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SKU portfolios grow faster than they shrink. A SKU Rationalization Playbook reads sales velocity, contribution margin, inventory cost, and operational overhead to identify the long-tail SKUs draining working capital and operational attention, ranked by retirement impact.
The Challenge
Product teams add SKUs to chase opportunities. The retirement process is heavier than the addition process, so the count climbs continuously. Each marginal SKU consumes a slice of working capital, warehouse space, planning attention, and reporting overhead.
A category total looks healthy because the top SKUs deliver the margin. Inside the same category sit hundreds of long-tail SKUs that consume operational overhead disproportionate to their contribution. Aggregate reporting hides the structural waste.
Every long-tail SKU has a stakeholder who values it (a sales rep, a customer relationship, an account exception). Without quantified retirement-impact data, those defenses win by default and the SKU stays. The aggregate retirement value never gets captured.
How eyko Solves It
A SKU Rationalization Playbook reads sales velocity, contribution margin, inventory carrying cost, operational overhead (planning, warehouse, reporting), and customer-attachment data to score every SKU on retirement value. It ranks retirement candidates by net benefit, surfaces stakeholder-attachment patterns where a SKU has a few important customers, and recommends retirement sequences that capture working capital and operational savings without breaking customer relationships.
The Playbook scored 4,200 SKUs by retirement impact. 680 SKUs sit in the long tail with contribution below operational overhead. Retiring the bottom 240 SKUs (the worst tail with no high-value customer attachment) projects $3.6M in freed working capital and $1.2M in annualized operational savings. 84 long-tail SKUs have customer-attachment patterns where retirement requires a relationship conversation rather than a clean prune.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
SKU Rationalization scores every SKU by retirement value using sales velocity, contribution margin, inventory carrying cost, operational overhead, and customer-attachment data. The Playbook ranks retirement candidates by net benefit, surfaces stakeholder-attachment patterns, and recommends retirement sequences that capture working capital and operational savings without breaking customer relationships so portfolio shape stays disciplined.
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FAQ
Everything you need to know about SKU Rationalization Map.
SKU Rationalization is an AI-driven scoring of every SKU by retirement value using sales velocity, contribution margin, inventory carrying cost, operational overhead, and customer-attachment data. The Playbook ranks retirement candidates by net benefit, surfaces stakeholder-attachment patterns, and recommends retirement sequences that capture working capital and operational savings without breaking customer relationships.
The Playbook reads from your ERP or sales system (per-SKU velocity, customer purchase history), finance system (contribution margin, inventory carrying cost), warehouse system (storage cost per SKU), planning system (planning overhead per SKU), and CRM (customer-attachment data on long-tail items). At least 18 months of paired SKU-and-cost data anchors the retirement scoring.
A slow-mover report flags SKUs by velocity alone. SKU Rationalization scores by net benefit including inventory carrying cost, operational overhead, and customer-attachment value. A slow-moving SKU with high customer attachment may be worth keeping; a moderately-moving SKU with low margin and high overhead may be a retirement candidate. The two are complementary, but net-benefit scoring is what produces actionable retirement decisions rather than just a slow-mover list.
Yes. The Playbook surfaces SKUs with customer-attachment patterns separately and recommends customer conversations before retirement, with substitute-product recommendations where the catalog supports them. The customer-attached cohort gets a different retirement motion than the clean tail, so the program preserves relationships while still capturing the working capital and operational savings from the broader retirement set.
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