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AI/MLRetail

Demand Forecasting System

ML-powered demand prediction with external factor integration and inventory optimization.

Duration

7 months

Team Size

4 developers

Industry

Retail

Category

AI/ML

Demand Forecasting System

An ML-powered forecasting platform that predicts product demand by combining sales history with external factors like weather, events, and economic indicators.

The Challenge

A retail chain struggled with inventory management:

  • Inaccurate forecasts - Simple moving averages failing
  • Stockouts - Losing sales on popular items
  • Overstock - Markdowns eating margins
  • Manual planning - Buyers using spreadsheets and intuition

They needed intelligent, automated forecasting.

Our Approach

We built an ML platform that considers hundreds of demand signals.

Forecasting Strategy

  1. Ensemble Models - Multiple algorithms for robustness
  2. External Factors - Weather, holidays, events, trends
  3. Hierarchical - Store, region, and company level
  4. Automated Pipeline - Daily forecasts without manual work

The Solution

Data Integration

  • POS sales history
  • Weather forecasts
  • Event calendars
  • Economic indicators

Forecasting Models

  • Time series decomposition
  • ML ensemble (XGBoost, Prophet)
  • Promotional lift modeling
  • New product forecasting

Inventory Optimization

  • Safety stock calculation
  • Reorder point recommendations
  • Allocation optimization
  • Markdown timing

Planning Interface

  • Forecast visualization
  • Override capabilities
  • What-if scenarios
  • Accuracy tracking

Technology Stack

LayerTechnologies
ML ModelsProphet, XGBoost, LightGBM
OrchestrationApache Airflow
BackendPython, FastAPI
DatabasePostgreSQL, Redis
FrontendReact, Recharts
CloudAWS (SageMaker, Redshift)

Results & Impact

The system transformed inventory management:

  • 35% more accurate forecasts
  • 25% fewer stockouts on key items
  • 20% less overstock and markdowns
  • $5M+ saved annually

ML Features

External Signals

  • Weather correlation
  • Holiday and event impact
  • Competitive pricing
  • Social media trends

Accuracy Monitoring

  • Forecast vs actual tracking
  • Model performance dashboards
  • Automatic model selection
  • Drift detection

Client Testimonial

"Our buyers went from spreadsheet guesswork to data-driven decisions. The accuracy improvement paid for the system many times over in reduced stockouts alone."

— VP of Merchandising, Retail Chain


Optimizing inventory? Contact us to discuss demand forecasting solutions.

Key Results

1

35% improvement in forecast accuracy

2

25% reduction in stockouts

3

20% decrease in excess inventory

4

$5M+ annual savings

Technology Stack

PythonProphetXGBoostAirflowPostgreSQLReact

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