BENTLY 60M100-00 With the application of Internet of Things, AI, 5G and other new technologies in recent years, the popularity of “intelligent manufacturing” is high. But opportunities often come with challenges. Whether it is the inefficiency of R&D design link, the lack of flexibility of production management link scheduling, the rigidity of supply chain management in traditional operation management, or the traditional operation and maintenance can not reflect the equipment running situation in real time, the warning analysis is not intelligent enough, the warning root is difficult to determine and other problems, all increase the obstacles for the industrial dual transformation.

Carefully observing these difficulties, it is not difficult to see that the problems involved are distributed in different production links of the manufacturing industry. In order to realize the digitalization of the whole life cycle of the manufacturing industry, it needs the joint efforts of the upstream and downstream enterprises of the industrial chain, including the joint participation of equipment providers, software developers, system integrators, service providers, end users and so on.

Generally speaking, intelligent manufacturing production link is divided into: R & D design, production management, operation management, operation and maintenance service four stages

First of all, in R & D design, traditional manufacturing enterprises are faced with such problems as slow response to market demand, inefficient internal team communication, and mismatch between product design and production conditions.

Secondly, with the continuous expansion of enterprise production scale and the enhancement of product customization attribute, problems caused by traditional production mode such as delayed inventory management, lack of flexibility in scheduling, and difficult material purchase to meet production control and control have gradually become numerous obstacles to enterprise development. How to apply digital and intelligent technology to enterprise production management has become the “urgent problem” for enterprises.

BENTLY 60M100-00 Taking metallurgical industry as an example, there are problems in basic management, spot inspection management, maintenance management and spare parts management in the field of equipment management.

To the enterprise operation management link, the current customer demand diversification, customization trend is more and more obvious, the traditional management of supply chain management rigid solidification, inventory management and order management lack of elasticity and other problems, become the soft shackles of enterprise development. Under the background of limited production capacity, how to flexibly and quickly meet the personalized needs of customers and improve the efficiency of enterprise operation and management has become an urgent problem to be solved under the background of digital transformation.

Taking the power industry as an example, the current development faces many challenges, such as weak electricity growth, inefficient and ineffective investment, high cost of purchasing electricity, and waste of capital use.

BENTLY 60M100-00 supports various types of data sources and data access. After data access to the system, the data is fully sorted and summarized, and the corresponding data business model is generated. Based on advanced AI technologies, such as text parsing, entity recognition, semantic understanding, and image recognition, a complete set of underlying engine capabilities of semantic analysis and image analysis is built. Combined with the industry knowledge graph and algorithm model built into the system, multiple top-level functions such as intelligent question and answer, document search, decision analysis, and data prediction are provided.

For industrial enterprises, safety and stable production is the bottom line. There are some problems in traditional operation and maintenance, such as not being able to reflect the running situation of equipment in real time, not intelligent warning analysis, and difficult to determine the root cause of warning. It is more and more necessary to provide fault warning in advance and realize remote and rapid diagnosis and maintenance of equipment.