Deliver Applications to the Factory Floor Faster: Cloud Native at the Manufacturing Edge

The integration of cloud native technologies is reshaping how businesses operate and deliver applications at speed. By leveraging cloud native solutions, manufacturers can deploy and update applications more efficiently, driving faster innovation cycles and enhancing production efficiency. 

Edge computing, a key component of this transformation, bridges the gap between the digital and physical worlds, enabling real-time data processing and decision-making right at the factory floor. Let’s explore how cloud native and edge computing together are revolutionizing modern manufacturing, from streamlining application delivery to optimizing IoT device performance and ensuring robust security. In the future of manufacturing agility, scalability‌ and real-time insights are the new norm.

Edge computing: Bridging the digital and physical worlds

By bringing computation and data storage closer to where it’s needed, edge computing is bridging the gap between the digital and physical worlds, transforming traditional factory floors into hubs of real-time innovation. In manufacturing, the proximity of edge devices to machines and sensors can reduce latency, enabling quicker data processing and decision-making. This is particularly crucial in environments where split-second responses can prevent downtime and optimize production efficiency. For example, edge analytics can detect subtle anomalies in machinery performance, allowing for proactive maintenance before a breakdown occurs. This not only minimizes the risk of unexpected downtime but also extends the lifespan of critical equipment.

Manufacturers are increasingly leveraging edge computing to optimize the performance of IoT devices, enhancing machine-to-machine communication. By processing data locally, edge devices can quickly relay insights and commands between machines, improving coordination and synchronization on the factory floor. This seamless communication is essential for complex manufacturing processes that require precise timing and coordination. 

SUSE’s edge solutions are designed to integrate seamlessly with existing factory systems, such as Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES). This integration ensures that data flows smoothly across the entire manufacturing ecosystem, from the factory floor to the corporate office, creating a cohesive and efficient operation. As a result, manufacturers can gain deeper insights into their operations, make data-driven decisions, and ultimately drive greater productivity and innovation.

The role of cloud native in modern manufacturing and faster application delivery

Cloud native solutions are not just about modernizing IT infrastructure; they are revolutionizing the way modern manufacturing plants deliver applications. By adopting cloud native technologies, manufacturers can significantly reduce the time it takes to deploy new applications and updates, enabling rapid iteration and quicker factory floor updates. This agility is crucial in an industry where the ability to adapt quickly to market changes and technological advancements can mean the difference between staying competitive and falling behind.

One of the key drivers of this transformation is the integration of DevOps practices. DevOps methodologies bring development and operations teams together, fostering a culture of collaboration and continuous improvement. This synergy enhances the agility and scalability of manufacturing processes, allowing for more frequent and reliable software releases. By automating many of the manual tasks involved in application development and deployment, DevOps practices reduce the risk of human error and free up valuable time for innovation and optimization.

Another significant advantage of cloud native solutions is the adoption of microservices architecture. This approach breaks down monolithic applications into smaller, independent services that can be developed, tested, deployed‌ and scaled independently. This modularity not only improves flexibility but also reduces downtime, as updates can be made to individual services without affecting the entire system. In a manufacturing environment, where even minor disruptions can have a substantial impact on production, the ability to make targeted updates quickly and efficiently is invaluable.

Continuous integration and continuous deployment (CI/CD) further accelerate the testing and deployment cycles, ensuring that new features and improvements are delivered to the factory floor with minimal delay. By automating the build, test‌ and deployment processes, CI/CD pipelines enable teams to detect and address issues early, reducing the time and effort required to bring new capabilities to market. This not only speeds up the delivery of new features but also enhances the overall quality and reliability of the applications used on the factory floor.

Implementing features across ERP, MES and edge systems

Integrating new features across ERP, MES and edge systems requires a cohesive strategy that ensures seamless communication and data flow, setting the stage for enhanced efficiency and productivity. In modern manufacturing, the ability to connect these systems is no longer a luxury but a necessity. Cloud native technologies play a pivotal role in this integration, allowing for real-time data exchange and unified operations. For instance, edge devices can collect and process data on the factory floor, sending real-time updates to ERP systems. This automatic updating ensures that production data is always current, enabling managers to make informed decisions quickly and efficiently.

Traditionally, when data exchange protocols need to be adapted or augmented to implement new features, small and large changes need to be coordinated in many different places, from the devices collecting the data to the aggregators on the factory floor level to the back-end systems in the cloud or data center that consume the data. Once cloud native technologies are adopted throughout the stack from edge to cloud, it becomes much easier to test such changes in virtualized “twins” of the production environment and then roll them out across the full stack with the same toolchain.

GitOps workflows further streamline this process by ensuring consistent deployment across all levels, from the cloud to the edge. By treating infrastructure as code, manufacturers can automate the deployment of updates and configurations, reducing the risk of human error and ensuring that all systems are in sync. This consistency is crucial in a dynamic manufacturing environment where rapid changes and improvements are the norm. The flexible architecture of cloud native systems supports this agility, allowing for the rapid implementation of new features and optimizations without disrupting ongoing operations. As a result, production workflows can be continuously refined, leading to higher quality outputs and more efficient processes.

Using familiar GitOps and Continuous Integration processes

Leveraging familiar GitOps and continuous integration processes, manufacturers can now deploy and manage applications at the edge with the same agility and reliability they enjoy in their cloud environments. GitOps, a methodology that uses Git as the single source of truth for infrastructure and application configurations, brings the benefits of version control and automated deployment to the factory floor. By treating infrastructure as code, manufacturers can ensure that every change is tracked, tested‌ and deployed systematically. This not only reduces the risk of human error but also enables faster and more frequent updates, keeping factory applications up-to-date and optimized for performance.

Continuous Integration (CI) further enhances this process by automating the testing and integration of code changes. In a manufacturing environment, the ability to quickly detect and resolve bugs is crucial. CI tools can automatically run tests whenever code is pushed to the repository, providing immediate feedback to developers. This rapid feedback loop allows teams to identify and fix issues early in the development cycle, ensuring that only high-quality code reaches the production environment. As a result, manufacturers can maintain a high level of reliability and performance in their edge applications, which are often critical for real-time data processing and decision-making.

Containers play a pivotal role in this ecosystem by ensuring consistent environments across the entire development and deployment pipeline. Containers encapsulate applications and their dependencies, creating a standardized environment that can be easily replicated from the development stage to the factory floor. This consistency reduces the “it works on my machine” problem, ensuring that applications behave predictably in production. Kubernetes, an open source platform for automating the deployment, scaling, and management of containerized applications, further enhances this consistency and reliability. By orchestrating Docker containers, Kubernetes ensures that edge applications are scalable, resilient, and optimized for performance, even in the dynamic and demanding environment of a modern factory.

DevOps practices, which emphasize collaboration and communication between development and operations teams, are instrumental in accelerating innovation cycles and improving product quality. In a manufacturing context, where speed and efficiency are paramount, DevOps fosters a culture of continuous improvement. By breaking down silos and encouraging cross-functional collaboration, manufacturers can more effectively address challenges and seize opportunities. This agile approach enables teams to quickly adapt to changing market conditions and customer demands, driving innovation and maintaining a competitive edge. As manufacturers continue to integrate cloud native and edge technologies, the adoption of GitOps and CI processes will be key to unlocking the full potential of these advanced solutions.

Real-world examples of edge innovation in factories

From predictive maintenance to real-time quality control, real-world examples of edge innovation in factories are showcasing the tangible benefits of advanced computing at the point of action. 

In the automotive industry, for instance, edge computing is revolutionizing quality control processes. By deploying smart sensors and machine learning algorithms, automotive plants can monitor production lines in real-time, identifying defects as they occur. This immediate detection allows for swift corrective actions, reducing the number of defective products that reach the market and significantly lowering waste. The result is not only a more efficient production line but also a more reliable end product, enhancing customer satisfaction and brand reputation.

“With our Rancher Prime environment running on NVIDIA edge devices, we are one step closer to realizing our goal of full automation with zero defects and no interruptions to manufacturing.” – Read the full Stylez customer success story

In consumer goods processing sectors, edge computing is being leveraged to optimize energy use, a critical factor in an area where efficiency can make a significant difference in profitability. Smart sensors installed throughout the factory floor can monitor energy consumption patterns and adjust machinery settings in real-time to minimize waste. This not only cuts operational costs but also aligns with growing environmental sustainability goals. By reducing energy waste, manufacturers can achieve both economic and ecological benefits, making their operations more sustainable and cost-effective.

“We are constantly discovering new optimization potential and are now able to produce recycled paper of the highest quality with even fewer resources.” – Steinbeis Papier is a true pioneer in sustainable business; read the full customer success story here. 

Pharmaceutical manufacturers are also embracing edge computing to enhance their operations. In this highly regulated industry, compliance is paramount. By deploying AI at the edge, pharmaceutical companies can ensure that every step of the production process adheres to strict regulatory standards. AI-driven systems can monitor and log every action, from raw material handling to final product packaging, providing a transparent and traceable record of compliance. Additionally, these systems can track inventory in real-time, reducing the risk of stockouts and overproduction, which are common issues in the industry.

Manufacturers across various sectors are integrating IoT devices to implement predictive maintenance, a strategy that can significantly minimize downtime and maintenance costs. By collecting and analyzing data from machinery in real-time, these systems can predict when equipment is likely to fail, allowing for proactive maintenance before issues arise. This approach not only extends the lifespan of machinery but also ensures that production lines remain operational, avoiding costly disruptions.

This improvements also enable innovation. As WEG found, simplifying their infrastructure and centralizing management helped them drastically improve their development productivity.

“We’ve definitely noticed an improvement in development productivity. Before Rancher Prime, infrastructure provisioning took days; now, we can get a new application environment up and running in hours – over 91% faster. It gives us a real shortcut to success.” – Eduardo Piccoli, Solution Architect, WEG

Another manufacturer, MTU Aero Engines, shared how a streamlined process was key to their success:

“Our principle is always to keep things simple. Rancher Prime helps us apply this principle to our container environment. With this solution, we can manage all of our Kubernetes clusters from a central interface and easily keep them up to date. We can roll out updates to the entire environment with just a few clicks. Additionally, built-in monitoring capabilities based on Prometheus and Grafana allow us to continuously monitor how our clusters are utilized and the health of individual workloads.” – Tobias Opel, MTU Aero Engines

Future trends: Predictive maintenance and AI at the edge

Looking ahead, the convergence of predictive maintenance and AI at the edge promises to further optimize factory operations, driving new levels of efficiency and reliability. One of the most significant advantages of AI at the edge is its ability to reduce latency. Traditional cloud-based systems often suffer from delays in data processing and response times, which can be critical in a manufacturing environment where machines need to make instant adjustments. By processing data locally, edge computing enables real-time decision-making, allowing machines to respond to issues or changes in the production line almost immediately. This not only enhances the speed of operations but also ensures that any potential issues are addressed proactively, minimizing the risk of production bottlenecks.

Predictive maintenance is another game-changer in the manufacturing sector, leveraging the power of AI to forecast equipment failures before they occur. By continuously monitoring the performance data of machines, AI models can detect patterns and anomalies that indicate impending failures. This proactive approach allows maintenance teams to schedule repairs or replacements during planned downtime, significantly reducing the likelihood of unexpected breakdowns and costly downtimes. The result is a more reliable production line, increased uptime and ultimately, higher productivity. Factories that have implemented predictive maintenance have reported substantial reductions in maintenance costs and improved overall equipment effectiveness (OEE).

Edge analytics play a crucial role in enhancing both security and efficiency. By processing data locally, edge systems minimize the amount of sensitive information that needs to be transmitted to the cloud, thereby reducing the risk of data breaches and ensuring compliance with industry regulations. This localized data processing also means that critical operations can continue even if there is a temporary loss of internet connectivity, ensuring that production remains uninterrupted. Furthermore, the integration of cloud native technologies with edge systems facilitates seamless software updates and continuous improvement. Cloud native architectures enable rapid deployment and scaling of AI models, ensuring that factories can quickly adapt to new technologies and processes without significant disruptions.

“We only have a very limited bandwidth to the vessel, and SUSE Linux Enterprise Micro enables us to have as simple an installation as possible. This is what makes SUSE Edge like a hand in a glove. We aren’t having to absorb excessive resources for the underlying OS. It fits seamlessly with the needs and constraints of our edge deployments.” – Hans Zenth, Danelec

In addition to predictive maintenance, factories are increasingly deploying AI models for quality inspection. These models can analyze images and sensor data in real-time to detect defects or inconsistencies in products, ensuring that only the highest quality items reach the market. This not only improves product standards but also reduces waste and rework, leading to cost savings and enhanced customer satisfaction. The combination of AI at the edge and cloud native technologies is transforming the manufacturing landscape, making factories smarter, more efficient and more resilient.

As we continue to embrace these advancements, the integration of AI and edge computing will not only streamline current operations but also pave the way for new innovations and opportunities in the manufacturing industry. The future is bright, and the potential for further optimization and growth is immense.

Learn more about SUSE’s manufacturing solutions and how to leverage the power of edge computing.