Trends in Artificial Intelligence

In recent years, artificial intelligence has shown a strong development trend in the intelligent equipment industry of the chemical industry. From the perspective of national policies, the General Office of the Ministry of Industry and Information Technology issued the "Guidelines for the Construction of Standard System for Intelligent Manufacturing in the Chemical Industry (2024 Edition)", aiming to effectively leverage standards to play a leading and supporting role in the intelligent manufacturing of the chemical industry. This indicates the high attention of the country to the intelligent development of the chemical industry and has pointed out the direction for the future development of the industry.

With the intensification of market competition, enterprises need to enhance production efficiency, reduce costs, and improve product quality. Artificial intelligence technology can achieve functions such as automatic control of chemical equipment, data analysis and prediction, and optimization of the production process. The development trend of artificial intelligence in the intelligent equipment industry of the chemical industry is unstoppable. Therefore, Jinwang Intelligent specially launched the Future Window series of soft articles, which will deeply analyze the application examples of artificial intelligence in the intelligent equipment industry. From the precise control of intelligent equipment to the in-depth mining of production data, it demonstrates how artificial intelligence helps enterprises enhance their competitiveness. Under the guidance of national policies, Jinwang Intelligent and the industry are jointly moving towards a new intelligent future.

AI - Artificial Intelligence

The application of AI in the industrial sector is extensive and profound. Its powerful data processing, analysis and prediction capabilities have brought revolutionary changes to industrial production. Through deep learning and big data analysis, industrial AI is gradually penetrating into every aspect of the manufacturing industry, from intelligent manufacturing, supply chain optimization to quality control, comprehensively promoting the transformation of traditional manufacturing towards intelligence and automation.

Specifically, the application of AI in the industrial sector mainly includes the following aspects:

1. Intelligent manufacturing

Production planning and optimization: AI can analyze production data in real time, optimize production plans, and improve production efficiency and flexibility. Through intelligent algorithms, enterprises can more accurately arrange production activities and reduce resource waste.

Predictive maintenance: Using machine learning and data analysis technologies, AI can predict equipment failures and carry out maintenance and replacement in advance, thereby reducing maintenance costs and extending equipment lifespan.

Intelligent manufacturing system: AI-driven robots can precisely perform complex operations and achieve intelligent and automated production processes. This greatly improves production efficiency and reduces the error rate of human operations.

2. Quality control:

Product inspection and defect identification: Through machine vision and deep learning technologies, AI can efficiently and accurately detect product defects, such as scratches and stains. This intelligent quality inspection system significantly improves product quality and inspection efficiency.

Quality data analysis: AI can also deeply analyze quality data during the production process to identify key factors affecting product quality and provide strong support for quality improvement.

3. Supply chain management
 Demand forecasting and inventory management: AI can analyze supply chain data in real time, predict market demand, and optimize inventory management strategies. This helps reduce inventory overstock and shortages, and improve customer satisfaction and enterprise operational efficiency.

Logistics optimization: AI can track the location of goods in real time, intelligently schedule inventory and transportation resources, reduce logistics costs, and improve logistics efficiency.

4. Energy management

Energy consumption optimization: AI can monitor and analyze energy consumption data in real time, propose energy-saving measures, and reduce enterprise energy consumption costs.

Energy demand forecasting: Through data analysis, AI can predict future energy demand, formulate reasonable energy procurement plans, and ensure the stability and economy of energy supply.

5. Safety monitoring

Environmental monitoring：AI uses image recognition and pattern recognition technologies to achieve real-time monitoring of the safety status of the production environment.

6. Personalized customization

Customer demand analysis: Through deep learning and big data analysis technologies, AI can deeply understand customers' personalized needs. Product design optimization: AI can assist designers in rapid prototyping and simulation testing, promoting the intelligence and personalization of product design. This helps enterprises better meet market demands and enhance market competitiveness.

In summary, the application of AI in the industrial sector not only improves production efficiency and quality, but also reduces costs and risks, promoting the transformation and upgrading of the manufacturing industry and its intelligent development. In the future, with the continuous advancement of technology and the increasingly rich innovative application scenarios, AI will play an even more important role in the industrial sector.

AI (Artificial Intelligence) Application Cases

Supply Chain Management: Walmart optimizes logistics through AI-driven demand forecasting and routing algorithms, enhancing productivity and customer satisfaction.
Intelligent Manufacturing System: BMW uses AI-driven automated guided vehicles (AGVs) in its manufacturing warehouses to simplify internal logistics operations, improve inventory management and visibility.
Demand Forecasting and Inventory Management: An automotive parts manufacturer uses machine learning models to predict spare parts demand, optimize inventory levels and reduce costs.
These application scenarios and success cases demonstrate the wide application and significant effectiveness of AI in the industrial sector.

Key Points in the Era of Industry 4.0

In the era of Industry 4.0, Jinwang will continue to focus on the research and application of intelligent production solutions. By introducing advanced artificial intelligence technologies and automated equipment, it will achieve highly intelligent production processes. From the intelligent selection and precise distribution of raw materials, to the automated operations on the production line and real-time quality monitoring, with intelligent technology as the core, production efficiency and product quality will be significantly improved.

In the field of intelligent logistics, the company is actively exploring innovative models. By leveraging Internet of Things technology, it enables real-time tracking of goods and intelligent scheduling. Through big data analysis, it optimizes logistics routes, reduces transportation costs, and enhances logistics efficiency, providing customers with efficient and reliable logistics services.

Intelligent sensors and visual inspection systems are installed on the production line. Using AI algorithms, the collected data is analyzed to achieve real-time monitoring and prediction of product quality. For instance, by analyzing product appearance images, AI can detect surface defects and size deviations; by monitoring and analyzing various process parameters during production, it can predict whether there will be quality issues with the products and take timely measures to correct them.

Establish a quality traceability system, combining blockchain and AI technologies to record and trace the information of products throughout the entire process from raw material procurement to production, manufacturing, and sales. In case of any quality issues, the cause can be quickly identified and corresponding measures can be taken.

Riding on the wave of new productive forces and the trend of intelligent transformation and digitalization, JWIM has built an intelligent factory to better serve customers. JWIM has launched the EPCOM intelligent factory turnkey service, which addresses the issues in the construction of intelligent factories in the agricultural chemical industry due to the uncertainty of project duration, huge consumption of consumables, unclear costs, unsmooth equipment operation, and failure to meet the expected standards of effectiveness. The innovative EPCOM model for agricultural chemical formulation intelligent factories, aimed at creating value, using intelligent equipment as the carrier, and leveraging digital technology as the handle, encompasses: intelligent processing, intelligent packaging, intelligent warehousing, digital operation, maintenance services. It integrates design, procurement, installation and commissioning, operation and maintenance, featuring ease of use, time-saving, cost reduction, and excellent results, ultimately achieving the effects of reducing staff, increasing efficiency, improving quality, reducing costs, ensuring safety, being environmentally friendly, and being low-carbon.

JWIM is also the earliest and the only company in the agricultural chemical industry to fully self-develop from 0 to 1 in the EPCOM chain. It leads the industry with multiple differentiated technologies! It has a hundred-person delivery team, responsible for quality control, safety control, progress control, and effect control, to realize customer value!

JWIM EPCOM - E Professional Design

JWIM EPCOM - P Professional Procurement

JWIM EPCOM - C Professional Construction

JWIM EPCOM - O Digital Operations

In the future, Jinwang will continue to uphold the development concepts of innovation, intelligence and efficiency, constantly exploring new fields and applications of intelligent technology, and making greater contributions to promoting the intelligent development of the industry.