Why Edge Computing Is Essential for IoT-Based Manufacturing

The Internet of Things (IoT) is reshaping manufacturing, allowing companies to get shop floor visibility, predict machine maintenance, track performance and improve asset management and quality control. IoT devices monitor key performance indicators (KPIs) across facilities, generating massive amounts of data. Sending data to a central location can overwhelm networks, raise storage costs and delay critical insights. Since much of the data requires real-time analysis, delays could have out-sized costs on modern production.

Edge computing addresses this concern by processing data locally where it’s created, avoiding network delays and latency issues. It provides frontline workers immediate access to the insights they need, allowing for near real-time decisions and addressing regional challenges such as environmental risks and regulations. Edge computing with IoT in manufacturing optimizes outcomes by distributing computing power and data processing locally while still benefiting from automation and management capabilities of global IT systems. 

By taking a burden off cloud systems, edge computing helps manufacturers reduce costs, optimize production, ensure worker safety and move toward more predictable, reliable operations. This article discusses a few use cases for computing on the edge in the manufacturing sector.

Edge Computing Delivers Real-Time Support for IoT-Based Manufacturing

Let’s take a closer look at some of the benefits of edge computing use cases for manufacturing: 

• Reduced Latency for Real-Time Decision Making. Edge computing allows for real-time adjustments as frontline workers get immediate access to data, whether it’s changing machine settings or detecting defects mid-production. 
• Improved Data Security and Privacy. With edge computing, data processing occurs locally, minimizing exposure to cyber threats during cloud transmissions.
• Efficient Resource Utilization. By cutting down on data transfers and optimizing bandwidth, manufacturers not only save costs but also ensure a more reliable, resilient operation.
• Increased Automation and Predictive Maintenance. By processing IoT sensor data in real time, manufacturers can adopt proactive machinery maintenance, instead of reactive maintenance when an issue arises.

Innovative Use Cases of Edge Computing in IoT-Based Manufacturing

Real-Time Quality Control

A fully automated production line can’t wait even seconds for data to be processed and analyzed as it comes through. With edge computing, the data collection and analysis from the sensors is happening right on site, giving feedback in milliseconds and ensuring production quality in real-time. 

Predictive Maintenance

IoT and edge computing combined help companies get a handle on preventative maintenance for critical machines, reducing downtimes and ensuring high-efficiency production. This allows companies to keep machines in production and avoid costly unplanned downtimes. 

One supplier combines AI, IoT and edge computing to help make proactive maintenance a reality. Using machine learning and embedded IoT sensors, devices measure and analyze patterns in normal operating behaviors. If anomalies are detected, the integration of the sensors with computing power at the edge ensures companies can proactively work to avoid equipment failure before it happens. 

Energy Management and Optimization 

IoT devices track energy consumption while edge computing finds patterns and automatically adjusts equipment to operate more efficiently.

Supply Chain Optimization

Real-time monitoring of supplies allows IoT devices and edge computing to work together to ensure seamless operations without delays, bottlenecks or excess inventory.

Worker Safety and Operational Efficiency

With edge computing providing real-time insights, companies can detect unsafe conditions or prevent equipment malfunctions with automatic shutoffs. 

Overcoming Challenges With Edge Computing in Manufacturing

While the benefits of edge computing are clear, implementation comes with challenges. 

• Connectivity and Integration Challenges. Operations technology teams are used to working with specialized hardware and software purposely built for monitoring and controlling physical devices, but it makes it hard to integrate new IoT devices or streamline IT connectivity. Utilizing cloud native solutions for edge computing with a centralized management platform can help facilitate connecting to legacy systems across different sites. 
• Managing Data at the Edge. Storing and processing large volumes of data locally presents challenges, as edge devices must handle substantial workloads. Utilizing edge storage and distributed computing models help manage this load efficiently, ensuring data is processed without overwhelming local resources. 
• Ensuring Scalability and Flexibility. As IoT systems grow, edge solutions need to scale accordingly. Designing flexible, scalable architectures allows manufacturers to expand their edge infrastructure without compromising performance and support 10s of 1000s of devices, ensuring they can adapt to future growth and evolving needs.

The Future of IoT and Edge Computing in Manufacturing

The future of manufacturing will be AI-driven, fully automated factories that produce no defects, protect workers and have no unplanned interruptions to manufacturing. In these smart factories, edge computing will play a critical role in supporting the computing power needed for AI and ML decision making. Plus, the arrival of 5G technology will further accelerate this transformation by providing the ultra-low latency necessary for real-time operations and increased device connectivity. 

Edge computing is already reshaping IoT-based manufacturing with safer, more reliable sites. Putting data in the hands of frontline workers right where and when they need it ensures manufacturing leaders who adopt edge computing will stay ahead of the curve in innovation, creating faster, smarter and more secure processes.

Discover how SUSE Edge customers are innovating at the edge in these case studies.