CargoConnect OCTOBER26_For DIGITAL EDITION - Flipbook - Page 44
FEATURE Interoperability
The interoperability
breakthrough won’t
come from mandates; it
will come from platforms
that make data sharing
commercially attractive.
The next major leap
is prescriptive decisionmaking, where AI not only
forecasts disruptions but
recommends the optimal
response.
Next-gen platforms will not
merely identify risks but
recommend actions including
alternate routes, inventory
repositioning and packaging
availability options.
MRINAL RAI
CO-FOUNDER AND CPO,
INTUGINE TECHNOLOGIES
SANDEEP CHATTERJEE
SUPPLY CHAIN ARCHITECT,
TRINAMIX
VIKAS KALRA
CO-FOUNDER AND CHIEF
BUSINESS OFFICER, FITSOL
He points to platforms that create tangible value at
individual nodes—better ETAs for manufacturers,
dispute resolution for transporters and automated gate
entry for plants—as the basis for wider participation.
Kalra likewise maintains, “The bigger hurdle is
stakeholder willingness to share operational data.”
He believes customers are willing to share shipment
and asset-movement information when there is a
clear business bene昀椀t. “The industry must move from
data ownership to data collaboration while protecting
commercially sensitive information,” he suggests.
Chatterjee argues that APIs, cloud platforms, IoT
sensors, AI analytics and ULIP have already made
end-to-end visibility technically achievable. The deeper
problem lies in fragmented data-sharing behaviour and
governance across shippers, transporters, warehouses,
ports, customs authorities and technology providers.
“Technology can enable visibility, but only trust, collaboration, and shared economic value can make it a
reality,” he underlines.
End-to-end visibility depends less
on technology than trust, protected
data collaboration and clear benefits
for participants across the logistics
ecosystem.
44 | CARGOCONNECT OCTOBER 2026
Moving From Reactive to
Predictive Supply Chains
The next stage of digital logistics is moving from visibility towards prediction and intervention. Leading
platforms increasingly combine AI, predictive analytics
and real-time signals to identify risks and recommend
corrective action before disruptions escalate.
“At Intugine, our AI control tower 昀氀ags SLA breach
risk 4-6 hours before it materialises, combining live
location data with lane-level history, driver behaviour,
and weather overlays,” informs Rai. When a likely breach
is detected, the system can trigger a driver call, escalate
to the 昀氀eet manager and update the consignee ETA.
However, important visibility gaps remain at 昀椀rst- and
last-mile nodes, where events such as loading completion,
gate-out and port entry may still be recorded manually.
Until these physical events are captured autonomously,
predictive systems continue to work with incomplete
or delayed inputs. AI is also increasingly being applied
beyond Tier 1 suppliers to identify sub-tier dependencies,
component shortages and capacity constraints before
they a昀昀ect production.
Kalra notes that Fitsol is combining transportation
visibility, packaging-asset tracking and sustainability
intelligence to build a more proactive supply-chain
ecosystem. The next generation of platforms, he says,
will not merely identify risks but recommend actions
including alternate routes, inventory repositioning and
packaging availability options.
Chatterjee sees the industry moving towards
prescriptive decision-making. “The next major leap is
prescriptive decision-making, where AI not only forecasts
