Optimal picking for all of your order pickers to increase productivity and cut travel time by up to 43%
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The new way of picking in the “never normal” era
Manual
• Determining the daily workload per warehouse area is difficult. • Batches aren't put together most efficiently. • There are no optimized picking lists
Automated
• Lower labor costs by forecasting workloads. • Intelligent batch picking increases productivity. • Picking path optimization cuts travel time nearly in half.
Common Picking Problems
“Our pickers are transferred from area to area in response to new order waves, as we have no visibility into the upcoming hours’ workload.”
“While pickers are provided with picklists, they should have their own paths to take.”
“Sorting operations necessitate a small batch size, whereas picking efficiency necessitates a large batch size. What is the cost-benefit analysis?”
AI Scheduling enables planners to understand the optimal picking sequences.
AI Scheduling on top of your WMS
AI-powered Warehouse Workforce Demand Planning
To avoid over- or under-staffing, adjust labor allocation throughout a shift to ensure that work is completed on time. Pickers can be reassigned to other areas based on shifts in projected workloads per warehouse area throughout the day.
Real-Time Batching & Waving
Have optimized batches of pick lines that are assigned to pickers. Creating optimal waves based on customer order batching across multiple areas, order drop time, and maximum wave sizes.
Picking Path Optimization
Determine the best path for the worker to take through the warehouse to complete their work. Picklist optimization within a wave or batch based on warehouse area layout and pick dimensions.
Key results, powered by Solvice
Customer details
40+
Warehouses
50-500
Workers per warehouse
€18 Mld
Revenue
Results
50%
Decrease in actual overtime
11%
More accurate workload prediction
Customer details
150k
Orders per day
3.000
Batches
90.000
Sqm.
32
Warehouse areas
Results
15-20%
Increase in order efficiency
39%
Decrease in travel time
Customer details
1.000+
Workers
€120 Mln
Revenue
Results
10%
Less congestion
20%
Shorter travel times
Aim high, start small, and iterate quickly
How AI-powered labor demand planning and picking optimization can be used to automate and optimize fulfilment operations.
The role of data in labor planning and picking scheduling.
How to integrate AI-powered capabilities to your current WMS.
In your demo, Solvice expert Michaël Saintobyn will show you how warehouses use automated, AI-driven scheduling to optimize picking operations.
To learn more, request your demo
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