Demand &
Forecasting AI
Predictive analytics system that forecasts demand, optimizes inventory, and models business scenarios. Combines time-series deep learning with market signal ingestion and auto-retraining pipelines to keep predictions accurate as conditions change.
What It Does
End-to-end demand intelligence from data ingestion to actionable forecasts.
Time-Series Forecasting
Multi-horizon demand prediction using temporal fusion transformers and ensemble methods. Models seasonality, trends, cyclical patterns, and exogenous variables across daily, weekly, and monthly granularity.
Scenario Modeling
Monte Carlo simulation and what-if analysis for strategic planning. Model the impact of price changes, promotions, supply disruptions, market entry, and economic shifts on demand curves.
Inventory Optimization
Dynamic safety stock, reorder point, and replenishment schedule optimization. Balances holding costs against stockout risk using probabilistic demand distributions and lead time variability.
Market Signal Ingestion
Integrates external signals — competitor pricing, weather data, economic indicators, social media trends, and search volume — into demand models for improved forecast accuracy.
Auto-Retraining Pipeline
Continuous model monitoring with automated drift detection and retraining. When forecast accuracy degrades, the system retrains on recent data and validates before deployment — no manual intervention.
Anomaly Detection
Statistical and ML-based anomaly detection for demand spikes, drops, and structural breaks. Distinguishes between genuine demand changes and data quality issues to prevent forecast corruption.
Architecture
The ML pipeline from raw data to production forecasts.
| Layer | Technology | Details |
|---|---|---|
| Data Ingestion | Kafka + Airflow | Real-time and batch data pipelines for sales, inventory, pricing, and external signals |
| Feature Engineering | Python / Pandas | Temporal features, lag variables, rolling statistics, holiday/event encoding, external signals |
| Forecasting Models | TFT / N-BEATS / Prophet | Ensemble of temporal fusion transformers, neural basis expansion, and statistical models |
| Scenario Engine | Monte Carlo / Custom | What-if simulation, sensitivity analysis, scenario comparison, confidence intervals |
| Optimization | OR-Tools / SciPy | Inventory optimization, replenishment scheduling, safety stock calculation |
| Anomaly Detection | Isolation Forest / LSTM | Demand spike/drop detection, structural break identification, data quality monitoring |
| Model Registry | MLflow | Model versioning, experiment tracking, A/B testing, automated deployment |
| Auto-Retrain | Custom Pipeline | Drift detection, triggered retraining, validation gates, gradual rollout |
| API Layer | NestJS / FastAPI | REST and GraphQL endpoints for forecast queries, scenario requests, dashboard data |
| Visualization | React + D3.js | Interactive dashboards, forecast vs. actual charts, scenario comparison views |
How a Forecast Is Generated
Data Ingested
Sales, inventory, pricing, and market signals collected in real-time
Features Built
Temporal features, lag variables, and external signals engineered
Models Run
Ensemble of TFT, N-BEATS, and Prophet generates predictions
Optimized
Inventory levels, reorder points, and safety stock calculated
Delivered
Dashboards, API responses, and alerts sent to stakeholders
Tech Stack
Need smarter demand planning?
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