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Through enterprise surveys, we found that 91% of AI projects failed to meet enterprise expectations, whether measured in terms of business benefits, budget and time commitment. It is a global phenomenon that the results of artificial intelligence projects fall short of expectations. This gap is often caused by the following problems:
Existing experience and organizational barriers;
Infrastructure constraints;
data acquisition methods and data quality issues;
Lack of engineering experience;
The project is too large and complicated.
83% believe AI has had or will have a tangible impact on their business in the next five years, with 27% of respondents believing AI projects have already brought value to their business. 56% of respondents believe AI will pay off for their businesses in the next 2-5 years.
From the perspective of technical orientation, more companies will invest in complex technology systems to optimize production, cost, inventory or quality control, or for sales, price predictive maintenance forecasts. There is relatively little enthusiasm for investing in a single technology category, such as vision monitoring, robotic positioning, and expert systems.
The industry generally believes that the industrial artificial intelligence platform allows enterprises to apply artificial intelligence at a lower cost, which is a necessary condition for the landing and popularization of artificial intelligence in the industrial field.
View one [Reference three] :
Artificial intelligence technology has achieved a certain range of applications in the field of intelligent manufacturing. By comprehensively considering the position of relevant applications in the product life cycle and the impact on the key elements of total quality management of products, the typical application matrix of artificial intelligence in intelligent manufacturing is given from two dimensions of product life cycle and key elements such as human, machine, material, method and environment.
Point 2 (Ref. 4)
From the technical point of view, there are eight key technologies of artificial intelligence that are widely used in the manufacturing industry, namely deep learning, reinforcement learning, pattern recognition, machine vision, data search, knowledge engineering, natural language understanding and brain-like interactive decision making. With the support of these eight technologies, the manufacturing industry can achieve self-perception, self-adaptation, self-learning and self-decision-making.
On the other hand, since 2012, the rise of artificial intelligence based on computing power, algorithms and data has been 7 years, and artificial intelligence has gradually entered the 2.0 era. At this stage, with the development of smart chips, cloud computing, and various open source algorithms, algorithms and computing power have basically no fortress, and data will become the main driving force to promote artificial intelligence change. In this phase, a large amount of structured and reliable data is collected, cleaned, accumulated and monetized.
In terms of industrial innovation, Academician Pan Yunhe proposed that artificial intelligence 2.0 will deeply integrate with industry and revolutionize the manufacturing industry at five levels:
First, intelligent production. This includes two aspects, one is the intelligence of the production process, with the assistance of artificial intelligence (such as machine vision, robotics, etc.), the factory will achieve complete automation and intelligence including parts sorting, assembly, welding, handling, etc., thus greatly liberating the labor force. The second is the intelligent production management, through the optimization of production, collaborative manufacturing, flexible manufacturing, employee management, energy management, safety management, factory optimization, etc., greatly reduce production losses. This is also the most talked about intelligent manufacturing content at present.
Second, the intelligent operation of enterprises. This includes forecasting and analysis of employment demand, including production cost management, financial management, asset management, information management, decision management, etc.
Third, the intelligence of product innovation. Including smart product innovation, AR/VR+ products, personalized customization and so on.
Fourth, the intelligent supply chain. Including supply chain risk management, logistics management, parts management, supply chain financial management, supply chain optimization and so on.
Fifth, the intelligence of economic regulation. Including industry market trend analysis, policy analysis, advantage analysis, competition and cooperation analysis, industry portrait, investment assistance decision, talent assistance decision, also includes regional economic analysis, economic boom early warning, economic coordination assistance decision.
