sku-profitability

SKU Profitability

Generated skill (example). This is the kind of bespoke skill fpa-learn-business proposes when the business profile says "product company with a discrete SKU set." It lives in skills/generated/ - in a real engagement it would be written into the client's repo after human approval, citing the profile facts that justify it (here: a limited-SKU D2C brand where per-product economics drive the mix decision).

Overview

The channel-level forecast tells you the business is healthy; it doesn't tell you which products carry it. This skill computes per-SKU economics and the Pareto curve so you can see the 80/20, find margin-dilutive SKUs, and make cut/reprice/push calls.

Core principle: Revenue flatters; margin and contribution decide. Rank by gross profit, not by sales.

When to use

• "Which products actually make money?" / "what should we cut?"
• Product-mix, pricing, or assortment-rationalization decisions
• Any product business with a discrete SKU set (especially limited-SKU brands)

Workflow

1. Load the SKUs (annual units, price, unit cost):

   import pyfpa
   skus = pyfpa.load_skus("examples/ridgeline/skus.yaml")   # or build [Sku(...)] inline
   df = pyfpa.sku_profitability(skus)
   

   df is sorted by gross profit (desc), indexed by SKU, with columns: units, revenue, cogs, gross_profit, gross_margin, revenue_share, cumulative_revenue_pct.

2. Find the 80/20:

   n = pyfpa.pareto_breakpoint(df, threshold=0.8)   # SKUs that make 80% of revenue
   

3. Read the signals:

   • Top of the list (high gross profit) - protect and push these.
   • High revenue, low gross_margin - reprice or renegotiate cost; they're buying share with your margin.
   • Low revenue_share AND low margin - candidates to cut (carrying cost without contribution).
   • The Pareto tail - if the bottom SKUs add complexity (SKUs to manage, inventory to hold) without margin, rationalize them.