Part 11/11:

• Handling Exogenous Variables & Store Openings: Static signals like holidays are encoded once annually, while dynamic data such as daily sales and inventory are updated daily. For new stores, models may rely initially on national or regional patterns until sufficient local data is collected.

• Store Clustering: Retailers also explore clustering stores based on attributes to improve forecasting accuracy and tailor inventory strategies further.

In essence, this presentation showcases how cutting-edge data science and machine learning are reshaping inventory management in retail, balancing technological innovation with practical constraints to make shelves smarter and customers happier.