Improving production efficiency is the foundation for the manufacturing industry to gain a firm foothold. The manufacturing industry achieves smart operations by introducing AI applications, automatically identifying abnormalities, or making adjustment suggestions, and assisting companies in achieving more accurate adjustments to machines and upgrading equipment. During the process, the traditional manufacturers are transformed into the smart manufacturers.
What Is AI in Manufacturing?
AI, or Artificial Intelligence is a combination of data from sensors, machines, and people and apply the data to algorithms designed to optimize operations or achieve better efficiency. Since big data and AI give Industry 4.0 a huge boost, artificial intelligence in manufacturing is the trend of the manufacturing industry.
There are many positive impacts on artificial intelligence in manufacturing, such as optimizing production processes, safer working environments, demand forecasting, product innovation, simplified supply chains, predictive maintenance, and customized manufacturing. AI in manufacturing aims to increase efficiency by enhancing manufacturing processes in monitoring every stage of the production cycle; for examples, lead times and quantities used. Engaged in smart manufacturing and transformed into a smart manufacturer are important now.
Since the outbreak of the global epidemic in 2020, manufacturers around the world have been affected by the current situation of broken production chains brought about by the epidemic, and enterprises have been affected by the epidemic, making uncertainty a new market practice. In addition to the impact of the epidemic on the international market, the manufacturing industry is undergoing a large-scale digital transformation. In recent years, small customized orders from customers and changes in the international situation have prompted manufacturers to find more flexible production solutions in pursuit of the epidemic. Smart manufacturing solutions that can still respond quickly. In the face of uncertainty, companies need to control all information such as production capacity, yield, supply chain scheduling, and product mix in real-time to respond quickly. Back to the most basic technology, that is, relying on data analysis to predict risks and needs, and then make decisions.
The manufacturing industry gradually attaches importance to and introduces related equipment and technology for data collection and analysis, and installs sensors to collect data on the machines to perform monitoring and data collection and analysis. As the demand for AI to pre-process data and use it on the ground grows, AIoT (Artificial Intelligence of Things) applications that combine the concepts of AI and IoT are gradually being widely used in the manufacturing industry.
Various large cloud companies have developed and launched their AIoT products, refined data processing technology, and reduced the size of machine learning models by as much as 80%, allowing complex models that used to consume a lot of storage space to be executed in a smaller memory storage space. Assist manufacturers with tens of thousands of data items to greatly reduce the cost of cloud storage and computing. In addition to processing quantitative data, new AI entrepreneurs also aim at contract text data that need to be managed under increasingly stringent laws and regulations, such as text analysis of documents such as bidding, contracts, and technology, and key information such as terms, locations, units, and personnel. Wait for the information to be extracted and assist manufacturers to master relevant laws and regulations.
Overall, whether it is qualitative or quantitative data processing, most new entrepreneurs in manufacturing data processing are highly automated data processing services, allowing companies that do not originally have professional data scientists to deal with important but cumbersome data. The data processing link of the company is handled by automated AI software services. The enterprise only needs to use a simple operation interface to enter the data analysis application stage of obtaining value for the enterprise.
Manufacturers import AI-related technologies, in addition to hoping to properly use their data for analysis, but also hope that AI has a basis for knowledge inheritance and future development in the empirical application of AI. In addition to building a large database of related manufacturing industries, accumulating suitable models of various industries on the platform, and completing testing and introduction is also the trend of AI services in the international manufacturing industry. In terms of manufacturing AI models across multiple industries, a large amount of manufacturing maintenance data from multiple industries is used to provide a variety of trained industrial AI models, and then the empirical application direction is adjusted according to needs. Overall, the manufacturing industry combines AI technology applications, many individual industry AI model sharing, and detailed model review services, which are all new trends in manufacturing AI. From the perspective of future AI application import trends, companies have begun to shift from production efficiency to operation-oriented AI applications, and from the past single-point testing to multi-point horizontal expansion. The manufacturing and retail industries have also shifted AI investment to logistics and warehousing to accelerate organizational efficiencies, such as warehouse automation, inventory forecasting, and delivery automation. On the other hand, information security is also an area that is increasingly developed and valued.
In the manufacturing industry, in addition to the traditional automatic optical inspection (AOI), the application of AI visual recognition-assisted inspection technology has gradually improved. Under the development goal of improving quality, AI has many application services in the improvement of production processes, but many production processes still rely on manual execution, so the first task is to apply AI to the improvement of manual work processes. The main goal of enterprises to introduce AI is to no longer prioritize traditional cost reduction, but instead regard improving organizational efficiency and increasing new sources of income as their top priorities.
In the face of the uncertainty of the post-epidemic era and the continuous changes in the global market, AI innovation aims at the key pain points of the manufacturing industry entering AI applications, and will provide corresponding services more comprehensive and in-depth, and provide quality data analysis. Including qualitative data file regulations, quantitative data equipment, and machines and other data processing services, laying the foundation for business analysis for manufacturers; and empirically subdividing manufacturing industries, providing AI model management services with specific industry knowledge, allowing AI Not only can pass on knowledge within the enterprise, but also can use the functions of AI applications in other industries; in addition, the AI services in the manufacturing industry are not limited to virtual data analysis, and use AI vision to analyze personnel movements, placement positions, and action timing services. And ultimately will be fed back to the physical person to operate, thereby assisting manufacturers to improve the overall production efficiency.