The manufacturing industry is a leading Internet of Things (IoT) solution adopter, with a report from IoT Analytics showing that IoT software will grow 37% by 2027 in the industrial software market. Another technology the sector is keen to integrate with its IoT-enabled machines is artificial intelligence (AI). Together, IoT and AI help manufacturers enhance the efficiency of many use cases, from equipment monitoring to supply chain management.
AI and IoT in Action: Automated Visual Inspection
A notable example of IoT and AI significantly improving the performance of an industrial process is vision inspection systems, shifting from manual inspections to automated data-capturing processes.
One vision inspection solution built by a leading end-to-end IoT solutions enabler leverages advanced algorithms and deep-learning techniques to evaluate wheels on the production line. The solution, having been trained on different images, can determine if there are any issues with the wheels, such as missing lug nuts, incorrect lug nuts, scratches, etc.
Should the solution spot an issue, it will create a trouble ticket for a human operator, allowing them to make data-driven decisions and streamline operations. Note that this data links with an existing enterprise resource planning (ERP) and cloud platform, ensuring the problem gets resolved quickly to maximize productivity.
The Integration Challenge
Integrating AI and IoT into manufacturing processes can be difficult. Manufacturers may have dozens of machines and software solutions, each using unique application programming interfaces and connectors. Put simply: synergy is a challenge because manufacturers’ machines speak different languages.
Likewise, many manufacturers have a combination of legacy and new machines on their shop floors. While these older machines still have utility and considerable life left, they predate modern connectivity standards and cannot communicate with IoT and AI-enabled ones.
Integrating AI and IoT With Legacy Machines and Enterprise Systems
Manufacturers must bridge the generational divide within their factories. One strategy, “digital retrofitting,” requires manufacturers to install IoT sensors on their legacy equipment to collect data. Then, manufacturers can use edge computing technologies, such as gateways or edge devices, to translate legacy protocols into IoT-compatible formats, allowing legacy machines to connect to the network and communicate with IoT device-enabled machines. As the name of this strategy suggests, it eliminates the need for manual, time-consuming retrofitting.
Another strategy manufacturers can use to standardize their machines’ communication involves deploying an IoT platform to create an abstract layer and collect data from any machine. Specifically, the platform creates metadata that can integrate with any IT system, ensuring that the data from IoT devices and AI-powered machines integrates seamlessly with the enterprise system data. Manufacturers should note that best-in-class IoT platforms typically come with larger lists of equipment connectors and languages than other offerings.
Wining in the Era of Industry 4.0
IoT and AI are essential components of digital transformation for manufacturing enterprises. The combination of these technologies enhances product quality and reduces errors, empowering manufacturers to elevate the overall quality of manufactured goods and boost customer satisfaction. Ultimately, those manufacturers that can overcome the integration challenge and bridge the generational divide between legacy and modern machines will gain a significant competitive edge in this new era of Industry 4.0.