Eliminate stockouts, reduce excess inventory, and optimize your supply chain with AI-powered demand forecasting that adapts to market changes in real-time.
Overstocking ties up capital, increases warehousing costs, and leads to markdowns when products become obsolete. Without accurate demand prediction, safety stock levels are too high.
Missing inventory when customers want to buy means lost revenue, damaged brand reputation, and customers switching to competitors. Poor demand forecasting causes chronic stockouts.
Reactive ordering based on current inventory levels leads to rush shipments, premium freight costs, and missed volume discounts from suppliers.
Inability to meet customer demand consistently results in SLA breaches, contract penalties, and erosion of customer trust and loyalty.
Our machine learning platform analyzes historical sales, seasonal patterns, promotional impact, and external factors to generate precise demand forecasts at SKU-location level.
Unlike simple historical averages, our demand prediction models incorporate dozens of variables: past sales velocity, promotional calendars, pricing changes, competitor actions, weather data, economic indicators, and market trends. This holistic approach captures the true drivers of demand.
Generate daily demand forecasts for every SKU at every location in your network. Our hierarchical forecasting approach ensures predictions are coherent across product categories, regions, and time horizons - from daily replenishment to quarterly capacity planning.
Demand forecasts feed directly into inventory optimization algorithms that determine optimal order quantities, reorder points, and safety stock levels. This closed-loop system continuously learns and improves, balancing service level targets against inventory costs.
Ready to optimize your supply chain? Our demand prediction platform integrates with your ERP, WMS, and planning systems.
Research shows that companies lose 3-5% of revenue annually due to poor demand forecasting. For a €100M revenue business, that's €3-5M in lost profits from stockouts, excess inventory markdowns, and inefficient logistics. The hidden costs are even larger: working capital tied up in inventory, expedited freight charges, lost customer loyalty, and reduced competitiveness.
Traditional forecasting methods - moving averages, exponential smoothing, or manual judgment - can't handle the complexity of modern supply chains with thousands of SKUs, multiple locations, promotional calendars, and volatile market conditions. Machine learning offers a fundamental step-change in forecast accuracy.
Gradient boosting excels at demand prediction because it can capture complex relationships between features without manual interaction terms. These models handle mixed data types (continuous, categorical), missing values, and non-linear effects naturally. We use them when you have rich feature sets: pricing, promotions, holidays, weather, competitor data, and economic indicators. Typical accuracy improvements: 15-30% reduction in MAPE compared to statistical methods.
Deep learning models shine when demand patterns are highly non-linear and involve long-term dependencies. LSTMs learn sequential patterns across multiple time scales - daily, weekly, monthly seasonality simultaneously. We deploy neural networks for high-value SKUs with sufficient history, complex seasonal patterns, and when feature engineering is difficult. These models can also be pre-trained on similar products and fine-tuned for new SKUs.
Generate forecasts at multiple aggregation levels (total company → region → store → category → SKU) and ensure they're coherent. Bottom-up approaches forecast at SKU level and aggregate up. Top-down starts with aggregate forecasts and disaggregates. We typically use middle-out approaches with reconciliation algorithms to get best of both worlds. This ensures total demand forecasts match the sum of individual SKU forecasts.
Promotions create massive demand spikes that standard models handle poorly. We build specialized models to estimate promotional lift by type (discount %, BOGO, loyalty offers), timing, product category, and channel. These models also account for cannibalization (sales stolen from similar products) and pull-forward effects (borrowing from future demand). Accurate promotional forecasting typically improves overall accuracy by 10-20%.
Feature engineering makes or breaks demand prediction models. Essential features include:
Demand forecasts are just the starting point. The real value comes from translating forecasts into optimal inventory decisions:
A European manufacturing distributor with 8,500 SKUs across 12 distribution centers was struggling with 30% forecast error (WMAPE) using moving averages. This resulted in €23M in inventory, frequent stockouts (82% service level), and expedited shipping costs exceeding €800K annually.
We implemented a two-tier forecasting system: XGBoost models for the top 1,500 SKUs incorporating pricing, promotions, customer orders, and economic data; Prophet models for mid-tier SKUs capturing seasonality; simple exponential smoothing for long-tail SKUs. The system generates daily forecasts and feeds an inventory optimization engine that sets dynamic safety stocks and reorder points.
Results after 6 months: Forecast accuracy improved to 18% WMAPE (40% reduction in error), inventory decreased from €23M to €19M (18% reduction), service level increased to 95%, stockouts reduced by 65%, and expedited shipping costs cut by 55%. Total annual benefit: €4.2M in released working capital plus €1.1M in operational cost savings.
New product forecasting is challenging but solvable. We use several techniques: (1) Identify similar existing products and use their launch curves as templates, (2) Leverage category-level seasonality and trends, (3) Incorporate market research, pricing, and promotional plans, (4) Apply transfer learning - train models on all existing products and fine-tune for new products with early sales data, (5) Use external data like search trends and competitor analysis. The key is starting with conservative estimates and rapidly updating forecasts as actual sales data becomes available.
Minimum requirements: 2+ years of daily or weekly sales history by SKU and location, product master data (category, attributes), and current inventory positions. Highly recommended: promotional calendar and discount data, pricing history, any stockout or lost sales records, supplier lead times. Optional but valuable: weather data, competitor pricing, economic indicators, search trends, and customer demographics. We can start with basic data and progressively incorporate additional features to improve accuracy.
Demand forecasts predict customer demand, independent of supply constraints. When stockouts occur, we use "lost sales estimation" to infer what demand would have been if product was available. For major disruptions (COVID-19, natural disasters), we implement rapid model retraining with more weight on recent data, scenario planning with different demand assumptions, and ensemble approaches that combine pre- and post-disruption models. We also provide tools for planners to manually adjust forecasts and create "what-if" scenarios.
Yes, integration is a core part of our implementation. We've integrated with major ERP systems (SAP, Oracle, Microsoft Dynamics), supply chain platforms (Blue Yonder, o9, Kinaxis), and WMS systems. Integration typically involves: (1) Automated data extraction from your systems to our forecasting platform, (2) API or database connections for real-time forecast delivery, (3) Export to Excel/CSV for manual review, (4) Direct integration with your demand planning or replenishment systems. We design the architecture to fit your IT landscape and workflows.
Results vary by industry, data quality, and current baseline, but typical improvements: 20-40% reduction in forecast error (MAPE/WMAPE) compared to simple statistical methods, 15-25% for companies already using basic ML, 10-15% for those with sophisticated existing solutions. The business impact is often larger than accuracy metrics suggest - better handling of promotions, new products, and seasonality leads to 15-30% inventory reduction while improving service levels by 5-10 percentage points. We always run pilot projects to establish baseline and demonstrate improvement before full deployment.
Optimize inventory, reduce costs, and improve service levels with AI-powered demand prediction. Get a free assessment of your supply chain forecasting potential.
Based in Lund, Sweden • Serving businesses globally