Case Study
SKU Portfolio Audit
SKU rationalization · portfolio optimization · contribution margin scoring
Nineteen of Cinderhaven’s fifty SKUs scored as kill candidates. Another thirteen as fix-or-kill. Each one earns less for its shelf space and production complexity than the rest of the portfolio.
Most SKU decisions happen by feel — the founder likes the product, a retailer asked for it, it’s been in the line since launch. The numbers that would make the decision clear (velocity, contribution margin, shelf-space cost, production complexity, cannibalization risk) rarely get scored together. So the worst SKUs survive on the strength of one flattering metric while quietly dragging the portfolio.
The worked example is Cinderhaven Provisions — a synthetic specialty food brand doing $33.2M in trailing-twelve-month retail scan revenue across 50 SKUs, 5 product lines, and 6 contracted retailers. The data is invented so the methodology can be shown in full. The scoring framework, the four-bucket classification, and the per-SKU action levers are exactly what a real engagement produces — and the results are genuine outputs of the pipeline run on that synthetic data, real as computed, not as a client’s past results.
What the audit finds
The Cinderhaven SKU audit scored all fifty SKUs across five dimensions and classified each into one of four action buckets:
kill
Weak on two or more counts: slow sales, high shelf cost, heavy production complexity, or a thin margin next to the rest of the portfolio. Cutting these frees production capacity and shelf space for the SKUs that earn it.
fix-or-kill
Viable products with one broken dimension. Each gets a specific lever: a price adjustment, a distribution change, a packaging consolidation. If the fix doesn’t move the score within a quarter, it’s a kill.
maintain
Solid performers, no action needed.
double-down
High velocity, strong margin, room to expand distribution.
The velocity gradient across lines is what makes the scoring honest: Artisan Sauces (the flagship) runs at nearly twice the velocity of Snack Bites (the newest line). None of the Snack Bites SKUs made the kill list. Five are fix-or-kill, mostly because they haven’t earned enough distribution yet, and SB-002 scores as a double-down — the framework separates “kill” from “not yet.”
See it worked through
SKU rationalization framework
Multi-dimensional scoring and visualization. Every SKU scored, classified, and mapped across six retailers over a three-year window.
sku.lailarallc.com →Velocity decision tool
Eight operating decisions, including SKU rationalization, answered from weekly scan data.
velocity.lailarallc.com →What you get
A full scored output for your portfolio: the kill list with quantified annual savings, a fix-or-kill action plan with one specific lever per SKU, and the methodology and queries your internal team needs to re-run it quarterly. This isn’t a one-time cleanup — it’s a decision framework you keep.
After the audit
An audit is a snapshot; defects are a flow. The findings stay fixed when validation runs where the defects enter — see the Validation Pipeline Build →
Next step — Which SKUs and channels actually earn
Get the number for your own data. Seven business days.
The worked example above is synthetic so the method can be shown in full. The offers below run it on your data — the Snapshot credits in full toward the audit.
Private, expiring upload — never email. Mutual NDA before anything moves. Files destroyed within 30 days of delivery, with a certificate. Methods published, tools open source.
Start in writing.
A few minutes by form — no call. Tell me about your portfolio and which SKUs you suspect. I’ll tell you how to score them and what a scoped audit looks like. No deck, no obligation.