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MsShawnP/short-ship-cost

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Short-Ship Cost Analysis — Cinderhaven Provisions

What does it cost when you can't fulfill orders as submitted? This tool traces every dollar of fulfillment shortfall cost to a specific platform event — a shipment line where units ordered exceeded units shipped.

Cinderhaven Provisions is a fictional ~25ドルM specialty food brand. The dataset is synthetic. The methodology is real. Every figure regenerates from a single pipeline against the Cinderhaven Data Platform (Postgres).

Live: https://shortships.lailarallc.com

What it finds

At a 92.7% portfolio fill rate, Cinderhaven loses 6ドル.6M over three years (2ドル.2M/yr) across four cost dimensions:

Dimension 3-Year Annual % of Shipped
Forgone revenue 5ドル.5M 1ドル.8M 7.85%
Compliance fines 369ドルK 123ドルK 0.52%
Chargebacks 344ドルK 115ドルK 0.49%
Deductions 331ドルK 110ドルK 0.47%

Every dollar traces to a platform event. No modeled soft costs, no forward projections, no assumed admin time.

Forgone contribution margin — the actual profit impact — runs 958ドルK/yr, roughly 52% of the forgone revenue figure.

The Costco finding

Costco generates 76% of all compliance fines (281ドルK of 369ドルK) because of its 250ドル flat fee per any-short PO. At 92% fill, 17% of Costco POs have at least one shorted line. Improving Costco fill from 92% to 95% is the single highest-return operational fix in the analysis.

Buffer simulation

The tool models what happens as fill rate improves:

Target Total Cost Recovery Recovery %
Baseline (92.7%) 6ドル.6M
95% 1ドル.2M 5ドル.4M 81.5%
97% 835ドルK 5ドル.7M 87.3%
98% 640ドルK 5ドル.9M 90.3%
99% 411ドルK 6ドル.2M 93.8%

Moving from 92% to 95% fill recovers 81% of the total shortfall cost. Diminishing returns set in above 97%.

Data contract

Consumes the Cinderhaven Data Platform directly:

  • fct_retailer_shipment_lines / fct_distributor_shipment_lines — units ordered vs shipped per line
  • raw.retailer_chargebacks / raw.distributor_chargebacks where reason = 'short_ship'
  • raw.retailer_deductions / raw.distributor_deductions where deduction_type = 'short_ship'
  • raw.sku_costs — COGS for contribution margin calculation
  • Compliance fines modeled from published retailer schedules (Walmart 3% of COGS, Costco 250ドル flat, etc.)

50 SKUs, 5 product lines, 6 retailers, 3 distributors. Canonical reference: CINDERHAVEN_CANONICAL.md.

Stack

  • Frontend: React 19, Vite
  • Charts: D3 / custom SVG
  • Data pipeline: Python → JSON from platform Postgres
  • Deployment: Cloudflare Workers

Run locally

npm install
npm run dev

To regenerate data from the platform:

python scripts/rebuild_from_platform.py

Requires a flyctl proxy to the Cinderhaven database.

What this replaced

This tool previously generated its own synthetic orders at a 69% fill rate, producing a 33ドル.1M cost figure across 8 dimensions. The plausibility audit found that figure was indefensible — three incompatible fulfillment realities coexisted in the portfolio. The rebuild replaced the synthetic engine with direct platform queries. 33ドルM became 6ドル.6M. Eight dimensions became four. Every dollar now has a receipt.


Built by Lailara LLC — data hygiene and analytics consulting for specialty food brands scaling into national retail.

License

MIT — see LICENSE.

About

Interactive analysis tool that quantifies the full cost of short-shipping orders for a specialty food brand. Eight cost dimensions, adjustable parameters, buffer simulation, and exportable PDF. React + Python.

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