Industrial Revolution 4.0: What is ‘smart’ anyway?
I started writing the article about the Industry 4.0 in my “Feierabend” and now that I am done, people are talking about Industry 5.0. Yet, according to the latest surveys, only 11 % of German industrial companies have exhibited maturity in implementing real-time monitoring of their assets based on predictive methods such as regression. With buzz words popping up on the web faster than technologies actually being employed, integrated into processes, and finally transformed into business values, we find ourselves lost in sexy tech words like ‘smart’, ‘deep’, and ‘brain’ that mean nothing.
Industry 4.0 is a complex term that encompasses a wide range of industrial sectors and technologies. One of the standard definitions of the industrial revolution 4.0 is a transformation based on Cyber-Physical Systems (CPS). CPS is a merge of the capabilities of physical and cybersystems towards digitization of the complete value chain of production. In literature, cybersystems are used interchangeably with smart devices or ‘things’ as in the Internet of Things (IoT). In layman’s terms, CPS is a joint effort of cybersystems with physical systems, aka humans towards achieving business targets. However, there is a thin line between the independent cybersystems actively participating in processes and devices that are entirely dependant on human supervision.
While some trendy technologies shine bright on the front page of AI magazines and conferences, others are working hard right now in factories and form the fundamental infrastructure for the industrial revolution 4.0. Four main technologies make a successful collaboration of smart cybersystems and humans possible: IoT, Cloud Computing, Big Data, and Data Science.
The first step of the infrastructure is obtaining data, which usually in case of anything ‘smart’ is a sensor data collected from devices. The interconnectivity and communication of the systems is a field of expertise of IoT. With the pervasive inclusion of devices into our society, IoT is becoming an integral component of almost every part of the society: smart home, smart city, and smart factory. Despite the integration of smart devices into processes, things remain passive components that need to be manually adjusted to the changing environment. That is not smart, now is it?
One of the community’s dreams is enabling the active participation of the things in the decision process. Active participation includes processing of past experiences, reacting to the environment, evaluating actions and their implications in the future. Isn’t that what independent and intelligent is all about? A possible example can include a wearable medical device that can be used by the patient before an operation to record vital health information, detect abnormalities, and send alarms. This analysis provides essential information about the patient and can predict possible symptoms before they occur. However, active decision making of the things in IoT is not trivial due to the complexity of the changing environment, which influences the decision-making process.
With various interdependent devices sending sensor data, we end up with a large amount of unstructured data that needs to be stored. This challenge brings us to another protagonist of the play: Cloud Computing. Cloud computing has been a trigger that allowed fast and efficient processing of large volumes of data. We, data scientists, know that if you want to derive insights from the data, to learn from the data, you have to have a lot of it. The more, the better. Yet, we need computational resources to store and process data. Resources are precisely what cloud computing offers: distributed storage and processing of data in less time and less money.
A large, rapid stream of unstructured data is a target of another field: Big Data. Big data analytics is not another hype trend that is soon to be extinct. Five targets Vs of Big Data, Volume, Velocity, Veracity, Variety, and Value, are as relevant today as they have ever been. The last V, Value has been added lately and rests at the most top of the big data pyramid. Value has the most relevant objective to our discussion: deriving valuable insights from data and transforming them into business value.
This final V also brings us to the field of Data Science, which enables us to get valuable information, and finally, learn from the data to predict future trends, and act accordingly. This concept defines the goal of such applications as real-time-condition monitoring and predictive maintenance. Specifically, if we can detect early symptoms of system failures, we can fix them before they happen: no manual labor → check, no downtime → check, avoid catastrophic failures → check, money saved → check.
The list of the essential technologies changing our times can go on. Honorary mentions go to augmented reality, cybersecurity, and additive manufacturing. These technologies fuel smart devices defining industrial revolution 4.0 that transforms such major industrial sectors such as manufacturing, electrical, chemical, and automotive.
Besides the technological challenges, mindset and culture is another important factor that hinders successful transformation towards the cyber revolution. Contrary to a simpleton’s understanding, Industry 4.0 is not a pretty dashboard you can buy or a tool you can hire someone to develop within three months. It is a fundamental change that is only possible with the help of a strong collaboration of data scientists and domain experts. These two components provide the recipe that gives the real value to businesses: take one ingredient away, and it is a farce.
TL;DR interconnected autonomous systems fueled by essential technologies allow smart products that are autonomous, reliable, and interconnected. The main objective of integrating these autonomous smart devices into processes from a business perspective is to bring value to the businesses. Yet, I will be the dreamy me, and say the goal of digitization is to serve society’s well-being as well. That and only that is what makes systems not only smart but intelligent, not only cost-reducing but efficient, not only autonomous but independent, and finally, not only sustainable but conscious. Because for what it’s worth, revolutions are about changing societies and bringing progress to the people.