Horizon AI Solutions
Retail

Retail Enterprise: 60% Improvement in Inventory Forecasting Accuracy

National Retail Enterprise
Australia
500+ stores
60%
More accurate demand forecasting
45%
Reduction in stockouts
30%
Decrease in excess inventory
Significant
Additional revenue captured

The Challenge

A national retail enterprise struggled with inventory management across hundreds of locations:

  • Frequent stockouts of popular items leading to lost sales
  • Excessive inventory tying up working capital
  • Inability to predict seasonal demand patterns
  • Poor coordination between stores and distribution centers

The Solution

Our Approach

We built a comprehensive inventory optimization platform:

1. Advanced Demand Forecasting

Implemented machine learning models that analyze historical sales, seasonal trends, and external factors.

2. Multi-Location Optimization

Developed algorithms to optimize inventory distribution across all locations.

3. Promotional Impact Analysis

Built models to predict the impact of promotions on demand patterns.

The Results

60%
More accurate demand forecasting
Significant improvement in prediction accuracy
45%
Reduction in stockouts
Better product availability for customers
30%
Decrease in excess inventory
Freed up working capital for growth
Significant
Additional revenue captured
Through improved availability and reduced markdowns

Implementation Details

Timeline
6 months
Team Size
11 specialists
Technologies
PythonTensorFlowAWSReactPostgreSQL

"Our inventory management has been revolutionized. We now have the right products in the right places at the right time."

E
Emily Watson
Chief Retail Officer

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