Infrastructure for Dynamic Slotting & Layout Optimization

Dynamic slotting requires documented layout constraints: fire safety clearance requirements, equipment turning radius minimums per aisle type, hazmat segregation rules, and throughput targets per zone. These constraints must be current and findable so the AI's layout recommendations comply with safety and operational policies. Without documented constraints, optimization proposals may violate OSHA clearance requirements or create equipment navigation conflicts.

Slotting optimization requires systematic capture of actual pick sequences, travel times per route, and pick frequencies per location over time. Each pick transaction recorded by the WMS—SKU, location, timestamp, picker route—builds the dataset needed to compute travel-distance matrices and identify suboptimal slot assignments. Seasonal pattern analysis requires consistent historical capture over multiple seasons.

Layout optimization requires formal ontology mapping SKU entities to demand profiles, location entities to spatial coordinates and zone constraints, and travel-path entities connecting locations with distance and equipment-type attributes. Without formally mapped relationships between these entities, the AI cannot compute travel-distance matrices or model how relocating one SKU affects travel paths to adjacent slots. This is graph computation requiring machine-readable spatial relationships.

Dynamic slotting must query historical pick patterns, current slot assignments, and SKU master data (dimensions, storage requirements) from the WMS to generate layout recommendations. It must also push approved slotting changes back as updated slot assignments in the WMS. API access enables this cycle of analysis and implementation without requiring IT-mediated data exports for each optimization run.

Slotting recommendations must reflect current SKU dimensions, velocity classifications, and storage constraint changes. When a new product introduction or demand forecast update changes a SKU's velocity tier, the slotting model should trigger re-evaluation of that SKU's optimal location. Event-triggered maintenance ensures slot assignments remain aligned with current demand reality rather than last quarter's pick patterns.

Layout optimization connects WMS (current slot assignments, pick history), demand forecasting systems (future SKU velocity), and order management (seasonal order patterns). API connections allow the optimizer to incorporate both historical pick data and forward-looking demand signals when generating slotting recommendations—critical for seasonal layout changes that must be implemented before demand spikes, not after.