Manufacturers are rapidly adopting digital technologies to improve efficiency, increase throughput, and control costs. Two important enablers of this digital transformation are edge architectures and cloud computing. When applied effectively, these technologies can significantly enhance Overall Equipment Effectiveness (OEE) by improving product quality, reducing downtime, and maximizing output.

Edge architectures work by processing data close to the machines and sensors that generate it. This reduces the time it takes to detect and respond to issues on the production floor, enabling near real-time decision-making. This is especially valuable in high-speed manufacturing environments where even brief delays can impact productivity or product quality. Keeping data local also reduces the demand on network infrastructure and improves system reliability in areas with unstable connectivity. In addition, processing data at the source helps manufacturers maintain greater control over sensitive information, which can be essential for protecting intellectual property or meeting data privacy requirements.

Cloud computing, on the other hand, offers a centralized approach. It provides access to scalable computing power and storage that can be shared across multiple facilities, making it easier to implement organization-wide analytics, forecasting, and enterprise applications. By aggregating data from different locations, cloud platforms help identify patterns that may not be visible when looking at individual sites. This enables better decision-making at the management level and supports long-term planning. Cloud platforms also simplify software deployment and updates, allowing manufacturers to roll out new capabilities without investing heavily in on-premise infrastructure. Their built-in redundancy and security features also make cloud systems a dependable backbone for business continuity.

Edge architectures and cloud computing aren’t competing technologies—they’re complementary. Edge architectures are ideal for tasks that require speed, autonomy, or local control, such as real-time equipment monitoring or safety shutdowns. Meanwhile, cloud solutions excel at coordinating information across the organization, managing large-scale data analysis, and supporting enterprise-wide collaboration. When combined in a hybrid approach, these technologies offer the best of both worlds: immediate, on-the-ground responsiveness from edge systems and strategic oversight and analysis from cloud-based platforms.

Cloud computing provides manufacturers centralized, scalable computing resources hosted off-site.

Edge architectures in manufacturing

In a manufacturing plant, edge architectures involve deploying compute power on the factory floor, co-located with machinery and associated sensors. Typical applications of edge architectures include:

Machinery condition monitoring: Increased local compute power enables the real-time monitoring of machinery, supporting predictive maintenance by detecting anomalies and scheduling maintenance activities before failures occur. This approach enhances equipment reliability, minimizes unplanned outages, and lowers maintenance costs.

Real-time quality control: Processing imagery and related data through edge architectures enables manufacturers to detect defects in products as they produce them. Immediate feedback reduces waste and rework, increasing the overall quality of output.

Energy and resource management: Edge architectures can support energy management more effectively. For example, edge devices can adjust heating and cooling in response to occupancy, plant floor activity, and external weather conditions. Similar edge technology can optimize the use of resources such as water and other raw materials.

One of the significant benefits of edge architectures is the ability to process data closer to its source in real time. This proximity to data enables more rapid decision-making, whether that involves energy consumption, quality control, or machinery maintenance.

The use of edge architectures allows sensitive data to be processed locally, minimizing the need to transmit large volumes of data to centralized cloud servers. This localization of data not only reduces network costs but can also reduce the risk of a data breach. For manufacturers dealing with proprietary processes, recipes, or sensitive client data, this localized data handling is particularly advantageous.

The distributed nature of edge architectures not only minimizes network load; it also enables more efficient resource utilization. It also helps to balance the network load and avoid bottlenecks associated with centralized data processing. Furthermore, for manufacturing plants located in remote areas with unreliable internet connectivity, edge architectures reduce the likelihood of operational outages due to connectivity loss or centralized processing failure. This decentralized approach enhances the overall resilience of the manufacturing process.

Cloud computing in manufacturing

In a manufacturing environment, cloud computing provides centralized, scalable computing resources hosted off-site to support a wide range of operations, from enterprise resource planning (ERP) to advanced analytics and cross-site coordination. Unlike edge architectures, which emphasize localized processing, cloud computing focuses on aggregating, analyzing, and managing data across the enterprise. Typical applications of cloud computing in manufacturing include:

Analytics and machine learning: Data collected from multiple production lines and facilities, centralized in the cloud, is used to identify trends, optimize production schedules, and enhance forecasting. Cloud platforms provide the processing power necessary to train and deploy machine learning models that support quality control, predictive maintenance, and supply chain optimization.

Remote monitoring and diagnostics: Cloud computing enables manufacturers to monitor equipment and operations across geographically distributed plants remotely. This capability allows for centralized support teams to perform diagnostics, analyze performance, and coordinate with on-site staff, thereby reducing the need for physical presence and enhancing responsiveness.

Scalable application deployment and integration: Cloud services facilitate the deployment and integration of new applications, including MES (Manufacturing Execution Systems), digital twins, and industrial IoT platforms. Manufacturers can roll out updates, test new features, and scale solutions without the constraints of on-premise infrastructure.

Two key benefits of cloud computing for manufacturers are scalability and centralized accessibility. Manufacturers can access computing resources without having to invest in or manage extensive on-site infrastructure. This flexibility allows them to scale operations quickly, respond to changing market demands, and improve overall agility.

Cloud computing also provides backup and disaster recovery services. Centralized storage ensures that data is readily accessible in the event of a local hardware failure. These features are particularly beneficial for manufacturers with global operations or those subject to regulatory compliance requirements.

Summary

Edge architectures can provide significant benefits to manufacturers by bringing compute power closer to the data source:

• Reduced latency enables faster response time to machinery and quality issues, resulting in increased throughput, higher quality, and reduced downtime.
• Localized intelligence enables faster decision-making within the organization, facilitating a more agile response to changes in demand.
• Distributed processing increases plant resilience, reduces data security risks associated with transferring data outside the facility, and reduces the costs of centralized infrastructure and networking.

Cloud computing can provide significant benefits to manufacturers by centralizing data and leveraging scalable, high-performance computing resources:

Enterprise-wide visibility: enables integration of data from multiple plants, suppliers, and systems, supporting advanced analytics, better forecasting, and improved coordination across operations.

Scalable infrastructure: allows manufacturers to deploy new applications, scale workloads, and implement digital transformation initiatives without heavy capital investment in on-site hardware.

Remote accessibility and collaboration: empower teams across departments and locations to access shared data, streamline product development, and coordinate more effectively on production and quality initiatives.

Robust data security and continuity: are supported through cloud providers’ built-in cybersecurity measures, automated backups, and disaster recovery capabilities, helping manufacturers maintain business continuity and regulatory compliance.

A hybrid cloud computing and edge architecture enables critical, real-time decisions to happen at the edge and strategic, data-intensive analysis in the cloud. This union of technologies enhances operational efficiency, supports innovation, and allows manufacturers to derive maximum value from their data.

Expect to see more edge architectures and cloud computing in manufacturing processes, driving further innovation and efficiency improvements.

Steve Mustard, president of National Automation, Inc. and former president of the Industrial Society of Automation