with the growing volume of stock and sales data. The manual tracking process led to frequent stockouts and overstock situations, causing operational inefficiencies and affecting customer satisfaction. The Solution: We leveraged SQL to automate and optimize our inventory management process. Here’s how we did it: Steps: 1.Centralized Database Creation: Consolidated inventory data from multiple sources into a single SQL database.   Example Query to Create Inventory Table: CREATE TABLE Inventory ( ProductID INT PRIMARY KEY, ProductName VARCHAR(255), StockLevel INT, ReorderLevel INT, LastUpdated DATE );   2.Automated Stock Monitoring: Developed SQL queries to automatically monitor stock levels and trigger alerts for reorder points.   Example Query for Reorder Alerts: SELECT ProductID, ProductName, StockLevel FROM Inventory WHERE StockLevel <= ReorderLevel;   3.Dynamic Reporting: Created dynamic reports to track inventory levels, reorder statuses, and historical stock trends.   Example Query for Inventory Report: SELECT ProductID, ProductName, StockLevel, LastUpdated FROM Inventory ORDER BY LastUpdated DESC;   Impact: Operational Efficiency: Reduced manual tracking efforts, saving time and minimizing errors. Optimized Stock Levels: Improved inventory turnover by maintaining optimal stock levels. Enhanced Customer Satisfaction: Reduced stockouts and overstock situations, ensuring product availability.   Visuals: Include screenshots of the SQL queries, inventory reports, and a before-and-after comparison of stock levels. How do you manage inventory in your organization? Share your strategies and experiences in the comments!  follow more for Priyanka SG #SQL #InventoryManagement #DataOptimization #OperationalEfficiency #BusinessIntelligence

Want to level up your forecasting skills: Check out Pythons NeuralProphet! Here is what you need to know about it: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁? NeuralProphet is an open-source time series forecasting Python package that combines the simplicity and interpretability of Facebook’s Prophet package with the advanced capabilities of neural networks utilizing PyTorch. It is designed to handle complex patterns in your data, such as multiple seasonalities, trends, and holidays, while being easy to use and integrate into your existing workflows. 𝗠𝗮𝗶𝗻 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Unlike traditional Prophet, NeuralProphet incorporates neural networks, which allow it to capture more patterns and dependencies in the data.    • 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: It supports daily, weekly, monthly, and custom time frequencies, making it adaptable to various forecasting needs.    • 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁-𝗕𝗮𝘀𝗲𝗱 𝗠𝗼𝗱𝗲𝗹: NeuralProphet models trends, seasonalities, and holidays as distinct components, making the forecasts more interpretable.    • 𝗔𝘂𝘁𝗼-𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗧𝗲𝗿𝗺𝘀: The inclusion of auto-regressive terms improves the model’s ability to predict future values based on past observations. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁? 1. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: By integrating neural networks, NeuralProphet can capture complex patterns and seasonality that traditional methods might miss. This leads to more accurate and reliable forecasts.     2. 𝗨𝘀𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: NeuralProphet matches the simplicity of Prophet, making it accessible even if you’re not a deep learning expert. Its intuitive interface allows you to set up and run forecasts quickly.     3. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Despite its advanced modeling capabilities, NeuralProphet maintains the interpretability of its components, helping you understand the underlying factors driving your forecasts.     4. 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Whether you’re dealing with daily sales data, monthly revenue, or weekly web traffic, NeuralProphet’s flexible framework can be tailored to meet your specific needs.     5. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: NeuralProphet can handle large datasets and multiple seasonalities, making it suitable for complex forecasting tasks in dynamic environments. Use the power of NeuralProphet to level up your forecasting game and deliver insights that drive business success. What tools are you using or plan to use for building forecasts? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanlaytics #datascience #neuralprophet #python #forecasting

Demand planning minds are wired differently. The infographic shows the mindmap of a great demand planner: # 1. Demand Drivers ↳ “What’s actually moving demand?” Seasonality, weather, promotions, competitor actions, economic signals, social trends ↳ Great planners don’t just look at numbers, they understand the business dynamics # 2. Data Quality Filters ↳ “Can I trust this data?” Missing weeks, phantom spikes, wrong hierarchy mapping, incorrect UOM conversions ↳ Experts fix bad data before it becomes a forecast disaster # 3. Bias Detection ↳ “Who is optimistic, who is pessimistic, and why?” Sales bias, marketing bias, planner bias, system bias ↳ They see patterns in adjustments and challenge them smartly # 4. Demand Risks & Upsides ↳ “What could go wrong?” Market shifts, competitor launches, seasonal anomalies, trend breaks, demand cannibalization ↳ Planners maintain a living risk register in their head # 5. Stakeholder Alignment ↳ “Who do I need to speak to today?” Sales for big customer updates, marketing for promo changes, finance for revenue expectations, supply for constraint impact ↳ Great planners don’t forecast alone, they orchestrate # 6. Scenario Thinking ↳ “What if demand changes by +10% or -10%?” Best case, base case, worst case ↳ They never walk into S&OP with one number, they walk in with options 7. Business Impact ↳ “What does this forecast mean financially?” Revenue, margin, inventory investment, service impact ↳ Demand planners who think like P&L owners rise the fastest Any others to add? Ready to improve and accelerate your planning career? Join me inside the Supply Chain Planning Circle community. https://lnkd.in/eVbY5YJF

🚀 𝗧𝗼𝗽 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝟮𝟬𝟮𝟱  Whether you're a quant, data scientist, or machine learning engineer... You’ve probably worked with time series data.  • Forecasting.                           • Signal smoothing.  • Anomaly detection.                    • Feature extraction.   I’ve spent months learning the most practical, scalable, and production-ready libraries for time series problems. Here’s the breakdown (sorted by GitHub ⭐): 📈 1. 𝗣𝗿𝗼𝗽𝗵𝗲𝘁 (𝗠𝗲𝘁𝗮) – 18.4k⭐ Perfect for quick, explainable forecasts with seasonality & holidays. e.g. Predict daily product sales including Black Friday surges. ⚡ 2. 𝗡𝗶𝘅𝘁𝗹𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 – 12k⭐ Includes statsforecast, neuralforecast, and TimeGPT. e.g. Train ARIMA or transformer models at lightning speed. 📊 3.𝗧𝗦𝗳𝗿𝗲𝘀𝗵-8.8⭐ Provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis. 🎯 4. 𝗗𝗮𝗿𝘁𝘀 – 8k⭐ Plug-and-play API for ARIMA, RNNs, and ensembles. e.g. Backtest multiple models on energy usage data. 🧠 5. 𝗦𝗞𝗧𝗶𝗺𝗲 – 7.8k⭐ Like scikit-learn, but for time series. e.g. Pipeline + tune multiple time series regressors. 🔮 6. 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – 4.6k⭐ Great for Temporal Fusion Transformers + interpretable DL. e.g. Forecast multi-variate sequences like inventory + demand. If you're serious about DL in time series, start here. 🎁 𝗕𝗢𝗡𝗨𝗦 𝗟𝗜𝗕𝗥𝗔𝗥𝗜𝗘𝗦 𝘁𝗼 𝗘𝘅𝗽𝗹𝗼𝗿𝗲:  • 𝗽𝗺𝗱𝗮𝗿𝗶𝗺𝗮 – Auto ARIMA, just like R, but in Python.  • 𝘀𝘁𝗮𝘁𝘀𝗺𝗼𝗱𝗲𝗹𝘀 – For traditional statistical modelling and diagnostics.  • 𝗔𝗥𝗖𝗛 – For financial time series & volatility modeling.  • 𝘁𝘀𝗺𝗼𝗼𝘁𝗵𝗶𝗲 – Smoothing and outlier removal in seconds.  • 𝗣𝘆𝗙𝗹𝘂𝘅 – Bayesian and classical methods in one package.  • 𝗾𝘂𝗮𝗻𝗱𝗹-𝗺𝗲𝘁𝗮𝗯𝗮𝘀𝗲 – Build visual dashboards in minutes. 💥 Who is this for?  • Data scientists working on production pipelines  • ML engineers deploying neural models  • Analysts needing fast, explainable forecasts  • This list will save you weeks of trial & error. 👉 Bookmark it. Share it. Test it. ♻️ Repost to share the time series magic, and follow Puneet Khandelwal for more such insights on quant and ML. P.S. What’s YOUR go-to Python library for time series? Drop it below 👇

Google quietly built and open-sourced a powerful time-series AI model called TimesFM that can analyze massive real-world data and predict future trends across industries. Developed by Google Research, TimesFM is trained on over 100 billion real-world data points and is designed for zero-shot forecasting, meaning it can generate accurate predictions on new data without any additional training or fine-tuning. Unlike traditional forecasting systems that require custom models for each use case, TimesFM works as a general-purpose engine that can detect patterns in time-based data like sales, traffic, markets, or demand, making predictions instantly with minimal setup. The model is available publicly on GitHub under an Apache 2.0 license, making it accessible for developers and businesses to run locally and build forecasting systems without relying on expensive infrastructure or proprietary tools. This shows where AI is heading next, not just generating text or images, but helping predict what happens next, and becoming a core layer behind real-world decision making across industries.

🔍 Forecasting in IBP vs Kinaxis vs OMP vs Relex vs Blue Yonder In today’s dynamic supply chains, accurate forecasting is essential for agility, efficiency, and resilience. Here’s how the top platforms stack up: Via 📊 SAP IBP • Uses time-series, AI/ML models via the Predictive Analytics Library (PAL) • Integrates forecasting into end-to-end S&OP, inventory, and demand planning • Real-time insights with SAP HANA for large volumes and collaborative planning ⚡ Kinaxis RapidResponse • Enables concurrent planning: forecast changes trigger real-time supply impact • Supports ML, causal forecasting, and demand sensing • Ideal for fast-moving, highly responsive supply chain environments 🏗️ OMP • Strong in multi-echelon and hierarchical forecasting • Forecasting is tightly integrated with finite capacity planning • Suited for complex manufacturing networks needing synchronized demand-supply logic 🛒 Relex Solutions • Designed for retail/FMCG, with store- and SKU-level forecasting • Uses AI/ML for promotions, weather, seasonality, and life-cycle forecasts • Automates replenishment and forecasting with strong daily granularity 🤖 Blue Yonder • Powered by Luminate AI/ML platform with probabilistic forecasting • Great for demand classification, demand sensing, and omni-channel retail • Strength lies in prescriptive recommendations and event-driven planning ✅ Quick Comparison: • IBP → Best for integrated enterprise planning (SAP users) • Kinaxis → Best for agility & real-time scenario planning • OMP → Best for manufacturing complexity and constraint-based planning • Relex → Best for retail-level granularity and automation • Blue Yonder → Best for AI-first, omni-channel retail and supply. 📌 Forecasting isn’t one-size-fits-all. Choosing the right platform depends on your industry, complexity, and decision velocity. Let’s connect if you’re evaluating tools or planning a digital supply chain transformation! #Forecasting #SupplyChainPlanning #SAPIBP #Kinaxis #OMP #Relex #BlueYonder #DemandPlanning #RetailTech #AIinSupplyChain #SOP #SupplyChainTransformation #Concurrengplanning 💬 👇🏻For queries on digital transformation using SAP IBP, Kinaxis, OMP, or Blue Yonder — feel free to reach out or drop a message. Let’s explore how the right tool can accelerate your supply chain journey. 🚀 Stefanini Group Stefanini North America and APAC Stefanini Brasil Stefanini EMEA Sandy Sankara Bala Upadhyayula Easwara Dhananjay Karanam Veerabhadra Rao Kakarapalli satish mallina Santosh Chavan Chaitanya Josyula