AI-powered application to predict demand for retail product line.
Client
Retail company offering fashion products manufactured in-house as well as items produced by the resale partners.
Objective
Improve the accuracy of the sales forecasting process for client’s most popular product lines. Existing COTS (commercial off-the-shelf) demand forecasting software generated unreliable numbers that significantly differed from the actuals.
Solution
Our data science team
analyzed the quality of the sales department data,
cleansed and labeled the data set to ensure adequate fit for the machine-learning engine,
experimented with a number of ML algorithms and identified the best match,
and finally built the AI service that greatly exceeded the accuracy of the existing forecasting software.
Once the initial ML model has been developed, we implemented the automated retraining mechanism to ensure the Service keeps improving on its own when new data becomes available.
Result
Product sales prediction process got a major accuracy boost which improved company operations by a significant margin.
