How CJ Darcl Is Using AI to Bring Discipline to One of India’s Most Complex Supply Chains

Why It Matters

The rollout proves AI can generate concrete safety, efficiency and cost gains in India’s fragmented logistics market, setting a benchmark for digital transformation. Combining automation with human oversight ensures scalability while preserving control over complex cargo movements.

Key Takeaways

• AI dashcams cut harsh braking 35%.
• MoveX reduces transit variability 25% across corridors.
• Driver fatigue alerts lower violations 40%.
• Asset utilization up 15% via unified TMS.
• Customer claims drop 25% with real-time visibility.

Pulse Analysis

India’s logistics landscape has long been plagued by unpredictable traffic, weather disruptions and fragmented data silos. CJ Darcl’s strategy flips this narrative by first imposing operational discipline through AI‑driven safety tools. Real‑time dashcams and fatigue monitors now alert drivers and control towers instantly, turning safety from a retrospective audit into a proactive shield. The measurable drop in harsh braking, overspeeding and violations not only protects lives but also trims downtime, a critical metric for time‑sensitive cargo owners.

The backbone of this transformation is MoveX, a cloud‑native, mobile‑first transportation management system that unifies full‑truck‑load, rail, coastal and air operations on a single data spine. By ingesting GPS, IoT sensor feeds and external traffic APIs, MoveX delivers predictive ETAs with about 85% reliability and dynamically reroutes shipments to the most efficient corridors. The platform’s self‑service features—WhatsApp bidding, automated invoicing and AI‑matched data reconciliation—have sharpened asset utilization by 15% and cut route costs up to 15%, delivering a projected 150% ROI within the first year.

Beyond technology, CJ Darcl emphasizes a calibrated human‑in‑the‑loop model. Operators validate system recommendations against on‑ground constraints, preserving agility for complex cargo while maintaining governance. This balanced approach has fostered internal adoption, turning dashboards into "co‑pilots" and reducing planning time by 10%. For customers, the single‑pane‑of‑glass visibility translates into fewer escalations, a 30% drop in picking errors and a 25% reduction in damaged‑goods claims, underscoring how AI, clean data and human expertise together can reshape supply‑chain economics in emerging markets.

How CJ Darcl is using AI to bring discipline to one of India’s most complex supply chains

By Ramarko Sengupta · Published Jan 15, 2026 at 07:07 AM IST

Dae Jun Lee, Deputy CIO at CJ Darcl Logistics

In Indian logistics, unpredictability is the norm. Weather shifts, traffic bottlenecks, driver fatigue, and fragmented data often collide on the same route. For CJ Darcl Logistics, the challenge has been less about chasing futuristic AI and more about imposing operational discipline at scale.

According to Dae Jun Lee, Deputy CIO at CJ Darcl Logistics Ltd., the most meaningful gains have come from applying digital and AI systems to problems that directly affect safety, reliability, and cost.

“One of the most meaningful shifts came from deploying Advanced Driver Assistance Systems and Driver Fatigue Monitoring Systems across our road fleet,” Lee tells ET Enterprise AI.

Earlier, safety decisions relied heavily on manual reporting and post‑incident reviews. With AI‑enabled dashcams and fatigue monitoring, both drivers and backend teams are alerted in real time when unsafe behaviour is detected, including drowsiness, seatbelt non‑compliance, harsh turns, or overspeeding.

The impact has been measurable. Within the first three months of deployment, harsh braking and overspeeding events dropped by 35 %, while speeding incidents fell 28 %. Every driver now wears a seatbelt.

“This use case created three clear shifts,” Lee explains. “Safety is now monitored in real time rather than retrospectively. That enabled targeted driver training based on individual performance. And incident risk is actively managed on routes that matter the most to enterprise customers.”

The shift from retrospective reporting to live intervention has also changed outcomes on the ground. CJ Darcl now runs its safety systems through central control towers operating 24 × 7, with telematics tightly integrated into fleet operations.

One night‑run incident illustrates the difference. A driver flagged for drowsiness received an instant alert that helped avert a potential swerve. The vehicle reached its destination on time, without escalation or downtime.

“Our long‑term aim is zero fatal accidents,” Lee says. “This framework has contributed to fewer violations by 40 % and reduced downtime by 20 %.”

The emphasis on safety is not isolated. It feeds directly into service reliability, especially for high‑value and time‑sensitive cargo.

Making route optimisation work in the real world

Route optimisation and predictive ETAs are built into the company’s cloud‑native, mobile‑first transportation management system, first deployed in 2021. In 2024, the platform was consolidated into a unified system called MoveX, bringing full‑truckload operations onto a single backbone.

“MoveX monitors vehicles in real time through GPS or SIM and FASTag‑based tracking,” Lee says. “It enables rerouting through the best possible routes and keeps customers updated on live ETAs.”

The operational impact has been tangible. Transit‑time variability has tightened by 25 % on key industrial corridors. Asset utilisation has improved by 15 % across full‑truckload, rail, coastal, air cargo, and project logistics.

More importantly, customers and vendors now operate on the same system. From lead management and contract creation to shipment tracking, invoicing, and payment settlement, workflows are increasingly self‑service and automated.

Scaling beyond pilots

“Scaling any system requires value before experimentation,” Lee says. “In digital systems, that value has to be measurable.”

MoveX began as a cloud‑based TMS (Transportation Management System) focused on core workflows. As results became visible, its scope expanded. By March 2024, it covered enquiry management, planning, advance disbursement, POD tracking, vendor and customer invoicing, and settlement across full‑truck‑load and rail.

Features such as WhatsApp‑based bidding and intelligent load allocation have improved planning efficiency and vendor responsiveness, contributing to cost optimisation of up to 15 % on key routes. Lee adds that a centralised ticketing system now supports faster issue resolution and greater transparency.

MoveX functions as the backbone of CJ Darcl’s integrated multimodal operations, standardising controls and visibility across branches.

Making sense of data

Predictive systems are only as good as the data beneath them. For CJ Darcl, the most valuable inputs include real‑time GPS data, historical shipment records, IoT sensor feeds for container conditions, and external weather and traffic APIs. Together, they support ETA predictions with around 85 % reliability, according to Lee.

The hardest data to integrate came from legacy vendor invoicing systems spread across branches.

“These records had inconsistent formats, duplicates, and missing entries.”

The company addressed this through automated ETL processes and AI‑driven data matching, reducing integration timelines from months to weeks.

Cleaner data flows have also sharpened downstream processes such as billing, claims, and vendor reconciliation.

Customer impact beyond dashboards

The real test of any digital system is whether customers feel a difference. In CJ Darcl’s case, visibility and predictability have translated into fewer escalations and lower claims.

Through MoveX, customers get real‑time visibility from order booking to delivery and payment settlement. API integrations allow customer ERP systems to mirror CJ Darcl’s operational data.

In the automotive sector, CJ Darcl has integrated its warehouse management system with customer systems using EDI (Electronic Data Interchange). Zone‑based picking and FIFO processes, supported by digital workflows, have reduced picking errors by 30 % and damaged‑goods claims by 25 %.

“For one major auto OEM, this saved about Rs 50 lakh annually in rework,” Lee says.

Customers increasingly value what Lee calls a “single pane of glass” view, where fewer surprises mean faster cycles and fewer disputes.

Humans still in the loop

Despite deep automation, CJ Darcl is careful not to remove human judgment from critical decisions.

While platforms such as MoveX, WMS, and telematics systems surface recommendations, alerts, and risk indicators, the operations teams validate these against real‑world constraints such as plant schedules, cargo characteristics, regulatory requirements, and multimodal availability.

Lee shares one instance where a monsoon‑delayed rail handoff triggered a system recommendation to reroute. The operations lead overrode it, choosing a trusted coastal option instead. The shipment arrived 24 hours early, avoiding a Rs 10 lakh penalty.

“This balance allows us to retain agility for complex cargo while maintaining governance and traceability at scale,” Lee says.

Building capability on the ground

Technology adoption has also reshaped roles inside the organisation. CJ Darcl has invested in domain data scientists and “field champions,” operational leaders trained to bridge AI systems and on‑ground realities.

Initial resistance was common. Dispatchers were wary of alerts and dashboards. Over time, that changed.

“One Hyderabad team lead told us it went from ‘another app’ to ‘my co‑pilot’ after we gamified training with leaderboards,” Lee says. Planning time dropped by 10 % as confidence grew.

For its 2024 MoveX upgrade, CJ Darcl is tracking a three‑to‑six‑month payback period. Early indicators include 18 % cost savings in vendor bidding and 22 % faster invoicing cycles. The company is targeting a 150 % ROI in the first year and is already at around 60 % driven by reduced claims alone.

Learning from a global ecosystem

CJ Darcl’s partnership with its promoter, CJ Logistics Corporation of South Korea, has accelerated access to advanced logistics technologies. CJ Logistics’ TES framework—spanning technology, engineering, and systems—underpins warehouse automation, robotics, and analytics‑led demand forecasting.

In one pilot warehouse, TES‑driven forecasting reduced overstock by 12 %, exceeding expectations, particularly for coastal operations, Lee shares.

He remains pragmatic about AI’s role in logistics.

“AI is now much more accessible and is being implemented across sectors,” he says. “At CJ Darcl, automation comes first, creating clean data flows and operational discipline before advanced AI layers are introduced.”

With the scale of daily transactions, AI‑driven systems in contract management, billing, and delivery coordination have improved predictability. But governance remains central.

“Models that are not monitored or explained can create operational blind spots,” Lee says. “Transparency, auditability, and human oversight remain non‑negotiable.”

For CJ Darcl, the future lies in a calibrated model. AI accelerates routine processes and supports decisions. Humans bring context, judgment, and control.