Sell Through Velocity Tracker

Methodology

Step 1: Sell-Through Rate Calculation

Compute sell-through at multiple time horizons:

Sell_Through_Rate = Units_Sold / (Beginning_Inventory + Receipts) × 100

Time-based variants:

Weekly_Sell_Through = Units_Sold_This_Week / BOW_Inventory × 100
Cumulative_Sell_Through = Total_Units_Sold_Since_Receipt / Total_Units_Received × 100
Sell_Through_Velocity = Units_Sold_per_Day  [absolute rate]
Days_to_Sell_Through = Remaining_Inventory / Average_Daily_Sales  [projected clearance timeline]

Step 2: Velocity Benchmarking

Compare actual velocity against multiple benchmarks:

Velocity_Index = Actual_Sell_Through / Planned_Sell_Through × 100

Category-specific benchmarks (cumulative sell-through by end of lifecycle):

Step 3: Seasonal Curve Alignment

Compare actual sell-through progression against the expected seasonal demand curve:

Curve_Alignment_Score = 1 - |Actual_Cumulative_%_Sold - Expected_Cumulative_%_Sold|

Detect timing misalignment:

• Ahead of curve: Selling faster than seasonal pattern — likely to sell out before season end
• Behind curve: Selling slower — late-season markdown pressure building
• Phase shift: Peak selling week occurred earlier or later than expected

Seasonal_Peak_Week_Actual vs. Seasonal_Peak_Week_Expected
Peak_Shift = Actual_Peak_Week - Expected_Peak_Week  [negative = early, positive = late]

Step 4: Cohort & Lifecycle Analysis

Track sell-through by product cohort (launch week) to establish velocity norms:

Week_1_Sell_Through by cohort → establishes early velocity benchmark
Week_4_Sell_Through by cohort → validates initial signal
Week_8_Sell_Through by cohort → determines lifecycle trajectory

Early warning system:

• If Week 2 cumulative sell-through is <50% of Week 2 benchmark for the category: flag as slow mover
• If Week 2 cumulative sell-through is >150% of benchmark: flag as potential winner, expedite reorder

Lifecycle velocity expectations:

Introduction: Increasing velocity (awareness building)
Growth: Peak velocity (market adoption)
Maturity: Stable velocity (replacement demand)
Decline: Decreasing velocity (markdown/exit phase)

Step 5: Financial Impact & Action Recommendations

Quantify the financial consequences of velocity deviations:

For slow movers (below plan):

Unsold_Inventory_Risk = Remaining_Units × Unit_Cost
Markdown_Exposure = Remaining_Units × (Original_Price - Expected_Clearance_Price)
Carrying_Cost_Exposure = Remaining_Units × Unit_Cost × Carrying_Rate × Expected_Weeks_to_Clear / 52
Opportunity_Cost = Shelf_Space_Occupied × (Avg_Category_GMROI - Actual_GMROI)

For fast movers (above plan):

Stockout_Risk_Revenue = Projected_Unmet_Demand × Selling_Price
Lost_Margin = Projected_Unmet_Demand × Unit_Margin
Reorder_Urgency = Days_to_Stockout < Supplier_Lead_Time  [boolean critical flag]

Action matrix:

Output Specification

sell_through_report:
  report_date: "2026-02-07"
  portfolio_summary:
    total_skus_tracked: 3200
    on_plan_pct: 64
    outperforming_pct: 12
    underperforming_pct: 18
    critical_pct: 6
    portfolio_sell_through_rate: 72.3
    plan_target: 75.0
  fast_movers:
    - sku_id: "SKU-JACKET-2201"
      velocity_index: 185
      cumulative_sell_through: 68
      weeks_in_market: 4
      days_to_stockout: 9
      remaining_units: 340
      action: "EXPEDITE REORDER — 2,000 units from supplier, air freight justified ($3,200 premium vs. $28,000 lost margin)"
      revenue_at_risk: 48000
  slow_movers:
    - sku_id: "SKU-SWEATER-1855"
      velocity_index: 42
      cumulative_sell_through: 18
      weeks_in_market: 8
      remaining_units: 4200
      inventory_value: 126000
      markdown_recommendation:
        timing: "Immediate"
        initial_markdown_pct: 25
        expected_velocity_lift: 2.5
        projected_sell_through_after_markdown: 55
        projected_margin_recovery: 73500
      root_cause_hypothesis: "3 competing styles launched by competitor at 15% lower price point"
  seasonal_alerts:
    - category: "Winter Outerwear"
      season_week: 14
      total_season_weeks: 18
      cumulative_sell_through: 52
      target_at_this_point: 70
      gap_pct: 18
      inventory_at_risk_value: 890000
      recommendation: "Category-wide 20% markdown event needed by Week 15 to clear before spring transition"

Analysis Framework

GMROI Integration

Connect sell-through velocity to return on inventory investment:

GMROI = Gross_Margin_$ / Average_Inventory_Cost
     = (Sell_Through_Rate × Gross_Margin_%) / (1 - Sell_Through_Rate / 2) × (365 / Avg_Days_in_Stock)

A SKU with high sell-through and healthy margins = high GMROI (keep/grow). A SKU with low sell-through regardless of margin = low GMROI (fix/exit).

Velocity Decay Analysis

Track velocity trend over time using exponential smoothing:

Smoothed_Velocity_t = α × Actual_Velocity_t + (1-α) × Smoothed_Velocity_(t-1)
Velocity_Trend = Smoothed_Velocity_t - Smoothed_Velocity_(t-4)  [4-week trend]

Accelerating decay (velocity dropping faster) = urgent action needed. Stabilizing velocity after markdown = markdown was effective but watch for further decay.

Examples

Example 1 — Fast Mover Stockout Prevention

"Winter Jacket SKU-2201 launched 4 weeks ago with velocity index of 185 (85% above plan). Cumulative sell-through is 68% with only 340 units remaining. At current velocity, stockout in 9 days. Supplier lead time is 21 days (ocean). Recommendation: Place 2,000-unit reorder via air freight. Air premium is $3,200; projected lost margin from stockout is $28,000 over the remaining 6 weeks of the winter season. ROI on air freight: 775%. Action: Approved expedite."

Example 2 — Slow Mover Markdown Optimization

"Sweater SKU-1855 has velocity index of 42 after 8 weeks in market. Cumulative sell-through is 18% vs. 55% benchmark. 4,200 units remain valued at $126,000. Root cause analysis: 3 competing styles from brand X launched at 15% lower price. Without intervention, projected end-of-season sell-through is 30%, generating $45,000 in markdown losses. Recommended: immediate 25% markdown projected to lift velocity 2.5×, improving end-of-season sell-through to 55% and recovering $73,500 in margin vs. delayed clearance scenario."

Guidelines

1. Calculate sell-through using receipt-based denominators (BOH + receipts), not current inventory
2. Track velocity daily for new launches in first 4 weeks — early signal is critical for reorder decisions
3. Normalize velocity for out-of-stock days — a product with 50% in-stock rate has understated velocity
4. Compare sell-through within like groups (same category, same price tier, same season) for meaningful benchmarks
5. For fashion/seasonal products, the sell-through clock starts at season start, not receipt date
6. Factor in planned markdowns when projecting forward sell-through — full-price velocity differs from markdown velocity
7. Use GMROI, not sell-through alone, to make keep/markdown/exit decisions — a slow seller with very high margins may be worth keeping
8. Track cannibalization — a new SKU's velocity may come at the expense of existing SKU velocity
9. Integrate velocity data with open-to-buy decisions — shift future budget from slow categories to fast ones

Validation Checklist

• Sales data excludes returns (net sales, not gross)
• Beginning inventory is accurate (reconciled with physical counts or WMS)
• Receipts include all inventory additions (POs, transfers in, return-to-stock)
• Seasonal curves are updated for current year trends, not just prior year replication
• Benchmarks are category-appropriate (don't compare fashion apparel to basic replenishment)
• Velocity index calculations handle zero-inventory periods (exclude from denominator)
• Markdown recommendations account for markdown budget and approval workflows
• Fast-mover reorder recommendations confirm supplier capacity and lead time feasibility
• Financial projections use current margin rates, including any cost changes
• Lifecycle stage assignments are current and accurate for each SKU