Warehouse Slotting Optimization Agent
Analyzes SKU velocity, pick paths, and order affinity to recommend and execute warehouse slotting changes that reduce travel time and improve pick efficiency.
Warehouse slotting — which SKUs live in which bin locations — is typically set once when a facility opens and rarely revisited as product mix and demand patterns shift, so fast-moving items end up in distant, hard-to-reach locations while slow movers occupy prime picking real estate near the front
Reslotting projects are seen as disruptive and labor-intensive, so they happen infrequently even though seasonal and lifecycle demand shifts make the existing layout increasingly inefficient between projects
Order affinity (items frequently ordered together) is rarely factored into slotting decisions, adding unnecessary travel to multi-item picks
This agent continuously analyzes velocity, seasonality, and order affinity data to recommend incremental slotting changes, prioritized by impact, and generates the move instructions to execute them with minimal disruption
The agent analyzes historical pick data, SKU velocity trends, and order affinity patterns (which items are commonly picked together) to build a slotting optimization model that minimizes total travel distance and picker congestion. It compares the optimal slotting layout against the current layout to identify the highest-impact incremental moves, rather than proposing a full facility reslot, and sequences move instructions to avoid disrupting active picking during peak hours. An LLM reviews product handling constraints (weight limits, ergonomic placement rules, hazmat separation) to ensure recommended moves comply with safety requirements before generating the execution work list for warehouse staff.
Analyze Velocity and Affinity Patterns
- Pull historical pick and order data by SKU
- Calculate velocity trends and seasonality by SKU
- Identify order affinity pairs (frequently co-picked items)
- Map current slotting layout against pick path distances
Model Optimal Slotting
- Run optimization model to minimize travel distance and congestion
- Apply weight, ergonomic, and hazmat placement constraints
- Compare optimal layout against current layout
- Identify highest-impact incremental moves
Generate and Schedule Move Instructions
- Produce detailed move instructions (from-bin to-bin per SKU)
- Sequence moves to minimize disruption to active picking
- Schedule execution during low-volume windows
- Update WMS bin locations upon move confirmation
Measure Impact and Iterate
- Track pick travel time and productivity before/after moves
- Calculate labor savings from reduced travel distance
- Monitor for new velocity shifts requiring further adjustment
- Publish quarterly slotting optimization impact report