Technology cloud report original.
With the in-depth advancement of my country's digital transformation and the vigorous implementation of intelligent manufacturing projects, the manufacturing industry is moving towards the "digital intelligence" era, and generative AI is regarded as the key driving force to promote the intelligent development of the manufacturing industry.
It is predicted that by 2027, 30% of the manufacturing industry will use generative AI to improve product development efficiency. On the basis of digital transformation, generative AI brings more powerful potential to the manufacturing industry.
Through the existing planning training model, generative AI can automatically generate new designs, thereby improving the efficiency of product development; at the same time, it also helps to improve the automation level of the production line.
After the digital transformation is completed, how can enterprises use generative AI to enter the era of "digital intelligence" and tap the huge opportunities contained in it?
From digitalization to digital intelligence: the transformation path of manufacturing industry
With the rapid development of artificial intelligence technology, the manufacturing industry is undergoing an unprecedented transformation.
In the past few decades, manufacturing companies have realized the automation and informatization of production processes through digital transformation. However, digitalization is only the first step in transformation.
Today, with the maturity and application of AI technology, the manufacturing industry is gradually entering the era of "digital intelligence", realizing the leap from digitalization to digital intelligence.
Digitization refers to the transformation of physical entities and processes into digital form so that they can be recognized and processed by computer systems.
Digital transformation has enabled manufacturing companies to realize the automation, informatization and collaboration of the production process, improving production efficiency and quality control capabilities. However, digitalization only uses digital technology to optimize the traditional production process, and still relies on manual decision-making and operation.
And digital intelligence goes a step further, it combines digitalization with artificial intelligence, and realizes intelligent production and decision-making through technologies such as machine learning, deep learning, and generative AI.
Digital intelligence is not just the optimization of the existing process, but through the application of AI technology, the machine can learn and adapt independently, and realize intelligent decision-making and autonomous operation.
With the development and breakthrough of AI technology, manufacturing enterprises have begun to shift their attention from digital transformation to digital intelligence.
AI technology can extract and analyze valuable information from big data, and provide intelligent decision support for manufacturing enterprises.
Through deep learning and pattern recognition of massive real-time data, the AI system can accurately predict production status, quality problems and equipment failures, and provide corresponding optimization plans and early warning mechanisms to help companies make timely and accurate decisions.
AI technology can also automate and intelligentize the manufacturing process . Through machine learning and visual recognition technology, the AI system can automatically monitor and control the production process, adjust parameters and optimize operations in real time, improving production efficiency and quality stability.
At the same time, AI technology can also be combined with robot technology to realize intelligent logistics and assembly, reduce labor costs, and improve the flexibility and responsiveness of the production line.
In addition, AI technology endows manufacturing companies with greater innovation capabilities . Generative AI technology can automatically generate new design solutions by learning a large amount of product data and design rules, helping companies quickly design competitive products.
AI technology can also simulate and optimize product performance, quickly predict and verify product feasibility and quality, speed up product development cycle, and improve product market competitiveness.
With the wide application of AI technology, the manufacturing industry is gradually entering the era of "digital intelligence". Digital and intelligent transformation enables manufacturing enterprises to realize intelligent decision-making, automated production and innovative design, further improving production efficiency and product quality.
The first step of "digital intelligence": do a good job in cloud infrastructure
In the industrial design link of the manufacturing industry chain, Haier Innovation Design Center (hereinafter referred to as Haier Design) keeps up with the tide of the times, from digitalization to digital intelligence.
Founded in 1994, Haier Innovation Design Center currently has more than 500 designers, who are engaged in design innovation and model exploration for the seven global brands of Haier Zhijia and as many as +8000 products.
According to Wu Jian, vice president of Haier Smart Home and general manager of Haier Innovation Design Center, in the field of industrial design, in the face of rapidly growing business demands and accelerated product cycles, industrial design also needs digital transformation, and several major problems are encountered during the transformation process:
High cost and time consumption : The traditional industrial design process usually takes a lot of time and resources.
From conceptual design to prototyping to product testing and validation, the entire process can take weeks or even months. This lengthens design cycles, increasing development costs and time-to-market.
Highly dependent on human experience and intuition : Many industrial design processes are still highly dependent on the designer's experience and intuition, which limits the innovation and efficiency of design.
Limitations of human experience can lead to limited innovation, and results can vary between designers.
Information asymmetry and difficulty in collaboration : In the process of industrial design, the flow of information among designers, engineers and manufacturers is often poor, and there is a problem of information asymmetry.
This can lead to a mismatch between design needs and technical requirements, which in turn affects product quality and performance. In addition, the collaborative work between different teams is also facing challenges, and there is a lack of efficient cooperation platforms and tools.
This directly leads to problems such as high labor cost, low concept output efficiency, and low concept pass rate in the concept design stage (that is, the preparation stage).
The first step to solve the above pain points is to realize comprehensive digitalization——to the cloud. In the cloud stage, Haier designed and locked the partner as Amazon cloud technology.
Previously, Haier designed and used its own private cloud system, which was deployed in its own IDC.
However, this private cloud system has many problems such as desktop system resource preemption, file storage system unable to save historical documents for a long time due to capacity limitation, rendering system due to resource limitation rendering tasks require long queues, complex maintenance of the basic system, inability to expand elastically, and difficulty in business system innovation, etc., which have a great impact on the business.
In this regard, Amazon Cloud Technology provided Haier Design with four complete cloud-based solutions, which fully replaced its own machine room, and made the workflow of the design center fully cloud-based and automated.
The solution provided by Amazon Cloud Technology for Haier Design includes four parts: 3D cloud desktop system, rendering farm system, file sharing system and automated design system:
Cloud desktop : In the Qingdao office designed by Haier, the 3D cloud desktop system provides a convenient and easy-to-use desktop environment for more than 300 3D designers and graphic designers.
Through resource isolation and division on the public cloud, Haier Design has completely solved the problems of the original self-built IDC VDI solution, such as "resource run-out causing freezes, crashes or downtime" and "performance degradation when multiple people use it", and can also improve performance by about 30%, which can be said to serve multiple purposes.
Shared storage : A file sharing system built on the basis of Amazon S3 features, allowing companies, groups, and individuals to share storage.
This unlimited-capacity storage system that automatically tiers hot and cold data improves data security by three times, and the previous self-built IDC allocates a maximum capacity of 500G per person, allows only one backup per day and retains it for a maximum of 7 days, and has since become history.
Rendering farm system : The rendering farm system uses Amazon Cloud Technology's own rendering product Amazon Thinkbox deadline software and HPC clusters for image rendering. It has high performance and flexibility, allowing designers to get rendering renderings after submitting tasks, completely solving the rendering task queuing problem.
When the load is low, it will automatically reduce the number of Amazon EC2 Spots and pay according to the actual usage time (accurate to the second), so that there will be no more waste.
Intelligent design system : The automated design system/intelligent design rendering system runs automated design software through Amazon EC2, Amazon Thinkbox Deadline, Amazon DynamoDB, etc., and can automatically generate large-scale rendering renderings that would take days to complete manually in 10 minutes, completely solving the computing power bottleneck problem of the original self-built IDC.
It is reported that after the launch, the application of the automated design system has shortened the original project cycle by 30%.
The second step of "digital intelligence": using AIGC to reduce costs and increase efficiency
At the end of 2022, a thunderstorm on ChatGPT set off a heat wave for generative AI large models. Based on the previous cooperation, Haier Design and Amazon started to explore "generative AI + industrial design".
As for why it chooses to actively embrace AI, Haier Design hopes to reduce costs and increase efficiency. Generative AI can be based on the company's existing processes and knowledge graphs, and avoid duplication, inefficient processes, and reuse after training.
Based on this, Haier Design cooperated with Amazon Cloud Technology to deploy generative AI applications, and created the country's first AIGC industrial design solution combined with actual business scenarios.
It is reported that at the infrastructure layer, the solution uses Amazon SageMaker to quickly build and train AIGC models. By applying the Amazon SageMaker machine learning platform, it provides services in the form of Fine-tune as a Service (tuning as a service). It uses Amazon SageMaker's online model training and management capabilities to provide creative assistance, content production assistance, and creation support for consumer goods, games, and other scenarios.
In addition, Amazon Cloud Technology provided elastic GPU computing power for Haier Design - Amazon EC2 G4dn instance, which is the most cost-effective general-purpose GPU instance in the industry, suitable for deploying machine learning models, such as image classification, object detection, and speech recognition, as well as graphics-intensive applications, such as remote graphics workstations, game streaming, and image rendering.
After the project was launched, Haier Design introduced AIGC solutions into product design, UI design, CMF design, brand design and other links, covering business scenarios of industrial design such as new product design, model modification and upgrade, and channel customization.
In addition, Haier Design and Amazon Technology have also jointly developed the first integrated virtual designer AI assistant "Co-designer". Through cooperation with Amazon Cloud Technology, Haier Innovation Design Center has obtained comprehensive support in terms of infrastructure, including 3D cloud desktop, file sharing system and automated design.
"Co-designer is a key point of cooperation. Although it is still not very perfect, as an important sub-scene, it brings many new applications to the design center.
In addition to Co-designer, Haier will further develop and apply other sub-scenes, such as the part before the designer and the fields of manufacturing, marketing, service and installation.
They plan to explore and apply AIGC technology in different links of the entire value chain, so as to achieve more work optimization and innovation,” Wu Jian said.
It is reported that through AIGC, Haier has achieved an 11.9% increase in the design center business efficiency.
Conclusion: In the era of generative AI, the future opportunities of intelligent manufacturing
From digitization to digital intelligence, the traditional manufacturing industry is moving towards the general direction of intelligent manufacturing. Under the guidance of generative AI technology, intelligent manufacturing is ushering in unprecedented opportunities.
Generative AI technology combines technologies such as deep learning, natural language processing, and image recognition to enable computers to automatically generate content, design solutions, and ideas, bringing revolutionary changes to the manufacturing industry.
In the future of intelligent manufacturing, generative AI will bring opportunities in many aspects, including automatic generation of design schemes, optimization of production processes, improvement of production efficiency, intelligent prediction, fault warning, and intelligent quality control and detection. It can even propose optimal solutions in supply chain and logistics management, improve efficiency and accuracy, and so on.
With the continuous development and innovation of generative AI technology, intelligent manufacturing will enter a new era .
Still, there are challenges to overcome to realize the potential of generative AI. These include data privacy and security protection, technical talent development and cross-departmental cooperation.
Only by comprehensively promoting technological innovation, strengthening cooperation and cultivating talents can the future opportunities of intelligent manufacturing be realized and the prospect of more prosperous and sustainable development for the manufacturing industry be realized.
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