Leverage advanced algorithms to analyse data on inventory, shipping, and other factors to
identify areas of inefficiency and suggest ways to improve efficiency. This can include
everything from reorganising the warehouse layout to implementing new processes and
technologies. By streamlining operations, the Warehouse Optimizer can help companies reduce
costs, improve productivity, and increase overall performance.
Problem Statement
Client has over 80 big inventory warehouses (bigger than 5000 sq. m) to store pharmaceutical
products.
Most of the warehouses have manual picking done by operators.
Pickers spend large time on the warehouse floor travelling between different bin locations.
In some automated warehouses, the time taken by final packing team is higher due to line
items coming in different totes.
Key Requirements
Optimize the picking layout to reduce time spent by pickers in the warehouse
Solution Advantages
The solution runs offline without any internet service
On-cloud deployment in client’s VPC.
Solution integrates with client’s SAP system to modify the bin allocation in WMS.
The solution predicts the amount of time required for pickers to pick items for efficient
planning.
On-premise deployment in client’s infrastructure
Fully customizable bank statement formats and analysis output
High Level Architecture
Output Accuracy
1.5 hours on average
Time spent after AI
recommendation picking top 20 products
2 hours on average
Time spent before AI
recommendation picking top 20 products
Client Engagement
Data For Training:
Historical data for training – 6 months
No of warehouses – 2 (1 automated and 1 non-automated)
Testing By Client For Goal Achievement:
1 month testing in actual warehouse Efficiency Result = 25% improvement in picker efficiency
Deployment:
Azure, Databricks
Benefit Achieved:
Better planning for workforce in the warehouse
Improved efficiency of pickers
Usage since:
Jun 2022
