Demand Forecasting

Use AI to predict customer demand and optimize inventory and supply chain operations

Business impact

• Forecast accuracy — AI models increase precision in predicting future demand, reducing forecast errors
• Inventory turnover — Better forecasts optimize stock levels, increasing inventory turnover rates
• Operational efficiency — Accurate demand predictions streamline supply chain and resource allocation processes
• Cost reduction — Minimized overstock and stockouts lower holding and lost sales costs

Data requirements

• Historical sales data (Numeric) — Used to identify past demand patterns and seasonality
• Market trends and economic indicators (Structured) — Incorporated to adjust forecasts for external influences
• Promotional and pricing data (Structured) — Helps model demand response to discounts and campaigns
• Weather and location data (Numeric) — Used to forecast demand variations influenced by environmental factors
• Social media and customer sentiment (Text) — Analyzed to capture emerging consumer preferences and trends

AI methods and techniques

• Predictive AI — Models forecast future demand based on historical and external data patterns
• Generative AI — Simulates demand scenarios to improve forecasting robustness and scenario planning

AI models and model families

Transformer, LSTM, XGBoost, Random Forest, TSMixer-Ext, Chronos-Bolt, Ikigai foundation model

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: French, Karlsruhe Institute Technology, Nissin Foods, SERVEO, Salesforce, Smart Tab, Walmart, Zalando SE.