CARGOCONNECT-JUNE2026 - Flipbook - Page 71
Multimodal Agenda FEATURE
to Point B, with physical veri昀椀cation possible at state
border check posts along the way. The moment you
introduce a rail leg, the routing changes. The cargo
goes to a rail terminal, moves by train to a di昀昀erent
terminal, and then gets picked up by another truck
for last-mile delivery. That doesn’t 昀椀t neatly into the
permit structure.”
He further informs there’s no standardised
mechanism across states for issuing multimodal transit
permits for excise goods. Some states don’t recognise
a rail terminal as a valid transit point in their excise
documentation. Others require physical inspection at
every modal transfer, which adds delays and defeats the
e昀케ciency purpose of going multimodal in the 昀椀rst place.
For instance, if there’s any discrepancy—a container
sitting at a terminal because of a permit mismatch—it’s
not just a logistics delay. It becomes a regulatory issue
with potential penalties. “It’s painstaking, it’s not
scalable, and it limits how much multimodal potential
we can actually unlock. The real resolution needs to
come at the policy level,” Reddy attests.
KESHAV TANNA
SECRETARY GENERAL, FIATA
Any disruption of one
single mode of transportation need not affect
the entire transport
chain. Multiple transport
options ensure continuity instead of stoppage
due to port congestions,
driver shortages, and
strikes etc.
Deploying Real-Time Tools to
Optimise Routes
Dynamic rerouting minimises wasted miles and fuel
consumption. Real-time, AI-driven tools are essential
for optimising multimodal routes, transforming logistics
from reactive, static planning to proactive, dynamic
execution that minimises disruptions. These systems
integrate data from road, rail, air, and maritime modes,
along with tra昀케c, weather, and IoT feeds to provide
instantaneous route adjustments and enhanced visibility.
Reddy highlights, “Our technology stack is practical,
we’re not chasing tools for the sake of it. A transport
management system with GPS-based tracking forms
the base, feeding into a central logistics dashboard that
handles road well. The challenge comes when you add
intermodal legs.” A standard Transport Management
System (TMS) wasn’t built to track a container hopping
from truck to train and back. For instance, when a
state announces a surprise dry day or an unexpected
movement restriction, knowing exactly where every
shipment is and what the next best option is—that’s the
di昀昀erence between a minor hiccup and a major service
failure. The question is never whether something will
go wrong. It’s about how fast you see it and how quickly
you can pivot. He points out that where we’re headed is
predictive—moving from “react fast” to “anticipate early.”
“Machine Learning (ML) and historical data are used
to predict Estimated Time of Arrival (ETA), forecast
tra昀케c congestion and delays before they occur, alerting
operators hours in advance. GPS tracking and data loggers assist in locating the position of the shipment. One
would also have to rely on track and trace information
re昀氀ected on websites of various transport modules,”
notes Tanna.
Taking cue from the above statements, Shah apprises
how ACAAI as an industry body actively participates in
all dialogues and initiatives and encourages its members
to consider increasing technology use in their work.
“We also actively interact with academic institutions
to develop a workforce capable of understanding and
appropriately applying logistics nuances with technology,” he noti昀椀es.
Therefore, piloting Arti昀椀cial Intelligence (AI) and
Blockchain in multimodal hubs (ports, airports, railway terminals) focusses on using AI for operational
optimisation like predictive maintenance, routing, and
blockchain for secure, traceable data exchange among
multiple stakeholders. Such integration aims to shift
from manual/isolated operations to smart, synchronised,
and resilient logistics chains.
From Fragmented Freight to
Integrated Supply Chains
The integration of multimodal logistics is a central
pillar of India’s strategy to improve its ranking in the
World Bank’s Logistics Performance Index (LPI). By
transitioning from a fragmented, road-heavy system to
an interconnected “Hub-and-Spoke” model, India has
already seen its LPI rank rise, driven by investments
in infrastructure and digitalisation.
Multimodal integration directly targets the key
parameters measured by the LPI. Development of
35 MMLPs under the Bharatmala Pariyojana and
the expansion of DFCs have improved India’s rank
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