A data science project analyzing dairy product sales data from India to predict out-of-stock situations and optimize pricing strategies.
This dataset provides a large dataframe containing data on the sale of different kinds of dairy products from different factory suppliers (Brand) from different farms and different states across several years in India. The dairy industry faces fluctuating demand throughout the year due to various factors. Out-of-stock (OOS) situations can significantly impact both suppliers and consumers. By analyzing year seasonality, price elasticity, and factory capacity, we aim to develop predictive models that can help optimize inventory management and pricing strategies.
We will create models to simulate these parameters, which would be best to suggest the best case scenario and the range of controls they need to stay within to create a stable supply of milk and avoid OOS.