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 (M2M) and the internet of things (IoT) are integrated for increased automation, communication and autonomous monitoring, resulting in smart manufacturing equipment that can analyze and diagnose issues without the need for human intervention.
Industries are under constant pressure – to improve product quality, boost factory efficiency, stay competitive, enhance safety, security and sustainability, and remain profitable. Yet driving operational efficiencies merely through traditional cost-cutting measures provides only marginal gains. In contrast, adopting Industry 4.0 technologies can fundamentally transform business models and revenue streams. To remain competitive, factories and warehouses are leveraging IIoT to gain visibility into their own practices and thereby become more agile and efficient.
IIoT vs. IoT
First, let’s distinguish IIoT from IoT. While both IoT and IIoT render devices smarter by connecting them to the internet, IoT aims at making consumer lives easier and more convenient while IIoT (IoT technology used in industrial settings) aims at improving efficiency and safety in factories and manufacturing facilities. IIoT is a key Industry 4.0 technology and is revolutionizing the way industrial organizations and supply chains are run.
The role of data in Industry 4.0
Although coined as a phrase back in 2012, the term “Industry 4.0” has gained increased popularity in recent years with its promise to revolutionize manufacturing through the power of data.
From the first industrial revolution (mechanization through water and steam power) to the mass production and assembly lines using electricity in the second, the fourth industrial revolution will take what was started in the third, with the adoption of computers and automation, and enhance it with smart and autonomous systems fueled by data and machine learning. The big difference between Industry 4.0 versus Industry 3.0 is that while in the former computers were introduced to enhance existing processes, the latter seeks to reinvent the entire process around the power of data.
Tim Hall, The Role of Data in Industry 4.0
As Hall states, data has long been treated in the manufacturing industry as the “orphan nephew living in the cupboard under the stairs”, and while operational and service industries have leapt on the benefits of data as the catalyst of business growth and efficiency gains, the manufacturing sector has been slow to adopt the culture of becoming a data-driven business.
Delivering on the promise of IIoT solutions
Data-driven operational intelligence can cut production cost, improve process efficiency, and support new innovative business models. In this context, data has been referred to as the proverbial new oil and the lifeblood of the ‘digital’ factory. Since the most valuable asset available to manufacturers today is the data they produce, the promise of IIoT solutions depends on the ability to harness data at the scale and speed of industrial data.
When effectively stored, analyzed, visualized and forecast, the data generated and transferred by equipment, transmitters and sensors provides a wealth of insight enabling real-time decision-making and action. The role of data in managing industrial assets (discussed in detail here) is perfectly summed up by Nortal, a global consultancy that enables enterprises to quickly meet high demands of scale, security, and performance empowered by the latest technologies:
The road to your digitalization journey and the Fourth Industrial Revolution is paved with data integrations. Data is needed when clarifying the big picture, aspiring for predictive maintenance, optimizing and debottlenecking processes, or implementing new systems...Industrial Internet of Things (IIoT) or Industry 4.0 are built on moving and combining data, making it accessible in real time. 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.
The design principles behind Industry 4.0
Critical for the adoption of Industry 4.0 is an understanding of its four design principles:
- Interconnection — The ability to have devices, sensors, and people connect and communicate with each other.
- Information transparency — Interconnection allows for the collection of large amounts of data from all points of the manufacturing process.
- Technical assistance — The ability to aggregate and visualize the data collected with a centralized dashboard allows operators to make informed decisions and solve urgent issues on the fly.
- Decentralized decisions — The ability for systems to perform their tasks autonomously based on data collected, and only on an exception basis, require interference.
Achieving the above design principles requires overcoming challenges that manufacturers encounter in becoming data-driven. These challenges include adopting new data flow systems and connecting them to their existing legacy solutions. Most industrial organizations use a system of software and hardware components called Supervisory Control and Data Acquisition (SCADA) to control factory machinery and systems in real time:
- SCADA systems control processes locally by gathering and recording event data from sensors, valves, pumps and motors.
- The relevant data is presented to the operator locally to make decisions about the machinery to keep it running optimally.
The data challenges of manufacturing companies
Delivering IIoT capabilities calls for a similarly capable database that can match industrial environments’ stringent performance, scalability, and availability requirements. The magnitude of data that industrial IoT applications must process — and the real-time processing and high availability they require — impose distinct demands on the database used to ingest and process their data.
Since sensor data is time series data, all process and event data includes a value and a timestamp and is stored in an industrial time series database (a data historian, also called a process historian) to show trends per machine or across a collection of machines. A data historian needs to allow for fast ingestion and query of time series data in near real-time and provide compression of the data to minimize storage. There are many commercial data historian solutions in the market, yet all these solutions come with a number of challenges — primarily cost, vendor lock-in, and scalability.
Transitioning to Industry 4.0
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 a data architecture that can ingest the huge volumes of real-time data generated by the IoT sensors and other devices, and enable nano-second control of the entire environment.
The critical element here is time, and a time series database offers the best route to providing this required precision:
- Maintaining production line efficiency — requires the ingestion of huge amounts of sensor data and the implementation of a time series database architecture to accommodate the scale and precision required.
- Minimizing production line downtime — ensured via data analytics to predict problems and equipment failures before they actually occur.
Seamless data flow through IIoT integrations
The open exchange of data is vital in an Industry 4.0 environment because an inappropriate data architecture can lead to data silos where critical data required for real-time process optimization is unavailable. This is why a data architecture that allows seamless integration from and to various data sources is critical for IIoT adoption and success. For example, Telegraf (an open source plugin-driven metrics collection agent native to InfluxDB) has IIoT- specific plugins for AMQP, ModBus, MQTT, OPC-UA and Sensors.
A complete time series platform with IIoT-specific integrations
IIoT use case examples
Learn how a time series database can serve IIoT in practice by:
- Reading these real-world IIoT customer use case examples in various sectors including renewable energy, smart buildings and others.
- Watching the webinars Using OPC-UA to Extract IIoT Time Series Data from PLC and SCADA Systems, How to Create a Modern IIoT Monitoring Solution on iOS Using Swift, MQTT and InfluxDB, and other IIoT webinars.
- Learning — through this podcast, this talk and the resources here — how Factry built its data historian on open source InfluxDB.
Available as InfluxDB open source, InfluxDB Cloud & InfluxDB Enterprise