Design a demand forecasting system for a large retail company like Costco, Walmart, or Target. The system should predict future product demand across stores and time to support inventory planning, replenishment, and promotions.
Answer
The demand forecasting system ingests diverse data from sales, inventory, weather, and promotions to predict product demand using ML models like time series, tree-based, or deep learning methods.
This demand forecasting system features a scalable architecture with data pipelines, real-time processing, and integration for inventory management.
Key benefits include reducing stockouts, optimizing supply chains, and improving accuracy through iterative model training.
Problem Definition & Success Metrics:
Define the forecast granularity (e.g., SKU-store-day), horizon (e.g., 2-week operational, 3-month tactical), and objective (e.g., minimize out-of-stocks and waste).
Key success metrics would be Weighted Mean Absolute Percentage Error (WMAPE) for overall accuracy and forecast bias to detect systematic over/under-prediction.
Data Strategy & Feature Engineering:
Integrate diverse data sources into a unified feature store:
(1) Internal: Historical sales, product hierarchies, pricing, promotional calendars, inventory levels, and online search/click data.
(2) External: Calendar events (holidays, paydays), weather, local events, competitor activity (scraped), and macroeconomic trends.
System Architecture:
(1) Data Ingestion Layer: Batch and real-time streams.
(2) Processing & Feature Store: Clean, validate, and compute features.
(3) Modeling Layer: A repository for multiple models, allowing experimentation.
(4) Serving Layer: Exposes forecasts via APIs to downstream systems (replenishment, pricing).
(5) Monitoring & Feedback: Tracks model performance, data drift, and incorporates actual sales as ground truth for retraining.
Modeling Approach with Hierarchical Ensemble Strategy:
Use a “Top-Down, Bottom-Up” approach. Forecast at the aggregate level (Category/Region) to capture macro-trends and reconcile these with granular SKU(Stock Keeping Unit)-level predictions to ensure total inventory alignment.
(1) Base Layer (Interpretability): Implement Prophet or Exponential Smoothing for high-level aggregates. This captures clear seasonalities (holidays, paydays) in a way that is easily explainable to business stakeholders.
(2) Granular Layer (The “Workhorse”): Use Global LightGBM or XGBoost models trained across entire product categories. This allows the model to learn shared patterns across similar items while efficiently handling categorical metadata like Store ID and Brand.
(3) High-Volatility Layer (Deep Learning): Deploy Temporal Fusion Transformers (TFT) or DeepAR specifically for high-volume or volatile items. These models capture complex, non-linear dependencies and multi-horizon temporal patterns that tree-based models might miss.
(4) Probabilistic Forecasting: Instead of a single point estimate, generate Quantile Forecasts (e.g., P10, P50, P90). This provides a range of uncertainty, allowing the logistics team to make data-driven decisions on safety stock levels.
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