FAQs about warehouse slotting and waving improvements
Slotting optimization analyzes 52 weeks of historical order data to identify movement patterns for each SKU. It then ranks items based on velocity, volume, and operational priorities, while also ranking warehouse locations based on accessibility and proximity to outbound areas. The system matches items to locations based on these rankings, ensuring high-velocity items are in the most accessible locations.
Our SKU forecasting uses the open-source Facebook Prophet time series model, which effectively handles seasonal patterns and trend changes. Each SKU has its own dedicated model that's tuned based on demand variability segmentation, allowing for customized forecasting approaches based on each product's unique characteristics.
Wave optimization reduces travel time by intelligently grouping orders that can be picked together with minimal aisle visits. The system analyzes incoming orders, assigns appropriate pick types, groups orders based on operational parameters, and applies our aisle minimization algorithm to create batches that minimize total travel distance while respecting constraints like cart capacity.
Slotting optimization focuses on where items are placed within the warehouse based on movement patterns, ensuring high-velocity items are in accessible locations. Wave pick optimization focuses on how orders are grouped and processed, creating batches that minimize travel time. While each component delivers benefits independently, they work together to create complementary effects that maximize overall efficiency.
The system identifies dead stock as items with no orders in the past 6+ months. Slow movers are products that have been in the warehouse for more than three months with a weekly average pick of 10 units or fewer. These items are flagged for potential relocation from prime picking locations to designated areas, freeing up valuable warehouse space.
For automated environments like those using Locus robots, the system makes special adaptations to prevent congestion. These include distributing high-velocity items across multiple aisles rather than concentrating them in a single area, creating multiple wave templates for varied robot paths, and implementing strategic high-mover placement to balance traffic throughout the facility.
Each component (slotting and wave optimization) typically takes 2-3 weeks to implement. Implementation requirements include historical order data (ideally 52 weeks), warehouse layout information, current location assignments, and SKU master data. The system is designed to work alongside existing operations with minimal disruption.
Yes, our solution supports multiple picking strategies including S-shape, return method, mid-point, and largest gap approaches. We analyze current picking methodologies and optimize within that framework, enhancing efficiency without disrupting established workflows.
The system analyzes 52 weeks of rolling historical data, refreshed weekly, allowing it to recognize seasonal patterns and adapt accordingly. Individual SKU forecasting models account for seasonality, and the continuous reassessment of inventory placement and order batching ensures that the warehouse remains optimized as conditions change.
Our pilot implementations have shown efficiency improvements ranging from 15% to 60%, depending on current warehouse organization. Specific metrics include up to 47% reduction in aisle visits per task, up to 30% improvement in units per hour processed, up to 10% reduction in task count, and up to 18% increase in orders per task. These improvements translate directly to reduced labor costs, increased throughput, and improved service level compliance.
