Industry 4.0 Defined and Explained

By Brian Gilmore / Use Cases, Company, Developer
Sep 21, 2021

With Industry 4.0 fundamentally transforming manufacturing systems and processes through IIoT technologies, manufacturers large and small are seeking the most efficient ways to reap its benefits. Potential gains include optimizing operations, generating data-driven insight, creating new revenue streams, and accelerating innovation. To paint the big picture, let’s start with a definition of Industry 4.0, followed by an explanation of what adopting it involves.

What Industry 4.0 means

Industry 4.0 – the Fourth Industrial Revolution also known as the Industrial Internet of Things (IIoT) – is the automation of traditional manufacturing and industrial practices, using cloud-native technology and data analytics. In Industry 4.0, industrial machine-to-machine communication and IoT are integrated for increased automation, communication and autonomous monitoring.

Industry 4.0, which focuses on cyber physical systems to reinvent the manufacturing process around the power of data, was preceded by the:

• 1st industrial revolution (mechanization, water power, steam power)
• 2nd (mass production, assembly line, electricity)
• 3rd (introduction of computers and automation to enhance existing processes)

Although coined as a phrase back in 2012, the term “Industry 4.0” has gained popularity in recent years with its promise to revolutionize manufacturing through the power of data.

Overcoming the data challenges of Industry 4.0

With data at the center of Industry 4.0 environments, the challenge becomes designing a data architecture that meets industrial environments’ demanding performance, scalability, and availability requirements. As such, a common challenge is adopting new data flow systems and connecting them to existing legacy solutions. This is where IIoT comes into play, offering heavy asset industries reduced cost, easy installation, improved data accuracy and remote monitoring. As IIoT is a newer technology than industrial automation mainstays such as Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) systems, its capabilities adapt well to current industry demands.

IIoT adoption requirements

Industry 4.0 marks the convergence of operational technology (OT) and information technology (IT) as well as the real-time interdependence between the process and analytics. Achieving this convergence necessitates reinventing the manufacturing process with an architecture that can ingest the huge volumes of real-time data generated by IoT sensors and other devices, and enable nano-second control of the entire environment. Transitioning to Industry 4.0 requires:

• Real-time data ingestion and querying capabilities — The magnitude of data that IIoT applications process and the real-time processing they require impose distinct demands on the database used to ingest and process their data. This is why a data historian (a time series database for industrial IoT) is a centerpiece of Industry 4.0. The data historian needs to allow for fast ingestion and query of time series data in near real-time; compression to minimize storage; and storage at the required precision for maintaining production line efficiency and minimizing downtime.
• A data architecture that allows seamless integration across data sources — Integrations maintain data availability for real-time process optimization. The role of data integrations in managing industrial assets is captured well by Nortal: "The road to your digitalization journey and the Fourth Industrial Revolution is paved with data integrations...Without data silos, you have a complete, accurate, and centralized view of your assets and their current state and health. The ability to visualize your asset performance and process efficiencies anytime and anywhere makes you agile and able to react quickly."

Designing an Industry 4.0 environment

In sync with the data requirements discussed above, an Industry 4.0 environment can be based on four design principles:

• Interconnection — Enabling communication between devices, sensors, and people
• Information transparency — Gathering large data volumes from all points of the manufacturing process
• Technical assistance — Aggregating and visualizing data in a centralized dashboard to solve problems in real time
• Decentralized decisions — Enabling systems to perform tasks autonomously based on collected data and only exceptionally require human interference

From cost efficiency to data democratization, operational agility, traceability, and customer retention through better products and services, Industry 4.0 adoption is empowering manufacturers to realize their productive potential. In contrast, failing to adopt Industry 4.0 technologies puts manufacturers at risk of falling behind as their competitors race to capitalize on automation and digitization.