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A pallet sits on a loading apron slightly longer than the ones around it. A temperature reading drifts outside its band and settles back. A container tag reports movement at an hour when nothing on the schedule calls for movement.
Taken one at a time, none of those is worth a phone call. Taken together, and in that order, they often are.
That gap, between signals that are individually tolerable and a problem that is already underway, is where Rohit Chudasama has spent much of his career. It is also the basis of his argument that the logistics industry has been asking its tracking systems the wrong question.
Chudasama is director of system architecture and product validation at an IoT and supply chain visibility company, where he designs the telemetry systems that follow shipments through aviation, logistics, healthcare and industrial supply chains. The commercial promise of that category has been close to continuous location data. The operational reality, by his account, is that location data on its own tells you remarkably little.
"Traditional tracking systems generate large volumes of location updates but very little actionable insight," he says.
Anyone who has run a logistics control room recognises the failure that follows. Once a system can see everything, it tends to report everything. Alert volume climbs, the proportion of alerts that turn out to be nothing climbs with it, and the people meant to act on them start triaging by instinct instead. A system that cries wolf at scale is not a safety net. It is background noise with a dashboard.
Chudasama describes the central problem of his work as designing a system that could distinguish between normal shipment movement and the early signs of a potential problem "without overwhelming users with false alarms." The phrasing is modest. The decision underneath it is not. It treats visibility as a classification problem rather than a reporting one, and classification requires something a location feed does not contain: context.
His approach has been to correlate several streams that most systems keep separate, including telemetry from the devices themselves, environmental conditions around the shipment, and the operational events happening to it. A reading is not evaluated on its own. It is evaluated against what else was true at the same moment.
Consider a freight container being loaded onto an aircraft. Movement, by itself, is ambiguous. It could be a scheduled load, a repositioning, or the opening seconds of a theft. In the container monitoring systems Chudasama has worked on, asset movement can be correlated with multiple telemetry and operational signals to determine whether it is consistent with an authorised loading event. By evaluating these signals together rather than independently, the system can distinguish expected operational movement from activity that may require further attention.
The logic runs in the other direction too. At fixed checkpoints, the systems he has helped architect watch not only for assets that leave without authorisation but for assets that appear without it, which is a harder thing to notice and a more interesting one.
In practice, that changes when a problem becomes visible at all. A shipment that has been left in the wrong temperature band, or has taken a route it should not have taken, or has been opened somewhere it should not have been opened, tends to announce itself through a sequence of small deviations well before it becomes a loss. Reading the sequence rather than the individual readings, Chudasama says, allows disruptions to be identified earlier, reduces the amount of manual intervention required, and gives operators more confidence when they are handling shipments that are valuable or time sensitive.
By his account, the platforms he has architected are now deployed by more than 50 enterprise customers and monitor millions of connected assets across aviation, healthcare, logistics and industrial operations. He has also pushed the engineering organisation toward reusable platform components rather than building each customer deployment as a separate implementation, so that new capability can be added without redesigning the core each time.
There is a less glamorous half of this work that he treats as inseparable from it. A detection system is only as trustworthy as the hardware reporting into it, and a device that ships misconfigured fails quietly at a customer site rather than loudly in a factory. Much of his validation work has gone into automated checks that run before devices leave the building, catching duplicate provisioning records, incomplete registration data and configuration mismatches while they are still cheap to fix.
That pairing of architecture and validation is the part of his position he states most directly. "Successful digital transformation is not driven by technology alone, it requires a well-designed architecture that connects devices, data, validation, and business applications into a single intelligent ecosystem," he says. Organisations, in his view, too often treat system architecture, product validation and business operations as three separate disciplines, "which leads to fragmented solutions and limited business value."
Where the field goes next is, in his telling, a question of ambition rather than instrumentation. The sensors are cheap and the connectivity is mostly solved. What remains is whether platforms are built to interpret what they are seeing. He expects the organisations that get this right to move beyond "simply monitoring operations to proactively predicting issues, orchestrating responses, and continuously optimizing business outcomes."
Whether that arrives on the timeline he expects is an open question, and the supply chain technology sector has a long record of promising interpretation and delivering dashboards. But the underlying observation is hard to argue with. Shipments rarely vanish in a single moment. They degrade in stages, through a series of small events that each look survivable on their own. Catching them is largely a matter of building systems that notice the stages.
