Over the past few centuries, the industrial sector has seen multiple revolutions that have changed the way manufacturing works and the ways people do business. The First Industrial Revolution in the United States ushered in the age of mechanical production, thanks to water and steam mechanization. With the second revolution, we saw electricity enable mass production. In the modern era, we’ve seen the third revolution, the digital revolution, enable fully automated systems and IT infrastructures. Today, we’re in the midst of the Fourth Industrial Revolution, also called Industry 4.0.
This revolution is largely defined by automated systems that interact with little to no human intervention. Machines can create specific products independently, monitor areas for safety and security, help large enterprises organize and monetize Big Data Certification, and much more. Here are just four important things that everyone should know about the Fourth Industrial Revolution.
1. Big data analytics allows for immediate action.
With traditional business intelligence tools, data analysis was typically a time-consuming process, and even worse, data from different systems had to be analyzed in silos. With modern master data solutions, disparate systems can collect data in one pool that you can analyze to discover ways to improve business processes. This could be anything from improving supply chain efficiency to analyzing customer data to find ways to increase sales.
Take the AARP Life Insurance Program from New York Life, for example. AARP is a group that focuses on the issues of U.S. residents over the age of 50. With modern data analysis, the New York Life Insurance Company could quickly gather information on insured AARP members to see what they most enjoy about membership to present life insurance products more effectively and focus on the positives of AARP membership. Gathering personal information automatically makes the underwriting process easier for brokers and clients to become AARP insured faster.
2. The Internet of Things is revolutionizing manufacturing.
Devices connected to the Internet of Things (IoT) are becoming more commonplace, and many people are already using them in their homes. The term Internet of Things refers to connections between multiple devices over the internet, such as sensors communicating with each other in a factory or your smart home devices sharing information. This technology is simultaneously making manufacturing more efficient and safer.
For example, sensors can be placed on the factory floor to monitor machines and automated processes. If a machine goes down, these sensors can automatically detect where the problem is and communicate it to supervisors. Even more impressively, they can sometimes use predictive analytics to spot problems before they even happen. GPS devices in trucks used in the supply line can also automatically communicate route data, so supervisors can find new ways to boost supply chain efficiency.
3. Virtualization can create copies of the plant floor.
Cameras and sensors on the manufacturing floor can also help create a virtual copy of the entire plant. This can be a useful way to spot operational inefficiencies that might otherwise go unnoticed. It’s also a reliable way to test new processes before they actually go live in the plant. Plant floors that use virtualization typically see a net profit increase since virtualization allows supervisors to test the best possible outcomes for the floor and quickly recover from security or manufacturing failures.
4. Machine learning is improving every aspect of the industry.
Many of the previously mentioned technologies, and more, are made possible thanks to modern advancements in machine learning. Essentially, machine learning is the study of computer algorithms to give them the ability to teach themselves using either supervised (taught) or unsupervised (more experimental) models. Machine learning can be applied to complex problems to give analysts little background in statistics the ability to solve them. In manufacturing, you can also use these to teach cameras the difference between a normal and defective product so that you can automatically take products with issues off the line.
Of course, this scratches the surface of machine learning and all the applications of the Fourth Industrial Revolution. These technologies will, no doubt, continue to advance and produce even more use cases.