  In this paper, we describe a novel solution to compute optimal warehouse
allocations for fashion inventory. Procured inventory must be optimally
allocated to warehouses in proportion to the regional demand around the
warehouse. This will ensure that demand is fulfilled by the nearest warehouse
thereby minimizing the delivery logistics cost and delivery times. These are
key metrics to drive profitability and customer experience respectively.
Warehouses have capacity constraints and allocations must minimize inter
warehouse redistribution cost of the inventory. This leads to maximum Regional
Utilization (RU). We use machine learning and optimization methods to build an
efficient solution to this warehouse allocation problem. We use machine
learning models to estimate the geographical split of the demand for every
product. We use Integer Programming methods to compute the optimal feasible
warehouse allocations considering the capacity constraints. We conduct a
back-testing by using this solution and validate the efficiency of this model
by demonstrating a significant uptick in two key metrics Regional Utilization
(RU) and Percentage Two-day-delivery (2DD). We use this process to
intelligently create purchase orders with warehouse assignments for Myntra, a
leading online fashion retailer.
