Taking manufacturing a step closer to the edge

The dawn of the Industry 4.0 revolution is marked by the rise of automation technologies transforming the manufacturing landscape. Emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and robotics are paving the way for faster and more advanced data-driven decision-making. With the promise of smart factories and warehouses that continuously collect and share massive data sets through connected devices and distributed infrastructure, manufacturers are increasingly relying on this data to make decisions more rapidly. They are harnessing these technologies to boost productivity, streamline processes, and increase flexibility.

Decentralized edge computing and AI

More specifically, manufacturers can use the data they collect and analyze to improve their processes, maintain systems, and respond to near real-time issues on the factory floor and in the warehouse and distribution channel. Edge computing is playing a crucial role in this transformation by providing a decentralized platform for data gathering, localized decision making and data processing, thereby reducing latency and bandwidth requirements normally associated with both PLCs and centralized computing.

The future of AI and its eventual widespread use, along with other automation technologies, relies on decentralized edge computing. This includes connecting IoT or other enabled devices to distributed network nodes. It also involves using localized AI-enabled chips capable of creating algorithmic models, such as machine learning.

As edge computing technology advances, manufacturers are increasingly interested in its capabilities, helping to build the factory of the future. Contrary to debates about data storage locations, edge computing complements the cloud instead of replacing it entirely. This synergy allows manufacturers to access and apply data-driven knowledge and “big data,” empowering smart factories and enhancing the quality of their products.

Revolutionizing quality control, diagnostics, and logistics

The following are examples of operations that can leverage edge computing.

• Automation of quality control includes quality control automation on a production line, for example, in a setting such as a bottling line in the beverage industry or the packaging process in the food or consumer goods sectors. Manufacturing facilities can range from minimally to fully automated production lines. Edge computing applies to both, but it is particularly useful in fully automated environments where autonomous quality data collection is crucial.

• Production line diagnostics uses edge-based data gathering separate from the control systems on production lines (usually PLC-based). This approach enables more precise monitoring and prediction of wear and breakdowns in complex systems with many moving parts. It also provides a clean separation between the critical machine control aspects, managed by the PLC, from data collection and processing by the edge computer.

• Warehouse, product logistics and tracking automation offer numerous data-driven benefits for enhanced efficiency and accountability. Edge computing allows for optimal decisions on local manufacturing within the warehouse, considering factors such as latency, cost, security, or any other reason. Moreover, advancements in product logistics and tracking extend this edge of the edge computing capability, facilitating real-time tracking of inventory and other uses even as products move from the manufacturing environment into other supply chain stages using technologies such as RFID and barcoding. This ensures near real-time product movement and tracking.

• AI/ML applications (artificial intelligence and machine learning), especially in manufacturing, could become the golden use case if increasing data collection and reducing its availability time to the manufacturing process remains the primary driver of edge computing strategies. AI’s ability to manage and drill into the big data possibilities allows it to recognize and define manufacturing and process trends, thereby driving efficiency improvements and finding possible lean options. Edge computing facilitates the application of ML at various levels of detail and complexity at a local on-the-line level like never before, relieving the PLC or machine controls and enabling reliable predictive data in new and innovative ways.

Manufacturers need edge computing technology to build the factory of the future and be ready to take advantage of everything it has to offer. Edge computing allows manufacturers to make flexible choices about processing data to eliminate time lags, decrease bandwidth use, and benefit from the data they can gather about the manufacturing process.