Software Platform
Slotting Optimization Platform
A cloud-enabled optimization platform that uses rigorous mathematical models to determine optimal SKU-to-location assignments — incorporating storage constraints, demand variability, and operational trade-offs that rule-based WMS slotting tools cannot capture.
$250K
Direct labor savings per DC annually
$300K–$400K
Total estimated value per DC annually
Scenario-based
Simulation across demand and layout changes
The Problem
Most distribution centers slot SKUs using simple ABC classification rules built into their WMS. These rule-based approaches fail to capture the multi-dimensional trade-offs that drive picker travel time — SKU affinity, cube utilization, replenishment frequency, labor balancing, and storage type constraints.
The result is suboptimal SKU placement, excessive picker travel, high replenishment frequency in forward pick areas, and poor space utilization — all of which compound when demand patterns shift and static slotting rules are not updated.
The Solution
The Slotting Optimization Platform formulates the SKU-to-location assignment problem as a constrained mathematical optimization, with an objective function that balances travel time, replenishment effort, and space utilization across all SKUs simultaneously.
The platform also provides scenario-based simulation — enabling operators to model the impact of demand changes, new SKU introductions, or layout modifications before committing to a relocation plan. Recommendations integrate back into existing WMS systems via API or batch export.
Core Capabilities
Platform Architecture
ERP / WMS Data
Source transactional, SKU, and demand data from existing operational systems.
Data Ingestion Layer
ETL pipelines and API connectors that normalize and validate incoming operational data.
Optimization Engine
Python-based mathematical optimization for SKU-to-location assignment using Gurobi, OR-Tools, or Pyomo.
Scenario Simulation
What-if analysis across demand scenarios, layout modifications, and operational constraint changes.
Recommendation Output
SKU-to-location mapping, relocation sequencing, and phased implementation plans.
WMS Execution Layer
Push recommendations back into WMS for directed, system-guided execution.
Get in Touch
Interested in a pilot engagement?
We are targeting mid-sized warehouses (50–200 employees) for early optimization-as-a-service pilots with measurable ROI validation. If your operation relies on manual re-slotting or WMS rule-based placement, contact us to discuss what a pilot would look like.
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