Turning Fragmented Global Shipping Operations into a Governed, AI-Powered Control Tower
How elsai connected shipment visibility, exception detection, costed resolution options, stakeholder communication, and supply-chain intelligence into one governed operating layer.
Exception detection vs 36-hour manual baseline
Savings from early detection across the 12-week POC dataset
Returned to each logistics coordinator
Overview
A renowned global CPG enterprise managing complex supply-chain operations across multiple geographies, carriers, ports, customs brokers, and retailer distribution centers.
With a diverse product portfolio, including perishable goods, the organization coordinates international shipments across multiple logistics networks, with operational requirements spanning shelf life, transit time, temperature conditions, and delivery schedules. Its operations are supported by a connected ecosystem of transportation, ERP, customs, carrier, and retailer platforms.
Business Challenge
Global shipping operations were connected by people, but fragmented across systems.
The operation involved multiple parties and systems, but there was no central intelligence layer connecting shipment status, product constraints, carrier performance, customs events, retailer windows, and financial exposure.
01
Shipment information was distributed across 8–12 systems, including carrier portals, TMS, ERP, customs systems, retailer portals, IoT sensors, and spreadsheets.
02
Delays, port congestion, customs holds, temperature deviations, and delivery issues could take 24–48 hours to reach the responsible decision-maker, narrowing the window for low-cost intervention.
03
Product shelf life, carrier transit time, and retailer DC acceptance windows were managed separately, making predictable failures difficult to identify before they became costly.
04
Shipment updates and intervention instructions moved through email, phone calls, and manual portal updates, creating delays between an event occurring and the right stakeholder being able to act.
05
Historical exception information was captured in aggregate reports, but teams lacked systematic intelligence into carrier, lane, seasonal, and product-level patterns driving recurring failures.
elsai Solution
elsai proposed a single orchestration layer that connects shipment data, business constraints, specialized AI agents, enterprise systems, and human decision-makers without requiring replacement of the underlying systems.
The operating model follows a simple principle:
The agents analyze, detect, calculate, and generate. The human decides.
The elsai solution enabled:
• Unified Shipment Visibility that creates a real-time shipment record combining shipment identity, product and batch information, shelf-life, DC booking windows, carrier ETA, vessel position, customs status, temperature readings, risk scores, and exception history.
• Constraint Validation that continuously cross-checks shelf-life, transit time, and retailer DC requirements and flags risks before a shipment moves beyond its intervention window.
• Real-Time Exception Detection that monitors carrier and vessel events, identifies affected shipments, calculates downstream impact, classifies severity, and notifies responsible stakeholders.
• Costed Replanning Options that generate 2–4 structured resolution options with revised ETA, shelf-life impact, DC compliance, additional cost, approval authority, and action deadline.
• Stakeholder Communication Automation that generates approved forwarder instructions, DC rebooking requests, TMS updates, supply-planning notifications, and ASN amendments.
• Customs & Compliance Intelligence that monitors clearance status, free-day balances, documentation gaps, and projected demurrage exposure.
• elsai Data for AI Intelligence that builds carrier performance scorecards, lane-risk intelligence, perishable-risk heatmaps, exception-cost tracking, and savings attribution from accumulated operational data.
Business Impact
4-Minute Exception Detection
The POC demonstrated an average 4-minute detection time compared with a 36-hour manual baseline, enabling stakeholders to see the impact while lower-cost intervention options remained available.
£1.178M in Early-Detection Savings
Across the 12-week POC dataset, early detection was associated with £1.178M in savings across 30 avoided exceptions, averaging £39,267 per avoided exception.
14 Hours Returned per Coordinator
The modeled POC outcome returned approximately 14 hours per week per logistics coordinator, shifting effort from status-chasing toward exception management and client engagement.
Under 40 Minutes to a Resolution Plan
For a critical exception, the workflow moved from triggering event to costed options and approved resolution in under 40 minutes, compared with a manual process of up to 48 hours.
Proactive Perishable Risk Management
Data for AI creates a perishable-risk heatmap across product, lane, and season combinations, enabling supply-chain teams to identify high-risk shipment patterns before booking decisions are made.
Evidence-Based Carrier Management
Carrier scorecards connect OTIF performance, exception rates, response times, and exception costs to provide evidence for carrier contract and routing decisions.
Stronger Carrier & Lane Decision Intelligence
Provides evidence-based insights into carrier performance, lane risk, and exception costs for better supply-chain decisions.
The elsai Advantage
• One control tower connects shipment data across carriers, TMS/ERP, customs, retailer portals, and operational sources.
• Specialized AI agents address specific supply-chain failure modes instead of relying on one generic AI agent.
• Human Approval Gate keeps high-impact shipment and financial decisions under authorized human control.
• Data for AI turns accumulated operational data into carrier, lane, perishable-risk, and exception-cost intelligence.
• ARMS provides agent monitoring, workflow traces, latency monitoring, failure alerts, and auditability.
• Reusable architecture allows the control-tower model to be configured for multiple CPG clients without rebuilding the underlying agent framework.
