“Forecasting demand and stock levels across categories and channels with high accuracy, to reduce both stockouts and overstocking risk for a fast-scaling apparel portfolio.”
A leading D2C ethnic Indian apparel brand operating across 10 distinct categories, offering a portfolio of 1,500+ SKUs, with over 150 new designs launched every quarter.
Inventory is critical to D2C fashion, with huge investments in production and finished goods. While a lack of inventory leads to major loss in sales, overstocking leads to cash outflow and, in some cases, sunk costs. The brand needed demand and stock forecasts across categories and channels, with high accuracy.
Engagement Tenure: 1 Year
Team: Data Scientists/Data Engineers, Business Analyst
Python, Power BI
A reduction in stockouts led to a better daily run-rate of 1.3%, relative to the industry benchmark of 1%. Better inventory prediction led to a 6.5X increase in the share of fast-moving SKUs, which grew from 1.75% to 11.33% of total SKUs. Channel- and category-specific inventory optimization also cut product update time from 2 weeks to 5 days.
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