How do you implement AI for supply chain demand forecasting?
AI-powered demand forecasting predicts future product demand to optimize inventory, procurement, and production. Data sources: historical sales data (daily/weekly by product, channel, location), external factors (weather data, economic indicators, holidays, events), promotional calendar (planned promotions, pricing changes), market data (competitor actions, market trends, industry reports), and digital signals (search trends, social media mentions, web traffic). Model architecture: hierarchical forecasting for products sold across multiple locations and channels. Forecast at the most granular level and reconcile across the hierarchy (product-location-channel) using top-down, bottom-up, or middle-out approaches. Model selection: statistical baselines (ARIMA, Exponential Smoothing for comparison), gradient boosting (XGBoost, LightGBM on engineered features) for strong tabular performance, deep learning (Temporal Fusion Transformer, N-BEATS) for capturing complex patterns, and foundation models (Chronos, TimesFM) for zero-shot forecasting without training. Feature engineering: lag features (sales at t-7, t-14, t-28, t-365), rolling statistics (7-day average, 30-day trend), calendar features (day of week, month, holiday proximity), promotional features (discount depth, promotional type, competitor promotions), and external features (temperature, precipitation for weather-sensitive products). Implementation: train separate models per product category or use a global model with product embeddings, implement forecast reconciliation across the hierarchy, generate probabilistic forecasts (prediction intervals) not just point forecasts, and implement automated model retraining on a weekly or monthly schedule. Evaluation: measure forecast accuracy using MAPE, RMSE, and weighted MAPE (give more weight to high-volume products), track forecast bias (systematic over or under-prediction), and implement forecast value-added analysis (compare AI forecast against naive baselines). Business impact: connect forecasts to inventory optimization (safety stock calculation, reorder point determination), production planning, and procurement scheduling.