Retail Forecasting Model

Generative Ecosystem

FMCG

1 min read

Our team developed and deployed a sophisticated retail forecasting model that enabled a beverage company to enhance its production planning and boost sales. By using advanced MLOps systems and a Recurrent Neural Network (RNN) architecture, we accurately predicted sales across various SKUs and regions, significantly improving forecasting accuracy and driving business growth.

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The problem overview

The client faced the challenge of forecasting sales for multiple SKUs across different regions. This task was complicated by the need to consider various factors like seasonality, holidays, sporting events, and temperature fluctuations. The client sought a solution that could not only generate accurate forecasts but also manage the complexity of running multiple models in parallel.

Our solution

To address this challenge, we implemented a robust forecasting model powered by Recurrent Neural Network (RNN) architecture. The model incorporated critical variables such as seasonality, holidays, significant sporting events, and temperature, which are crucial for predicting demand in the beverage industry. Additionally, we employed MLOps systems to manage the complexity of running hundreds of models simultaneously for different SKUs and regions.

The results

Exceptional results were achieved through innovative solutions, turning the challenge at hand into measurable success.

#1 Surpassed MAPE Target

Achieved an average Mean Absolute Percentage Error of 22% for various SKUs, exceeding the initial goal of being lower than 25%

#2 Enhanced Overall Stability

Achieved a 10% to 15% improvement in overall stability, with minimum accuracy rising from approximately 70% to around 90%.

#3 Increased Sales and Market Share

The improved forecasting capabilities directly contributed to higher sales and a larger market share.

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