🚀 Fly, Cloud, and Hybrid Approach: The Future of Smart Factories!

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Data Management in Smart Factories: What's the Best Strategy?​

As the integration of artificial intelligence (AI) and machine learning (ML) technologies into industrial environments increases, IT managers are seeking the best strategies to enhance factory efficiency. Particularly in smart factories, the real-time capture, processing, and analysis of data from connected machines, critical systems, and sensor-equipped devices pave the way for new capabilities such as predictive maintenance. According to an IDC survey, the most common use of machine learning in IoT is predictive analytics, with 83% of respondents developing these capabilities.

Challenges of Data Management and the Hybrid Solution​

To optimize this massive data flow, IT managers need to store, transport, manage, and properly utilize data. However, what is the best strategy to capture the content (what it is and where it comes from) and context (why it's important and how it can be used) from all connected edge devices?

The answer is complex. If we consider the factory environment, a hybrid approach collects data from the edge and moves it to the cloud. By having the context of all edge devices within the factory, IT managers can filter, segment, organize this enormous amount of data, and determine the best ways to integrate and optimize AI technologies. In a smart factory, data is not directly streamed to the cloud because valuable factory floor context can be lost during this transition. Furthermore, latency, security, and data integrity are also important considerations for accuracy and security.

New Hybrid Model: Edge Analysis, Cloud Strategy​

Instead, a new hybrid model is emerging: Data is collected from all devices at the edge and stored in a local gateway equipped with the necessary AI and machine learning capabilities. This allows analysis, inference, and other critical tasks to be performed in real-time, with lower latency and without certain security risks.

Data held in the gateway is sent to the cloud during off-peak hours when timing is no longer an issue. Over time, more machine learning algorithms are run in the cloud, generating patterns and insights that can be used for predictions and future operations.

The Power of Sensors and Business Benefits​

In recent years, sensor diversity has increased, enabling detailed monitoring of the entire factory. IDC estimates that the number of sensors deployed worldwide will exceed 80 billion by 2025. Thanks to leaps in technology from chipsets to applications, the data generated by these sensors provides significant benefits. From a strategic business perspective, factories can improve their operations and production through data analysis at both the edge and in the cloud. Data processed at the edge can prevent disruptions caused by machine failures on the production line, while data sent to the cloud ("core") enables long-term analysis of variance factors to achieve savings or increase efficiency within the factory.

Machine performance deviations can be detected (and even predicted), allowing factory managers to correct issues rather than reactively and expensively replacing faulty units.

Hybrid Approach for Efficiency, Quality, and Innovation​

This blend of real-time analysis and long-term archival monitoring frees up factory workers' time, allowing them to focus on productivity, quality, and innovation. This hybrid approach provides significant benefits for factories, employees, their customers, and the community.

Real-time analysis helps prevent disruptions and provides context for factory operations to make relevant decisions on the ground. Archival analysis, on the other hand, helps factory owners make strategic decisions over time to improve the layout of the business, their machines, cost savings, and overall production efficiency. Industry players keep pace with constantly changing technologies to create optimal floor environments. As quality increases and quantity multiplies, the work environment becomes more positive, productive, and conducive to innovation.