A large brick-and-mortar retail chain was managing inventory across multiple locations and struggled to accurately predict demand and maintain optimal stock levels. Overstocking tied up capital and increased storage costs, while stockouts resulted in lost sales opportunities and lower customer satisfaction. The retailer needed a more data-driven solution to manage inventory with changing demand across locations.
The Challenges:
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Excess overstock tied up operational capital and inflated store storage costs.
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Frequent stockouts created missed sales opportunities across key locations.
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Inaccurate demand predictions led to unbalanced product distribution.
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High holding expenses reduced total operating profit margins.
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Poor inventory control lowered overall customer shopping satisfaction.
The Solution:
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Implemented an AI-driven inventory optimization solution using time-series forecasting and demand prediction models.
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Analyzed historical sales data to identify demand patterns and forecast future inventory requirements for each location.
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Used AWS Glue, Google BigQuery, SQL Server, Scikit-learn, and Keras to process data and build the machine learning solution.
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Optimized inventory levels based on location-specific demand, helping reduce both overstocking and stockouts.
The Business Outcomes:
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Reduced overall overstock levels by 25% across all store outlets.
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Improved product stock availability by 20% in physical locations.
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Increased total net profit margins by 10% through reduced overhead.
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Integrated custom prediction models into existing supply chain platforms.
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Maximized working capital by eliminating unnecessary inventory accumulation.
