UST AI-Powered Retail Demand Forecasting Solution on AWS

UST AI-Powered Retail Demand Forecasting Solution on AWS helps retail and CPG enterprises generate accurate, explainable SKU-by-store retail demand forecasts and inventory optimization signals using machine learning and AWS-native analytics services. Built on Amazon SageMaker, Amazon S3, AWS Glue, and related services, the solution ingests POS, inventory, promotion, pricing, and external signals (seasonality, holidays, local events, demographics) to produce high-quality time-series forecasts that drive smarter replenishment, allocation, and promotion planning across your omnichannel network.

Designed for buying, merchandising, supply chain, and data science teams, the accelerator combines proven demand forecasting models with an end-to-end AWS reference architecture and MLOps capabilities. UST brings 15+ years of retail experience and global case studies where customers have reduced stockouts and shrink, improved WMAPE, and unlocked significant sales and margin uplift by moving away from manual Excel-based forecasting and legacy systems.

Key features

• AWS-native, modular architecture Built on Amazon SageMaker for training and hosting, Amazon S3 as the data lake, AWS Glue for ETL, AWS Lake Formation and AWS IAM for governance and security, with AWS Lambda, AWS Step Functions, and Amazon CloudWatch for orchestration, automation, and monitoring.

• Advanced ML and time-series model ensemble Uses a champion–challenger framework with multiple models (e.g., ARIMA, gradient boosting, random forest, LSTM, CNN, Transformer-based time series) to select the best-performing algorithm per SKU, store, and category, with automated retraining to track changing demand patterns.

• Granular SKU-by-store demand forecasting Generates forecasts at multiple levels of granularity: SKU-by-store, category, banner, and region. Supports daily and weekly horizons for operational replenishment and strategic planning, with configurable aggregation logic.

• Promotion- and event-aware forecasting Incorporates promotions, flyers, discounts, price changes, holidays, local events, campaigns, and omnichannel signals to quantify uplift and cannibalization, and supports what-if analysis for promotion planning and ad optimization.

• Feature engineering and data enrichment pipeline Prebuilt pipelines create features for seasonality, trend, store clusters, product hierarchies, and customer behavior metrics. Designed to handle multiple source systems, including POS, ERP, inventory, CRM, and planning tools.

• MLOps and model governance on AWS Includes templates for SageMaker pipelines, model registry, versioning, and automated deployment. Centralized monitoring of model drift, data drift, forecast bias, and WMAPE/MAE across products and locations.

• Planner- and buyer-friendly dashboards Integrates with BI tools such as Amazon QuickSight to provide interactive dashboards that visualize forecast vs. actual, confidence intervals, promotion uplift, stockout risks, and recommended replenishment quantities.

• Secure, scalable design Supports enterprises with thousands of stores and hundreds of thousands of SKUs. Uses AWS best practices for security, data isolation, encryption, and access control to meet enterprise governance standards.

Benefits

• Improve forecast accuracy and WMAPE: Replace manual spreadsheets and legacy tools with ML-driven demand forecasting, reducing forecast error at SKU-by-store level and improving WMAPE across key categories.

• Reduce stockouts and lost sales: Anticipate demand by location and channel so the right products are available at the right time, protecting revenue and customer experience.

• Lower shrink and markdowns: Align orders with true demand and shelf life to cut over-forecasting, reduce waste, and minimize margin-draining markdowns.

• Optimize inventory and working capital: Right-size safety stock and reorder quantities using accurate demand signals, lowering excess inventory and carrying costs while maintaining service levels.

• Enhance promotion performance: Quantify the uplift and profitability of promotions, flyers, and campaigns. Use scenario planning to select more effective offers and reduce promo cannibalization.

• Accelerate time-to-value on AWS: Deploy a proven AWS reference architecture, data model, and model library instead of building from scratch, shortening time from data ingestion to production-grade forecasts.

• Empower planners, buyers, and merchandisers: Provide intuitive dashboards, explainable forecasts, and actionable recommendations so teams spend less time wrangling spreadsheets and more time on strategic decisions.

• Standardize forecasting across banners and regions: Establish a consistent forecasting process across stores, regions, and brands while still allowing local nuance through store clustering and configurable models.