The last decade has brought a lot of innovation into warehouse operations, targeted at making it faster, more efficient and less error-prone. While historically most warehouses have relied heavily on human labor for the picking and placing of items, most warehouses today adopt some level of goods-to-person (GTP) automation. By bringing goods directly to pickers, rather than having them travel to the goods, these systems significantly reduced the time and physical strain involved in the picking process. GTP systems fall into two general categories: 1. Early iterations of GTP technology focused on mechanized solutions like conveyor belts and static picking stations, which already marked a considerable improvement in efficiency and accuracy over manual methods. In addition, horizontal/vertical carousels and vertical lift modules (VLMs) were used to present items to pickers. 2. Currently, random access, high-density systems, including cube and shuttle-based systems, are used to deliver items to a picker at a pick station. This article focuses on GTP systems of the second category, which truly revolutionized GTP systems by integrating advanced technologies such as robotics, artificial intelligence and machine learning. An efficient GTP system ensures that SKUs are presented ergonomically with sufficiently high throughput for optimal picker efficiency. High throughput rates are crucial for optimizing picker efficiency in warehouses due to several key factors.
Full opinion : Robotics For Efficient Fulfillment.
