Fashion Retail: Turnover from 64 to 41 Days in 90 Days

Company Overview

A fashion distributor and retailer managing 6 stores and 2 warehouses. The assortment covers approximately 2,000 SKUs including clothing, footwear, and accessories. The business is highly seasonal, with spring/summer and fall/winter collections operating within tightly defined selling windows.

The Challenge

The core challenge was managing seasonal collections. The company repeatedly found itself in situations where:

• Markdowns launched too late, when products had already lost relevance
• Bestselling styles sold out weeks before the season ended
• Different stores had dramatically different stock levels for the same items
• Inter-store transfer decisions were made on gut feeling

Average inventory turnover was 64 days — with seasonal cycles of 120–150 days, this meant nearly half the merchandise remained unsold by the time collections changed. End-of-season write-offs reached 12% of purchase cost.

The AI Analysis & Solution

The platform processed 24 months of sales data and classified every SKU by its seasonal pattern:

Seasonal Classification

Key Findings

• 420 SKUs (21%) had stock exceeding the remaining selling window — meaning they were guaranteed not to sell at full price
• Location mismatch: 4 of 6 stores showed simultaneous stockouts and overstock in the same categories, just different sizes
• Late markdown start: historically the company began markdowns 2 weeks before season end. The AI recommended a differentiated approach — from 6 weeks for peak items to 2 weeks for basics

Recommendations

The platform generated 247 recommendations:

• 156 early markdowns — products where starting markdowns immediately would maximize recovery
• 43 inter-store transfers — redistribution of stock between locations to balance inventory
• 28 urgent purchases — bestsellers with only 2–3 weeks of supply remaining
• 20 assortment recommendations — categories to expand or reduce in the next season

Results After 90 Days

Key Takeaway

In fashion retail, time is the critical resource. Every day of delay on markdowns reduces recovery. AI seasonality classification enabled a shift from reactive ("season's over — discount everything") to proactive, where each SKU gets an optimal action window. The result — 36% faster turnover and half the write-offs.