Data Analysis Software Improves Order Picking Efficiency

Author: | Posted in Uncategorized No comments

Effective order picking is important to have maximum efficiency in warehouse storage. Finding the ideal mix of storing and slotting products is like a puzzle. Discovering what that ideal mix is for the puzzle pieces can be achieved using order picking analysis software and adjusting the layout and set-up of a warehouse storage operation.

img 20210712 wa0006


Using existing data on carton weight, length, height and demand, the software recommends the most efficient type of storage for each product. The product that moves through inventory the fastest should be the easiest for order pickers to access. The more quickly order picking is completed, the higher the efficiency rating, which creates more profit potential for the company.

IBM Data Analyst Course – Start Learning Data Analytics

Jira┬« for Data Analysis – Trusted by 65K+ Customers

Data Analysis Software – Try For Free

The size and weight of the cartons affects how many items can be placed on a shelf or carton flow bed without exceeding height and weight restrictions. The space needed for maximum efficiency in track width compared to product width and the roll center as affected by carton length are also considered. By adding these puzzle pieces of information, software can help to determine which products can be stacked on top of each other for maximum efficiency in order picking, as well.


The cartons that move with the most frequency should be placed in the most convenient slots for order picking. Consulting inventory and using data analysis software to determine the slotting of products with the highest turnover completes a critical piece of the puzzle. Another way to improve the process is to find the most efficient means possible to restock the most frequently picked products. For example, some pick shelves can be loaded from the back, rather than the front. Replenishing product while order picking is taking place does not prevent the worker from completing orders while restocking takes place.

Using data analysis to maximize the picking system is not a one-step process. It should be re-evaluated with large changes in product demand or inventory changes. For example, some items are more popular in the wintertime than the summertime. Cold weather products will be in higher demand for a short time, before warm weather products increase in popularity. As a result, winter products need to be more accessible during peak demand, then moved out of the way as their summer counterparts have a higher turnover. Preparing for and adjusting to product demands during different seasons through inventory placement will help a company and its employees be more efficient and more profitable.

Add Your Comment

Your email address will not be published. Required fields are marked *