DHL research shows 80% of supply chain leaders still rely on spreadsheets for planning. In a $9.4 trillion global logistics market, the gap between available data and actionable decisions costs you margin on every shipment.
The Strategic Gap
McKinsey reports that AI-enabled supply chain management can reduce logistics costs by 15%, cut inventory levels by 35%, and improve service levels by 65%. Yet most logistics organizations still route decisions through disconnected tools and tribal knowledge.
80% of supply chain leaders still rely on spreadsheets for critical planning decisions, creating version conflicts and stale data across teams (DHL Supply Chain Research).
The average cost of a late delivery in B2B logistics ranges from $150 to $350 per shipment. Across thousands of daily shipments, these penalties erode margin rapidly.
Trucks run empty on roughly 20% of miles driven in the U.S., burning fuel and driver hours with zero revenue. Most carriers lack the route intelligence to reduce deadhead systematically.
AI-enabled inventory management can reduce stock levels by 35% (McKinsey), but most warehouses still rely on static reorder points that ignore real-time demand signals.
TMS, WMS, ERP, and telematics systems generate data in silos. Operations teams spend hours reconciling shipment status instead of acting on exceptions.
Service levels could improve by 65% with AI-enabled management (McKinsey), yet most organizations lack the structured reasoning to connect fulfillment performance to root causes.
What Lumina Does
Lumina does not replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Specialist agents work the way a certified analyst or a production superintendent does, in the vocabulary your teams already use, and they show the evidence behind every conclusion.
Severity zones, defect frequencies, crest factor and the days remaining before a threshold is reached are calculated by the analytical engine. The charts are built from your measurements, and the agent describes what it sees rather than drawing the picture itself.
The rules, thresholds and standards your experts spent years building live outside the model, owned by you. Change a rule and you change the behaviour, audit a rule and you audit the system, because that logic is your intellectual property.
What a senior engineer teaches the system stays with the team afterwards. Corrections to a machine record and judgments about how an asset behaves shape the next analysis, so understanding accumulates instead of resetting.
When reliability and production pull in different directions, the specialists argue it out on the record and one recommendation comes back. The veto belongs to the role that owns the risk, and it is enforced rather than negotiated.
The analysis runs on the hardware you already own, so the working dataset is processed where it sits. You choose how your data interacts with AI, and you can point Lumina at a model inside your own network.
How It Works
Most AI tools are a wrapper around a single language model. Lumina is built on DIF, so working with your data is a reasoning dialogue: a plain-language question goes in, and a traceable, computed answer comes out.
Every answer is one you can inspect, not a black box. And the memory the system builds as you work compounds over time, so each question starts from everything your team has already asked.
Use Cases by Domain
Route Optimization, Fleet Health, Carrier Performance, Last-Mile Intelligence
Transportation represents 50-60% of total logistics costs, yet route planning, carrier selection, and fleet maintenance decisions are often made with incomplete data. From multi-stop LTL routing to FTL lane rate analysis, structured reasoning helps operations teams move from reactive dispatching to proactive fleet intelligence.
Analyze multi-stop routing efficiency, fuel cost modeling, driver hours-of-service compliance, and deadhead reduction opportunities across your fleet.
Transform telematics data, tire pressure monitoring, and DTC code analysis into predictive maintenance schedules that reduce breakdowns and extend asset life.
Score carriers on on-time delivery, damage rates, cost-per-mile, and lane rate competitiveness. Identify underperformers and renegotiate with data.
Optimize delivery density, time window allocation, and failed delivery prediction. Reduce cost per stop while improving customer satisfaction.
American Transportation Research Institute (ATRI), 2024 Operational Costs of Trucking
Inventory Positioning, Labor Productivity, Order Fulfillment
Warehouse operations generate massive volumes of transactional data, from picks-per-hour and dock-to-stock times to SLA adherence and order accuracy. Yet most fulfillment teams lack the analytical tools to connect labor performance to inventory positioning decisions, leading to overtime spikes, backlog accumulation, and missed service windows.
Analyze SKU velocity, ABC classification, safety stock levels, and slotting efficiency. Reduce pick travel time and improve replenishment accuracy.
Track picks-per-hour, wave planning efficiency, dock-to-stock time, and overtime correlation. Identify staffing imbalances and productivity bottlenecks.
Monitor SLA adherence, order accuracy, backlog prediction, and seasonal surge readiness. Surface fulfillment risks before they become customer-facing failures.
Warehousing Education and Research Council (WERC), 2024 DC Measures Report
Demand Sensing, Network Design, Supplier Risk
Supply chain planning requires synthesizing signals from POS data, supplier lead times, geopolitical events, and network cost models. The complexity overwhelms traditional planning tools, leaving teams to make network design and sourcing decisions based on outdated assumptions. AI-enabled planning can reduce forecasting errors by 20-50% (McKinsey).
Integrate POS signals, promotional lift analysis, seasonal decomposition, and new product forecasting into a unified demand picture that updates in real time.
Evaluate DC placement scenarios, mode selection tradeoffs (LTL vs. FTL vs. intermodal), and cost-to-serve modeling across your distribution network.
Track lead time variability, single-source exposure, geopolitical risk scoring, and supplier quality trends. Build resilience before disruptions hit.
McKinsey & Company, "Supply Chain 4.0," 2024
Across the Enterprise
Beyond operations, Lumina supports the corporate functions that keep logistics organizations financially sound, compliant, and staffed.
Analyze cost per unit shipped, margin by lane, freight spend variance, and accessorial charge patterns. Connect financial performance to operational decisions.
Monitor customs documentation, hazmat compliance, DOT audit readiness, and regulatory change impact. Reduce the risk of fines and shipment holds.
Track driver retention rates, warehouse staffing models, CDL pipeline health, and overtime trends. Address workforce gaps before they disrupt operations.
Monitor TMS and WMS integration health, EDI transaction success rates, API latency, and data quality across your logistics technology stack.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
See how Lumina brings structured reasoning to logistics operations. From route optimization to demand sensing, turn your supply chain data into decisions.
Learn how structured, multi-turn reasoning transforms logistics data from passive dashboards into active decision support for supply chain teams.
Deep DiveWhy logistics organizations need local-first AI architecture that keeps sensitive shipment, pricing, and supplier data under their control.
InsightSupply chain dashboards show you what happened. Dialogue intelligence helps your team figure out why it happened and what to do next.
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