How I Built a Python Demand Forecasting Model That Saved $18,000

1. 1Collect historical sales data using SQL to analyze trends and seasonality
2. 2Build a demand forecasting model using Python libraries such as Pandas, NumPy, and Scikit-learn
3. 3Configure the model to account for seasonal fluctuations and anomalies
4. 4Test the model using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE)
5. 5Apply the model to forecast demand and optimize inventory management

Data scientists and operations teams can benefit from this approach to optimize inventory management and reduce costs

💡 Accurate demand forecasting can significantly reduce emergency procurement costs and optimize inventory management

💡 Built a Python demand forecasting model that saved $18,000 in emergency procurement costs!