On the global economic stage, manufacturing has always played a pivotal role. From ancient times to the present, whether it is the invention of the iron plow and textile machine in the agricultural era, or the birth of steam engines and electric equipment during the Industrial Revolution, they have fully demonstrated the role of manufacturing in promoting the progress of human society. With the rapid development of science and technology, the importance of manufacturing has become increasingly prominent. It is not only the cornerstone of national economic growth, but also the key to improving the country's comprehensive strength.
The combination of digitalization and AI has always been the focus of the manufacturing industry. Gu Fan, general manager of the Strategic Business Development Department of Amazon Cloud Technology Greater China, said: Generative AI is accelerating innovation and change in the manufacturing industry. Generative AI will further empower Chinese manufacturing companies and provide disruptive innovation and change. Amazon Cloud Technology is committed to promoting generative AI to reshape the growth path of the manufacturing industry, by lowering the threshold in the critical path of building generative AI applications, fully penetrating into manufacturing value chain scenarios, and working with partners to provide industry-leading end-to-end Technical solutions, developing customized solutions in industrial design, marketing creativity, knowledge base and other scenarios, allowing manufacturing companies to fully realize the potential of generative AI.
AI technology + digitalization brings new momentum to the manufacturing industry
It can be seen that my country is moving from a manufacturing country to a manufacturing power. It has mastered high-end manufacturing technologies such as high-speed rail, large aircraft, LNG ships and large cruise ships, and the manufacturing industry has begun to fully enter the digital era.
Currently, China's rapidly developing manufacturing industry is facing several challenges and opportunities. If enterprises can seize them in time, they will surely usher in all-round "leap-forward" development.
AI will usher in a big explosion in 2023. At the same time, the digital transformation of the manufacturing industry has also entered the "deepening" stage. It is more expected to organically integrate digital technology with core business and use data analysis and artificial intelligence to improve corporate operations. At the same time, companies from the manufacturing industry are shifting their focus from artificial intelligence and machine learning to generative AI technology, which will affect manufacturing (production), product development and design, sales, and supply chain links.
Manufacturing overseas and sustainable development are still eternal topics. Enterprises going overseas must have a global vision and local operation capabilities, take into account safety compliance and efficient resource allocation, and vigorously develop green manufacturing in order to satisfy complex customers. need.
Based on the above points, it can be seen that the integration of digitalization and AI in manufacturing will become the key to future development.
AI improves industrial design efficiency several times
When talking about the combination of AI and manufacturing, the first thing that comes to mind is industrial design. McKinsey research shows that if the development of new products is delayed by six months and the product is launched, profits will decrease by 33% in the next five years. How to use digital tools and methods to improve R&D efficiency is crucial to the company.
Currently, Amazon Cloud Technology focuses on three key scenarios: computing-assisted engineering (CAE), electronic design automation (EDA), and design and engineering desktop and environment eVDI. It is working with partners to launch corresponding cloud solutions for business scenarios in some industries.
Siemens Chengdu Factory cooperated with Amazon Cloud Technology to successfully build an industrial waste sorting system that trains local reasoning in the cloud. The system uses advanced machine learning algorithms and cloud computing technology to greatly improve the accuracy of waste classification, reaching more than 95%. For the identification of hazardous waste, 100% accuracy has been achieved.
In terms of model training, by using the computing power of Amazon Cloud Technology, Siemens Chengdu Factory reduced the model training time from more than 10 hours to 2 hours, an increase of 5 times. This not only greatly saves time costs, but also reduces labor costs. The work that originally required three full-time personnel to perform model training can now be done with other more valuable tasks.
INVISTA, a leading global manufacturing company, is transforming its operations from business intelligence (BI) to artificial intelligence (AI). For its data science workflow, INVISTA selected Amazon SageMaker to build, train, and deploy internally developed third-party machine learning models. After system implementation, INVISTA successfully migrated 600 local servers to the cloud, including multiple manufacturing applications and global INVISTA SAP. This transformation not only improved efficiency, but also brought significant economic benefits to INVISTA. They've saved more than $2 million in annual costs and created $300 million in value from company-wide data.
Generative AI helps the manufacturing industry through three major scenarios
Gu Fan believes that generative AI is different from other IT technologies in that it brings huge room for imagination to an industry. However, no matter how big your imagination is, you need to put it into practice first to ensure that these ideas can take root. This means that enterprises need to pay attention to the performance of generative AI in practical applications and how to combine it with existing technologies and business scenarios to achieve real value.
Currently, there are three major application scenarios that have been closely integrated with the manufacturing industry and created huge value.
The first is industrial design. As mentioned above, generative AI can help accelerate the efficiency of concept drawing design in the field of industrial product design. Although generative AI cannot completely replace humans, it can quickly generate concept maps based on human descriptions. This way, designers can more quickly transform their ideas into images, thus speeding up the entire design process.
Taking the car case mentioned by Thunderstar as an example, this technology can help car designers accelerate the design process in the concept drawing stage and integrate these concept drawings into the entire workflow. This can improve design efficiency, shorten product development cycles, and reduce design costs.
The second industrial scenario is marketing. Generative AI is also widely used in marketing material design. By using schemes such as Vincentian diagrams and Tusheng diagrams, marketing materials for the product can be automatically generated based on the description of the product's selling points. These materials can be adapted to different communication channels, including various online and offline scenarios.
The third scenario is functional support. Amazon Cloud Technology uses generative AI technology to build an enterprise-level intelligent knowledge base. By integrating search engines and large language models, this intelligent knowledge base can help enterprise employees quickly find the most accurate and effective content, thereby effectively improving production and office efficiency.
Of course, the above three application scenarios are only part of what generative AI can do, and there are many more scalable scenarios in the future.
Large models and small models
Gu Fan said that for To B manufacturing industry, the application of large models is not just about using a tool, but to achieve the best results by identifying core business application scenarios, solving business problems, improving efficiency, reducing costs and increasing efficiency. solution. Through the rational application of large models, companies can achieve greater competitive advantages in the manufacturing industry.
Both large and small models play an important role in application solutions in the manufacturing industry. Large models are often used to handle complex problems that require large amounts of data and computing resources for training and inference. They are suitable for scenarios that require higher levels of analysis, prediction, and optimization, such as intelligent knowledge bases and innovative design centers.
Small models pay more attention to real-time performance and flexibility, and are suitable for use in assisted decision-making, real-time monitoring and control scenarios. Amazon Cloud Technology's industrial visual inspection and supply chain prediction are good examples. They can provide accurate results and predictions through rapid analysis and processing of real-time data, helping companies optimize production and supply chain management.
In practical applications, manufacturing companies may need to use both large and small models to solve different problems. Large models can perform complex large-scale data analysis and predictions to provide strategic decision-making support for enterprises; while small models can monitor and control the production process in real time and discover and solve problems in a timely manner.
Therefore, it is foreseeable that in the future, large models and small models will continue to coexist in the manufacturing industry and complement each other to provide enterprises with more comprehensive and accurate solutions.
Generally speaking, today's manufacturing industry has begun to fully embrace AI. A series of complex problems such as design, management, operation and maintenance, supply chain and other aspects can all use AI to improve efficiency. Amazon Cloud Technology is using advanced technologies to bring infrastructure, generative AI, and solutions for various manufacturing scenarios to the industry, allowing it to better integrate digital technology with core businesses to enhance competitiveness.
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