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The Five Stages of Halagon
Our framework for building AI decisional intelligence for logistics, unlocking maximally optimal decisions at breakneck speed across the entire supply chain.
Stage 01
Data Infrastructure
Data infrastructure unifies operational data across the systems you already run: WMS, TMS, OMS, YMS, ERP, the data lakehouse, and more. IDs, timestamps, and event semantics are reconciled so that SKUs, orders, shipments, lanes, pallets, and shifts become first-class objects, ready for the process intelligence stage to consume.
Integrations and the unified schema are shaped to each customer's data landscape, ensuring seamless connectivity without disrupting existing workflows.
Key Capabilities
- Multi-system data unification (WMS, TMS, OMS, YMS, ERP)
- ID and timestamp reconciliation across sources
- Event semantics normalization
- Custom schema mapping per customer
- Real-time and batch data ingestion
Stage 02
Process Intelligence
Process intelligence reconstructs the operation from the events your systems produce, building a live digital twin of the warehouse floor, the lanes, and the labor schedule.
Object-centric process mining tracks pallets, orders, waves, shipments, and appointments simultaneously. The digital twin and process is built and mapped around the customer's own operation, providing unprecedented visibility into operational dynamics.
Key Capabilities
- Live digital twin of operations
- Object-centric process mining
- Multi-object tracking (pallets, orders, waves, shipments)
- Labor schedule integration
- Custom operation mapping
Stage 03
Models & Optimization
Models and optimization brings together ML predictions and constraint solvers. ML models predict the uncertain quantities the operation depends on: demand, dwell, ETA, no-show risk, and more.
Solvers turn those predictions plus the constraints surfaced by process intelligence into optimized decisions across vehicle routing (VRP), pick-path optimization, wave release, dock-door assignment, slotting, labor allocation, and inventory positioning. Models, solvers, and decision classes are scoped per customer implementation.
Optimization Areas
- Vehicle routing problem (VRP) optimization
- Pick-path optimization
- Wave release scheduling
- Dock-door assignment
- Slotting and labor allocation
- Inventory positioning
Stage 04
Implementation
Implementations are made upon customer preference during integration. The framework supports several channels for executing decisions.
Operator review for high-stakes calls, direct push into the WMS, TMS, YMS, or OMS for well-constrained decisions, and AI agents for multi-system coordination across carriers and customers. The mix shifts over time as the framework proves itself on specific problem types.
Implementation Channels
- Operator review for high-stakes decisions
- Direct system integration (WMS, TMS, YMS, OMS)
- AI agents for multi-system coordination
- Carrier and customer coordination
- Progressive automation trust building
Stage 05
Monitoring & Observability
Monitoring and observability provides live system state, decision traceability from inputs to action, and recommended-versus-executed comparison with operator overrides captured at the moment they happen.
Failures are attributed back to the layer that caused them—whether data drift, workflow drift, forecast error, or solver infeasibility—so the framework continuously corrects itself. Metrics, thresholds, the improvement loop, and root-cause taxonomies are calibrated to each customer deployment.
Observability Features
- Live system state monitoring
- End-to-end decision traceability
- Recommended vs. executed comparison
- Operator override capture
- Root-cause failure attribution
- Continuous self-correction loop
Ready to transform your logistics operations?
Schedule a call with our team to learn how Halagon can optimize your supply chain with decisional AI.
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