10 Frequently Asked Questions about IIoT and Industrial Analytics
By now the phrases Industry 4.0, IIoT, smart factories, industrial analytics, and other buzzwords floating around today’s manufacturing landscape are probably nothing you haven’t heard before, but do you really know what these mean? Could you explain these to someone if they asked you? Maybe, maybe not. Maybe only partially. Whatever the case, it’s good to know their basic definitions in order to understand the context in which modern day process manufacturing operates.
So because we know it’s nice to have them together in one place, we’ve compiled some of the most frequently asked questions about IIoT and industrial analytics and answered them in quick, bite-size ways:
What is Industry 4.0 and what will Industry 5.0 look like?
In 2011, the fourth industrial revolution (or Industry 4.0) began. This industrial revolution was brought on by the information era and the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). What resulted are plant ecosystems of connected industrial infrastructures of smart and autonomous systems fueled by the capture of huge amounts of process data. The next industrial revolution, which is inevitable and expected within conceivably within 5 to 10 years, will be Industry 5.0. It will showcase manufacturing that uses greater teamwork and alliance between humans and smart systems, in particular robots. Smart machines will do the repetitive and dangerous work, and humans will do the creative work.
What is the difference between IoT and IIoT?
IoT or the “Internet of Things” refers to the connectivity of electronic smart devices that contain sensors which transmit and receive data through a wireless network. These smart devices are encountered throughout everyday life from mobile phones, watches, GPS tracking devices, to car and house sensors and much more. IIoT is the “Industrial Internet of Things” and is the industrial level of IoT. It consists of networked sensors and smart devices on assets and equipment in a manufacturing plant that capture an immense amount of data referred to as Big Data. The Industrial Internet of Things (IIoT) brings new possibilities for the process manufacturing industry. Originally, data was captured using hard wired sensors; today since there are new wireless sensors and devices and cloud storage capacities, a much more extensive capture and storage of process data is possible.
What is the difference between digitization, digitalization, and digitalization transformation?
Digitization is the creation of a digital representation of physical objects or features or in other words about converting something non-digital into a digital representation. A manufacturing example would be when a measurement is converted from a manual or mechanical reading to an electronic one.
Digitalization refers to enabling or improving processes by applying digital technologies and digitized data. It takes a human-driven process to software-driven process. Digitalization then brings about digitalization transformation and is the process of using digital technologies to modify or create processes, meet modern day changing business and market requirements. Think of it as a reimagining of business in the digital.
What is a smart factory?
A smart factory is a factory that has the connectivity of IIoT and that has the ability to capture and store process data. In turn, a smart factory enables smart manufacturing with centralized networks that link assets and that can connect factories digitally worldwide.
To understand more about smart factories and the relationship with Industry 4.0, have a look at “Industry 4.0, the Smart Factory, and Self-Service Industrial Analytics”
What is Big Data?
The immense amount of operational process data that is captured in smart factories is referred to as “Big Data”. It is commonly defined by the three “V’s” – volume, velocity, and variety. The amount of big data is mind boggling as thousands of sensors on hundreds of different machines and processes continuously collect readings throughout each day every day. Envision this systematic data collection scenario, and you can understand the speed and array of the data thus the velocity and variety.
What is analytics?
In a nutshell, it’s the process of analyzing and interpreting data to find meaningful and significant patterns out of it.
What is advanced industrial analytics?
Advanced analytics includes several sub-fields of analytics that uses advanced statistical models, high level tools and methods, and predictive capabilities to determine future trends, events, and behaviors for industrial manufacturing processes.
And what about self-service analytics?
Self-service analytics is a software solution that puts the analytics into the hands of the subject matter experts, so they themselves, without the help of a data scientist and without a data science background, can analyze, monitor, and predict process and asset performance and maintenance to improve operational excellence and overall profitability.
What is machine learning?
Machine learning the use of algorithms to find patterns in data and automatically learn from this to make decisions, predictions or determinations about the future. The objective is to allow computers to learn without human intervention and to modify actions accordingly.
What is artificial intelligence?
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because these require human intelligence and discernment. Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
What to Make of All This
So, these are just a few important questions and answers about industrial analytics. Today is the age of data. It is the age of IIoT, digital transformation, and the transformation of the industrial manufacturing plant into the smart factory that captures Big Data.
With advanced analytics, in particular self-service analytics, that uses pattern recognition and machine learning, organizations can have greater insight and predictive capabilities into operations which will allow them to stay in the game of modern production processes. It’s an exciting time in industrial manufacturing and tomorrow promises much more.