Warehouses that have embraced computer vision and machine learning are successful in automating some of the most labor intensive and error prone tasks in a warehouse. These tasks occur during critical warehouse workflows including warehouse receiving, inventory put-away, cycle counting, order packing and of course outbound shipping. Accurately recording inventory during these workflows provides the warehouse system of record (i. e., WMS) the ground truth supporting more efficient inventory processing and management. The most common AI enabled applications during these workflows include:
One of the fundamental tasks in a warehouse is accurately identifying and tracking products. Traditionally, this required manually scanning barcodes or QR codes on each item. However, warehouse computer vision has changed the game by not just automating, but even enhancing this process.
AI-enabled computer vision systems equipped with cameras and image recognition algorithms can swiftly scan and decode barcodes and QR codes on products. In the event a barcode is damaged or obscured, the CVML system looks for the human readable number string that accompanies barcodes and can read the text, thus making it less sensitive to barcode condition and therefore more reliable for barcode reading (see “Label Text Reading” section below for more detail.) This automation significantly reduces the risk of human error and increases the speed of inventory management. As products move through the supply chain, computer vision ensures that each item is accurately tracked, from its arrival at the warehouse to its shipment to the customer.
Ensuring the quality of products in a warehouse is crucial, especially when it comes to customer satisfaction. Detecting damaged items manually can be time-consuming and unreliable. Computer vision provides a more efficient and consistent solution.
By analyzing images of products, computer vision and machine learning systems can detect subtle signs of damage, such as dents, scratches, or cracks. Detection before inventory enters the warehouse eliminates blame for pre-damaged goods. Additionally detecting damage before orders LEAVE ensures that customers do not receive imperfect goods. Vimaan AI powered systems record photographic evidence of inventory condition which aids in the inventory condition disputes with customers and transportation agents.
Accurate inventory management is at the core of warehouse operations. Traditional manual counting methods are prone to errors and can be a significant drain on resources. Computer vision, however, has the capability to count packages with precision and speed. Using cameras strategically placed in the warehouse, AI powered computer vision systems can capture images of packages and automatically count them as they move through conveyor belts, transported on forklifts or stored in racks. This real-time counting capability provides warehouse managers with up-to-the-minute inventory data, helping them make informed decisions about restocking, order fulfillment, and overall warehouse optimization.
Optimizing warehouse space is essential for maximizing efficiency and minimizing costs. Understanding how bins and storage spaces are utilized can be challenging without the aid of technology. Computer vision and machine learning analyze bin utilization allowing for further optimization of bins space.
The Vimaan cycle count vision system, StorTRACK, captures images of storage areas allowing Vimaan algorithms to analyze these images to determine how efficiently rack space is being used. This AI-enabled analysis includes identifying empty bins, partially filled bins, and overstocked bins. Warehouse managers can then make data-driven decisions about rearranging inventory, adjusting storage locations, and optimizing space utilization to reduce waste and increase storage capacity.
In addition to product identification, there are often instances where reading text is crucial to warehouse inventory tracking. This can include reading labels on products, boxes, or shipping documents. Unlike other inventory tracking solutions, Vimaan computer vision is well-equipped to handle these tasks with precision.
Text recognition, also known as Optical Character Recognition (OCR), is a computer vision application that can extract and interpret text. In the context of warehousing, OCR can be used to read labels on products, invoices, packing slips, and shipping labels. This technology reduces the risk of errors in data entry and ensures that the information on labels is accurately recorded in the warehouse management system. Most inventory tracking systems solely scan barcodes, but Vimaan can read, capture and interpret any label text important to warehouse.
Vimaan computer vision and machine learning automates the process of capturing pallet dimensions (and weight with the aid of 3rd party tool integration) of a palletized load automatically using Vimaan proprietary equipment and AI-powered technology. The goal of pallet dimensioning is to gather accurate information about the size and weight of palletized goods for various purposes, including inventory management, space optimization, shipping cost calculations, and compliance with industry regulations.
By recording precise measurements of pallets and their contents, Vimaan customers maintain more accurate inventory records, reducing the risk of overstocking or understocking items and also minimize the use of expensive labor to dimension incoming and outgoing pallets.
. Understanding the exact dimensions of palletized loads helps in optimizing warehouse storage space, calculating shipping costs, adhering to regulations and improving customer satisfaction.
The Cycle Count Guide delivers insight not available from any other resource including:
– Top 5 Ways to Improve Inventory Accuracy
– The Truth Behind all WMS’s
Warehouse inventory tracking with Vimaan delivers the truth from your floor to your WMS, providing the most precise inventory management accuracy possible.