The main differences between Industrial Revolution 3.0 and Industrial Revolution 4.0 are as follows:
Digital Transformation: While Industry 3.0 represented a significant leap forward in manufacturing technology, it was primarily focused on automating existing processes. Industry 4.0 represents a more comprehensive digital transformation of industries. It requires a fundamental rethinking of business models, processes, and value creation. It involves the adoption of new technologies, the development of digital skills, and the integration of digital capabilities into every aspect of the organization.
Focus and Scope: Industry 3.0 primarily focused on automation within factories, replacing human labor with machines and computers to optimize production lines. It aimed to improve efficiency and productivity. Industry 4.0, on the other hand, encompasses a broader scope. It emphasizes the integration of digital technologies across the entire value chain, including design, manufacturing, distribution, and even customer interaction. It involves the use of artificial intelligence, machine learning, data management, and the Industrial Internet of Things (IIoT) to create interconnected cyber-physical systems.
Technological Advances: Industry 3.0 introduced automation and the use of computers and programmable controllers in manufacturing processes. However, the technology was limited compared to the advancements in Industry 4.0. Industry 4.0 leverages advanced technologies such as artificial intelligence, machine learning, big data analytics, cloud computing, robotics, augmented reality, and the IIoT. These technologies enable real-time connectivity, data-driven decision-making, predictive maintenance, autonomous systems, and the merging of the physical and virtual worlds.
Data and Connectivity: In Industry 3.0, data and information were primarily stored and utilized within local servers of individual companies. There was limited integration and sharing of data across sectors. In contrast, Industry 4.0 promotes the sharing of data and information across industries. It relies on interconnected systems and platforms where data is accessible to multiple stakeholders in real-time. The availability and analysis of vast amounts of data enable better decision-making, optimization of processes, and improved collaboration between different entities.
Automation and Intelligence: While both revolutions involve automation, Industry 4.0 goes beyond physical automation. It integrates automation with intelligence through the use of AI and machine learning algorithms. This enables machines and systems to become more intelligent, adaptive, and autonomous. Industry 4.0 systems can learn from data, make predictions, and optimize operations without human intervention. It enables more flexible and responsive production processes that can adapt to changing demands and market conditions.
Collaborative Ecosystems: Industry 4.0 promotes collaboration and connectivity within an ecosystem of stakeholders, including manufacturers, suppliers, customers, and even machines. It fosters the concept of “collective intelligence,” where different entities collaborate and share knowledge and insights to drive innovation and continuous improvement. This collaborative ecosystem enables seamless communication, transparency, and interoperability between different components of the value chain.
In summary, while Industry 3.0 brought automation to manufacturing processes, Industry 4.0 represents a broader and more transformative revolution that combines advanced technologies, data connectivity, and intelligence to create interconnected, adaptive, and efficient cyber-physical systems.
Solulever, a Dutch Technology Startup, aligns with the principles of Industry 4.0 and provides industrial connectivity platforms to assist manufacturers in their digital transformation journey.
The Brabo Edge Platform® enables real-time data integration and analysis through the process of data mashups. It consolidates data from different sources, such as sensors, machines, and other connected devices, and combines them to provide valuable insights. These insights are then made available to the development teams in real-time.