Demand Planning Software

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

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

 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

Leveraging predictive analytics offers a clear edge—not just to anticipate change but to adapt in real time and act with greater precision across complex decision chains. Predictive models use historical data to forecast future events, allowing businesses to fine-tune strategies before market shifts happen. For example, retailers can anticipate demand spikes, optimize inventory, and avoid overstocking. These models rely on big data—massive, diverse datasets from transactions, sensors, or social media—that are processed using cloud-based tools to extract actionable insights. Ensuring data ethics and legal compliance is key, especially with privacy-sensitive information. When done right, advanced analytics strengthens operational agility and supports faster, more accurate decisions. #PredictiveAnalytics #BigData #AIforBusiness #DataEthics #DigitalTransformation

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

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

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

A few months back, I interviewed a senior demand planner from a global skincare brand. I asked a simple question: "How do you improve your forecast when the system gives you a number that feels... off?" She replied, "We talk to the right people before we talk to the system." That line stayed with me. In Demand Planning, we often focus heavily on historical data, statistical models, and software outputs. But what truly differentiates an average forecast from a high-confidence, actionable one - is the process of Demand Enrichment. And no, it’s not just a buzzword. It’s a discipline - a method of adding intelligence beyond what the system predicts. In fact, according to a McKinsey study, companies that effectively integrate enriched demand signals (like promotions, competitor moves, distribution expansion, influencer campaigns, and even climate effects) can improve forecast accuracy by up to 25%. When I worked for a consumer brand in North India, we noticed our system forecast underestimated demand by 18% during Q4. Why? Because it didn’t factor in the impact of a regional festival that doubled store footfall across 3 key states. Our statistical model was flawless. But our insights were incomplete. That’s when we built a cross-functional "Demand Intelligence Loop" - gathering inputs from marketing, sales, trade partners, and retailers - and feeding it back into planning. The result? Forecast accuracy jumped. Inventory positioning improved. And stockouts during peak weeks were cut in half. If you're a planner reading this: Don't just accept the forecast. Enrich it. Challenge it. Elevate it. That’s how Demand Planning transforms from reactive to strategic.

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?

Are we really delivering the best possible forecasts with state-of-the-art foundation models if our models stop at historical patterns and ignore the external signals shaping the future? In the last few years, we've all seen how foundation models started transforming time series forecasting — unlocking strong zero-shot performance and making high-quality predictions possible without task-specific tuning. But the problem is that most of these models are univariate: they treat time series as isolated signals, leaving out exogenous factors that are often critical for accurate prediction. And that's not how forecasting works outside of a benchmark. Promotions, holidays, weather, pricing — these external influences often explain as much of the future as the past itself. Ignoring them leads to wider prediction intervals and forecasts that are harder to translate into real business decisions. So the real challenge now is: how do we bring that missing context into foundation models? That's the problem Chronos-2 was designed to solve. We built Chronos-2 to handle covariates and multivariate data in a zero-shot manner, and on benchmarks focused on these tasks, it achieves significant reductions in forecast error. But building a foundation model that can handle such diverse, context-dependent signals is not straightforward. Each forecasting task is unique — the number of features, their semantic meaning, and their interactions differ. The solution is a model that can adapt with in-context learning (ICL). Chronos-2 tackles this with two key components: 1. Architecture. In addition to standard temporal attention, we introduce group attention layers that enable information mixing across dimensions, allowing the model to learn from exogenous signals. 2. Training data. Multivariate and covariate time series data are extremely scarce, so we use synthetic data augmentation, adding multivariate structure on top of the univariate series commonly used for pretraining. The result is strong empirical performance across domains. In retail, Chronos-2 captures the impact of promotions on sales. In energy, it learns how weather influences energy consumption. In both cases, incorporating covariates significantly improves forecast accuracy and narrows prediction intervals — making forecasts more actionable. Chronos-2 is available under the Apache 2.0 license and ready to use. Give it a try and let us know what you think! 📄 Technical report: https://lnkd.in/d4RZG8Rq 💻 GitHub: https://lnkd.in/d9mvFT5B 📓 Example notebook: https://lnkd.in/dz69pCyu Abdul Fatir Ansari, Jaris Küken, Andreas Auer, Yuyang (Bernie) Wang, George Karypis, Huzefa Rangwala, Michael Bohlke-Schneider, Nick Erickson, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Amazon Science