The Platform

How Aerotrax turns removal history into parts forecasts

From raw task card data to a ranked weekly demand report. The full forecasting pipeline, explained.

Ingest Queue Parsing
LDG-STRUT-B737 Code 04 / 2026-03-12
APU-AIR-FLTR Code ? / 12-MAR-26
BRAKE-ASSY-MLG Code 02 / 2026-03-09
HYD-PUMP-EDP Code 07 / Mar 8 2026
4 records parsed, 1 date format normalized

Data Ingestion

Removal-Log Ingestion

Aerotrax parses task card records, unscheduled removal entries, and reason-for-removal codes from your existing maintenance records system. The ingestion layer handles messy real-world data with missing fields, inconsistent date formats, and mixed part number conventions. No data-cleaning project required before you start.

Wear Curves 3 near threshold
0 100 3m 6m 9m limit YNJ LZT PLK
LDG-STRUT-B737 / high-cycle tails

Wear Modeling

Component Wear Modeling

The model builds degradation curves per component type and fleet variant, calibrated to your specific aircraft utilization pattern. Tail-to-tail variation in removal rates is a first-class input, not an averaging artifact. A high-utilization tail on a regional route degrades differently than a low-cycle tail doing long haul.

Demand Forecast 2026-W29
Part 30d 60d 90d Risk
LDG-STRUT-B737 2 5 9 HIGH
APU-AIR-FLTR 4 7 14 MED
BRAKE-ASSY-MLG 1 3 6 MED
HYD-PUMP-EDP 0 1 3 LOW
Fleet: B737-800 x12 / ranked by criticality

Demand Forecast

Demand Forecast Engine

The engine produces 30/60/90-day point-estimate demand with confidence bands, ranked by criticality and current stock-depth. Output is a ranked demand report by part number and tail registration. A ranked list a planner can act on before the Thursday purchase order cutoff, not a dashboard that requires interpretation.

AOG Risk Score 2 critical
LDG-STRUT-B737 84
Single-source vendor / 18d lead time
APU-AIR-FLTR 71
AOG freq 3x per year / limited spot
BRAKE-ASSY-MLG 52
3 approved vendors / 8d avg lead
HYD-PUMP-EDP 28
Deep spot market / 4d typical lead
Score 0-100: supply risk x AOG freq x lead-time

Risk Scoring

AOG Risk Scoring

Aerotrax assigns risk weights based on single-source supply vulnerability, lead-time volatility, and historical AOG frequency for each part class. Parts that can ground an aircraft on a 2-day vendor lead time surface above parts with deep spot-market availability. Not all shortages have equal operational consequences.

What data Aerotrax reads

Three data sources, one ranked forecast

REMOVAL LOGS task cards + reason codes TASK CARDS maintenance event records FLIGHT CYCLE FC + FH per tail AEROTRAX ENGINE DEMAND FORECAST 30/60/90-day by part + tail AOG RISK ALERT high-criticality part flags
  • Minimum 12 months of removal history required for baseline model accuracy
  • CSV upload available in all tiers. No integration project needed to start a pilot
  • API connectors for AMOS and CESIUM available in Pro tier
Connects with your existing systems

Works alongside the tools your team already uses

AMOS
CESIUM
SAP PM
Mxi Maintenix
CSV / Excel

API connections available in Pro tier. CSV import available in all tiers.

Forecast performance

What the model delivers in practice

87%
directional accuracy on 30-day part-class demand: up/flat/down matched actual removal trends
2.1 wks
average forward window before a shortage event is flagged, based on early-access validation

Measured across 3 anonymized fleet datasets during Aerotrax early-access validation period.

Start your 30-day pilot

Upload your removal history CSV. We build your baseline forecast in 5 business days.

Request a pilot