In recent years, artificial intelligence has become an important driving force for a new round of scientific and technological revolution and industrial transformation. As the pace of the fourth industrial revolution continues to accelerate, the demand for enterprises to improve quality and efficiency and accelerate the process of sustainable development has reached an unprecedented peak, and the emergence of AI technology is marking an important inflection point for enterprises.
What obstacles do manufacturing companies face today in the process of digital transformation? What priorities and priorities do companies need to have when investing in IT and AI technologies?
Improving operational efficiency: Top priority for digital transformation in manufacturing
Now, as the world enters the "post-epidemic era", a new map of manufacturing is slowly taking shape. No matter from the manufacturing scale, quality and supply chain ecosystem level, the global manufacturing industry is in the "strong stronger" and more innovative, more high-end direction. Looking at the country, the market share and influence of Chinese manufacturing are steadily rising, and manufacturing highlands with the potential of "new quality productivity" are also being formed.
In the process of steadily advancing into high-quality development in various industries, digital transformation is undoubtedly one of the most important programs and trends. Because the cost structure of the manufacturing industry and the service industry is completely different, generally speaking, the total cost of raw materials and labor in the manufacturing industry can account for 70%-80%, so it is very important to reduce costs and increase efficiency.
In summary, focusing on the excellent digital intelligence transformation examples in various industries, digital technology can help enterprises bid farewell to the traditional manual post-maintenance mode and reap the huge benefits of preventive maintenance. For manufacturing enterprises, the marginal effect of producing one thousand of the same products is very different from that of producing one hundred thousand, so it is of great significance for enterprises to maintain long-term high-quality operation of equipment.
At the same time, digital transformation also plays an important role in reducing the working capital of enterprises in the screening, planning and accurate forecasting, and centralized optimization. With the deepening of digital transformation, once digital technology is combined with enterprise "people, machines and materials" or application facilities and processes, it can be solidified in the system to create more reuse value, which is very typical in mechanical engineering and large-scale discrete manufacturing industries.
Insights and Suggestions: Build a digital middle desk to revitalize data assets
Industrial manufacturing is a typical capital-intensive industry, and its digital transformation is bound to be accompanied by huge investment. Therefore, understanding the investment situation and trend of IT technology has important reference significance for enterprises. Gong Huiwei said that it is best for enterprises to follow the model of modularization and gradual development, so that the design and implementation of digital technology deployment process can get better business and resource coordination.
To accelerate the landing of AI, high-quality data and talents are the key
Why is AI getting so much attention? The main purpose of the introduction of AI by manufacturing enterprises is to reduce costs and rationally use resources, which is also highly consistent with the purpose of reducing costs and increasing efficiency. Fortunately, in many cutting-edge cases, generative AI and industrial metauniverse are slowly landing in the industrial field, and the technical penetration rate is also gradually increasing, which creates a feasible path for the wide application of AI technology.
It is true that in the process of AI landing and accelerating iteration, enterprises will still face many difficulties. On the one hand, with the rapid development of China's manufacturing industry, the large amount of data generated by the operation is one of the most valuable assets for the development of AI, however, Gong Huiwei said that the massive data is not 100% usable, and low quality data often leads to situations such as "vertigo" of large language models, especially for generative AI. As the cornerstone, "data quality" directly determines the accuracy of AI models.
In addition, manufacturing enterprises can develop and plan the roadmap of generative AI and AI use cases for enterprises at the level of research and development process, planning and scheduling of production, logistics procurement, OEM and quality traceability, after-sales and operational excellence, which can play an important role in realizing intelligent manufacturing and improving production efficiency, business revenue and customer experience. At the same time, the shortage of talent and resources is a major obstacle for enterprises to apply AI technology in the short or long term. Therefore, the majority of manufacturing enterprises not only need to introduce AI technology algorithms and other related talents from outside, but also need to set up corresponding personnel "library" in order to better meet the challenges of future AI innovation and integration application.
