Driving new levels of manufacturing efficiency and responsiveness with the adaptive cloud approach

Manufacturers are excited by the prospect of using advanced technology to drive higher levels of efficiency, agility, and quality. However, their ability to scale these kinds of Industry 4.0 initiatives have been hindered by significant challenges at the edge, including legacy systems, siloed operational models, disparate data, and inconsistent standards.?What these customers need to lay the foundation for scalable digital transformation is a trusted and unified framework for deploying infrastructure, configurations, applications, and AI models in a consistent manner across all factories.

Azure’s adaptive cloud approach unifies siloed teams, distributed sites, and sprawling systems into a single operations, security, application, and data model that spans edge and cloud. This approach, enabled by Azure Arc, allows organizations to leverage cloud-native and AI technologies consistently across hybrid, multi-cloud, edge, and IoT environments – ultimately providing a more scalable and agile foundation for transformation. And one that enables the application of innovative Copilot and AI capabilities across an enterprise at the speed of the cloud.

Azure IoT Operations as a Key Component of the Adaptive Cloud Approach

The adaptive cloud approach is supported by Azure's global to local infrastructure and a set of Arc-enabled services across identity, management, security, applications, data, and AI. Azure IoT Operations provides customers with the ability to gather edge data, process it, and flow that data to the cloud. In line with the adaptive cloud approach, all the controls for gathering, processing, and flow use configuration interfaces that reside in the same location as the configuration interfaces for the customer’s cloud services. Once configured, Azure IoT operations flows processed data to cloud data services like Microsoft Fabric, making it available in the common data lake powering the company’s data estate.

Practically speaking, the adaptive cloud approach requires that the edge and cloud have a shared management plane where each configuration associated with data collection and infrastructure resides in a shared graph backed by a common identity system. Additionally, a shared data plane provides company-wide data sharing based on the same identity system, allowing for innovation at the edge at the same pace as innovation in the cloud. For example, providing a new employee with appropriate access to data and configurations based on their role or applying a new policy configuration to all edge software and cloud software simultaneously can be executed by orchestrating the configuration of the relevant Azure Resources using the same tools and interfaces.?

Bringing the Value of Azure IoT Operations to Life

An adaptive cloud approach can help make organizations more responsive and cost-efficient.

When organizations invest in adopting Azure in their physical operations, devices and resources in the factory are joined into a common management plane and can be controlled with actions, tools, and services that have proven success in managing at-scale cloud operations. These capabilities enable common configuration, streamlined deployment, security and health monitoring, application model, and data flow across the entire infrastructure down to machines on the factory floor.

?The first step in adopting Azure across distributed infrastructure is Azure Arc-enabling industrial PCs, databases, and servers, or using distributed compute devices running Azure Stack HCI with Azure Arc built-in. Azure Arc-enabled devices appear as Azure Resources in the Azure portal, which serves as a shared management plane, offering a single pane of glass view with centralized visibility of all resources.

In addition to centralized management, organizations can invest to enable an enterprise-wide data culture using a shared data plane in Azure.

Microsoft Fabric makes data available in contextualized, democratized, AI-ready formats across various departments of an organization. This is enabled through a common data lake, and shared data plane tools that provide role-based access for individuals and workloads. This allows for effective data governance without traditionally complex access delays and overhead. The ability to get access to the data, build dashboards, perform analytics, and find conclusions is all in one surface area. These common tools empower OT teams with the same visualization capabilities that business analysts and executives use for global decision making.

Azure IoT Operations extends the cloud infrastructure pattern down to the shop floor, using the same deployment and management controls as other Azure workloads. It enables OT engineering and staff to use shared management plane tools with assets at the edge. Industrial Assets and OT systems can be discovered and onboarded into the shared management plane with ease, appearing as assets in a new operator-focused user experience and resources in the Azure Portal. OT engineers can configure readings to be pulled from equipment by specifying new data points, or tags, on an asset’s configuration in the operations experience.

For example, if an organization wants to assess how ambient temperature may affect its product quality, an OT engineer can directly add a new temperature tag to the asset. Once the tag is valid, the temperature data starts to appear in the shared data plane and can be visualized in a quality report. Here, the real-time dashboard and subsequent temperature simulation models demonstrate that 1.58% product quality improvement can be made by simply controlling the ambient temperature on the factory floor near five machines.

Authorizing software updates for industrial PCs so that all the correct capabilities are installed is another common data collection task. These software updates are enabled using the same set of shared management plane tools via Azure Update Manager, using permissions to ensure that individuals have the right approvals from the shop floor to make those updates at the appropriate time. An organization’s OT engineers and IT staff can proactively coordinate changes using Azure’s centralized management and data plane tools.

By using the adaptive cloud approach, complex projects spanning organizational boundaries as well as cloud and physical systems can be executed efficiently and securely. Shared data and management planes can improve the safety and affordability of complex projects, unlocking the potential for improved profitability.

In Summary

In a traditional factory, completing the projects described above would be costly and time-consuming. Instead, with an adaptive cloud approach and democratized data, all responsible parties can quickly gain access to view data and make configuration changes based on their identity and role. This system auto-generates audit logs, security events, and health data, creating a company-wide safety net for employees.

Customers adopting Azure’s adaptive cloud approach and data culture can see similar benefits across areas like deployment, inventory management, observability, governance, and security. These scenarios scale easily across sites and infrastructure, bringing your edge into the same paradigm as your cloud. Advanced cloud-native tooling and infrastructure patterns that scale cloud services across regions and tenants are available to elastically scale your operations. Customers can replicate edge infrastructure, applications, and security where needed. By configuring edge systems with Azure Resources, just like in the Azure public regions, you can unlock the rapid advancements of Copilots and AI, instantly bringing value to edge components aligned with common Azure Resources.

We are excited to roll out more adaptive cloud-enabled technologies over the next year, bringing even greater capabilities to your physical systems.? Check out Azure IoT Operations for yourself by visiting https://aka.ms/AzureIoTOperations and get hands-on with Azure Arc Jumpstart.