Case Study
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Our retail forecasting model successfully predicted sales of various SKUs and Regions, allowing our customer to plan their production schedule in a timely manner. This, in turn, enabled them to supply more products to their customers, resulting in increased sales and market share.
Creating multiple forecasts for various SKUs is a highly complex task that requires effective MLOps systems capable of generating and managing multiple models concurrently.
By incorporating variables such as seasonality, holidays, significant sporting events, and temperature, we developed a robust forecasting model that accounted for significant fluctuations in these factors and accurately predicted end-customer demand for our client, a beverage company.
Our forecasting model leverages the power of a Recurrent Neural Network architecture, which allows it to effectively analyze past data and account for significant variations. The model is also highly resilient to outliers in certain SKUs, as it can incorporate both endogenous and exogenous data to generate accurate predictions.
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Data Collection
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Baseline Model Produced
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Model Deployment
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