Industry 4.0 Revolution
Sometime in the last decade, manufacturing entered a period of transformation. New technologies found their way onto the shop ﬂoor in force. Advances in computer processing power and data storage resulted in new manufacturing use cases for a range of products. A climate favorable to development made prohibitively expensive technologies affordable and scalable. In rapid succession, new industrial applications emerged for artiﬁcial intelligence, cloud computing, Internet of Things connectivity, big data analytics, quantum computing, 3D printing, cyber-physical systems, and a host of other technologies.
Manufacturing is changing at a rate and scale comparable to the advent of steam power or software-driven automation. The impacts of this digital transformation, known as Industry 4.0, have been profound, and the potential remains great. But Industry 4.0 has implications beyond proﬁt, and its reach extends beyond manufacturing. At its core, Industry 4.0 is not reducible to any one technology, or even a suite of technologies. Rather, it is a fundamental reconﬁguration of work in the digital era.
How businesses strategize their digital transformation will determine who will sink and who will thrive during Industry 4.0. McKinsey and WEF, for example, have observed that early adopters experience a 112% cashﬂow advantage from their Industry 4.0 ventures over those who wait for the price of new technology to drop. Similarly Accenture estimates the ﬁrst wave of IIoT adopters could see 30% improvements in productivity, and Symantec found that early adoption can lead to 26% reductions in annual inventory.
Industry 4.0 is not strictly technological. It is a new way of connecting and communicating that links digital technology to the human body and physical objects. Industry 4.0 is characterized by:
● A suite of digital technologies achieving scalability and ROI in industrial contexts
● A changing relationship between humans, machines, and labor
● A dispersion and pace sufﬁcient to earn the title “revolution”
What distinguishes our current moment from others is the potential of new digital technologies to disrupt established orders. Commentators have noted that blockchain, artiﬁcial intelligence, and threats to cybersecurity–in their most extreme implementations–have the potential to undermine core institutions. Governments, banks, energy infrastructures–all could be massively transformed by decentralized, intelligent technologies.
We are a long way from science-ﬁction scenarios of intelligent computers toppling nations, or even from blockchain democratizing banking through distributed consensus networks. But the underlying point remains valid. The effects of advanced digital technology have much in common with paradigm-shifting industrial advances that instigated previous industrial revolutions: steam, electricity, and computing.
Most historians attribute the First Industrial Revolution to the invention of the modern steam engine in the 18th century. Though relatively weak at ﬁrst, the steam engine improved in power and reliability throughout the 18th and 19th centuries. By 1886, steam engines reached a capacity of 10,000 horsepower. Steam and water power allowed humans to build machines that allowed for mechanization of basic processes. In the ﬁrst half of the 19th century, manufacturers developed processes that converted a number of repetitive, manual tasks into machine work. Many of the world’s most powerful nations build their fortunes through advantages gained on the back of mechanical advances during this era.
The Second Industrial Revolution is characterized by electriﬁcation and conveyor production. These changes reached national and then global scale at the beginning of the 20th century. The development of modern industrial science greatly accelerated the pace of development and the breadth of dispersion of the technologies. By the early 20th century, electricity and conveyor belt production could be found in the world’s most and least developed nations alike.
The Third Industrial Revolution did not occur with a fundamental transformation in energy. Instead, it was a result of advances in computing and communications technology. Incipient robotics, capable computers, and breakthroughs in data storage and communication introduced digital electronics into the factory. They also greatly increased the number of processes that could be automated. It was a short leap, then, to more sensitive forms of automation, data analysis, and global connectivity.
The Fourth Industrial Revolution is fundamentally rooted in advances in computation and communications. In this case, ubiquitous connectivity created and infrastructure that allowed numerous other technologies to communicate across space and distance. It has been identiﬁed velocity, breadth and depth, and systems impact as the factors that make this transformation a revolution rather than an acceleration. In other words, this transformation moves faster, affects us more powerfully, and has the capacity to alter world systems. This revolution impacts who we are, not just what we do and how we do it.
There are as many use cases for Industry 4.0 technologies as there are manufacturers. Even so, the many use cases can be boiled down to a few categories. McKinsey recently identiﬁed four areas in which early adopters have achieved reliable success :
Digital Performance Management : It serves as a crucial ﬁrst step in developing Industry 4.0 capabilities and infrastructure. Digital performance management tools rely on Industrial Internet of Things connectivity and cloud storage to processes continuous, real-time data from workers and machines. Digital dashboards and manufacturing apps let operators view and respond to process performance in real time. Flexible performance management solutions let engineers tailor KPIs to their operations. Constant interaction with data encourages an evidence-ﬁrst mentality, an important early step toward a more analytical operation.
Predictive Maintenance : As MES, manufacturing execution software, and analytical systems have improved, so has predictive maintenance. But with advances in big data, human performance tracking, and machine learning, predictive maintenance tools are growing more sensitive by the day. For factories with a base level of connectivity, deep-learning algorithms can create maintenance schedules that only grow more accurate over time. They have already driven huge improvements in OEE, and delivered large reductions in machine downtime as AI hones in on inefﬁciencies. More advances in this area will arrive as computer vision and wearable sensors turn human movements into actionable data.
Process Optimization : Industry 4.0 promises to collect data from machines, and to analyze that data with sophisticated algorithms. But this need not be limited to single processes, or single lines. Rather, early-adopters are seeing signiﬁcant gains as they use their data to develop systems within departments, and then connect those systems into a responsive, fully connected whole. Some of the greatest Industry 4.0 gains will come from optimizing the full value stream.
Advanced Automation : Most of the leading research ﬁrms project that the use of robotics will expand in manufacturing in the next ten years. But automation doesn’t end with robotics. It is also predicted that many knowledge workers will also contend with automation, as algorithms are increasingly capable of managing demand, scheduling inventory, and performing root-cause analysis.
See you in next blog with the following topics :
- So what does the modern connected factory look like?
- Some common stumbling blocks along this road
- How to Implement a Digital Transformation?
Kadıköy, İstanbul – TURKEY
M. Temel AYGÜN, Ph. D. in Aerospace Eng.