SKU Rationalization Advisor

Overview

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.

When to Use

• Annual assortment reset or planogram reset cycle
• Warehouse or shelf-space capacity constraints require SKU reduction
• Category has grown to excessive SKU count through years of additions without removals
• Supply chain or procurement team flags complexity costs
• Preparing for private label expansion (need shelf space for new PL items)
• Post-acquisition portfolio integration requiring duplicate elimination
• User provides SKU-level data and asks which items to cut

Required Inputs

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

Methodology

Step 1: SKU Performance Scoring

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)

Step 2: Strategic Role Assessment

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

Step 3: Redundancy & Cannibalization Analysis

Identify clusters of overlapping SKUs:

1. Group SKUs by attribute similarity (same brand + same segment + similar price ± 10%)
2. Within each cluster, calculate Incremental Contribution: revenue that would be lost if the SKU were removed (net of substitution to remaining SKUs)
3. Apply a Substitution Rate assumption: typically 40–65% for CPG (i.e., 40–65% of a delisted SKU's sales transfer to remaining items)

Net Revenue at Risk = SKU Revenue × (1 − Substitution Rate)

Step 4: Action Classification

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

Step 5: Impact Simulation

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.

Output Specification

1. SKU Action Summary

SKU	Brand	Segment	Revenue	Composite Score	Strategic Role	Action	Net Revenue Risk	Substitute SKU
—	—	—	—	—	—	Keep/Delist/Consolidate/Replace	—	—

2. Portfolio Impact Dashboard

• Total SKUs: current → proposed
• Revenue at risk (gross) and retained (net of substitution)
• Complexity cost savings
• Net margin improvement
• Shelf space / warehouse slots freed

3. Delist Candidate Detail Cards

For each delisted SKU:

• Performance scores breakdown
• Top 3 substitute SKUs and substitution rationale
• Customer impact assessment (loyalty segment affected)
• Supplier impact (any volume commitments or deductions at risk)
• Recommended exit timeline

4. Consolidation Recommendations

Clusters of SKUs to merge, with proposed surviving SKU and rationale.

Analysis Framework

Key Metrics

• SKU Productivity: Revenue per SKU (benchmark: top quartile > 2× category average)
• Pareto Ratio: % of revenue from top 20% of SKUs (benchmark: 75–85%)
• Long-Tail Burden: % of SKUs contributing < 0.1% of category revenue
• Inventory Turns by SKU: Turns < 4×/year in center store signals overstock or low demand
• Incremental Contribution: Revenue uniquely attributable to the SKU (not transferable)
• Complexity Cost per SKU: Fully loaded cost including procurement, warehousing, handling, shelf management
• Customer Reach: % of total buyers who purchased the SKU in the last 52 weeks

Decision Thresholds

• Delist threshold: Bottom 15–25% of SKUs by composite score, unless strategically protected
• Minimum viable assortment: At least 1 SKU per decision-tree node that has demonstrated demand
• Maximum delist batch: Recommend no more than 15% of SKU count in a single reset to manage disruption
• Substitution rate validation: If observed switching data is available, use it; otherwise apply category-level default (50%)

Examples

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:

1. Scoring: 1,200 SKUs scored; 187 score below 20 (delist zone), 124 score 20–40 (replace zone), remaining 889 score > 40
2. Redundancy: 93 SKU clusters identified with high overlap; 156 SKUs are redundant within clusters
3. Recommendations: Delist 168 SKUs, consolidate 84 into 42, replace 48 → net reduction of 258 SKUs (to 942)
4. Impact: Gross revenue at risk $4.2M (3.8%); estimated retained after substitution $2.9M; net loss $1.3M (1.2%). Complexity savings: $2.1M/year. Net benefit: +$0.8M/year.
5. Top delist clusters: 14 niche air freshener variants (0.3% of revenue, 1.2% of SKUs), 22 legacy packaging sizes in dish soap, 11 private label duplicates with identical formulations

Guidelines

• Never recommend delisting the only SKU serving a valid consumer need state — that creates an assortment gap
• Apply a "buyer count" safety check: if a SKU has > 2% of category buyers, flag for manual review even if scores are low
• Consider seasonal SKUs separately — annualized metrics understate their in-season importance
• Respect contractual and promotional commitments; flag timing constraints for delistments
• Communicate delist recommendations with sensitivity: provide data-backed rationale, not just scores
• Stagger delistments over 2–3 reset windows rather than a single mass cut
• Monitor post-delist performance for 8–12 weeks; have contingency to relist if substitution doesn't materialize
• Always pair rationalization with gap-fill recommendations (use Assortment Gap Analysis skill) — rationalize and renovate simultaneously

Validation Checklist

• Every SKU has been scored on all four dimensions
• Strategic roles are assigned and protection levels applied
• Redundancy analysis uses attribute-based clustering, not just performance data
• Substitution rates are documented and defensible
• Net revenue impact is calculated (not just gross delist revenue)
• Complexity cost savings are estimated
• No single-representative segments are left empty after delistments
• Seasonal SKUs are evaluated on in-season performance
• Supplier and contractual constraints are flagged
• Delist count stays within the recommended maximum per reset cycle