Python CLI that profiles every column in an Excel file and flags hidden data-quality problems (mixed types, sentinel values, missing data, cardinality anomalies) in plain English. Outputs PDF, HTML, JSON, or annotated-Excel reports, with strict cell-level type detection that catches mixed-type columns pandas silently coerces.
One-page reference on GTIN, GDSN, item-setup, and freight-class rules for specialty food operators, with a downloadable PDF.
Product data validation tool for specialty food brands preparing for national retail
The data model a specialty food product master should have — documented, contracted, and running on Postgres + dbt.
Audits messy Excel workbooks for the data-quality issues that show up in real consulting engagements: mixed formats, misused fields, placeholder floods, and phantom duplicates. Generates HTML, Excel, and PDF reports.
Interactive capital allocation analysis — which channel actually pays after all deductions, and is the capital allocation wrong? React + D3.
Retailer/distributor item-setup pre-flight — codified partner schemas + typed validation engine that flags new-item form rejection risk before submission
Revenue lifecycle analysis tracing where money leaks between invoice and cash receipt. For every dollar invoiced, 87 cents arrived. React.
Trial-vs-repeat analysis: how many triers came back, and whether penetration growth is real adoption or expensive sampling.
Velocity decision tool for specialty food brands. 8 decisions — shelf defense, production planning, promo ROI, distribution expansion, SKU rationalization, launch diagnostics, pricing power — built on a synthetic 50-SKU, 1.2M-row retail dataset.
Sales-penetration analysis separating distribution gains from velocity gains for specialty food brands (SPPD, ACV%, door counts).
Weekly Excel report template for specialty food founders — three critical Monday numbers, tiered by growth stage. Python + openpyxl.
Content-level EDI reconciliation across the full PO lifecycle, surfacing every mismatch as a dollar-ranked exception.
Trade spend diagnostic workbook for a specialty food brand. 7-tab Excel with executive pulse, leak diagnostic, promo ROI, retailer risk, and deduction ledger. Built on the Cinderhaven Data Platform (Postgres). Python + openpyxl.
Parses Walmart, Costco, UNFI, and KeHE remittance PDFs into a unified, reconciled deduction ledger — and flags what to dispute before the window closes.
Modern data platform for a fictional specialty food brand. Demonstrates source-to-mart pipelines, data quality testing, orchestration, and lineage for CPG data shapes.
Competitive shelf intelligence dashboard — tracks pricing, placement, and assortment across retailers for a specialty food brand. Python + Dash.
Free web tool for specialty food brands doing EDI by hand. Parses 850 Purchase Orders and validates 856 Advance Ship Notices against retailer-specific specs. Supports Walmart, Amazon, UNFI, KeHE, and Costco.
Multi-dimensional SKU scoring and visualization for specialty food brands. Five dimensions, four action buckets, interactive portfolio view. Python + Dash.
OTIF blind spot diagnostic — reconciles internal fulfillment metrics against retailer scorecards and quantifies the gap. Python + Dash.
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