Portfolio

Engagement case studies, free tools, open-source libraries, and the internal tooling that powers the practice. Different problems, different tools — the common thread is data that hasn’t been cleaned, validated, or interpreted yet.

Engagements

Worked examples on synthetic data. The methodology, deliverables, and dollar figures are real — the company is invented so the work can be shown in full. Every engagement sits somewhere on one arc: recover the money already leaking, prevent the repeat, preserve the margin.

Product Data Health Audit

Every product record traced against GS1 standards, every downstream failure tracked to the field that caused it, annualized cost in dollars.

Trade Spend & Deduction Recovery

Where the waste is, which deductions are disputable, what's recoverable, and a prioritized action plan.

Chargeback Prediction & Prevention

Chargebacks harmonized into root causes, attributed to the data state at ship time, and ranked into a prevention roadmap by dollars prevented. Proves which data conditions cause the bill and prices each fix.

Remittance Stub Parsing

Four retailer remittance formats parsed into one reconciled deduction ledger. Arithmetic validation flags only the stubs that do not balance, while the dispute window is still open.

Monday Morning Report

A weekly operating rhythm for founders: three numbers, the same three every Monday, tiered to revenue, that catch the cash crunch and the velocity slide while they are still cheap to fix.

Fulfillment & OTIF Diagnostic

Reconciles internal fulfillment metrics against retailer scorecards, quantifies the gap, attributes root causes.

Channel Profitability & Capital Allocation

Which channels earn after all costs, where revenue-to-cash leakage concentrates, whether capital flows to the right shelf. Retailer-level cost-to-serve scoring with renegotiation simulation.

Where the Money Comes From

An interactive walk from gross revenue to contribution across 10 channels, ending in a priced answer: where does the next $1M of growth actually pay? The revenue leader is rarely the contribution leader.

SKU Portfolio Audit

A scored kill list with quantified savings, a fix-or-kill action plan with one lever per SKU, methodology to re-run quarterly.

Retail Readiness & Launch

Readiness across eight dimensions for a specific retailer launch. Cash-flow modeling, GTIN validation, EDI preflight, gap-closing action plan.

Trade Promotion Leakage Audit

Forensic match of promo plan to deductions to settlements. Double-funded promos, phantom promos, rate discrepancies — found, priced, and prevented.

Production Demand Forecast & S&OP

Demand signals, capacity constraints, and seasonality in one model. What the next launch does to existing commitments, before you accept the PO.

Validation Pipeline Build

The audit finds the problems once; the pipeline stops them forever. Automated validation, item-setup preflight, exception protocols your team owns.

The Blast Radius — Recall Traceability

When a shared-ingredient lot touches multiple production runs, a recall isn’t one batch — it’s every batch that lot ever entered. Scenario B expands a one-day recall into 23 affected lots, 14 SKUs, six retailers, and 5,785 cases in channel: $52K–$81K in retrieval before legal or brand cost. The variable that sets the blast radius isn’t ingredient risk — it’s whether the lot genealogy exists when the call comes in.

Door Math — Distribution Penetration Tracker

Tracks which stores are actually carrying a brand’s products versus which were authorized — the gap where revenue dies. Built on Cinderhaven synthetic data (50 SKUs, 6 retailers, 640 doors). Four views: door count with authorization-to-scan gap analysis, ACV% and TDP trend lines, exception drill-down, and a printable buyer-meeting scorecard.

Spin Rate — Penetration × Velocity Quadrant

Once a brand is on the shelf, is it actually selling — or wide but dead? Plots every item by distribution penetration (x) against velocity/SPPD (y), bubble-sized by dollars, across four quadrants: stars, hidden gems, wide-but-dead, and question marks. Ranked expansion and at-risk lists, plus a quadrant-migration view showing what moved since last period. Built on Cinderhaven synthetic data (50 SKUs, 6 retailers, 640 doors).

Void Finder — Authorized but Not Selling

Finds every store where an item is authorized but not scanning, classifies each void as never-scanned or went-dark, and prices the gap from the median velocity of comparable stores in the same volume tier and region. Output is a ranked, broker-ready work list with store numbers and addresses — including a regional never-scanned cluster traced to a single botched shelf reset. Built on Cinderhaven synthetic data (50 SKUs, 6 retailers, 640 doors).

Decompose — Which Lever Moved Sales

Splits a brand's period-over-period sales change into the only three levers that can move it — buying households × purchase frequency × spend per trip — and reconciles them exactly to the sales delta with a Shapley waterfall. The demo catches growth that's actually erosion: sales up $1,954 while penetration falls 2.2 points and price carries the entire number. Board-ready verdict computed from the data, never scripted. Built on a seed-locked synthetic household panel.

Leaky Bucket — Trial vs Repeat Analyzer

Of the households that tried you, how many came back — and is your penetration growth real adoption or just expensive sampling? Two launches side by side: a big trial spike with ~14% repeat (a promotion that looked great in month one) against modest trial with 51% repeat (the quiet winner). Cohort retention triangle with right-censoring shown, not smoothed; every summary number applies a maturity cutoff. The integrity check on Decompose — #3 counts the buyers, #4 says whether they stuck. Built on a seed-locked synthetic household panel.




Open Source

Published on PyPI. Install with pip.

Data Hygiene Auditor

CLI that audits Excel files for mixed formats, misused fields, placeholder floods, and phantom duplicates.

datascope

Data profiling and quality scoring for tabular datasets. Summary statistics, anomaly detection, quality reports.

Cinderhaven Data Platform

Modern data platform for CPG data shapes: source-to-mart pipelines, quality testing, orchestration, lineage. Built around Cinderhaven, a synthetic demonstration dataset — not a client.

Dimension & Weight Integrity

Guided narrative analysis of case dimension and weight divergences. Five chapters from raw measurement audit through cost modeling and retailer compliance risk.

Source on GitHub →

Product Master Data Model

The entity model a specialty food product master should have — one SKU walked from brand to pallet, a GTIN at every packaging level that carries one, and the attribute sets each retailer requires, mapped to the entity that owns them. Written as working Postgres DDL with dbt contracts, not a whiteboard diagram. The model that closes the gaps behind the $93K/yr in chargebacks the Product Data Health Audit traced to product-data defects.

Source on GitHub →


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