Analytics based on large data sets which optimizes quality of production and saves energy has come out recently in the world of manufacturing.

Manufacturers in numerous industries have used robots to tackle complex assignments, but now they’re progressing towards greater utility.

A digital twin is a virtual simulation of a real-world product, machine, system, or process based on IoT sensor data.

Augmented-reality-based systems support various services, like sending repair instructions over mobile devices and selecting parts in a warehouse.

Cloud computing in the present scenario goes way beyond scalability, speed, cost efficiencies, and storage. It gives the foundation for most advanced technologies and provides organizations with a way to organize. The data that power Industry 4.0 cloud computing technologies reside in the cyber-physical systems and cloud at the core of Industry 4.0 use the cloud to coordinate and communicate.

With the use of standard communications protocols and increased connectivity with Industry 4.0, the need to protect manufacturing lines and critical industrial systems from cybersecurity threats increases drastically. As a result, reliable and secure communications and access management of machines and users are a must. During the past year, various industrial equipment vendors have joined forces with cybersecurity companies through partnerships or acquisitions.

The Industrial Internet of Things is so central to Industry 4.0 that the two terms are used interchangeably. Most of the physical things in Industry 4.0 such as devices, machinery, robots, products and equipment use sensors to provide real-time data about their performance, condition, or location.

This technology lets organizations run smoother supply chains, rapidly modify, and design products.

A drive-and-control-system vendor, Bosch Rexroth, outfitted a production facility for valves with a decentralized production and semi-automated process. Products are identified by identification codes, radiofrequency and workstations to know which manufacturing steps must be performed for each product and can adapt to perform the specific operation.

Analytics based on large data sets has come out recently in the world of manufacturing, where it optimizes quality of production, improves equipment service, and saves energy. Big data analytics in Industry 4.0 is in the context that the comprehensive evaluation and collection of data from many different sources will become standard to support decision making in real-time.

For example, Infineon Technologies, a semiconductor manufacturer has reduced product failures by correlating single-chip data encapsulated in the testing phase at the end of the production process with process data collated in the wafer status phase in the early process. In this way, industry 4.0 analytics helped Infineon to quickly identify patterns that assist them in discharging faulty chips early in the production process and improved production quality.

Currently, most of the IT systems are not fully integrated. Organizations, customers and suppliers are rarely closely linked. Functions in the organization are not fully integrated. From plants to products to automation—lacks complete integration.

But with Industry 4.0, organizations, functions, departments, and capabilities will become much more tenacious, as cross-organization, universal data-integration networks evolve and enable truly automated value chains.

Organizations have begun to adopt additive manufacturing, such as 3-D printing, mostly to produce individual components and prototypes. Having Industry 4.0 in place, these additive-manufacturing methods will be widely used to make small batches of customized products that offer construction advantages, like high performance, lightweight designs, and more.

Currently, most of the IT systems are not fully integrated. Organizations, customers and suppliers are rarely closely linked.