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6 findings from IoT Signals report: Manufacturers prepare their shop floor for AI

Manufacturers are embracing AI to deliver a new level of automation, optimization, and innovation. To unlock the full potential of AI on the shop floor, organizations are testing and investigating technologies and paradigms that empower them to leverage their data more effectively.

Microsoft, in partnership with IoT Analytics, market research firm, conducted a global survey of manufacturers to gain insight into how they are seizing the AI opportunity while navigating key industry challenges. We asked manufacturers about their current priorities and future visions, their adoption of modern technologies and paradigms, and the benefits they expect from those technologies 

In this report, we share the key findings from the survey, to show how manufacturing enterprises are preparing their shopfloors for AI to make them secure, scalable, and automated and how they are adopting advanced technologies such as centralized device management, software containerization at the edge, and unified industrial data operations to accelerate that process.

Accelerate industrial transformation

How manufacturers prepare shopfloors for a future with AI

Download report 

Read on to discover the six key lessons learned from manufacturers rethinking their operations for AI and how Microsoft is supporting the factory of the future with Azure’s adaptive cloud approach.

Six findings from manufacturers preparing their shop floor for AI

1. Scale matters the most in the era of AI

Scalability was the main concern for 72% of survey respondents, who highlighted this paradigm as crucial for their factory’s future. Scalability came first, followed by automation and serviceability. These paradigms ensure that factories can efficiently expand with demand, optimize with minimal manual decision making, and maintain high uptime through easy troubleshooting and maintenance. 

What does scale look like for industrial environments? 

Manufacturers face the challenges of keeping up with the changing demands of the market, the regulations, and the competition. They also recognize the potential of AI to transform their operations, optimize their processes, and enhance their products. But they don’t have the luxury of spending months or years on deploying and scaling solutions across their plants. Manufacturers need a faster way to move, a smarter way to manage, and a more flexible way to adapt. That’s why we have introduced a new approach—the adaptive cloud approach. 

To learn more, see how the adaptive cloud approach is designed to help manufacturers unify their teams, sites, and systems with cloud-native and AI technologies that work seamlessly across hybrid, multicloud, edge, distributed computing, and IoT. The adaptive cloud approach empowers manufacturers to deliver value faster, manage devices more efficiently, and run applications more securely to prepares them for the AI-powered factory of the future.  

2. Cybersecurity and data management are top of mind right now 

Security risks and data handling difficulties pose serious problems, with 58% of respondents seeing cybersecurity as a severe issue and 49% seeing data management as a severe issue. These concerns are motivating customers to improve network security and ensure data is reliable and accessible for decision-making. 

What does security look like for industrial environments? 

Security and data protection are critical for the manufacturing sector, as the sector faces increasing regulatory standards and cyber threats. Manufacturers need to secure existing devices, and plan during device refresh to choose devices that meet industry security standards, will enable them to more easily comply with regulatory standards, and provide security to defend from the latest security threats.  

To learn more, see Microsoft’s comprehensive approach to security, from device to cloud, that helps customers meet their compliance needs and defend against attacks. For existing devices, Microsoft provides firmware and network scanning with Microsoft Defender for IoT, which allows customers to inventory their devices and monitor for vulnerabilities and threats. For new devices, customers can choose from leading industry original equipment manufacturers (OEMs) devices labeled Secured-core, which meet the latest security requirements. Both existing and new devices can be monitored and remediated using Microsoft Defender and Microsoft Sentinel.

3. Device management is critical for security and data handling 

Device management’s value is evolving beyond updates and device health monitoring to also address security risks and data flow management. The survey data supported this trend, with 68% of respondents noting that the security monitoring aspect of device management was very or extremely important to their organization and 59% of respondents highlighting data management as the second most important aspect of device management. 

Why is centralized device management important? 

Centralized device management is vital for ensuring the performance and security of operations in a factory setting. It helps to keep devices secure and functioning optimally, which contributes to the overall efficiency and productivity of a manufacturing environment. Effective management also enables better oversight and control over the factory processes, improving operational reliability and supporting scalability and adaptability in a dynamic industrial landscape.

To learn more, see how Azure Arc delivered centralized management for IT and OT environments. Manufacturers can define resources, such as hybrid, multi-cloud, edge, and IoT, to Azure Resource Manager so services such as system health monitoring, security, and many others can be easily applied across a globally distributed digital estate.

4. Containerized workloads are coming to the shop floor 

The adoption of containerized software on the shop floor is rising, with 85% of survey respondents already utilizing this technology. This shift towards containerization at the edge signifies a move to improve operational efficiency, system stability, and security. 55% of respondents indicated that containerized software could significantly or extremely mitigate reliability and uptime challenges, while 53% indicated it could do the same for cybersecurity challenges.

What is containerized software? 

Software containerization enables consistent and repeatable development and deployment of solutions across different environments, in the cloud and in factory. Containerization of OT software is essential for the AI-powered factory of the future, as it enables seamless technology deployment in scalable, serviceable, and automated factories. Kubernetes automates the scaling and management of containerized applications, saving time and resources for manufacturers.

To learn more, see how Azure Kubernetes Service helps to securely modernize and optimize Kubernetes environments with unified management, governance, and monitoring. Azure Kubernetes Service (AKS), helps teams accelerate app development and deployment with best-in-class tools and generative AI. With AKS, enabled by Arc, these benefits can also be extended to on-premises and edge-based applications 

5. Industrial data operations optimize OT data management

Companies want to combine information technology (IT) and operational technology (OT) systems for context driven decision making. 52% of respondents indicated that having a combined IT and OT data platform was very or extremely important for their company. Industrial data operations enhance the integration of IT and OT data by improving data flow, quality and value; therefore, 87% of companies have already adopted industrial data operations technology in some form or are planning to do so.

What are industrial data operations? 

Industrial data operations delivers data in a reliable, real-time manner for optimizing factories and plants. Industrial data operations manages and unifyies data from various sources, facilitates seamless integration of information, and ensures data is accessible and usable for decision-making purposes. Industrial data operations helps break down data silos and improve predictive insights through an exchange and integration between shop floor and cloud environments.

To learn more, see how Azure IoT Operations handles data from equipment and systems in OT environments, ensuring that data is collected, pre-processed, and integrated into applications running onsite. This service, announced in public preview at Ignite 2023, embraces industry standards—such as, open packing conventions unified architecture (OPC UA), message queuing telemetry (MQTT), transportOpen telemetry (OTel)—and natively integrates into Microsoft Fabric. Microsoft Fabric, handles data for cloud environments, centralizing data on one open, organization-wide data lake to eliminate sprawl and reduce duplication. It allows creating and managing AI models on a single foundation, reducing data movement and time to value.

6. Respondents are investing in underlying data architecture for AI 

According to the study, manufacturers plan to invest in AI-powered factories of the future within the next two years. On average, respondents expected their organizations to increase their investments in software for orchestrating edge AI by 11%. This investment shows that they recognize the need to overcome technical and skill gaps to fully exploit AI’s capabilities in future manufacturing processes. 

How to invest in underlying architecture for AI? 

Microsoft recommends embracing advanced technologies such as centralized device management, software containerization at the edge, and unified industrial data operations to accelerate industrial transformation and prepare for AI.

Accelerate industrial transformation in manufacturing

To learn more read the full 2024 IoT Signals report, a comprehensive survey of manufacturers’ priorities, challenges, and plans for adopting new technologies, such as these, in their factories to prepare for AI. The report shows that manufacturers are looking for solutions that can help them secure, scale, and automate. Microsoft Azure is responding to these needs with its adaptive cloud approach, which offers a flexible and scalable platform for managing devices, applications, and integrated data across the edge and the cloud.

To view a presentation of this survey by IoT Analytics’ CEO and Microsoft’s GM of Azure IoT and Edge, recorded at HMI 2024, click here.

To discover more insights and best practices for accelerating industrial transformation, download the 2024 IoT Signals report below.

Download the 2024 IoT Signals report
The post 6 findings from IoT Signals report: Manufacturers prepare their shop floor for AI appeared first on Azure Blog.
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New Beta Feature: Deep Dive into GitHub Actions Docker Builds with Docker Desktop

We’re excited to announce the beta release of a new feature for inspecting GitHub Actions builds directly in Docker Desktop 4.31. 

Centralized CI/CD environments, such as GitHub Actions, are popular and useful, giving teams a single location to build, test, and verify their deployments. However, remote processes, such as builds in GitHub Actions, often lack visibility into what’s happening in your Docker builds. This means developers often need additional builds and steps to locate the root cause of issues, making diagnosing and resolving build issues challenging. 

To help, we’re introducing enhancements in GitHub Actions Summary and Docker Desktop to provide a deeper understanding of your Docker builds in GitHub Actions.

Get a high-level view with Docker Build Summary in GitHub Actions 

We now provide Docker Build Summary, a GitHub Actions Summary that displays reports and aggregates build information. The Docker Build Summary offers additional details about your builds in GitHub Actions, including a high-level summary of performance metrics, such as build duration, and cache utilization (Figure 1). Users of docker/build-push-action and docker/bake-action will automatically receive Docker Build Summaries. 

Key benefits

Identify build failures: Immediate access to error details eliminates the need to sift through logs.

Performance metrics: See exactly how long the Docker Build stage took and assess if it met expectations.

Cache utilization: View the percentage of the build that used the cache to identify performance impacts.

Configuration details: Access information on build inputs to understand what ran during build time.

Figure 1: Animated view of Docker Build Summary in GitHub Actions, showing Build details, including Build status, error message, metrics, Build inputs, and more.

If further investigation is needed, we package your build results in a .dockerbuild archive file. This file can be imported to the Build View in Docker Desktop, providing comprehensive build details, including timings, dependencies, logs, and traces.

Import and inspect GitHub Actions Builds in Docker Desktop

Initially announced last year, the Build View in Docker Desktop now supports importing the dockerbuild archive from GitHub Actions, providing greater insight into your Docker builds. 

In Docker Desktop, navigate to the Builds View tab and use the new Import Builds button. Select the .dockerbuild file you downloaded to access all the details about your remote build as if you ran it locally (Figure 2). 

Figure 2: Animated view of Docker Desktop, showing steps to navigate to the Builds View tab and use the new Import Builds button.

You can view in-depth information about your build execution, including error lines in your Dockerfile, build timings, cache utilization, and OpenTelemetry traces. This comprehensive view helps diagnose complex builds efficiently.

For example, you can see the stack trace right next to the Dockerfile command that is causing the issues, which is useful for understanding the exact step and attributes that caused the error (Figure 3).

Figure 3: Inspecting a build error in Builds View.

You can even see the commit and source information for the build and easily locate who made the change for more help in resolving the issue, along with other useful info you need for diagnosing even the most complicated builds (Figure 4). 

Figure 4: Animated view of Docker Desktop showing info for inspecting an imported build, such as source details, build timing, dependencies, configuration, and more.

Enhance team collaboration

We aim to enhance team collaboration, allowing you to share and work together on Docker builds and optimizing the build experience for your team. These .dockerbuild archives are self-contained and don’t expire, making them perfect for team collaboration. Share the .dockerbuild file via Slack or email or attach it to GitHub issues or Jira tickets to preserve context for when your team investigates.

Get started

To start using Docker Build Summary and the .dockerbuild archive in Docker Desktop, update your Docker Build GitHub Actions configuration to:

uses: docker/build-push-action@v6

uses: docker/bake-action@v5

Then, update to Docker Desktop 4.31 to inspect build archives from GitHub Actions. Learn more in the documentation.

We are incredibly excited about these new features, which will help you and your team diagnose and resolve build issues quickly. Please try them out and let us know what you think!

Learn more

Subscribe to the Docker Newsletter.

Get the latest release of Docker Desktop.

Vote on what’s next! Check out our public roadmap.

Have questions? The Docker community is here to help.

New to Docker? Get started.

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