Revolutionizing Manufacturing: The Ultimate Equipment Digitalization IoT Platform for High-Speed, High-Precision Data Management
When we talk about IoT for equipment, in the wave of digitalization, it seems omnipresent—from gateways to SCADA systems, to even customized project solutions, there are mature solutions available. However, despite these mature technologies, we have invested tens of millions in developing a new generation of equipment digitalization IoT platforms, with plans for further significant investments in the future. Today, I'd like to share our insights and understanding of the industry.
Industry Background
- The starting point for any successful product or technological innovation must be to serve industry trends and specific scenarios, creating value for customers.
- In the past, the manufacturing sector focused for decades on solving problems related to production automation, catering to large-scale, standardized, mass production. This led to rapid growth in both standard and custom equipment markets, which in turn propelled the industrial automation market forward. For instance, Siemens' automation division has seen high revenue growth over the past 20 years, and Inovance Technology has achieved an astonishing annual growth rate of 50% over the same period. The business architecture of manufacturing companies is generally based on the ISA95 standard, as shown in Figure
This architecture, a product of the automation era, has led to a common separation between IT and OT in manufacturing companies, causing the following issues:
(1) There is a widespread lack of standardized physical and data interfaces at the equipment level, making equipment integration time-consuming and labor-intensive.
(2) The equipment layer struggles to dynamically adjust based on business changes, including automatically receiving process recipes from MES systems to achieve flexible production.
(3) There's a lack of high-quality data needed for process quality analysis, production process modeling, etc. Most data is at a second-level resolution, and there's no effective cleaning or storage.
Currently, manufacturing industries generally suffer from overcapacity, needing solutions for small-batch, multi-variety, high-quality production while reducing costs. From an operational perspective, this necessitates an end-to-end business cycle from order to automated production to enhance operational efficiency in manufacturing enterprises. Through business modeling and data-driven approaches, we aim to replace modes that rely solely on the experience and knowledge of personnel with more scientific and sustainable methods to achieve sustainable, high-quality development in manufacturing. However, the old architectures and models have led to a general scarcity of high-quality production data in manufacturing firms.
Looking to the future, with the widespread adoption of new-generation AI technologies, manufacturing must evolve from being highly dependent on the experience of workers and engineers to a model-driven, data-centric approach. This evolution is crucial for sustainable, high-quality development in manufacturing.
Therefore, the manufacturing industry urgently requires an equipment digitalization IoT platform tailored for the digital era to support the digital transformation of manufacturing companies, as shown in Figure
2. Market Demand
Looking specifically at today's industrial enterprises' needs for equipment digitalization, for example, in the automotive parts industry where high-speed, high-tempo automated assembly lines require data collection at 100ms intervals and batch industrial recipe data distribution, traditional solutions struggle to cope, as illustrated in Figur
instance, in analyzing extrusion process curves or welding voltage and current data, there's a need to collect vast amounts of high-precision data (like at 10ms intervals) to understand quality fluctuation factors in production processes deeply. This provides crucial decision-making data and a foundation for process optimization and control.
In summary, the market demands an equipment digitalization IoT platform that is both fast and stable.
3. Traditional Solutions
(1) Productized Solutions
Traditional productized IoT solutions for equipment generally use gateways for data collection or SCADA for basic production monitoring, which indeed offers a low-cost solution in some sectors. However, for the customer scenarios mentioned in the previous section, especially when it comes to high-quality data collection and real-time control distribution, like data collection and processing at 10ms or 100ms intervals, or batch process data distribution for real-time control, these solutions are inadequate.
Traditional IoT gateways and SCADA systems also struggle with millisecond-level feature parameter collection and analysis, making it difficult to establish mathematical models based on production data, thus hindering process and quality optimization.
(2) Customized Solutions
There are also many project-based companies offering custom development, but software developers often specialize in software technology without a deep understanding of industrial communication protocols or the factory floor. They might even "borrow" from open-source solutions that haven't undergone rigorous reliability and stability testing, leading to frequent issues in real-world applications. For instance, when dealing with large data volumes and frequent real-time interactions on equipment or production line IoT platforms, issues like data loss, missing data, or failure to distribute data accurately to PLCs can delay project implementation and acceptance, resulting in significant economic losses for factories.
Moreover, customized solutions face maintenance challenges; as business needs evolve, long-running systems can become a "burden" for enterprises, especially when they encounter supply chain issues or lose contact with original developers, making system expansion or maintenance impossible.
4. Positioning of the Equipment Digitalization IoT Platform
Given the industry background and market needs, an equipment digitalization IoT platform for manufacturing, whether for individual machines or production lines, should have the following key features:
(1) Fast: Ultra-high-speed, high-precision data collection, capable of capturing millisecond-level data, even down to 10ms, to catch subtle changes and critical information in the production process, providing a solid data foundation for precise decision-making.
(2) Stable: Efficient, stable data distribution for control, enabling real-time control with batch process data distribution to ensure production strictly follows preset process parameters, guaranteeing product quality stability and consistency.
(3) Broad Southbound Compatibility: Can interface with mainstream PLCs, CNCs, and support the integration of third-party custom equipment for seamless connection and efficient collaboration.
(4) Strong Horizontal Integration: Integrates various hardware on automated lines like barcode scanners, cameras, printers, and machine vision, creating an intelligent, integrated production environment.
(5) Flexible Northbound Data Interfaces: Offers standardized data interfaces like OPC UA/DA, MQTT, APIs, for seamless, efficient integration with third-party MES, WMS, ERP systems, bridging the entire data chain from production to management.
Based on a deep understanding of industry needs and precise product positioning, our self-developed SIOT (Super&SYC IoT) product architecture is shown in Figure 3 and supports Windows, Linux, as well as domestic operating systems.
5. Conclusion
As manufacturing companies push forward with digital transformation, traditional IoT product solutions fall short in high-quality data collection and real-time control distribution, while customized solutions are unstable and hard to maintain. Therefore, our new generation of equipment digitalization IoT platform, characterized by being both fast and stable, provides robust technical support for the digital transformation and sustainable development of manufacturing. We look forward to further discussion and collaboration with industry peers to drive the digital transformation of manufacturing.