E-Commerce Inventory Assortment Optimizer
Optimize e-commerce inventory assortment using SKU performance data
What You Can Do
Analyze SKU performance across size, color, and price dimensions to segment products into expand/maintain/reduce/discontinue categories. You'll evaluate seasonal trends, margin health, and channel-specific performance to build data-driven assortment strategies that balance selection breadth with inventory efficiency. The skill helps you identify profitable size-color-price combinations and plan phased inventory adjustments that protect margins while improving sell-through.
Features
Categorize products using velocity, margin, and seasonality metrics to identify expansion opportunities and discontinuation candidates
Uncover which size runs, colorways, and price tiers drive profitability within each product category
Map phased inventory adjustments for collection cycles with specific reorder and markdown timelines
Optimize product mix differently for DTC, marketplace, and wholesale channels based on their unique demand patterns
Flag SKUs with escalating markdown pressure and recommend discontinuation or closeout strategies
Compare your size curves, color distribution, and price tier balance against direct and indirect competitors
Calculate reorder quantities and vendor allocation priorities based on sell-through velocity by SKU
Structure raw data to identify inventory turnover gaps and dead stock aging
Example Output
Example 1: Category Review Output
- Expand: Black ponte pants (size 0-8, 8-14) — 85% sell-through, 45% margin, consistent velocity
- Maintain: Navy blazer (size 2-16) — 72% sell-through, 52% margin, seasonal spike in Q3/Q4
- Reduce: Floral print dress (all sizes except XS/S) — 38% sell-through, markdown-heavy, size 10+ below 25% conversion
- Discontinue: Cropped linen shirt in white — 18% sell-through, aged 180+ days, negative margin after markdown
Example 2: Channel-Specific Assortment
- DTC: Expand basics (black, navy, white) in core sizes (4-12); reduce novelty colors
- Amazon: Shift allocation to size XS and XXL; reduce size 6-8 depth
- TikTok Shop: Feature trending colors (sage, chocolate); reduce classic navy weighting by 25%
Example 3: Seasonal Transition Plan
- Q2 (April-May): 60% summer inventory, 40% spring clearance; pause cold-weather SKU reorders
- Q3 (June-August): Transition to 30% summer, 70% fall allocation; begin fall color depth in core silhouettes
What's Included
- SKILL.md instruction file with SKU analysis framework and decision matrices:
- SKU Performance Segmentation Template: categorize products by velocity, margin, and seasonality
- Size-Color-Price Analysis Worksheet: map profitability by size run, colorway, and price tier
- Seasonal Transition Planning Checklist: phased inventory adjustment timeline with reorder and markdown gates
- Channel Assortment Comparison Framework: segment assortment recommendations by DTC, marketplace, and wholesale
- Markdown Risk Assessment Scorecard: identify SKUs for discontinuation based on age, velocity, and margin erosion
Who It's For
- Fashion Buyers — optimize seasonal assortment decisions and balance selection breadth with inventory turnover
- Inventory Planners — allocate budget across size curves and color distributions based on channel demand
- E-Commerce Merchandisers — manage SKU depth for DTC, marketplace, and wholesale channels with data-driven prioritization
- Category Managers — conduct quarterly assortment reviews and negotiate vendor allocation based on sell-through performance
- Operations/Supply Chain Teams — improve reorder accuracy and reduce obsolete inventory through structured SKU analysis
Best For
- Seasonal collection planning — optimize size and color assortment before Q1, Q3 transitions
- Dead stock elimination — identify aged, low-velocity SKUs for discontinuation or closeout
- Channel expansion — tailor assortment for new marketplaces (Temu, TikTok Shop, Amazon) with channel-specific size curves
- Margin protection — balance expansion opportunities with margin-aware SKU recommendations
- Vendor negotiations — support allocation discussions with sell-through velocity and reorder quantity data
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