In-Transit Visibility (ITV) is the technological capability to monitor a shipment's precise real-time location, environmental condition, and custody status across the entire supply chain journey. It aggregates data from IoT sensors, GPS trackers, and RFID tags to provide a continuous, unbroken digital record of a shipment's physical state and geographic progress, moving beyond simple periodic check-ins to persistent streaming telemetry.
Glossary
In-Transit Visibility (ITV)
The capability to monitor the real-time location, condition, and integrity of a shipment from the point of origin to the final destination, providing a continuous chain of custody.
SUPPLY CHAIN INTELLIGENCE
What is In-Transit Visibility (ITV)?
In-Transit Visibility (ITV) is the real-time capability to monitor the geographic location, environmental condition, and chain-of-custody status of a shipment from origin to final destination.
Effective ITV systems fuse geofencing alerts, temperature telemetry, and shock detection into a unified control tower interface. This allows logistics managers to proactively identify dwell time anomalies, route deviations, and cold chain excursions the moment they occur, enabling automated exception management and preserving the integrity of sensitive pharmaceuticals or perishable goods.
PILLARS OF VISIBILITY
Core Characteristics of ITV
In-Transit Visibility is not a single technology but a convergence of data streams, hardware, and analytics. These core characteristics define a robust ITV architecture that moves beyond simple track-and-trace to predictive, condition-aware monitoring.
01
Real-Time Location Streaming
The continuous ingestion of geospatial data via Active RFID, GPS, or LoRaWAN protocols. Unlike passive batch updates, true ITV requires sub-minute latency for high-value or critical shipments. This enables dynamic geofencing and automated alerts when a shipment deviates from its planned corridor.
< 60 sec
Target Latency
02
Multi-Sensor Condition Fusion
ITV extends beyond location to ingest IoT sensor telemetry—temperature, humidity, shock, and light exposure. The system fuses these data streams to create a holistic digital fingerprint of the shipment's environment, not just its coordinates. This is critical for Cold Chain Compliance and Mean Kinetic Temperature (MKT) calculations.
03
Chain of Custody Ledgering
An immutable, time-stamped record of every transfer, stop, and exception event. Modern ITV systems often use a Blockchain Ledger or distributed database to create a shared, tamper-proof audit trail across carriers, 3PLs, and shippers. This provides the verifiable data integrity required by 21 CFR Part 11 and FSMA 204.
04
Predictive Exception Management
Moving from reactive alerts to proactive intervention. By applying Edge AI Inference and Causal Inference models to streaming telemetry, ITV systems can predict a Cold Chain Break or a delayed handoff before it occurs. This triggers automated Excursion Management workflows, such as re-icing instructions or dynamic re-routing.
05
Digital Twin Synchronization
The ITV data pipeline feeds a live Digital Twin of the physical shipment. This virtual replica allows supply chain managers to run simulations, stress-test alternative routes, and visualize the impact of a delay on downstream inventory. It transforms visibility from a passive dashboard into an active decision-support tool.
06
Interoperable Data Standards
True end-to-end visibility requires breaking down data silos between carriers and logistics platforms. Robust ITV architectures rely on lightweight, standardized messaging protocols like MQTT Protocol and API-first designs to ensure seamless data handshakes across heterogeneous systems, from ocean freight to Last-Mile Cold Chain delivery.
IN-TRANSIT VISIBILITY
Frequently Asked Questions
Clear, technically precise answers to the most common questions about achieving end-to-end shipment monitoring, chain of custody, and real-time condition tracking.
In-Transit Visibility (ITV) is the capability to monitor the real-time location, condition, and integrity of a shipment from the point of origin to the final destination, providing a continuous chain of custody. It works by integrating data from multiple sensor and identification technologies—such as GPS, Active RFID, LoRaWAN-connected loggers, and IoT sensor telemetry—into a centralized supply chain control tower. This platform aggregates and normalizes disparate data streams, applying edge gateway preprocessing for protocol translation before transmitting critical events to the cloud. The system correlates location pings with environmental readings like temperature and shock, creating a single, auditable record that confirms not just where a shipment is, but whether it remained within specified parameters throughout its journey.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
Read more
Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
Read more
Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read more
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
LinkedIn
Limited slotsGet a Free AI Consultation
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us