System Overview

What it is: Walmart uses machine learning models to forecast product demand across thousands of stores. The system incorporates data on location, weather, local events, and seasonal trends to optimize inventory levels, reducing stockouts and overstock situations.

Core Capabilities

ML-driven demand forecasting at store and regional levels
Integration of external factors like weather, holidays, and events
Automated inventory adjustments and replenishment recommendations
Real-time dashboards for supply chain visibility
Scenario modeling for promotional or seasonal campaigns
Continuous model retraining with new data inputs

Business Problems Solved

Stockouts leading to lost sales and poor customer experience
Overstock causing excess carrying costs and waste
Inconsistent demand planning across different store locations
Limited visibility into external factors influencing demand
Inefficient manual forecasting methods

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Actor Identification

Primary actor: Walmart supply chain and inventory managers.
Secondary actors: ML forecasting models, store systems, warehouse teams, ERP platforms.

Actor Goals

Supply Chain Manager: Maintain optimal inventory levels with fewer stockouts and overstocks.
Store Manager: Ensure products are available when customers need them.
ML Models: Predict demand accurately using multiple internal and external data sources.
ERP/Inventory Systems: Execute replenishment orders and update stock levels.

Context and Preconditions

Historical sales data integrated across Walmart stores
External data sources (weather APIs, event calendars) connected
ML models trained and validated on past performance
ERP systems integrated for automated replenishment
Compliance with logistics and distribution SLAs

Basic Flow (Successful Scenario)

ML models analyze historical sales, weather, and event data.
Demand forecasts are generated for each store and product category.
ERP system receives recommended replenishment levels.
Warehouse and distribution centers adjust shipments accordingly.
Inventory accuracy improves, reducing both stockouts and excess stock.
Forecast performance is tracked and models retrained regularly.

Outcome: Walmart improves inventory accuracy by up to 90%, reduces waste, and ensures customers find products in stock when they need them.

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Alternate Flows

A1: Data anomaly: If unusual spikes (e.g., sudden panic buying) occur, forecasts are adjusted manually.
A2: API downtime: If weather or event data sources fail, models revert to historical-only forecasting.
A3: Forecast error: If predictions deviate significantly, alerts notify planners for manual review.
A4: Distribution delay: If replenishment shipments are delayed, system prioritizes high-demand stores first.

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AI-Powered Demand Forecasting Driving Smarter Inventory at Walmart