Evaluates and recommends SKU-level actions (keep, delist, consolidate, replace) to optimize assortment efficiency, reduce complexity costs, and improve inventory productivity. Use when the user wants to reduce SKU count, identify underperforming items, streamline assortment, or conduct a portfolio pruning exercise. Triggers on requests about SKU rationalization, tail-SKU analysis, assortment pruning, delisting candidates, or long-tail optimization.
SKU Rationalization Advisor systematically evaluates every SKU in a category or portfolio against performance, strategic, and operational criteria to recommend keep/delist/consolidate/replace actions. The goal is to reduce complexity costs and free shelf space or warehouse capacity for higher-potential items without sacrificing meaningful consumer choice.
In CPG retail, the long tail is expensive. Industry analysis shows that the bottom 20% of SKUs typically contribute only 1–3% of revenue but consume 15–25% of inventory carrying costs, supply chain complexity, and shelf space. However, naive pruning destroys value—some low-velocity SKUs serve as strategic traffic drivers, assortment signals, or niche segment anchors. This skill applies a multi-criteria framework to distinguish truly unproductive SKUs from strategically necessary ones.
| Input | Required | Description |
|---|---|---|
| SKU master list | Yes | Full catalog with attributes: brand, size, flavor, segment, pack, price |
| Sales data (12+ weeks) | Yes | Units, revenue, and ideally margin by SKU; 52 weeks preferred |
| Inventory data | Recommended | Average inventory $, turns, days of supply, stockout rate |
| Margin/cost data | Recommended | Gross margin $, gross margin %, COGS, landed cost |
| Distribution data | Recommended | Store count, %ACV, or warehouse allocation |
| Substitution/switching data | Optional | Which SKUs shoppers switch to when one is unavailable |
| Supplier/vendor data | Optional | Supplier dependencies, exclusive arrangements, MOQ requirements |
| Strategic item flags | Optional | Items with contractual obligations, brand commitments, or strategic role |
Score every SKU on four dimensions (each 0–100):
A. Revenue Contribution Score
Revenue Score = (SKU Revenue / Category Revenue) × 10,000
Normalize to 0–100 scale. Pareto benchmark: top 20% of SKUs should score > 60.
B. Profitability Score
Profit Score = (SKU Gross Margin $ / Top-SKU Gross Margin $) × 100
If margin data unavailable, use ASP relative to category average as proxy.
C. Velocity Score
Velocity Score = (SKU Units per Store per Week / Category Avg Units per Store per Week) × 100
Cap at 100. Measures inventory productivity and consumer demand intensity.
D. Trend Score
Trend Score = 50 + (SKU YoY Growth Rate − Category YoY Growth Rate) × 5
Centered at 50; above 50 = outperforming category trend, below = underperforming. Cap at 0–100.
Composite Score (weighted):
Composite = (Revenue × 0.30) + (Profit × 0.30) + (Velocity × 0.25) + (Trend × 0.15)
Assign each SKU a strategic role that may override pure performance scores:
| Role | Definition | Protection Level |
|---|---|---|
| Traffic Driver | Generates store/site visits; high search volume, destination item | High — keep even if margin is low |
| Basket Builder | High attach rate; frequently bought with other items | Medium-High |
| Margin Anchor | Above-average margin %; may have low velocity | Medium |
| Assortment Signal | Represents a segment that defines category credibility | Medium — keep at least 1 SKU |
| Niche/Loyalty | Small but fiercely loyal customer base; high repeat rate | Medium — evaluate switching risk |
| Filler/Redundant | No unique role; interchangeable with other SKUs | Low — primary delist candidate |
Identify clusters of overlapping SKUs:
Net Revenue at Risk = SKU Revenue × (1 − Substitution Rate)
Based on Composite Score, Strategic Role, and Redundancy:
| Composite Score | Strategic Role | Redundancy | Recommended Action |
|---|---|---|---|
| > 60 | Any | Any | Keep — core performer |
| 40–60 | Traffic/Basket/Signal | Low | Keep — strategically important |
| 40–60 | Filler | High | Consolidate — merge with stronger variant |
| 20–40 | Any non-critical | High | Replace — swap for higher-potential alternative |
| < 20 | Filler/Redundant | Any | Delist — remove from assortment |
| < 20 | Niche/Loyalty | Low | Review — manual decision required |
Simulate the impact of recommended delistments:
Total Delist Revenue = Σ (Delisted SKU Revenue)
Retained Revenue = Σ (Delisted SKU Revenue × Substitution Rate)
Net Revenue Loss = Total Delist Revenue − Retained Revenue
Complexity Savings = # SKUs Removed × Cost per SKU (inventory carry + handling + space)
Net Impact = Complexity Savings − Net Revenue Loss
Industry benchmark: Cost per SKU ranges from $5,000–$50,000/year depending on category and supply chain complexity.
| SKU | Brand | Segment | Revenue | Composite Score | Strategic Role | Action | Net Revenue Risk | Substitute SKU |
|---|---|---|---|---|---|---|---|---|
| — | — | — | — | — | — | Keep/Delist/Consolidate/Replace | — | — |
For each delisted SKU:
Clusters of SKUs to merge, with proposed surviving SKU and rationale.
Input: "We have 1,200 SKUs in our Home Cleaning category. Management wants to reduce to 900 SKUs while maintaining at least 95% of current revenue. Here's our 52-week sales, margin, and inventory data."
Output: