inventory-demand-planning

To forecast demand for a new SKU with no historical data, this Skill applies modeling techniques using Python libraries like numpy and scikit-learn to generate baseline predictions. It handles new product introductions by analyzing available attributes to estimate initial demand.

Calculating optimal safety stock involves analyzing demand variability and target service levels across multiple locations. This Skill uses pandas and scikit-learn to process historical data, computing the necessary inventory buffers to prevent stockouts while minimizing holding costs.

Yes, you can estimate promotional lift and plan replenishment for retail promotions. The Skill calculates the expected sales increase and forward buy quantity, while also modeling the post-promo demand dip to ensure accurate inventory replenishment planning.

Yes, this demand planning Skill works with Python machine learning libraries like XGBoost. It requires numpy, pandas, scikit-learn, and xgboost as dependencies to perform advanced data analysis and build predictive models for retail inventory management.

The best way to plan inventory replenishment for seasonal transitions is using a model-driven approach that forecasts demand shifts and adjusts safety stock. This Skill handles seasonal transitions by optimizing replenishment schedules to align with changing demand patterns.