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Retail Industry

Predictive Inventory Management

Large Retail Chain

Location
Pan-India
Duration
6 months
Team Size
10 specialists
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The Challenge

The retail chain was struggling with inventory management across 200+ locations. Stockouts were costing sales while overstock was tying up capital and leading to markdowns. Traditional forecasting methods couldn't keep up with rapidly changing consumer preferences and seasonal variations.

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Our Solution

We deployed an advanced predictive analytics engine that forecasts demand at the SKU level for each location. The system considers historical sales data, weather patterns, local events, promotional calendars, and social media trends to optimize inventory levels. It automatically generates purchase orders and suggests inter-store transfers.

Implementation Steps

1

Integrated with Point of Sale (POS) systems across all locations

2

Connected to supplier systems for automated ordering

3

Deployed machine learning models for demand forecasting

4

Created centralized dashboard for inventory managers

5

Implemented mobile alerts for store managers

6

Established automated replenishment workflows

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Measurable Impact

67% Reduction
Stockouts
Fewer lost sales due to out-of-stock items
31% Decrease
Inventory Costs
Reduced capital tied up in excess inventory
15% Improvement
Profit Margins
Increased profits from better pricing and less markdown
89%
Forecast Accuracy
Improved demand prediction accuracy

Technologies Used

Predictive AnalyticsMachine LearningData VisualizationCloud ComputingPythonPower BIAzure
"

The inventory optimization system has fundamentally changed how we operate. We now have the right products in the right places at the right time.

VP of Supply Chain
Large Retail Chain

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