Industry 4.0″, “new quality productivity”, “artificial intelligence +”… We are experiencing an era of productivity transformation. New quality productivity with technological innovation as the core and breaking the boundaries of industrial scenarios has emerged, and the important representative force is the development of artificial intelligence.

In March of this year, “artificial intelligence +” was written into the government work report for the first time, which means that China will accelerate the formation of new quality productivity with artificial intelligence as the engine. This coincides with Schneider Electric’s long-term layout of digitalization and intelligence. Schneider Electric firmly believes that putting AI technology into practical applications in the industrial industry will stimulate unlimited potential for the “advancement” of China’s industry.

With the continuous iteration and evolution of AI technology, how to play its value in the complex and changeable industrial field to promote the development of new quality productivity? What scenarios can make AI play its greatest role and realize the large-scale application of AI, thereby accelerating the new industrialization?

Efficiency evolution, exponential transformation of productivity

In the transformation from traditional industry to new industry In the process, creating tangible value with cutting-edge technology is the only way – Schneider Electric is further breaking down the barriers between IT and OT, penetrating into the entire life cycle of enterprises from design, construction to operation and maintenance, and putting AI technology into practice.

In the early R&D and design stages, Schneider Electric is using AI technology to innovate the traditional software development methods, such as using large models to assist in generating basic code and help check code integrity, saving engineers a lot of repetitive work, and injecting more vitality into the development of new technologies and new functions. In the key production and manufacturing stages, AI technology is used to help factories improve quality and efficiency, such as using AI intelligent decision-making to help coordinate multiple factors and formulate accurate production plans; using AI visual inspection to efficiently identify product defects and improve product quality. In the process of operation and maintenance management, Schneider Electric is using AI algorithms and machine learning to help companies efficiently manage assets and equipment, improve operational efficiency, optimize energy use, and help companies improve the efficiency and resilience of operation and maintenance.

It can be seen that whether it is visual recognition, machine learning, large language models, or generative AI, they have now penetrated into all aspects of the industrial production process. So what is the key to maximizing the value of AI scenarios?

In-depth scenarios, deep integration of technology and applications

The key to unleashing the potential of AI technology lies in promoting the integration and innovation of AI technology and actual application scenarios. As a “practitioner” and “enabler” of AI scenario applications, Schneider Electric is committed to deeply integrating AI technology with a series of vertical industry scenarios to enable production quality and efficiency:

Process optimization: Schneider Electric uses AI algorithms to formulate intelligent control strategies and provides a disruptive production line optimization solution for a beer manufacturer. By aggregating, analyzing, and sensitively monitoring the working conditions of the entire production data, and predicting and fine-tuning the optimal control strategy, it helps customers achieve 20% material savings and 15% production efficiency improvement while achieving safe and high-quality production.
Industrial full-process carbon reduction: In an application example of a chemical company, Schneider Electric deployed a customized machine learning model to monitor six carbon emission sources in a vacuum distillation unit. The model uses the AVEVA PI System operational big data management platform to analyze data streams every 5 minutes, providing timely feedback on potential deviations in CO2 emissions. This enables operators to respond quickly, investigate root causes, and make targeted adjustments to optimize processes and minimize CO2 emissions. The model is not only applicable to vacuum distillation units, but can also be migrated to different industrial processes.
Refined management of energy consumption: Schneider Electric provides a semiconductor company with an ice machine cooling capacity prediction solution. Based on AI algorithms, it accurately predicts the cooling capacity on the demand side based on the historical data of ice machine operation. Refined management of energy consumption is achieved through more accurate control of energy demand. Actual measured data shows that the solution has an energy saving effect of 3-5%. If hardware modification is provided, a comprehensive energy saving of 5-10% can be achieved.
Improved energy efficiency of air compressors: Schneider Electric uses AI intelligent algorithms to achieve optimized control and intelligent management of air compressor stations, helping companies significantly improve energy efficiency. In a station management system project of a new energy vehicle company, through data collection, modeling and analysis, the optimal operating parameter suggestions are provided for the factory’s integrated station air compressor station control system and HVAC control system, achieving control logic optimization and energy saving and efficiency improvement, so that the company can achieve twice the result with half the effort on the road to building an efficient, energy-saving modern and green factory.
Dynamic refrigeration efficiency improvement: In a HVAC energy-saving renovation project of a data center, Schneider Electric injected AI modeling and data analysis algorithms into traditional PID closed-loop control. Through modeling and data collection, accurate prediction, optimization solution and strategy output, the terminal precision air conditioner in the computer room is optimized to dynamically output refrigeration according to actual needs. At the same time, the cold station control system is globally optimized to achieve 31% power saving of the terminal air conditioning system, and the cold station refrigeration efficiency is expected to increase by 20%.
Predictive maintenance: The equipment fault prediction and diagnosis system based on vibration mechanism + mathematical model, combined with the process mathematical model fault diagnosis tool, can not only help users diagnose mechanical aging and wear problems, but also diagnose equipment failures caused by electrical faults or process changes for users. Schneider Electric’s Xiamen factory has deployed an AI-based predictive maintenance solution for vacuum furnace equipment, enabling real-time data monitoring of equipment status 24 hours a day, 7 days a week throughout the year, and scheduling equipment maintenance according to prediction curves, saving approximately RMB 1.2 million in maintenance costs each year.With the rapid development of digital technologies such as artificial intelligence, the global industry is undergoing major changes. Schneider Electric will continue to be innovation-driven, promote the deep integration of AI technology with specific application scenarios in more industries, and work with more partners to create industry influence and move towards a smarter, innovative and sustainable future industry.
Related product recommendations:
PPD512 A10-15000
TC-PPD011
PPD113B03-26-100110 3BHE023584R2634
PPD512 3BHE040375R1023
PPD113B01-10-150000 
PPD115A102
5SHX1445H0001 3BHB003230R0101
5SHY4045L0001 3BHE009681R0101
5SHY4045L0004 GVC736CE101
5SHY3545L0016 3BHB019719R0101
5SHX2645L0004 5SXE04-0150/GVC707AE01
5SHY3545L0005
5SHY35L4520 5SXE10-0181
5SHX1445H0002
5SHX06F6004
5SHY4045L0006 3BHB030310R0001
5SHY5055L0002 GVC736BE101
5SHY35L4512
5SHY6545L0001 AC10272001R0101 
5SHX2645L0004 5SXE05-0156
5SHY3545L0009 GVC750BE101
5SHY4045L0003 GVC736BE101
3500/91-01-01 (161204-01+161216-01)
3500/22M 138607-01
3500/42M 140734-02 
125680-01 3500 
3500/50 133388-02
3500/15 133292-01 
3500/15 127610-01
3500/22M 288055-01
3500/42M 140734-02
3500/05-01-02-00-00-01
3500/15 129489-01
3500/40M 140734-01
3500/20 125760-01
3500/32 125712-01
3500 Series 15
3500/25-01-01-00
More……