Pick Aisle Layout Optimization Using Demand and Travel Simulation
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Solution Overview
Problem
Warehouse workers face inefficiencies in locating and navigating to pick items due to random arrangement of items in the pick area, leading to increased time and energy consumption in fulfilling pick orders.
Innovation Solution
A system and method to determine optimal pick aisle arrangements based on historic pallet information, considering factors like frequency of picking, pallet weight, and seasonal demand, to allocate bays and aisles efficiently, grouping high-velocity and high-demand pallets together, and minimizing travel distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If items are arranged randomly in the pick area, then storage flexibility is maintained, but warehouse workers spend more time and energy locating and navigating to pick items
Solution Approach 1:
The system performs preliminary analysis of historic pallet information, velocity data, and pick order patterns before arranging items. By pre-calculating optimal pick aisle arrangements based on anticipated demand and item characteristics, the system prepares the pick area in advance, reducing the time workers spend searching and navigating during actual picking operations
Solution Approach 2:
The system applies different arrangement strategies to different zones within the pick area based on local characteristics. High-velocity items are placed in easily accessible locations, while low-velocity items are positioned in less accessible areas. This localized optimization of item placement based on specific pickup frequency and demand patterns improves overall picking efficiency without requiring complete randomization
2Ease of operation
If pallets are placed closer to the front of pick aisles, then travel distance is reduced, but space utilization decreases
Solution Approach 1:
The system dynamically adjusts pick aisle arrangements based on changing demand patterns, velocity data, and seasonal factors. Rather than maintaining a static layout, the system recalculates optimal positions for pallets based on current priorities, allowing high-demand items to be positioned closer to the front during peak periods while maximizing space utilization during lower-demand periods
Solution Approach 2:
The system changes key parameters such as item velocity, demand frequency, and seasonal factors to determine optimal pallet positioning. By continuously monitoring and adjusting these parameters, the system finds the optimal balance between minimizing worker travel distance and maximizing space utilization in the pick area
3Productivity
If pick aisle arrangements are optimized for high-velocity items, then picking efficiency improves, but the system becomes less adaptable to seasonal demand changes
Solution Approach 1:
The system continuously monitors pick order patterns, velocity data, and seasonal demand trends, using this feedback to dynamically adjust pick aisle arrangements. By incorporating real-time and historical feedback loops, the system adapts to changing seasonal demands while maintaining optimization for high-velocity items, ensuring both picking efficiency and adaptability are achieved simultaneously
Data Source
AI summary
Disclosed are systems and methods for determining a pick area arrangement for a storage facility. A method can include retrieving, by a computer system, historic pallet information for pallet SKUs and pick area information, determining, based on the information, a number of bays to allocate for each pallet SKU, determining, based on the information, a number of dynamic bays to allocate in the pick area, which receive pallet SKUs in high demand during a particular time, determining, based on the information, quantities of aisles to allocate as pick aisles in the pick area by simulating aisle arrangements with pallet SKUs from the historic pallet information and determining average travel times to complete pick orders having the pallet SKUs in the aisle arrangements, selecting the quantity of aisles providing a lowest average travel time, and assigning each pallet SKU from the historic pallet information to bays in the quantity of aisles.