Overview
Warehouse-to-arrival cargo risk intelligence
ATENEA is a cargo risk evaluation engine designed to predict damage risk in containerized transport by combining route climate exposure, cargo behaviour, packaging configuration, loading conditions and mitigation systems.
Status
Architecture shaping
Primary output
CPI
Risk logic
Lifecycle-based
Core scope
Dry cargo + beverages
Modes
Predictive + stuffing validation
Current focus
Visible system structure
Risk Lifecycle
The system should not start at the ship. It starts at the warehouse.
Warehouse
Risk starts before stuffing. Product moisture, warehouse RH, storage time, packaging condition and pallet condition define origin vulnerability.
Stuffing
Load compactness, air circulation, wall clearance, vapor accessibility and protection setup determine whether mitigation can actually work.
Transit
Route climate, thermal gradients, solar radiation, humidity and voyage duration create condensation, vapor migration and thermal cycling risk.
Arrival
Visible damage appears here: mold, odor contamination, product degradation, claim events and cargo rejection.
System Formula
Current working mental model
Base risk is driven by loading conditions, route dew point exposure, cargo system configuration and commodity moisture behaviour. Mitigation systems then reduce, but do not automatically eliminate, risk.
Risk Engine
Core scoring components currently shaping the model
LCI
Loading Condition IndexMeasures loading-time condition: product moisture, temperature, ambient RH, rainfall and packaging condition.
RDPE
Route Dew Point ExposureMeasures route climate exposure through thermal contrast, humidity, seasonality and transit duration.
CSC
Cargo System ConfigurationScores packaging, palletization, density, wall clearance, circulation and container setup.
CMB
Commodity Moisture BehaviourCaptures intrinsic cargo sensitivity: hygroscopicity, mold risk, odor sensitivity, condensation sensitivity and infestation risk.
Inputs
The engine should work with minimal information, and improve when richer data exists.
- Required shipment inputs: origin, destination, planned loading date, commodity, transport mode, container type.
- Automatic route/climate inputs: temperature range, route humidity, seasonal patterns, solar radiation, transit duration.
- Packaging inputs: primary packaging, pallet type, load type, fill level, wall clearance, air circulation, protection systems.
- Stuffing validation inputs: product temperature, product moisture, warehouse temperature, warehouse RH, container dryness, visual notes.
- Outcome inputs: arrival condition, damage type, claim value, evidence, protection system used.
Commodity & Packaging Logic
Where physical cargo behaviour and packaging geometry become visible.
Commodity behaviour
Safe moisture range, hygroscopicity, mold risk, odor sensitivity, condensation sensitivity, infestation risk.
Packaging matrix
Typical packaging, alternative packaging, typical load configuration, packing density.
Critical geometry
Air circulation, vapor accessibility, wall clearance, floor contact, compactness.
Working insight
Protection systems are context-dependent. Desiccants or liners can underperform if circulation and vapor access are poor.
Risk Framework
ATENEA distinguishes between external transport exposure and internal cargo-system vulnerability.
External Risk
Climate differences, thermal gradients, solar radiation, external humidity and transit duration.
Internal Risk
Product moisture, wet packaging, hygroscopic materials, trapped air and stored moisture already inside the cargo system.
Data Layers
Architecture backbone for route intelligence and climate exposure.
Outputs
What the system should eventually explain, score and recommend.
Roadmap
Rough project sequencing from reference architecture to operational engine.
Now
Visible architecture board, shared language, lifecycle framing, core drivers, source document consolidation.
Next
Data model, scoring v0, route/climate integration, mitigation logic, structured dashboard sections.
Later
Dynamic disruptions, sensor data, shipment feedback loops, calibration, machine learning assisted refinement.
Documents
Current material already feeding the visible architecture board.