From automobiles and airplanes to hairpin buttons, each product is inseparable from the support of data from the inspiration of R&D and design, to the careful preparation of material procurement, to the roar of the manufacturing machine, as well as the ingenious marketing of product sales, and finally to the after-sales care service of the product. For manufacturing enterprises, how to turn data into intelligence, intelligence into insights, and insights into action, improve security and security, and accelerate digital transformation?
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This article was originally published by e-works Wu Jie.
Related to this, the explosive growth of data from the edge to the cloud has also brought the digital transformation of the manufacturing industry into a state of accelerated change. So, for manufacturing enterprises, how to turn data into intelligence, intelligence into insights, and insights into action, improve security and security, and accelerate digital transformation?
Turn data into intelligence
The manufacturing industry is facing challenges
The key to the digital transformation of the manufacturing industry is to allow data to flow freely and be effectively managed within the enterprise, so that data can be burned efficiently like fuel and exert greater value to establish a more efficient and flexible production process. IDC predicts that by 2025, the total amount of data in the world will reach 175 zettabytes. As enterprise data grows and expands like never before, and workloads flow in a variety of ways across complex and dynamic systems, the challenges of critical workloads and data management are exacerbated:
First, the challenge of data at the edge. With the deepening application of industrial Internet, digital twins, and 5G technology, a large amount of edge data is expanding rapidly, bringing a huge demand for edge computing power in enterprises. In the face of many scenarios such as remote control of equipment in manufacturing enterprises, remote maintenance of personnel, and high-definition video collection and transmission, it is necessary to use computing power to analyze and mine massive data, reduce the delay and cost of data transmission, and achieve the goals of cost reduction, efficiency increase, and quality improvement.
The second is the challenge of data silos. In the manufacturing industry, data is often scattered in a variety of different IT systems, such as ERP, MES, SCM, etc., and there is a lack of effective data exchange and integration mechanisms between these systems, resulting in data that cannot be associated and shared with each other. This not only wastes data resources, but also affects the ability of data analysis and decision support.
Third, the data value of old equipment has not been brought into play. There is a large number of old equipment in the traditional manufacturing industry, which may not have sensors or data collection tools installed, and the data generated by their operation is often not captured and utilized, resulting in the potential value of the equipment not being tapped.
Fourth, the challenge of migrating data to the cloud. For manufacturing companies, there are many benefits to going to the cloud. However, migrating the huge amount of data from manufacturing enterprises to the cloud is a complex process, which may involve large amounts of data, diverse data types, complex data structures, etc., while ensuring the security and integrity of data during the migration process. In addition, post-migration data management and analysis requires specialized skills and resources.
Figure 1 Manufacturing is facing challenges
As a result, the number of connected devices and the resulting data available is exploding, and the environment in which critical workloads are located is undergoing tremendous changes. This also means that in the future, enterprise computing power, storage and other infrastructure will usher in a new reconstruction cycle to meet the challenges of digital transformation and the acceleration of intelligent upgrading of industries.
Turn intelligence into insights
Provide computing power and storage support
In this context, as a natural market innovator, HPE has always been at the forefront of technology and applications, and continues to innovate and evolve with the changing times and enterprise needs. Especially in the past few years, HPE has shifted from "cloud-first" to "data-first", delivering an overall product portfolio from edge to cloud in the industry through the "as-a-service" model, providing the computing power, storage and other services required by enterprises, laying a solid foundation for enterprises to accelerate workload transformation and drive data value innovation.
On the one hand, computing power is a new engine that activates the potential of data elements and drives the digital transformation of the manufacturing industry. HPE has built and optimized a compute portfolio that scales from the edge to exascale to deliver workload-optimized computing power in an innovative as-a-service model that enables more enterprises to accelerate their digital transformation. The latest generation of HPE ProLiant servers provides users with a cloud-native management hub that enables users to securely and automatically access, monitor, and manage servers for efficient operations, regardless of where the computing environment resides.
At the same time, HPE ProLiant Gen11 supports multiple architectures, including 4th Gen AMD EPYC™ processors, 4th Gen Intel® Xeon ® Scalable processors, and Ampere®Altra® and Ampere®Altra®Max cloud native processors. The new HPE ProLiant Gen11 servers deliver twice the I/O bandwidth for the most demanding applications compared to the previous generation. At the same time, the number of cores per CPU is increased by 50%, allowing for better workload consolidation. High-performance GPU density has also increased by 33% per server to better support AI and graphics-intensive workloads.
Figure 2 HPE ProLiant Gen11 server
On the other hand, with the deepening of digital transformation, enterprises have put forward new requirements for storage performance, availability, and user experience—on top of storage, users demand more powerful data management capabilities. In this regard, HPE creatively integrates cloud O&M experience and artificial intelligence advantages into data management while innovating hardware products, effectively supporting the innovation of customers in different fields at all business stages. HPE Alletra series storage is the first centralized storage product in China to adopt the full NVMe protocol, based on the end-to-end NVMe architecture, Alletra series storage implements front-end and back-end NVMeoF interconnect, NVMe SSD and NVMe SCM support.
Based on HPE Alletra-series storage, users can enjoy a cloud-as-a-service experience, traditional storage requires users to pay attention to the underlying hardware, in the use of Alletra-series storage, users only need to focus on their own needs, and the storage will actively allocate appropriate resources according to capacity, application scenarios, performance requirements, etc. In addition, HPE Alletra-family storage, powered by the HPE InfoSight AI platform, solves complexity, transforms the way resources are managed, and redefines the support experience.
Figure 3 HPE Alletra-series storage
It is not difficult to find that HPE's computing power and storage products, by optimizing different workloads, can be said to really help enterprises release more data value in the era of "data first", and its innovative "as-a-service" model also really helps enterprises to respond to different workloads more flexibly, which is also the unique value of HPE in the market.
Insight into action
Exploring the Future of "Intelligent Manufacturing"
As all walks of life actively promote digital and intelligent upgrading, HPE has begun to move towards thousands of industries, and has taken manufacturing as a key expansion industry. In recent years, HPE has continued to deploy in the manufacturing industry, and has provided all-round support to enterprises in manufacturing industries such as packaging, electronics, and automobiles through innovative technology integration and scenario practice.
For example, as the company grew, the company's IT infrastructure was under tremendous pressure, and the hardware equipment was aging, which could lead to an increased risk of downtime, system failure, and data loss. In addition, the rate at which the company's business is growing means that any unplanned server failure can have a significant impact on its operations. In order to help the company reduce development costs and improve market responsiveness, HPE provides the latest hardware infrastructure, including HPE ProLiant servers, HPE switches, HPE storage, Aruba networking, VMware vSphere, etc., which effectively shortens the processing time of the company's critical reports, provides timely and accurate information support and real-time analysis capabilities, and is critical for decision-making and operations management. In addition, the company's data was successfully migrated to SAP S/4HANA® in the HPE GreenLake environment, creating a scalable and future-proof operations platform that enables end-to-end data traceability and improved product quality control.
In the electronics industry, many companies are under pressure to ensure that no quality issues occur with any product on the production line, especially as the demand for customization grows. However, relying on workers and manual processes for inspections often fails to keep up with the speed of today's production lines and meet stringent quality requirements. HPE partnered with Relimetrics to implement video analytics and machine learning on an electronics manufacturing company's production line, including the installation of five cameras to capture high-resolution images of the products entering the conveyor belt and transmit the images to an embedded system for on-site processing using machine learning algorithms. The system uses HPE Edgeline Converged Edge System, an HPE OEM solution with NVIDIA® GPUs, to analyze images at the edge rather than in the cloud. The resulting machine vision system enables real-time video analysis on the production line, saving time and preventing delays, improving the speed and accuracy of defect detection, resulting in improved productivity and product quality.
In the field of autonomous vehicles, test vehicles will be equipped with a large number of sensors to conduct extensive testing on real roads, generating terabytes or more of data every day, and then having AI algorithms process this data and record the reactions of the autonomous driving software, and if the responses are incorrect or too slow, the algorithms will need to be adjusted. An automotive company uses HPE GreenLake to deliver a high-performance solution that solves the computational challenges of autonomous driving software modeling and simulation. The solution provides the massively parallel computing power required by the company, covering the entire process of data processing, thereby greatly improving R&D efficiency. For example, one component of the solution is the HPE Data Management Framework, a large-scale, tiered storage and data management platform designed to manage large volumes of content generated by highly scalable, dynamic HPC, data analytics, and AI workflows.
These are just a few of the ways in which HPE is helping to transform the digital transformation of manufacturing. As one of the world's leading technology companies, HPE has thoughtfully released relevant industry solutions to create greater value for the manufacturing industry and customer business.
postscript
To support the digital transformation of the manufacturing industry, HPE has never stopped, not only based on the current needs and challenges, but also focusing on the future development direction of the industry, constantly innovating and upgrading platforms and products. In the future, with the in-depth application of AI, industrial Internet, digital twins, 5G and other technologies in the manufacturing industry, the data of the manufacturing process will be multiplied, HPE will continue to put data first, systematically mine and analyze production and manufacturing data, turn data into intelligence, turn intelligence into insights, and promote insights into actions, so as to empower manufacturing enterprises to evolve from informatization and networking to digitalization and intelligence, and realize the construction and high-quality development of enterprise core competitiveness!