Warehouse Tote Layout and Pick Path for Faster Order Picking
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Solution Overview
Problem
Conventional methods and systems do not optimize the pick rate based on optimizing the arrangement of products in totes used by pickers in warehouses.
Innovation Solution
A system that includes an analytics server connected to customer devices and autonomous robots, which determines an optimal arrangement of products in a tote and a pick path by considering parameters of the tote and attributes of the products, such as fragility and orientation limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If products are arranged in totes without optimization, then the picking process is simple, but the pick rate and productivity are low
Solution Approach 1:
The system performs preliminary arrangement of products in totes before the picking process begins. The analytics server determines the optimal arrangement of products in totes based on product attributes, tote parameters, and picking path optimization, so that when pickers retrieve items, the arrangement is already optimized for maximum efficiency.
Solution Approach 2:
The system creates a digital model or representation of the tote arrangement through the analytics server, which calculates and stores the optimal product placement information. This digital copy guides the physical arrangement process without requiring complex manual planning during actual picking operations.
2Quantity of substance
If products are densely packed in totes to maximize space utilization, then storage efficiency improves, but accessibility and retrieval speed decrease
Solution Approach 1:
The system applies different arrangement strategies to different products within the same tote based on their specific attributes. Fragile products are positioned to prevent damage, products with orientation limitations are placed accordingly, and high-priority items are positioned for quick access. This localized optimization allows dense packing while maintaining retrieval speed.
Solution Approach 2:
The analytics server dynamically adjusts arrangement parameters such as product position, orientation, and layering based on product attributes and picking patterns. By changing these parameters optimally, the system achieves both high density and fast retrieval without compromise.
3Productivity
If products are arranged without considering product attributes, then arrangement is faster, but product damage and compliance issues occur
Solution Approach 1:
The system performs preliminary analysis of product attributes (fragility, orientation limitations, weight) and pre-determines the optimal arrangement that ensures product integrity. This upfront planning prevents damage during both arrangement and picking operations while maintaining efficient workflow.
Solution Approach 2:
The analytics server automatically processes product attribute information and generates optimal arrangement plans without requiring manual intervention. The system uses the product data itself to determine the correct placement, ensuring compliance with product requirements while maintaining high arrangement speed.
4Productivity
If traditional path optimization is used without tote arrangement optimization, then picking path efficiency improves, but overall pick rate remains limited
Solution Approach 1:
The system merges tote arrangement optimization with picking path optimization into a unified approach. The analytics server simultaneously determines both the optimal product arrangement in totes and the optimal picking path, considering how they interact to maximize overall pick rate rather than optimizing them separately.
Solution Approach 2:
The analytics server performs multiple functions: it analyzes product attributes, determines optimal tote arrangement, calculates picking paths, and provides guidance to pickers. This multi-functional system achieves comprehensive optimization without requiring separate specialized systems for each function.
Data Source
AI summary
Disclosed are systems and methods for optimizing a pick rate based on an optimized arrangement of products in a tote and an optimizing pick path. The arrangement of the products in the tote are based on one or more parameters of the tote and one or more attributes of each of the products. Parameters of the tote include a pack density and maximum weight capacity. Attributes of the products include a volume, fragility, weight, hazard, and orientation. The arrangement of the products in the tote dictate the path a picker takes to collect the products. Products collected at a first instant in time are placed at the bottom of the tote while products collected at a later instant in time are placed closer to the top of the tote. The analytics server transmits instructions for arranging the products in the tote such that a picker optimally orients the products.