retail-allocation

Optimize retail inventory allocation by clustering similar stores, grading performance, and forecasting demand to distribute SKUs. This ensures the right product quantities reach each location, maximizing sales while minimizing markdowns and waste across the network.

Store clustering groups similar stores based on demographics and sales, while store grading ranks locations by performance. These techniques simplify retail allocation by tailoring inventory depth and product mix to predicted store-level demand.

Yes, you can allocate size-specific distributions for fashion SKUs using Python. The process applies size curve optimization to adjust the mix of sizes allocated to each store, matching specific customer profiles and sales volume.

Yes, you need pandas, numpy, and scikit-learn to run this algorithmic store inventory distribution. These Python libraries provide the foundational data analysis, mathematical operations, and machine learning capabilities required for demand forecasting and store clustering.

The best way to distribute new merchandise is demand-based allocation. It distributes inventory proportionally to predicted store-level demand, considering customer size preferences and sales volume to maximize sales and minimize markdowns.

Size curve optimization adjusts the mix of sizes allocated to a store based on its specific customer profile. It ensures that size-specific distributions match local preferences, preventing stockouts of popular sizes and excess inventory of slow-moving ones.