Everyone is talking about how artificial intelligence (AI) is revolutionizing manufacturing. Heavy industry and the tech sector were early adopters, now the apparel industry is starting to consider how this technology can help it reduce costs, boost productivity and even raise quality levels. However not all AI-driven technology will be applicable to the apparel industry. Here are five ways where AI will be likely to make a significant impact on apparel manufacturing. <h3><b>IoT Sensors </b> </h3> One of the growing AI innovations in manufacturing is the execution of internet of things (IoT) sensors. <b>How It Works</b><b> </b> Sensors are placed on machines that feed performance data to a central (usually cloud-based) server. Management can view this data in real time using dashboards on their computers. <b>Best Use Cases </b> Predictive monitoring potential equipment failures. It can also be used to help spot areas where workflows can be improved. <h3><b>Machine Vision Error Detection </b></h3> Machine vision frameworks let manufacturers computerize quality control processes at both the front and back finish of production lines. <b>How It Works</b><b> </b> Manufacturers develop a deep learning-based algorithm and train it with examples of defects it must detect. With enough data, the neural network will eventually detect defects without any additional instructions. <b>Best Use Cases</b> Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. For apparel manufacturing, there still is not sufficient ability to detect less generalized flaws in garments. However more standard errors can be picked up saving time during mass production. <h3><b>Automated Visual Inspection Tools </b></h3> Some flaws in products are too small to be noticed by the naked eye, even if the inspector is very experienced. <b>How It Works </b> Machines can be equipped with cameras many times more sensitive than human eyes that can detect even the smallest defects. The system recognizes defects, marks them, and sends alerts. <b>Best Use Cases</b> For technical or functional garments that need to provide assurance of a high level of performance, often under extreme conditions, this technology can elevate the level of product inspections. This includes performance sports gear, protective gear and medical-related (wearable tech) products. <h3><b>AI-Controlled Software Powering Robots</b> </h3> Tasks that were previously done by humans or physical machines can now be done by AI-controlled software powering robots. This increases flexibility and traceability for the robots, and in many cases, reliability. <b>How It Works</b> Instead of pre-programming robots to perform specific tasks, AI-controlled software enables robots to be more ‘responsive’. This creates a ‘hybrid’ of sorts between human capabilities (to think and respond) and robotic (ability to repeat the same task over and over with stable efficiency). <b>Best Use Cases</b> Still in its early stages for apparel manufacturing, AI-controlled robotics could be the answer to performing manufacturing tasks that are too customized or complex for standard robots. <h3><b>Inventory Management Tools</b> </h3> PLM and other software has greatly aided inventory management. AI will take current systems to the next level. <b>How It Works</b> Machine Learning is used to manage inventories based on demand and supply. More importantly, to provide better predictive data. <b>Best Use Cases</b> The ability to fine tune management of both materials and finished garments can save costs, as well as play a key role in sustainability by reducing product and materials waste.