Supply Chain Analytics Case Study – Boost Sales & Revenue2021-08-02T08:26:12+00:00

30pp improvement in accuracy of demand forecasting models using ML algorithms

Delivered a demand forecasting solution to improve sales, shorten planning cycle, reduce stock-outs, and optimize inventory management solutions

Business Challenges

The client is a global healthcare and pharmaceuticals company and wanted to create a demand forecasting solution that works for different categories of products. Their existing system lacked a single source of true forecast that could be consumed across different business teams. They needed to shorten the current forecasting cycle and make the process more efficient.

Sigmoid’s Solution

Sigmoid improved their existing forecast solution using ML algorithms to improve WAPE, BIAS, and coverage. The process involved removing the seasonality and trend from the products to get base sales. The algorithm catered to different patterns in the data like non-linearity, high volatility, extreme values, non-stationarity, sudden peaks, and troughs, etc. Sigmoid also developed new models using ML algorithms for new product launches using SKU-SKU similarity and pattern matching based on their sales values.

Business Impact

Created an MVP for 3 product categories with improved accuracy of demand forecasting models by 30 percentage points.

For detailed understanding and solution, please download the case study here

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