Demand Forecasting for Retail with Amazon Forecast

Retail success hinges on having the right products in the right place at the right time. Too much inventory ties up capital and leads to markdowns; too little means lost sales and disappointed customers. Amazon Forecast applies the same ML technology that powers Amazon.com's supply chain to help retailers build accurate demand forecasting systems.

The Forecasting Imperative

Demand forecasting touches nearly every retail operation. Procurement teams use forecasts to place orders with suppliers. Distribution centers allocate inventory across stores based on predicted demand. Marketing plans promotions knowing their expected impact. Finance budgets revenue using sales projections.

Traditional forecasting relies on statistical methods: moving averages, exponential smoothing, and ARIMA models. These methods work well for stable, predictable demand patterns. They struggle with the complexity of modern retail: thousands of SKUs across hundreds of locations, influenced by promotions, weather, holidays, and competitive actions.

Machine learning forecasting captures these complex relationships. ML models learn from historical patterns while incorporating external factors that influence demand. The result is more accurate predictions, especially for products with irregular demand patterns.

Amazon Forecast Overview

Amazon Forecast is a managed time-series forecasting service. It automatically trains and deploys forecasting models using your historical data, handling the ML complexity so you can focus on using forecasts to improve operations.

How It Works

Forecast ingests your historical demand data (sales transactions, inventory movements) and optional related data (prices, promotions, weather). The service trains multiple algorithms automatically, selecting the best performers for your specific data patterns. Trained models generate forecasts at configurable horizons and granularities.

The service uses deep learning techniques developed at Amazon, including the DeepAR+ algorithm that models complex seasonality and handles missing data gracefully. AutoML capabilities test multiple algorithms and select optimal configurations without manual tuning.

Data Requirements

Forecast requires historical time-series data with timestamps and demand values. For retail, this typically means daily or weekly sales by product and location. The service recommends at least two years of history to capture annual seasonality patterns, though shorter histories can work for products without strong seasonality.

Related time-series data improves accuracy by capturing demand drivers. Include pricing history, promotion calendars, and holiday indicators. Weather data helps for weather-sensitive categories. Economic indicators may improve forecasts for discretionary purchases.

Building Retail Forecasts

Effective retail forecasting requires thoughtful data preparation and model configuration.

Demand Signal Selection

Choose the right demand signal for your use case. Point-of-sale data represents actual customer demand but may be distorted by stockouts, customers who couldn't buy what wasn't available. Order data captures what stores requested from distribution centers. Forecast what you need to predict: sell-through for replenishment, customer demand for assortment planning.

Hierarchy Design

Retail data has natural hierarchies: products roll up to categories, stores to regions. Forecast supports hierarchical forecasting that ensures consistency across levels. Forecasts at the product-store level sum correctly to category-region totals, enabling both detailed execution and high-level planning.

Feature Engineering

Transform raw data into features that capture demand drivers:

• Calendar features: Day of week, month, holiday flags, promotional periods
• Price features: Current price, discount depth, price relative to competition
• Product attributes: Category, brand, size, color for cross-product learning
• Store attributes: Format, size, demographics for cross-location learning
• External factors: Weather forecasts, local events, economic indicators

Forecast Generation

Trained models generate forecasts through the prediction API or batch export.

Probabilistic Forecasts

Forecast provides probabilistic predictions: not just a single expected value, but a distribution of possible outcomes. Request forecasts at specific quantiles (P10, P50, P90) to understand the range of likely demand. Use higher quantiles for critical inventory (avoiding stockouts) and lower quantiles for cost optimization (minimizing overstock).

Forecast Horizons

Configure forecast horizons matching your planning cycles. Short horizons (days to weeks) support store replenishment. Medium horizons (weeks to months) inform distribution center inventory. Long horizons (months to quarters) guide procurement and capacity planning.

Forecast Frequency

Balance forecast freshness against processing costs. Daily forecast updates capture recent demand signals but increase compute costs. Weekly updates suffice for stable categories. Trigger ad-hoc updates when significant demand shifts occur.

Integration Patterns

Forecasts deliver value only when integrated into business processes.

Inventory Management Integration

Feed forecasts into inventory management systems for automated replenishment. Calculate safety stock from forecast uncertainty (the gap between P50 and P90). Set reorder points based on forecast demand during lead time. Automate purchase orders when inventory drops below replenishment thresholds.

Merchandise Planning Integration

Export forecasts to planning systems for assortment and allocation decisions. Aggregate forecasts to category level for buyers. Disaggregate to store-product level for allocation. Compare forecasts against open-to-buy budgets for financial alignment.

Operational Dashboards

Visualize forecasts and actuals for operational monitoring. Highlight products with demand significantly above or below forecast. Track forecast accuracy metrics over time. Enable planners to review and adjust forecasts for special circumstances.

Measuring Forecast Quality

Forecast accuracy determines business value. Track metrics that align with business objectives.

Accuracy Metrics

Amazon Forecast provides built-in accuracy metrics:

• WAPE (Weighted Absolute Percentage Error): Aggregate accuracy across products, weighted by volume
• MAPE (Mean Absolute Percentage Error): Average percentage error across items
• RMSE (Root Mean Square Error): Emphasizes large errors, useful for high-stakes forecasts

Business Metrics

Connect forecast accuracy to business outcomes. Track inventory turnover improvements from better forecasting. Measure stockout reduction from forecast-driven safety stock. Calculate markdown reduction from demand-aligned purchasing. These business metrics justify continued investment in forecasting capabilities.

Advanced Techniques

Cold Start Handling

New products lack historical data for training. Forecast handles cold start through item metadata: products with similar attributes inherit demand patterns from established products. Provide rich product attributes to enable effective cold start forecasting for new introductions.

Promotion Modeling

Promotions create demand spikes that confuse simple models. Include promotion indicators as related time series so models learn promotion effects. Separate base demand from promotional lift for more accurate regular-price forecasting.

Demand Sensing

Combine long-range statistical forecasts with short-range demand sensing. Use recent sales patterns to adjust near-term forecasts. This hybrid approach captures both seasonal patterns and recent trend shifts.

Key Takeaways

• Amazon Forecast applies Amazon's ML forecasting technology to retail demand prediction
• Related time-series data (pricing, promotions, weather) significantly improves forecast accuracy
• Probabilistic forecasts enable risk-based inventory decisions using different quantiles
• Integration with inventory and planning systems is essential for realizing forecast value
• Track both statistical accuracy metrics and business outcomes to measure forecasting ROI