Portfolio Project · Excel · JavaScript · Freight Audit

Pre-Audit vs Post-Audit
Recovery Dashboard

200,416 simulated invoices across a real-world international-heavy freight audit portfolio. Prevention vs recovery cost efficiency — quantified with domain-accurate error logic and seasonal client behavior.

Advanced Excel Python Data Model D3.js Interactive No Real Client Data
200K+
Annual Invoices
$9.86M
Total Error Pool
62.3%
Pre-Audit Catch Rate
12.3%
True Leakage Rate
10.1x
Post vs Pre Cost
3 Regions
Domestic / Asian / EU
Portfolio simulation notice: All figures are entirely simulated. Client mix (30% domestic / 40% Asian / 30% European), seasonal volume curves, error type distributions, and catch rates reflect domain-accurate freight audit logic — not real client, carrier, or proprietary data.

Live Interactive Dashboard

Click any month — everything updates

Chinese New Year slowdown (Jan–Mar) European slowdown (Oct–Jan) October rate table anomaly Normal
Monthly Error Split — Pre-Caught · Post-Caught · Leakage
Pre-Audit Catch Rate Trend
Selected Month Detail January
Error Type Breakdown January
🟡 Chinese New Year Slowdown
Asian clients (40% of this portfolio) reduce freight activity significantly from January through mid-March due to Chinese New Year factory shutdowns. Invoice volumes drop 55–62% on the Asian segment. Error rates on remaining invoices tick slightly higher as billing systems process a backlog of pre-holiday shipments during this period. Volume ramps back mid-March through April.
⚠ October — Carrier Rate Table Update
October's error pool spikes to $1.2M — 35% above the surrounding monthly average — while Pre-Audit catch rate drops to 56.3%, the lowest of the year. A simulated carrier rate table update that the pre-audit system hadn't yet synchronized caused Rate Mismatch errors to spike. More errors flowed to the expensive 28% contingency Post-Audit channel. This is the exact pattern a Pre-Audit team lead would investigate: a sudden catch rate drop almost always traces to a rate table, staffing gap, or system sync failure.
🟣 European Client Slowdown
European clients (30% of this portfolio) naturally slow down October through January, then pick back up in February. Combined with October's rate table anomaly, the Q4 shift in carrier mix toward domestic (which has lower average invoice values) explains the lower error pool dollar figures despite relatively steady total invoice counts in November and December.
Full Year Summary — All 12 Months

Technical Skills

Tools & Techniques

Excel & Data Modeling

Advanced ExcelINDEX / MATCH SUMIFS / IFERRORData Validation Dropdowns Seasonal Volume ModelingCost-per-Dollar Analysis Live Cross-Sheet Formulas

Visualization & Web

D3.jsSVG Area & Line Charts Animated TransitionsGradient Fills Tooltip InteractionsResponsive Layout

Freight Audit Domain

Pre-Audit Avoided Cost Post-Audit Contingency Recovery Duplicate Invoice Batch Logic Demurrage: Confirmed vs Pending Fuel Surcharge Domestic vs Intl Accessorial Summer Spike Chinese New Year Seasonality European Client Slowdown Catch Rate Analysis by Error Type True Leakage Quantification 10x Cost Multiple

Download the Excel workbook

245 live cross-sheet formulas — change any input on the Raw Data sheet and the entire model recalculates.