CARGOCONNECT-OCTOBER2024 - Flipbook - Page 28
COVER STORY
COLD CHAIN LOGISTICS EXCELLENCE
routes, and inventory levels—companies
gain invaluable insights that allow them to
optimise operations. Predictive analytics, in
particular, helps anticipate potential issues,
such as equipment failure or temperature
昀氀uctuations, before they occur. This proactive
approach not only ensures product integrity
but also minimises waste and loss. With
real-time dashboards providing a clear view
of all operational parameters, companies are
better equipped to maintain the consistency
of temperature-sensitive goods.
Sagar reiterates that data aggregation
and predictive analytics play crucial roles
in enhancing control and visibility within
the supply chain. He o昀昀ers an in-depth look
at their signi昀椀cance.
Data Aggregation
* Uni昀椀ed Data View: Aggregating data from
various sources (suppliers, transportation, inventory, sales) provides a uni昀椀ed
view of the entire supply chain.
* Improved Decision-Making: Centralised
data allows for more informed decisionmaking as it provides a comprehensive
overview of supply chain operations.
* Real-Time Updates: Real-time data aggregation ensures that all stakeholders have
access to the latest information, enhancing
communication and collaboration.
* Transparency: Increased transparency
through data sharing helps build trust
among partners and suppliers.
* KPIs Tracking: Aggregated data helps
track key performance indicators (KPIs)
across di昀昀erent supply chain segments,
identifying areas that need improvement.
* Benchmarking: Performance can be
benchmarked against industry standards
or historical data to measure progress.
* Proactive Risk Identi昀椀cation: Aggregating
data from various points in the supply
chain helps identify potential risks early,
allowing for proactive mitigation.
* Contingency Planning: Comprehensive
data enables better planning for contingencies and disruptions.
Predictive Analytics
* Accurate Predictions: Predictive analytics
uses historical sales data, market trends,
and other variables to generate accurate
demand forecasts.
* Inventory Optimisation: Better demand
forecasting helps optimise inventory
levels, reducing both stakeouts and
excess inventory.
* E昀케cient Resource Allocation: Predictive
analytics helps allocate resources more
e昀케ciently by anticipating demand and
supply 昀氀uctuations.
28 | CARGOCONNECT OCTOBER 2024
ROHAN BELLIKATTI
Managing Director – India
and Srilanka,
LAMILUX India
AI-integrated sensors
continuously monitor
product conditions such
as temperature, while AI
autonomously adjusts storage
and delivery parameters
to prevent deviations. This
ensures product integrity is
maintained throughout the
logistics process. AI models
help to predict and address
potential disruptions, ranging
from equipment failures to
geopolitical challenges. By
designing more cost-efficient
logistics networks, AI evaluates
infrastructure, demand, and
risks to optimise the overall
cold chain. Additionally, AI
enhances sustainability by
optimising energy consumption
in cold storage facilities and
reducing waste through realtime tracking of goods.
* Cost Reduction: Optimising routes,
inventory, and procurement based on
predictive insights can lead to signi昀椀cant
cost savings.
* End-to-End Visibility: Predictive analytics
provides end-to-end visibility into the
supply chain, highlighting potential
bottlenecks and ine昀케ciencies.
* Disruption Prediction: Predictive models
can forecast potential disruptions (e.g.,
supplier delays, transportation issues)
and suggest preventive measures.
* Scenario Planning: Analytics enables
scenario planning, allowing businesses to
prepare for various potential disruptions
and their impacts.
* Contract Negotiations: Data-driven
insights can enhance contract negotiations with suppliers by providing
informed, evidence-based recommendations.
Bellikatti elaborates on the transformative role of technological advancements
in cold chain logistics, stating, “Technological advancements in robotics and AI
are set to reshape cold chain logistics by
signi昀椀cantly elevating e昀케ciency, reliability,
and scalability. Robotics, for example, now
handle various tasks within cold storage
facilities, including picking, packing, and
palletising, while operating continuously
in harsh, low-temperature environments.
This automation not only boosts e昀케ciency
but also minimises errors.”
He further emphasises the importance of
automation in distribution, adding, “Automated
Guided Vehicles (AGVs) and autonomous
robots are now transporting goods within
warehouses autonomously, enhancing safety
and productivity while reducing dependency
on human labour. Moreover, robotics in highdensity storage solutions optimise space
utilisation, which leads to considerable reductions in energy costs.”
Rohan also echoes the role of AI in
predictive analytics, stating, “AI’s ability
to harness data for demand forecasting is
vital for optimising inventory levels and
reducing spoilage. It also aids in route optimisation by analysing real-time factors such
as tra昀케c and weather conditions, ensuring
that deliveries are more efficient while
minimising temperature excursion risks.”
He further highlights how AI improves
real-time monitoring, noting, “With IoT and
AI, real-time monitoring of temperature
conditions becomes more precise, allowing for proactive interventions that can
prevent spoilage. Additionally, Blockchain
technology, when combined with AI, o昀昀ers
unparalleled transparency and traceability,
