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How Generative AI Will Reshape the Manufacturing Industry

Company News > How Generative AI Will Reshape the Manufacturing Industry

For decades, Amazon has been focusing on artificial intelligence (AI) and machine learning (ML), continuously lowering the threshold for machine learning usage and making it easy for everyone, including over 100000 customers in various industries, to get started. Among these customers, companies from the manufacturing industry are shifting their focus from artificial intelligence and machine learning to generative AI technology, in order to achieve more exciting results.

The research report on the current situation of manufacturing ind

ustry and the application of generative AI by manufacturing enterprises released by IDC shows that respondents from the manufacturing industry believe that the top three areas where generative AI can have the greatest impact in the next 18 months are manufacturing (production), product development and design, sales, and supply chain. This article will focus on the potential of generative AI in innovating product design, significantly improving manufacturing productivity levels, and optimizing supply chain applications.

Innovating in the field of product engineering through generative AI

In the field of product engineering, artificial intelligence and machine learning have been combined with high-performance computing to enhance the design of discrete product components, providing innovative designs that humans typically cannot conceive. These technologies provide manufacturing enterprises with a faster and more effective way to explore various design options, in order to find the best solution while minimizing costs, material consumption, engineering design time, and even production time.

As a leading enterprise in the field of 3D design, engineering, and entertainment software, Autodesk has been developing software for the architectural design, construction, engineering, manufacturing, media, and entertainment industries since 1982. In order to accelerate and simplify the development process, Autodesk has been steadily expanding its use of Amazon cloud technology services and reducing the use of its own data centers. Autodesk provides generative design functionality (a service similar to generative AI) through its Fusion 360 software, helping product designers provide innovative designs within user specified parameter ranges, including materials, manufacturing process constraints, safety factors, and other variables. At the Hannover Messe in April 2023, Autodesk introduced a mobile travel startup that improved its process of creating new mobile solutions, ultimately reducing delivery times and controlling engineering and manufacturing costs while rapidly exploring new mobile design concepts. This startup adopts Autodesk Fusion 360 and utilizes Amazon SageMaker to enhance artificial intelligence generative design and additive manufacturing, thereby shortening the launch cycle of new designs from 3.5 years to 6 months, achieving an 86% speed increase.

In addition to the enormous potential in the design process, engineers can also use generative AI to analyze large datasets to improve product safety, create simulated datasets, explore how to manufacture or process components faster, and thus push products to the market faster. These datasets can serve as information sources or basic models for manufacturing enterprises to construct generative AI strategies, allowing them to build their generative AI strategies on this basis. This allows enterprises to fully benefit from this technology while also protecting their ownership and data security.

Amazon Cloud Technology's Amazon Bedlock hosting service allows users to access AI21 Labs, Antiopic, Cohere, Stability AI, and basic models provided by Amazon through APIs. Amazon Bedlock is the easiest way for customers to build and extend generative AI applications using basic models, reducing the usage threshold for builders. One of the most important features of Amazon Bedlock is that it allows customers to easily customize models. Customers only need to present a few annotated data examples from Amazon Simple Storage Service (Amazon S3) to Bedrock, and Bedrock can fine-tune the model for specific tasks with a minimum of 20 examples, without the need to annotate a large amount of data.

Optimizing production processes using generative AI

Due to the risks of high costs and production losses faced by manufacturing enterprises, they often hesitate to adopt and implement new technologies in the production environment. In factory production, the application cases of generative AI are still in the early stages, but many factory managers have clearly stated that generative AI can help optimize overall equipment efficiency (OEE). Due to the large amount of data required to create basic models in generative AI, manufacturing enterprises will face unique industry challenges, namely obtaining factory data and migrating it to the cloud, embarking on a journey of generative AI. Data is the foundation of any digital transformation. For many manufacturing enterprises, the first step is to develop and adopt industry data strategies, which can help business teams easily and effectively utilize data to cope with various application scenarios in the enterprise. The reason is that manufacturing enterprises often face unrelated and isolated data sources, which make collaborative work difficult, making it difficult for basic models to obtain high-quality datasets that are economical, secure, structured, and easy to access. Now, the Industrial Data Fabric solutions provided by Amazon Cloud Technology can help businesses solve these problems.

Georgia Pacific has been using artificial intelligence and machine learning to optimize paper production quality for many years. The company uses Amazon Cloud Technology data analysis technology to predict the running speed of production lines to avoid the problem of paper tearing during the production process, thereby increasing profits and maximizing the utilization of factory resources. And how will generative AI help enterprises improve production? In conversations with business and manufacturing leaders, a recurring issue is the loss of production knowledge and experience caused by employee turnover. Experienced workers can identify which machine bearings require lubrication and which machines are experiencing abnormal vibration during operation. But as they retire, the knowledge and experience accumulated over these decades are also taken away. Therefore, the challenge that enterprises often face is how to quickly equip inexperienced operators with professional knowledge, maintain efficient operation of complex production processes, and maximize output and ensure quality.

If manufacturing enterprises are committed to digital transformation and record historical machine maintenance data, repair data, equipment manuals, production data, and even data from other manufacturing enterprises, in order to make basic models more effective, real change will come. For example, if a machine that continues to malfunction causes unplanned downtime, engineers can use generative AI to investigate possible causes of the malfunction and obtain advice on equipment adjustments, maintenance operations, and spare parts procurement, which can reduce downtime. Therefore, in the absence of experienced engineers and operators, generative AI can promote the maximization of OEE in production environments.

Optimizing Supply Chain Using Generative AI

Amazon Cloud Technology provides a variety of services to cope with various supply chain scenarios. Amazon Supply Chain can help businesses improve supply chain visibility, reduce risks, save costs, and improve customer experience while making informed decisions faster. Amazon Supply Chain can automatically merge and analyze data from multiple supply chain systems, allowing enterprises to observe their own operational conditions in real-time, grasp changes faster, and generate more accurate demand forecasts to ensure sufficient inventory to meet customer needs. Based on Amazon. com's nearly 30 years of logistics network experience, Amazon Supply Chain enhances the flexibility of the supply chain by providing a unified data lake, machine learning based insights, action suggestions, and in application collaboration capabilities.

The epidemic, geopolitical conflicts, raw material shortages, natural disasters, and other factors have brought various uncertainties to the supply chain, and the supply chain of manufacturing enterprises remains a worrying area, even a source of anxiety. But because of this, suppliers have developed value-added areas for generative AI. For example, if a manufacturing enterprise has run out of customized mechanical parts and is looking for alternative suppliers to complete some of the customized processing work, generative AI can be used to provide the necessary capabilities for the alternative suppliers and help the enterprise provide urgently needed professional services. When conditions permit, generative AI can also be used as a substitute for daily interpersonal communication, such as answering questions that previously took hours or days to obtain correct data and understand its meaning. Generative AI can also serve as a supply chain control tower, proactively assessing risks such as transportation difficulties, natural disasters, strikes, or other geopolitical events, helping enterprises allocate scarce resources to the supply chain and mitigate the impact of supply disruptions.

Compared to the infinite possibilities that generative AI brings to the manufacturing industry, its current applications in product design, production, supply chain, and other aspects are only the tip of the iceberg. However, in any case, this is an unprecedented step forward. Amazon Cloud Technology is committed to enabling developers of any level and enterprises of all sizes to innovate through generative AI. I believe that generative AI technology will bring infinite new possibilities to the manufacturing industry.

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How Generative AI Will Reshape the Manufacturing Industry

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