Almost three hundred years ago, the combination of steam power with manufacturing machines revolutionized our production. For the first time in history, we could produce more goods without spending more time.
Now, we are amidst the Fourth Industrial Revolution.
In industry 4.0, we use smart technology to automate our production further. Machines gather information with their sensors, process the information using AI, and put it to use via the IoT.
With these new technologies come many buzzwords and confusing acronyms.
Here, you can learn in simple terms what those industry 4.0 buzzwords and acronyms mean, how the technologies behind them work, and why they are important for industry 4.0.
This is the industry 4.0 glossary.
With the help of Sébastien Meunier, our Director Industrial Transformation, we update this glossary regularly.
Bookmark it and stay up-to-date.
What is the Internet of Things (IoT)?
The Internet of Things (IoT) is the network on which devices communicate with each other and without any human interaction.
When we open our browser on our phone and google something, the phone and the Google server communicate via the internet.
But if, for example, our phone unlocks our door’s smart lock when we are within 20m of our house, the phone and the lock communicate over the IoT.
To be precise, we should call it an Internet of Things instead of the Internet of Things. Devices within one network can’t communicate with devices outside of their network. Otherwise, our phone could open our neighbour’s smart lock and vice versa.
What is the Industrial Internet of Things (IIoT)?
The IoT is made for consumers. Devices that are connected via an IoT are designed to make our everyday life easier.
The IIoT does the same for production and manufacturing. The IIoT connects machines in a factory and lets them exchange information. Combined with AI, those machines can then autonomously identify and solve problems that previously needed human intervention.
Let’s take a tour through a fictive cookie factory.
Everything is automated. There is a machine that mixes the dough, one that forms the cookies, one that sprinkles them, and the last one that checks if size and form are as they should be.
Suddenly, the last machine registers that the cookies are missing sprinkles.
Without the IoT, the production would have to stop, and a worker would have to investigate.
With IoT, an AI can pull data from all the machines, figure out the problem, and send commands back to them. The machines solved the problem on their own. Faster than if humans would have done it and without stopping production.
How the Internet of Things (IoT) works.
At the heart of every IoT is a hub. The hub is a physical device, like a box, that acts as the central point of the IoT.
Every device with a connection to the IoT has a digital twin that can send information about itself to the hub and receive commands back.
Just like humans use their senses to gather information, machines use cameras, microphones, and other sensors.
Through the sensors and the constant communication among themselves, the devices know exactly what we’re doing. In combination with AI, they can even predict what we’ll do next.
How many IoT devices are there?
We estimate that there are currently over 31 billion devices connected to an IoT.
While it’s interesting to know how many IoT enabled devices there are, it’s not practical information. Keep in mind that those devices aren’t all connected to the same IoT.
Nowadays, we can connect almost any type of device to an IoT. There are smart fridges, smart coffee machines, and even smart water bottles. Companies are enhancing even the most basic products with a network connection to—supposedly—make our life easier.
Why the Internet of Things (IoT) is important
Although there are many seemingly unnecessary gadgets flooding the market, the IoT is crucial in Industry 4.0.
Humans learned to communicate with one another between 50,000 and two million years ago. That was one of the most important milestones in our history.
Now, machines are taking that step.
Machines are faster than us, more accurate, and never tire out. By sharing data, they can find solutions and directly implement them without any human interaction.
This is the core of Industry 4.0.
What is IOTA?
IOTA is a foundation with its headquarters in Berlin, Germany. They provide an open-source distributed ledger that allows secure data and value transfer for free. The technology IOTA uses is called the Tangle.
Simply put, IOTA’s Tangle makes the transfer of information and payment between devices secure and free of cost.
An application could be you recharging your electric car. Your car measures how much electricity you put in it and then automatically pays the charging station without you having to do anything.
How IOTA’s Tangle works—in simple terms.
One of the main concerns of Industry 4.0 is safety. How can we keep information stored safely and know for sure it hasn’t been tampered with?
Enter the Tangle.
With the Tangle, every user automatically becomes a contributor to the network. Once someone initiates a transaction on the Tangle, their device has to confirm two other transactions first. This means that with every user and with every transaction, the Tangle gets more powerful.
How the confirmation process actually works is highly complicated.
Simply put, every time a device makes an entry on the ledger, a secret algorithm creates a hash, like a digital fingerprint, based on the device’s input. Then it adds the previous entry’s hash to the new entry. This means that one new entry contains the data, the hash, and the previous entry’s hash. We call this information package a site.
To confirm a transaction (to verify if the last entry hasn’t changed), we compare the ‘previous hash’ to the previous entry’s hash. If the data has been changed, the algorithm would have changed the hash as well.
Why IOTA is important.
IOTA contributes to solving two of the biggest problems in Industry 4.0: Cybersecurity and scalability.
Its technology, the Tangle, allows for unlimited, free, and most importantly verified (secure) exchange of information and transactions between devices.
What is blockchain?
The purpose of a blockchain is the secure recording of information on a digital, distributed ledger.
You can literally imagine it as a digital chain of blocks. Each block has the user’s input data in it, a hash (a digital fingerprint for identification purposes), and the hash of the previous block.
Often, blockchain is synonymously used with Bitcoin, one of many cryptocurrencies. While Bitcoin uses the blockchain technology to keep its transactions secure, it’s not one and the same thing.
Bitcoin needs blockchain. But blockchain doesn’t need bitcoin
How blockchain works.
To explain blockchain as simply as possible, I’ll compare it to an Excel sheet.
First off, there isn’t one single blockchain. Everyone can start a blockchain, just like anyone can create an Excel sheet.
When you want to record data and make sure nobody can tamper with it, here’s how you’d do it in Excel:
Create a file with three columns: Data, hash, previous hash.
We can call one row a block.
In the column ‘hash’, a secret formula creates a unique code based on the ‘data’ column. Those cells are locked. The ‘previous hash’ just mirrors the previous block’s generated hash code. Those cells also have to be locked.
Now, send that Excel file to ten other people who all save a local copy of it. They can only input new data, but not change the formula or the ‘hash’ or ‘previous hash’ cells.
When someone adds new data, they have to copy-paste their block into an email they send to the ten other people.
Everyone checks their own Excel sheet to see if the sender’s ‘previous hash’ cell matches the hash of the previous block.
If it does, it’s all good. The data hasn’t been changed. Everyone still has the same code, and the data is verified. Everyone adds the new block to their local Excel sheet.
If the codes don’t match, it means the sender has changed the value of the previous block’s data, which automatically changed the ‘hash’ cell that their ‘previous code’ cell mirrors.
So, a blockchain stores its data on every device that has joined the blockchain and downloaded a copy of the ledger. When someone adds a new block, every member device can verify with its own copy of the ledger that nobody has changed the previous block.
Why blockchain is important.
In Industry 4.0, cybersecurity is a big concern. Blockchain allows us to keep records safe and verified.
The main use case of blockchain technology today is cryptocurrencies. But other fields are working with blockchain technology as well. Notaries can use it to verify documents, the healthcare industry uses it for patient records, and some countries are even thinking about voting with blockchain.
What is Artificial Intelligence (AI)?
In short, AI is the science of machines that can solve problems the way humans do.
In 1951, the two first AI programs were presented: One that plays chess, and one that plays checkers. The term artificial intelligence was coined in 1956, at a conference at Dartmouth College in Hanover.
Since then, AI has come a long way. Today, AI can drive cars, talk to us, and even write full articles.
How Artificial Intelligence (AI) works.
To make machines solve problems the way humans do, it’s not enough for them to execute our commands.
AI must be able to learn and combine new information to come to entirely new conclusions.
There are myriad technologies involved, most of which are only important to those working in the field of AI. Some though, we would consider general I4.0 knowledge. Here they are:
Machine Learning (ML)
We take in new information with our senses, store them in our brain, and use them to make better decisions in the future.
That’s what we call learning.
Machine learning is the science of machines that improve autonomously through experience.
To do this, ML uses algorithms to work the information that the machine has gathered through sensors or from direct data input.
For example, we can use ML to predict how much stock of a product we need. The algorithm analyzes how much was sold in the past and predicts on this basis how much we sell in the future. If it finds that its predictions were off, it will learn from it, and adapt the numbers.
Artificial Neural Networks (ANN)
An artificial neural network is made to mimic the human brain. It’s composed of various algorithms that use large data sets and communicate with each other to find new solutions to complex problems.
You can imagine our neurons being ML machines. Each neuron uses a specific algorithm to handle a specific type of information. For an ANN, we connect all neurons—or algorithms— with each other, so they can communicate.
Deep Learning (DL)
Deep Learning uses an artificial neural network and is the next step up from Machine Learning.
For example, when we use DL to process an image, one algorithm may be used to identify colours, another algorithm maps shapes, another one tries to find context in the database, and so on. In the end, the results from the various algorithms are put together and the machine can, for example, describe what is on the image.
Why Artificial Intelligence (AI) is important.
AI is literally the brain of Industry 4.0.
AI already makes our daily working life easier. It’s our digital assistant, our smart email inbox, and our Google search bar.
New developments in AI has brought forward programs with the ability to write code, design apps, and answer philosophical questions.
But the real potential of AI is still untapped. Imagine a never tired, not by emotion or personal interest influenced CEO that could read thousands of reports a day.
It goes even further. In the future, we will have smart cities. Everything will be connected and, therefore, has to be managed. Instead of employing hundreds of people, a single AI could do the job.
Are you worried about AI becoming too smart? If so, let me ease the worry with our FAQ about the dangers of AI.
What is AR?
AR is short for augmented reality. To put it simply, AR uses the real environment and adds digital information on top of it.
Defined by the dictionary, AR means: An enhanced version of reality created by the use of technology to overlay digital information on an image of something being viewed through a device (such as a smartphone camera).
Snapchat uses AR to generate funny camera effects like adding dog ears and a snout to your face, and in Pokémon Go, you can hunt virtual Pokémon in the real world.
But AR isn’t just for fun. Giants like Microsoft, Google, and Facebook are creating AR applications for better education, increased productivity, and higher comfort.
How AR works.
Before the term AR was coined in 1990, machines like the Sensorama could already mix the real world with a virtual one.
AR began taking off at the beginning of the 21st century when game developers caught on. After games like AR Quake (2000) and AR Tennis (2005), BMW began using AR for commercial purposes with its enhanced print ads.
We already make AR work with various devices. Among them are screens, glasses, phones, and head-mounted displays. In the future, we may even be able to project AR content directly onto our retina or use our contact lenses as screens.
We have three main approaches to making AR work.
- SLAM (Simultaneous Localization and Mapping). SLAM allows us to map our environment while we’re in it. Because the device knows where we are and what’s around us, it can add digital objects on top of it.
- Recognition-based. Our sensors recognize a specific QR code or a natural feature tracking marker like an edge of a table or the shape of a ball. The device displays the digital object on top of said marker. When you move the marker, the digital overlay moves as well. You can twist and turn the marker to look at the digital object from various angles.
- Location-Based. The location-based approach relies on a GPS, digital compass, velocity meters, or accelerometers to provide data about the location. Since all smartphones are equipped with this technology, it’s a popular approach for everyday use. With this location-based method, you can use your phone as a heads-up display while driving.
Why AR is important.
The use of AR goes far beyond the gimmicks of Snapchat and Pokémon. Here are some examples of how AR enhances our education, productivity, and comfort:
- Imagine how nervous first-time surgeons must be. With an AR overlay over a patient, they could see exactly where and how they’d have to make their first incision.
- Kids could examine virtual dinosaurs in the classroom while taking notes.
- Budding chemists can mix virtual ingredients without danger.
- When everyone in the room wears AR capable glasses, companies can showcase their prototypes without producing them. Holograms are enough. They can even adapt those holographic prototypes in real-time.
- A plumber can stay in his office and give you instructions on how to repair your sink remotely. You’re wearing your AR glasses with a camera and the plumber sees what you see. The plumber can mark the right screw on his screen and you’ll see his mark through your smart glasses.
- Onboarding new employees takes time. A hologram of yourself could give your new employee the room tour.
- When you look at your dinner plate, your smart glasses display the number of calories along with important nutrition facts right on the food.
- You could see shopping lists, directions, and messages without having to take your phone out of the pocket.
- Instead of video calling, you could talk to someone’s hologram and see their facial expressions in real-time.
What is Additive manufacturing (AM)?
Additive manufacturing is the process of making 3D objects by adding layer after layer of material. Like this:
Yes, it’s 3D printing. The two are synonyms. Here are some more names for it:
- Additive layer manufacturing
- Layer manufacturing
- Additive fabrication
- Additive processes
- Additive techniques
There are many methods beyond the wide-spread plastic 3D printing. We can print 3D objects out of metal, concrete, and even organic tissue.
How 3D printing works.
We use a computer program to design a 3D object. The software breaks the 3D object down layer by layer and guides the nozzle of the 3D printer.
To print plastic objects, we use plastic wire on coils.
The machine feeds the wire to the nozzle, the nozzle heats the plastic to its melting point and drips it on the surface below.
The most common methods to print metal objects are SLM (Selective Laser Melting) if the object is small and DED (Direct Energy Deposition) if it’s big.
SLM uses a laser beam to fuse powdered metal together:
DED works similarly to a plastic 3D printer. The nozzle feeds the metal wire directly onto the surface below, where an electron beam melts it to the previous layer.
For more metal printing methods, check out this article about the types of 3D printing in metal.
Our 3D printing capabilities go beyond mere manufacturing. With bio-ink, we can print living tissue. This is bioprinting.
The challenges of 3D printing.
We can print with many materials, but we do have some serious limitations. We can’t print anything from wood, paper, cloth, or similar material.
Manufacturing cost is an important factor. When it comes to mass manufacturing, 3D printing might not be your best choice. The machines are slower than traditional production methods and heavier on your budget.
The standard nozzle is 0.4mm thin. If you need to print plastic parts that smaller than 0.4mm, 3D printing isn’t the way to go.
Why 3D printing is important.
We mostly use 3D printing for manufacturing. It allows us to manufacture exactly the parts we need, we can modify a design between prints, and use the same machine for many different parts.
The more we advance in 3D printing, the more interesting uses we start to see.
To combat the meat industry’s environmental impact, a crazy company called Redefine Meat is 3D printing meat.
“Redefine Meat technology produces animal-free meat with the same appearance, texture and flavor of animal meat, from natural and sustainable ingredients.—Redefine Meat
Our technology combines proprietary 3D meat modeling, food formulations and food printing technology to deliver a new category of complex-matrix “meat” in a cost effective and scalable way.
Redefine Meat™ Products have 95% smaller environmental impact, no cholesterol, and is more affordable compared to animal meat.”
Looks delicious, doesn’t it?
With bioprinting, we can even print organic tissue like hearts, lungs, or livers. This technology is not yet at a stage where we can implement one of the 3D printed organs. But in the future, bioprinting may provide healthy organs for everyone who needs them, making lengthy transplant lists obsolete.
What is Big data?
Big data is simply a big amount of data. So much data that normal computers can’t handle it.
Big data aims to make sense of massive amounts of collected data and derive value from it. If you just store it, you’re doing nothing but a fancy tech exercise.
Everything we store digitally is data. This Industry 4.0 buzzwords article is data. Your clicks and scrolls on our website are data.
Everything we do when we interact with the digital world creates data. Per minute, we make:
- 2.1 million Snaps
- 3.8 million Google searches
- 1 million Facebook posts
- 500 hours of YouTube video uploads
- 188 million emails
To determine if our data is big data or just normal data, we use the Vs of big data. The Vs of big data used to be the three identification markers.
Over time, everyone wanted to add their own two cents, and now you find articles talking about the 7 Vs, 8 Vs, or even 10 Vs.
The three Vs of big data: Volume.
Volume is the primary marker. A company that makes storage devices estimated in 2010 that there were 900 exabytes (1 exabyte is 1 million terabytes) of data — and growth of 50% every year. Nobody knows how much data we really generate and store.
If you’re working with so much data that your normal hard- and software can’t handle it, it’s big data.
The three Vs of big data: Variety.
We used to digitize information into neatly organized databases, like Excel. This is called structured data.
We upload images and videos to social media, save voice recordings in our cloud, and we tweet and snap and DM all day. This data doesn’t fit any criteria. We can’t categorize the contents of a voice message in a database file. This is unstructured data.
Big data aims to somehow make sense of the unstructured data with human intervention or artificial intelligence.
If you’re dealing with a whole lot of unstructured data on top of your structured data, it’s big data.
The three Vs of big data: Velocity.
Remember how much data we create every minute? You need to store and process as quickly as possible. Preferably in real-time. Standard systems can’t handle such data flow.
If you get a continuously high amount of data in a short time, it’s big data.
How big data works.
Big data isn’t new. CERN has been struggling for decades with the amount of data they collect with their experiments.
The detectors can be compared to digital cameras with 100 million electronics channels that would be taking 40 million pictures per second (which currently corresponds to 1 billion proton-proton interactions per second)—CERN Data Center
It’s just that today also other industries can collect and use big data. It’s not limited to science anymore. 90% of all data we have currently stored has been collected in the past two years.
There are three fronts where we generate big data: people, machines, and companies.
Big data generated from people.
People generate data in every interaction with the digital world. Some of it is conscious, like browsing the internet, posting on social media, or having a Zoom meeting. Some of it is unconscious, like the GPS tracking of your phone, the sensor-activated light switches, and the automatic connection to our Bluetooth headphones.
Big data generated from machines.
Machines generate data constantly, without human interaction. Satellites take pictures, computers store log files, cameras never stop recording, phones are in constant exchange with messaging servers to receive push notifications, and so on.
Big data generated from companies.
Company-created data comes from transactions, credit cards, e-commerce, research, accounting, and so on. Companies tend to store a lot of data to optimize anything they can for more profit.
How we store and use big data.
Generating data is easy. Storing and making sense of it is hard.
Most companies don’t want to buy their own storage facility. Thanks to the cloud, they don’t need to. Giant tech companies like Amazon or Google offer cloud storage and processing solutions. Companies can pay for as much storage and power as they need and leave the heavy lifting to the rented machines. They just transfer the raw data and receive the computed results via the internet.
Why big data is important.
“I want you to think of data as the next natural resource.”—Virginia Rometty, CEO of IBM
We’re living in a digital economy. Data has immense value and is key to the functionality of the world. From governments to businesses, data drives everything.
Governments use big data to detect tax fraud. It automatically analyzes tax reports of all citizens and compares them to last year. If there is any discrepancy, the computer flags the report and an employee can look into it.
Businesses like Netflix and YouTube analyze our every click. Based on data, they suggest shows and videos we like while hiding those we dislike.
The advertising industry relies heavily on data. When you visit an online shop, you’re almost guaranteed to see their ads in the next few days. It’s no coincidence, and it gets even crazier. When you talk, your phone listens. Based on the data it gathers from its microphone, you will see ads about corresponding products or services.