Demand Planning Software

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.

What can you do with Python in Excel for FP&A and #finance? I have received this question many times since the launch! Python in Excel can be a game changer for FP&A and finance professionals. If you learn how and for what to use it. You can now do: ✅Cohort Analysis with Heatmaps ✅Time Series Forecasting Using ARIMA ✅Outliers Identification ✅Statistical Advanced Outliers Identification ✅Headcount Analysis ✅Monte Carlo Simulations I created this cheat sheet to help you. But if you want to learn how to leverage AI and Python for Finance, Nicolas Boucher and I have a course coming up: https://lnkd.in/e4FugWeY Comment "Python in Excel is here" and I can send you the Excel file with all the code in the examples! And a bit more detail on the examples: 1) Cohort Analysis with Heatmaps Easily track and visualize customer retention or employee performance trends over time with beautiful, interactive heatmaps. 2) Time Series Forecasting Using ARIMA Predict future financial outcomes like revenue or expenses using advanced ARIMA models that can capture patterns in historical data. 3) Outliers Identification Quickly spot unusual data points (e.g., abnormally high expenses or revenues) with scatter plots and advanced visuals. 4) Statistical Advanced Outliers Identification Go deeper with statistical methods to identify outliers based on standard deviation or interquartile range, providing a robust analysis of deviations from the norm. 5) Headcount Analysis Analyze workforce trends across departments or time periods using visually engaging box plots and scatter diagrams, highlighting fluctuations and unusual spikes. 6) Monte Carlo Simulations Simulate thousands of financial scenarios to model risk and uncertainty, providing a data-driven approach to decision-making and forecasting.

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

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

How Zinnia Used AI to Forecast Daily Call Volumes with 95% Accuracy 📞 At Zinnia, we needed a better way to forecast call center volumes — our existing tool often missed the mark by 10–20%, making staffing plans unreliable So, we rolled up our sleeves and built our own AI forecasting solution: ✅ Combined Prophet (seasonality + trends) with XGBoost (learn from errors) ✅ Used real-world signals like holidays, month-ends, and even Mondays after weekends ✅ Tuned everything with time-aware cross-validation We tested A LOT (even LSTMs and SHAP-based pruning!), but our hybrid model consistently delivered 95%+ accuracy across clients. 🔍 I’ve shared the full breakdown, code, and what worked (and what didn’t) in this Medium article — practical, real-world AI for ops. If you're a data scientist, ML engineer, or even an ops leader — this one’s for you. Josh Everett | Pawan Choudhary | Daniel Gremmell | Eti Gupta #DataScience #Forecasting #AI #XGBoost #Prophet #TimeSeries #MLinProduction #CallCenterAI #WorkforcePlanning #ZinniaTech #AIinOps

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

🚀 Excited to share my latest project: a fully autonomous Smart Warehouse Management System built using the Agent Communication Protocol (ACP)! This innovative system features four intelligent agents InventoryBot, OrderProcessor, LogisticsBot, and WarehouseManager working seamlessly together to manage stock, schedule deliveries, and handle reorders, all through standardized, real-time communication. 🌟 What is ACP?   ACP is a framework that enables autonomous agents to communicate effectively using structured messages with defined performatives (e.g., ASK, REQUEST_ACTION, TELL, CONFIRM). It ensures clear, reliable interactions, making it ideal for complex systems like smart warehouses where coordination is key. 🌟 How It Works:   Scenario 1: Stock Alert & Reorder - The OrderProcessor checks stock levels with InventoryBot and triggers reorders to maintain minimum availability (e.g., reordering to fill low laptop stock).  Scenario 2: Delivery Scheduling - The WarehouseManager directs LogisticsBot to schedule deliveries of goods, with LogisticsBot confirming the schedule including a tracking ID for transparency.  Scenario 3: Low Stock Management - InventoryBot alerts the WarehouseManager of low stock (e.g., 5 tablets), prompting a confirmation that 15 tablets are needed; the WarehouseManager then requests OrderProcessor to place an order for 15 tablets, with OrderProcessor confirming via a PO number.  The interactive frontend visualizes these interactions, complete with a Statistics dashboard (e.g., total messages: 6, active conversations: 3, registered agents: 4) to monitor performance, making it perfect for real-world adoption. 🏭Impact on Logistics: This solution transforms the logistics industry by reducing manual oversight, optimizing stock levels, and streamlining delivery schedules. With real-time data and automated reordering, warehouses can operate 24/7, cut costs, and improve customer satisfaction key drivers in today’s fast-paced supply chain. This showcase how AI and ACP can revolutionize warehouse management. Check out the demo video to see it in action!

What Are The Top Innovations in the Analytics and Decision-Making Platform Market in Supply Chain in 2025? 🔵 Heritage supply chain applications exhibit limitations that constrain leaders. However, Analytics and Decision-Making (ADM) platforms in supply chain leveraging technologies like graph technology, generative AI (GenAI), and agentic AI offer enhanced insights and recommendations. This enables faster, more intelligent, and higher-quality decision-making, particularly for complex cross-functional processes. Supply chain executives recognize the value and need to embed these tools within existing application portfolios to maximize investment value. 🔵 Supply chain leaders face challenges including heritage application limitations and persistent data quality issues. Evaluating ADM platforms in supply chain functions requires assessing capabilities like collaboration, composability, automated insights, and storytelling. Strategic choices between buy, build, or partner models are key. Integrating new technologies involves addressing data privacy, security, and compliance for sensitive datasets. Distinguishing viable AI capabilities from market hype necessitates thought leadership support due to limited understanding. 🔵 Given system limitations, data challenges, integration complexities and evaluation needs, how can supply chain leaders effectively identify, prioritize, and strategically leverage the latest innovations in ADM platforms incorporating AI, #DecisionIntelligence and prescriptive analytics to augment or automate decision-making, improve operational efficiency, foster enhanced collaboration, and ultimately drive digital value realization and business outcomes? 🔵 Key innovations in ADM platforms include artificial supply chain intelligence, which uses composite AI (GenAI and ML) for decision augmentation and automation via a closed-loop Analyze, Decide, Act, Learn (ADAL) approach. Current features also include AI-powered connectors, knowledge graphs, simulation and optimization engines, self-service analytics, and conversational interfaces. 🔵 Future innovations emphasize intelligence and automation, featuring agentic AI for accelerating decision automation, intelligent simulation for adaptive planning, prescriptive analytics, and decision flow automation with continuous feedback loops. These are supported by AI-powered user experience and advanced collaboration advancements. 🔗 Link in comments for Gartner clients to read this new research in full.

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