The online fashion market is booming. Around 21 % of all fashion items worldwide are already ordered online, with an upward trend. This holds enormous potential for the fashion industry but also presents a complex challenge. Given the shortage of skilled workers due to demographic change, companies find it increasingly difficult to find enough qualified workers, especially for monotonous and repetitive tasks like order picking.
Dynamic changes, such as seasonal fluctuations in demand or sales offers, also characterize the fashion industry. Especially during peak periods, logistics companies struggle to cope with high order volumes and, at the same time, meet the expectations of modern customers for fast and error-free delivery. To remain competitive and exploit the enormous potential of fashion e-commerce, automating central processes such as order picking is essential. However, fashion items posed significant challenges for previous automation solutions, mainly due to the type and nature of the items.
To protect textiles from external influences, they are usually packed in polybags. These bags are typically transparent and, especially when they are stacked on top of each other, are difficult to distinguish, even with the human eye. In addition, depending on the lighting, reflections can occur on the packaging. This can be problematic, as many cameras can no longer recognize the depth correctly. The Sereact software works with high-precision AI models that enable objects to be reliably recognized even with low-cost cameras, despite transparency, reflection, and overlapping.
Picking textiles in polybags also places specific demands on the hardware. If the wrong gripper is used, for example, this can cause the items to fold up and fall off, as a vacuum can no longer be generated at the crease. Or too much pressure is generated, and the bag is damaged. Sereact, therefore, works with special grippers and cups. Depending on the size and surface properties, the AI software can select the gripper with which the robot can pick the object optimally. For example, for larger objects, a multi-suction gripper is needed to maintain their shape, while the single-suction gripper is sufficient for smaller objects. Special cups also ensure that the bags are not damaged.
Of course, not only T-shirts, pants, etc. are ordered from online mail order companies, but also many other products, such as shoes, accessories, or decorative items, which need to be handled differently to textiles. In addition to shape and size, the software must, therefore, also be able to recognize the surface properties of the objects and then select the correct gripper.
Just like picking, textiles also present specific challenges when packing. The robot's path planning must be optimized so that the items do not rock too much during movement so that they do not fall off and can be placed neatly in the shipping carton.
The fashion e-commerce market will continue to grow in the coming years, which means that logistics companies will have to handle ever larger order volumes in ever shorter periods. With Sereact's AI-based robotics solution, online fashion retailers can fully automate their picking and packing processes, making their logistics more efficient, crisis-resistant, and future-proof.