Modeling Supply Chain Evidence: Condition, Asset, and Custody Data Schemas for IoT Hardware

Most IoT tracking projects start with a GPS coordinate and a timestamp. Ship a device, poll its location, plot it on a map. That works for fleet management. But when the use case involves pharmaceutical cold chain compliance, food safety traceability, or reusable asset management, the data model needs to answer three fundamentally different questions - and the hardware architecture follows from there.

This article breaks down the three evidence dimensions that supply chain visibility hardware should produce, with concrete data schemas and payload examples.

The Three Evidence Dimensions

1. Condition Evidence

Condition evidence captures environmental exposure during transit and storage. The minimum viable payload for a condition event:

{
  "device_id": "BTT02-SN-00482",
  "event_type": "condition_sample",
  "timestamp": "2026-06-10T14:32:18Z",
  "location": {
    "lat": 51.4700,
    "lng": -0.4543
  },
  "sensors": {
    "temperature_c": 7.2,
    "humidity_pct": 42,
    "light_lux": 0,
    "shock_g": 0.3,
    "door_open": false
  },
  "device_meta": {
    "firmware": "2.1.4",
    "battery_pct": 87,
    "calibration_date": "2026-01-15",
    "calibration_cert": "CAL-2026-00482",
    "accuracy_c": 0.5
  }
}

The critical fields that most tracking platforms skip: calibration_date, calibration_cert, and accuracy_c. Without these, a pharmaceutical QA team cannot determine whether the sensor data is trustworthy enough for a deviation investigation.

Why this matters: When a temperature excursion occurs (e.g., 14°C for 45 minutes on a 2–8°C shipment), quality teams need to assess whether the product is still within its stability budget. That assessment requires the ALCOA+ data integrity framework: Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, Available. A CSV export without audit trail fails this test regardless of sensor accuracy.

2. Asset Evidence

Asset evidence tracks reusable logistics containers as capital assets. The data model is event-driven, not coordinate-driven:

{
  "asset_id": "PLT-EU-2024-08821",
  "asset_type": "insulated_pallet",
  "event_type": "custody_change",
  "timestamp": "2026-06-10T09:15:00Z",
  "location": {
    "lat": 50.1109,
    "lng": 8.6821,
    "zone": "FRA-CARGO-DOCK-3"
  },
  "previous_custodian": "carrier:LH-CARGO",
  "new_custodian": "handler:FRA-GND-OPS",
  "condition": "in_service",
  "cycle_count": 47,
  "days_since_dispatch": 3
}

The KPIs that matter for RTI management derive from this event stream:

• Average cycle time: mean days from dispatch to return
• Loss rate: assets not returned within N days, segmented by customer/route
• Utilization rate: active-in-circulation / total fleet
• Dwell time: time at each node - identifies retention hotspots

Industry data suggests 5–15% annual loss rates for RTI fleets. For 50,000 pallets at USD 15–30 replacement cost, that is real budget impact.

3. Custody Evidence

Custody evidence records who had control and when control transferred. This is where condition and asset evidence get linked to accountability:

{
  "shipment_id": "SHP-2026-EU-US-04471",
  "event_type": "handover",
  "timestamp": "2026-06-10T09:15:00Z",
  "location": {
    "lat": 50.1109,
    "lng": 8.6821
  },
  "from_party": {
    "id": "carrier:LH-CARGO",
    "type": "airline_cargo"
  },
  "to_party": {
    "id": "handler:FRA-GND-OPS",
    "type": "ground_handler"
  },
  "confirmation": "geofence_trigger",
  "condition_at_handover": {
    "temperature_c": 5.8,
    "door_open": false,
    "device_battery_pct": 82
  }
}

The architectural insight: each handover event should snapshot the condition state at the moment of transfer. This creates a chain where any subsequent excursion can be attributed to the party who held custody when it occurred.

The Evidence Package

When these three streams converge for a single shipment, the output is what the industry calls a cold chain evidence package:

Hardware Architecture Mapping

Regulatory Context

The data schemas above are not academic exercises. FSMA 204 (enforcement extended to July 2028) requires Critical Tracking Events and Key Data Elements for specific food products. EU GDP mandates documented temperature control and deviation investigation for pharmaceutical distribution. 21 CFR Part 11 sets the bar for when electronic records are trustworthy enough for regulated use.

The common thread: regulators are not asking "where was the shipment?" They are asking "what happened to it, and can you prove it with structured, auditable data?"

Key Takeaways

• Structure IoT output as three evidence types: condition, asset, custody
• Include calibration metadata and audit trail in every condition payload
• Model RTI assets as event streams, not coordinate histories
• Snapshot condition state at every custody handover
• The evidence package - not the raw data dump - is what survives audit scrutiny

The cold chain monitoring market is projected to grow from USD 8.3B (2025) to over USD 15B by 2030 (MarketsandMarkets). That growth is driven by industries that need proof, not pins on a map.

This article was written with AI assistance for research and drafting.