introduction
Industry 4.0 represents the digital revolution in manufacturing, which brings the manufacturing process into the digital age. However, Industry 4.0 is not a single concept, but a multi-dimensional category, including different technologies, application fields, enterprise sizes and implementation methods. But within this multi-dimensional concept, low-code technology is emerging. This article will explore how Industry 4.0 is classified to help readers better understand the complexity and diversity of this concept.
一. Classification bytechnical dimensions
The technology dimension of Industry 4.0 includes a variety of key technologies that drive digital transformation. Among them, low-code technology is gradually emerging, bringing more flexibility and speed to manufacturing companies.
1,Internet of Things: Through low-code platforms, manufacturing companies can build IoT applications and connect devices more quickly and sensors to achieve real-time data collection and analysis.
The Internet of Things (IoT) is a technology and concept that enables smart devices, sensors, software and other physical objects to connect and communicate with each other. The main idea of the Internet of Things is to enable objects in the physical world to communicate and collaborate with each other through the Internet to achieve data collection, transmission and analysis, thereby providing more intelligent, efficient and automated solutions.
Key elements of IoT include:
(1) Smart devices and sensors: These devices have built-in computing and communication capabilities and are able to sense and transmit environmental data to other devices or systems on the Internet.
(2) Connectivity: The Internet of Things relies on network connections, including wireless and wired connections, for communication and data transmission between devices.
(3) Data processing and analysis: The Internet of Things generates a large amount of data, which needs to be collected, processed and analyzed to provide useful information and insights.
(4) Applications and solutions: IoT data is often used to develop applications and solutions for automation, remote monitoring, intelligent control and more functions.
IoT has a wide range of applications in various fields, including smart homes, smart cities, industrial automation, agriculture, healthcare, transportation, and energy management. It can improve efficiency, reduce costs, increase safety, and bring more convenience and smart experiences to people's lives and work.
2,Big Data and Analytics: Low-code tools can be used to develop data analysis applications to help enterprises process and visualize Massive production data.
Big data and analytics refers to the process of collecting, storing, and analyzing large amounts of complex data to derive valuable insights and decision support. Big data typically includes structured data (such as data in databases), semi-structured data (such as XML files), and unstructured data (such as text documents, social media posts, and multimedia content). Data analytics is the use of a variety of techniques and tools to understand this data, revealing patterns, trends, and relationships to help organizations make more informed decisions.
Here are the key elements of big data and analytics:
(1) Data collection: The big data process starts with the collection of data, which can include data from multiple sources such as sensors, social media, mobile applications, Internet browsing, etc.
(2) Data storage: Big data requires effective storage before analysis. Traditional relational database management systems (RDBMS) are often unable to handle big data, so distributed storage systems such as Hadoop and NoSQL databases are used to store data.
(3) Data processing: Big data processing usually includes the cleaning, transformation and integration of data to prepare the data for analysis.
(4) Data analysis: Data analysis covers the use of various techniques, including data mining, machine learning, statistical analysis and visualization, to extract insights from data.
(5) Decision support: The ultimate goal of data analysis is to provide information about business, operational and strategic decisions to improve efficiency, innovation and competitiveness.
The applications of big data and analytics are vast and span a variety of industries, including finance, healthcare, retail, manufacturing, energy, transportation, social media, and government. It can be used for customer insights, market analysis, forecasting, security monitoring, product optimization and many other areas. Big data and analytics technologies are evolving to become critical assets for modern organizations, helping them better understand their data and make smarter decisions.
3, Cloud computing: Low-code development can quickly implement cloud applications and support collaboration and data storage across geographical locations. .
Cloud Computing is an Internet-based computing model that allows users to access and use computing, storage, databases, networks, analysis, applications and other IT resources through remote server resources provided by cloud service providers.
Key features of cloud computing include:
(1) On-demand self-service: Users can access cloud resources by themselves according to their own needs without purchasing or configuring hardware and software in advance.
(2) Broad network access: Cloud services are provided through the Internet, and users can access cloud resources from any location and any device.
(3) Resource pooling: Cloud computing service providers bring together a large number of computing and storage resources, which can be allocated to multiple users on demand to achieve resource sharing and efficient utilization.
(4) Rapid elasticity: Users can expand or reduce cloud resources according to needs to adapt to changing workloads. This elasticity can be adjusted in real time.
(5) Service measurement: Cloud computing services are usually based on a service level agreement (SLA), and users can be billed based on their usage instead of paying a fixed fee.
Cloud computing is divided into the following types according to different service models and deployment models:
(1) Clothing model:
① Infrastructure and services (IaaS): Provide basic computing, storage and network resources on which users can build their own applications and environments.
②Platform as a Service (PaaS): Provides a higher-level application development and operating environment, and users can use development tools and services on the cloud to build applications.
③Software as a Service (SaaS): Provides fully managed applications that users can access and use directly without worrying about the underlying infrastructure.
(2)Department model:
①Public cloud: Cloud resources are hosted and managed by third-party cloud service providers, and multiple customers share these resources.
②Private cloud: Cloud resources are independently hosted and managed by a single organization, usually to meet specific security and compliance requirements.
③Hybrid cloud: Mixes public cloud and private cloud, allowing data and applications to flow between the two.
Cloud computing is widely used in various fields, including enterprise IT, software development, big data analytics, artificial intelligence, Internet of Things and more. It brings flexibility, efficiency, cost savings and opportunities for innovation, becoming a critical infrastructure for modern businesses and organizations.
4,Artificial intelligence and machine learning: Leveraging low-code platforms, manufacturing companies can more easily develop and deploy AI and machine learning models.
Artificial Intelligence (AI) and Machine Learning (ML) are two closely related but different fields. They are both dedicated to allowing computer systems to simulate human intelligence and learn from data.
(1) Artificial intelligence (AI):
Artificial intelligence is a field of computer science that aims to create systems and programs that exhibit human-like intelligence.
AI systems can perform complex tasks such as natural language processing, computer vision, expert systems, planning and decision-making, etc. These tasks often require intelligent decision-making and problem-solving capabilities.
AI systems can be implemented based on different methods such as symbolic processing, expert rules, and data-driven methods, and machine learning is an important branch of AI.
(2)Machine Learning (ML):
Machine learning is a subfield of artificial intelligence that focuses on building algorithms and models that can learn from data and automatically improve.
Machine learning uses statistical and mathematical methods to allow computer systems to recognize patterns, generate predictions, or make decisions based on input data without explicit programming rules.
Machine learning can be divided into different categories such as supervised learning, unsupervised learning and reinforcement learning. The choice is based on the nature of the problem and the required learning method.
(3)Different sum system:
AI is a broader field that includes various methods and techniques that attempt to simulate human intelligence, while machine learning is a specific branch of AI that focuses on learning from data.
Machine learning is a method of realizing artificial intelligence. Through machine learning, AI systems can become smarter through continuous improvement.
AI systems can include non-learning algorithms such as rule-based expert systems, while machine learning involves the process of extracting knowledge from data.
Artificial intelligence and machine learning have a wide range of applications in modern technology and application fields, including natural language processing, computer vision, speech recognition, autonomous driving, financial analysis, medical diagnosis, recommendation systems and more. As one of the core technologies for realizing artificial intelligence, machine learning continues to promote the development of intelligent applications.
5, Augmented reality (AR) and virtual reality (VR): Low-code tools can accelerate AR/VR applications Developed for training, maintenance and visualization.
Augmented Reality (AR) and Virtual Reality (VR) are two digital technologies that interact with the real world. They differ in terms of immersive experience and virtual interaction.
(1)Augmented Reality (AR):
AR technology interweaves virtual elements with the real world by overlaying digital information (such as text, images, audio or 3D models) into the user's field of view, thus enriching the user's perceptual experience.
Users are still able to see and interact with the real environment, but AR technology allows digital information to be added to this environment to provide additional information, guidance or entertainment elements.
AR applications are widely used in mobile applications, headsets, glasses and tablets to provide functions such as mobile games, virtual navigation, maintenance and training.
(2) Virtual reality (VR):
VR technology creates a completely virtual digital environment. After users put on a VR headset, they will be immersed in a virtual world that is completely isolated from the real world.
Users can see, hear, and interact with virtual objects and scenes in a VR environment, often using handles, gloves, or other controllers to simulate interactions.
VR is mainly used in fields such as virtual games, simulation training, virtual travel, medical therapy and virtual meetings.
(3)Different sum system:
AR enriches the perception of the real world by adding digital elements to the real world, while VR completely brings users into a virtual environment.
AR maintains a connection to the real world, and users can still see their surroundings; VR is a completely closed experience, and users are isolated in the virtual environment.
AR typically uses devices such as cameras, displays, and sensors to identify and interact with the user's environment; VR requires headsets, audio equipment, and controllers to provide a fully virtual experience.
Both AR and VR technologies are widely used in many fields, including entertainment, education, medical care, military, design and corporate training. They provide new immersive experiences and ways of interacting, and are constantly evolving and improving, bringing unlimited potential to the future of technology and entertainment.
二. Classification byapplication fields
The application fields of Industry 4.0 are diverse, and low-code technology provides faster development paths for various applications.
1, Smart Manufacturing: The low-code platform enables manufacturing companies to quickly build customized production management applications, including intelligent Factory monitoring and automated production line control.
Smart Manufacturing is a manufacturing model that comprehensively utilizes advanced technology and digital methods to improve production efficiency, product quality and corporate competitiveness. It involves the application of intelligent equipment, automated processes, data-driven decision-making and industrial Internet to achieve highly flexible, efficient and sustainable manufacturing.
The following are the key features and elements of smart manufacturing:
(1) Automation and automated control: Intelligent manufacturing relies on automated equipment and control systems to reduce manual intervention and improve production efficiency and product consistency.
(2) Industrial Internet: Real-time monitoring, predictive maintenance and remote control are achieved through the interconnection between devices and the collection and analysis of sensor data.
(3) Digital twin: The manufacturing digital twin is a digital copy of the actual product or production process, which can be used for simulation, design verification and production process monitoring.
(4) Data analysis and big data: Using data analysis technology, smart manufacturing extracts insights from large amounts of data for decision-making and process optimization.
(5) Sustainability: Smart manufacturing also focuses on the sustainability of resource utilization, including energy efficiency, waste reduction, and environmental protection of the production process.
(6) Flexible production: Intelligent manufacturing supports the rapid adjustment of production lines and the flexibility of production plans to adapt to changing market demands.
(7) Collaborative robots and autonomous systems: Robots and autonomous systems are used in smart manufacturing to support the automation of task distribution, logistics and production processes.
The application scope of smart manufacturing covers various industrial sectors, including automobile manufacturing, aerospace, electronics, life sciences, food and pharmaceuticals, etc. It provides significant improvements in production efficiency, quality improvement, cost reduction and visual monitoring of the production process. Intelligent manufacturing is a core component of Industry 4.0. It represents the trend of future manufacturing and will continue to evolve and develop to adapt to changing market needs.
2, Smart supply chain: Leveraging low-code technology, companies can more easily develop supply chain collaboration applications to Achieve real-time monitoring and demand forecasting.
Intelligent Supply Chain is a supply chain management method based on advanced technology and data-driven, aiming to improve the efficiency, visibility and responsiveness of the supply chain. Smart supply chain integrates technologies in areas such as logistics, inventory management, order processing, and data analysis to achieve more intelligent and flexible supply chain operations.
The following are the key features and elements of a smart supply chain:
(1) Real-time data and visibility: Using sensors, IoT devices and real-time data analysis, smart supply chains can monitor logistics, inventory and order status in real time, providing a higher level of visibility.
(2) Predictive analysis: Using big data and machine learning technology, smart supply chains can predict demand, inventory changes and delivery times to help make more accurate decisions.
(3) Automation and intelligent decision-making: Smart supply chain adopts automated decision-making and execution systems, which can automatically adjust inventory, transportation routes and order processing according to real-time conditions.
(4) Supply chain visualization: Using digital technology, smart supply chains can create digital twins of the supply chain, reflecting actual supply chain operations in real time to help optimization and decision-making.
(5) Internet of Things and Sensors: Internet of Things devices and sensors are used to monitor goods, equipment and the environment, and provide data for supply chain management and decision-making.
(6) Blockchain technology: Blockchain can be used to ensure the transparency, security and authenticity of transactions and deliveries, and to prevent fraud and errors.
(7) Artificial Intelligence and Machine Learning: AI and ML technologies are used to analyze big data, discover patterns and provide intelligent suggestions to improve supply chain processes.
Smart supply chain applications span a variety of industries, including manufacturing, retail, logistics, healthcare, agriculture, and energy. Its goals are to improve supply chain efficiency, reduce costs, reduce inventory and improve customer satisfaction. Smart supply chain is the future trend of modern supply chain management and will continue to develop and evolve to adapt to changing market and technological requirements.
3,Smart products: Low-code development helps manufacturing companies integrate IoT technology into their products to achieve remote control Monitor and provide personalization features.
Smart Products refer to physical products with built-in intelligent technologies and capabilities. These products can sense, analyze and respond to the environment, and provide value-added functions and interconnectivity. Smart products often integrate sensors, communication modules, data processing units and user interfaces to enable higher levels of interaction and automation.
The following are the main features and elements of smart products:
(1) Sensing technology: Smart products are equipped with various types of sensors, such as temperature sensors, motion sensors, optical sensors, etc., to sense the surrounding environment and product status.
(2) Data collection and processing: These products are able to collect data generated by sensors and analyze and interpret this data using built-in data processing units.
(3) Communication capabilities: Smart products are usually equipped with communication modules that can be connected to other devices or the Internet to achieve remote monitoring and control.
(4) Automation and intelligent control: Intelligent products can automatically perform certain tasks or perform intelligent decision-making and control based on data and user input.
(5) User interface: They are usually equipped with user-friendly interfaces such as applications, touch screens, or voice recognition to interact with users.
(6) Interconnectivity: Smart products are usually interconnected and can be integrated with other smart devices, cloud services or applications to provide a wider range of functions.
(7) Upgrade and remote management: These products can be upgraded and managed via remote firmware to keep their functionality and performance up-to-date.
Smart products have a wide range of applications, including smart home devices, smartphones, smart watches, smart vehicles, smart medical equipment, smart city solutions, etc. These products change people's lifestyles and provide more convenience, efficiency and interactivity. Smart products are also widely used in the enterprise field to help improve production efficiency, monitor equipment status and optimize operational processes. As technology continues to advance, smart products will continue to develop, bringing more innovations to people's daily lives and work.
4, Smart maintenance: Through low-code platforms, companies can quickly build predictive maintenance applications and use sensor data to Reduce equipment downtime.
Smart Maintenance is a method that uses advanced technology and data analysis to monitor, predict and maintain equipment and assets. The goal is to minimize unnecessary downtime, reduce maintenance costs, and increase equipment availability and performance.
The following are the key elements and characteristics of smart maintenance:
(1) Sensor technology: Intelligent maintenance uses sensors to monitor various parameters of equipment, such as temperature, pressure, vibration, current, etc. These sensors collect data in real time for analysis and determination of equipment status.
(2) Data analysis and predictive maintenance: The collected data is analyzed to detect anomalies and trends. With the help of machine learning and data analysis, possible equipment failures can be predicted and maintenance can be carried out.
(3) Remote monitoring: Intelligent maintenance can remotely monitor the operating status of equipment, reducing the need for on-site inspection and repair. This saves time and costs.
(4) Maintenance optimization: Maintenance tasks can be optimized based on the actual condition of the equipment. Maintenance is only performed when needed, reducing the frequency of planned maintenance.
(5) Visualization and reporting: The results of data analysis can be displayed in a visual form and maintenance reports can be generated for maintenance personnel to refer to and take action.
(6) Equipment health management system: Smart maintenance usually includes an equipment health management system, which integrates all relevant information so that maintenance personnel can better manage equipment status and maintenance history.
(7) Preventive and predictive maintenance: Intelligent maintenance is divided into two main types. Preventive maintenance is based on a prescribed maintenance schedule, while predictive maintenance is based on data analysis to predict equipment failures and take action when needed.
Smart maintenance is widely used in manufacturing, energy, transportation, healthcare and facilities management. It helps reduce maintenance costs, improve equipment reliability, and reduce production interruptions. With the development of technologies such as the Internet of Things, big data and artificial intelligence, intelligent maintenance will continue to develop and provide higher levels of equipment maintenance and management for various industries.
3. Classification byenterprise level
The degree of implementation of Industry 4.0 can be classified according to the size of the enterprise (large enterprises, small and medium-sized enterprises). Low-code technology provides digital transformation opportunities for enterprises of different sizes.
1, Large enterprises:Large manufacturing enterprises can use low-code tools to quickly build and deploy customized applications to support digital strategies.
Large enterprises typically have more resources, including financial, technical and human resources, making it easier for them to undertake widespread Industry 4.0 implementations.
Larger companies typically have more capital to invest in equipment, sensors, automation systems and data analysis tools.
These companies usually have larger production scales, making it easier to achieve economies of scale and improve production efficiency through Industry 4.0.
Key challenges that large enterprises may face include the complexity of change within the organization, data integration and maintenance.
2, Small and medium-sized enterprises: may choose specific Industry 4.0 technologies or may benefit from low-code technologies to meet their Specific needs and resource constraints.
Small and medium-sized enterprises may have limited resources, including financial and technical resources, so the implementation of Industry 4.0 may require more careful consideration and planning.
These companies often need to better select and optimize Industry 4.0 technologies to ensure maximum benefits with limited resources.
Small and medium-sized enterprises are generally more flexible and able to adapt to market changes faster and adopt smaller-scale Industry 4.0 projects.
Key challenges may include funding constraints, insufficient technical capabilities and staff training needs.
Regardless of the size of the enterprise, the goal of Industry 4.0 is to increase production efficiency, reduce costs, improve quality and customer satisfaction. Large enterprises may be able to implement a broad range of Industry 4.0 solutions more quickly, while small and medium-sized enterprises may need to more strategically select the appropriate technologies and solutions to meet their specific needs and resource constraints. However, Industry 4.0 provides opportunities for companies of all sizes to innovate and compete.
4. Classification by degree
The degree of implementation of Industry 4.0 can generally be divided into two main categories: partial implementation and full implementation. These two categories reflect the varying degrees of adoption of digitalization and automation technologies by manufacturing companies.
1, Partial implementation:Enterprises may only implement Industry 4.0 technologies in part of the production process to meet specific needs.
① Partial implementation of Industry 4.0 means that enterprises have adopted some digitalization and automation technologies in their production processes, but they have not yet been fully applied in the entire production process.
②This may include installing sensors on some equipment to monitor performance, adopting some automated control systems, or starting to collect some production data.
③Partial implementation is usually a gradual process, with companies gradually introducing more Industry 4.0 elements to improve production efficiency and quality.
2, Full implementation: Enterprises may fully implement Industry 4.0 in the entire production process to achieve full coverage of digitalization and intelligence.
① The full implementation of Industry 4.0 means that an enterprise adopts a wide range of digitalization and automation technologies throughout its production process, from supply chain management to production, quality control and logistics.
②This includes the use of a large number of sensors, automated robots, data analysis tools, Internet of Things (IoT) and artificial intelligence to achieve comprehensive production process automation and optimization.
③The fully implemented Industry 4.0 enables enterprises to achieve highly flexible, efficient, personalized and sustainable manufacturing.
In practical applications, many companies start with partial implementation and then gradually transition to full implementation. This incremental approach allows businesses to adapt to the introduction of technology, gradually increasing their level of digitalization and automation while reducing risks and costs. However, full implementation of Industry 4.0 is often the ultimate goal to maximize production efficiency, reduce costs, improve product quality and customer satisfaction.
5. Classification by country and region
The degree of implementation and adoption of Industry 4.0 varies between countries and regions. The development level of manufacturing and digital infrastructure, policy support and industrial demand in various countries and regions will all affect the adoption of Industry 4.0. The following is the classification of some countries and regions in terms of Industry 4.0:
1, China:
China is one of the largest manufacturing countries in the world, and the government actively supports the development of Industry 4.0. China's manufacturing companies have adopted Industry 4.0 technologies on a large scale, including smart manufacturing, automated factories, industrial Internet of Things and big data analysis.
2, Doku:
Germany is considered the birthplace of Industry 4.0, and its manufacturing industry has made significant progress in digitization and automation. German manufacturing companies widely apply Industry 4.0 technologies, including smart manufacturing, the Internet of Things, automated production lines and digital twins.
3, Bikuni:
Manufacturing in the United States is also adopting Industry 4.0 technologies, including advanced automation equipment and large-scale data analysis. American manufacturing companies focus on improving production efficiency and providing highly customized solutions.
4,Korea:
South Korean manufacturing companies are also actively applying Industry 4.0 technologies, especially in the fields of electronics and automobile manufacturing. Korean manufacturers adopt automated production lines, IoT and smart factory technologies.
5, Other European countries:
European countries have also made significant progress in Industry 4.0, including France, Italy, Switzerland, etc.
European companies adopt smart manufacturing, automation and digital twins to improve competitiveness.
6, Emerging market countries:
Some emerging market countries, such as India and Brazil, have also begun to adopt Industry 4.0 technologies to improve the competitiveness of their manufacturing industries.
The adoption of Industry 4.0 is diverse globally and depends on the specific conditions and needs of each country and region. However, this trend continues to grow globally, with countries striving to achieve digital transformation to make manufacturing more efficient and innovative.
六. ByClassification of production environment
Industry 4.0 technologies can be applied in different types of production environments, including discrete manufacturing, process manufacturing, agriculture and medical fields.
1, Dispersed production:
Discrete manufacturing involves the production of discrete units of products, such as automobiles, electronic equipment, mechanical parts, etc.
In the field of discrete manufacturing, Industry 4.0 technology can be used for intelligent manufacturing, automated production lines, quality control, predictive maintenance and supply chain management.
2, Process production:
Process manufacturing involves producing products in a continuous process such as chemicals, food and beverages, oil and gas, etc.
Industry 4.0 technology is used in process manufacturing to monitor and control production processes, improve safety, reduce energy consumption, and optimize production planning and execution.
3、农业：
Agriculture is an important field where Industry 4.0 technologies can be used to improve the efficiency and sustainability of agricultural production.
Industry 4.0 applications in agriculture include smart agricultural machinery, agricultural robots, sensor monitoring, precision agricultural management and digital agricultural solutions.
4,Medical/Health:
The healthcare sector utilizes Industry 4.0 technologies to improve medical equipment, healthcare processes and patient monitoring.
This includes telemedicine, connectivity of medical devices, smart ward management and electronic health records.
5,Transportation and Logistics:
The transportation and logistics industry uses Industry 4.0 technologies to improve the intelligence of transportation systems, as well as the visibility and efficiency of logistics and supply chains.
This includes smart traffic management, logistics tracking, autonomous vehicles and smart warehousing management.
6, Buildings and Infrastructure:
Industry 4.0 technologies are used in the construction and infrastructure sectors to monitor and maintain buildings, roads and bridges.
This includes building intelligence, structural health monitoring and urban infrastructure management.
Regardless of the field, the goals of Industry 4.0 are to increase efficiency, reduce costs, improve quality and sustainability. It can be customized to meet the needs of different industries to meet the challenges and opportunities of specific production environments.
in conclusion
The classification method of Industry 4.0 covers multiple dimensions and helps us better understand the diversity of digital transformation. Different enterprises can choose appropriate technologies and implementation methods based on their needs and resources to achieve goals such as improving efficiency, reducing costs, and improving product quality. The future of Industry 4.0 will continue to evolve, bringing more innovation and opportunities to the manufacturing industry.