Overview

The client is a large retail company managing thousands of stock-keeping units across its inventory. Accurately predicting demand was difficult because purchasing patterns were affected by seasonality, market trends, promotions, and other external factors.

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Inaccurate forecasts regularly resulted in stockouts of in-demand products and excess inventory of slower-moving items. HBM developed an AI-powered forecasting system that analyzed historical sales, market trends, and external data to generate more accurate, granular demand forecasts and support better inventory decisions.

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Client Needs and Challenges:

Demand forecasting at scale: The client needed accurate forecasts across thousands of SKUs while accounting for seasonality, market trends, promotions, and other external factors.
‍Inventory imbalance: Inaccurate predictions caused stockouts of in-demand products and excess inventory of slower-moving items.
‍Revenue optimization: The client needed better forecasting insights to improve product availability and capture more sales.

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Services Delivered

AI forecasting algorithm: Developed machine learning models that combined historical sales, market trends, and external data to produce granular SKU-level demand forecasts.
‍Solution implementation & Integration: Built the forecasting system using Python, TensorFlow, and scikit-learn to support purchasing and stock allocation decisions, and integrated into company’s existing infrastructure and working tools.

Outcomes

20% reduction in stockouts: The forecasting solution improved the availability of in-demand products.
15% decrease in excess inventory: More accurate demand predictions reduced surplus stock.
30% increase in revenue from optimized stock: Better product availability helped the client capture more sales.

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Improving Retail Demand Forecasting with AI

Overview

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Client Needs and Challenges:

Services Delivered

Outcomes

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