The Industry 4.0 Advantage

This visceral image of “industry” being gritty and exclusively blue-collar is true to some degree, but when “4.0” is added to it, it takes on a whole new meaning, and blue-collar workers end up believing the narrative that robots and artificial intelligence (A.I.) will delete their jobs.

Though common, this fear is unwarranted. Despite the now-proven Hard Trend that A.I., advanced automation and robotics, 3D printing, and other industrial Internet of Things (IoT) advancements often replace mundane tasks in manufacturing, Industry 4.0 transformations allow us to work alongside machines in new, highly productive ways.

Industry 1.0 to 4.0

Manufacturing in every industry has evolved as four distinct industrial revolutions since the 1800s. The first industrial revolution took place between the late 1700s and early 1800s. Manufacturing evolved to optimized labor performed by the use of water- and steam-powered engines with human beings working alongside them.

The second industrial revolution began in the early part of the 20th century, introducing steel and use of electricity in factories. These developments enabled manufacturers to mobilize factory machinery and allowed for capitalizing on manpower in mass production concepts like the assembly line.

A third industrial revolution began in the late 1950s, which brought with it automation technology, computers, and robotics, increasing efficiency and repositioning the human workforce. Near the end of this period, manufacturers began experiencing a shift from legacy technology to an increase in attention to digital technology and automation software.

The current industrial revolution is Industry 4.0, which increases interconnectivity and networked intelligence through the Internet of Things (IoT) and other cyber-physical systems. Industry 4.0 is far more interlinked than revolutions before, allowing for improved company communication and collaboration.

The general definition of Industry 4.0 is the rise of digital industrial technology. To better understand, let’s take a look at nine building blocks of Industry 4.0.

Big Data and Analytics

Industry 4.0 allows for streamlining, collecting and comprehending data from many different sources, including networked sensors, production equipment, and customer-management systems, improving real-time decision making.

Autonomous Robots

The ability for robots to interact with one another while accomplishing rhetorical tasks increases productivity and opens new job opportunities for employees willing to learn new things. These future autonomous robots will cost less while having greater range of capabilities.

Advanced Simulation

Advanced simulations will be used more extensively in plant operations to leverage real-time data, mirroring the physical world in a virtual model. This includes machines, products, and humans and allows operators to test and optimize the machine settings in the virtual world first, accelerating a predict-and-prevent operational strategy for downtime issues.

Horizontal and Vertical System Integration

Universal data-integration networks in Industry 4.0 increase connectivity among departments, suppliers, and partners. This resolves lack of communication or miscommunication within a project crossing departmental boundaries.

Industrial Internet of Things (IIoT)

Decentralizing analytics and decision making while enabling real-time feedback is key in today’s age. IIoT means connected sensors, machines communicating with each other, and more devices having embedded computing enabling Edge Computing, where networked sensors get new data instantly and automated decisions happen faster.

Agile and Anticipatory Cybersecurity

Secure means of communication and identity management is quite important to cybersecurity in Industry 4.0, as increased interconnectivity brings the risk of security issues. Manufacturing companies must pre-solve problems in cybersecurity and implement anticipatory systems by adding a predict-and-prevent layer to A.I.

Advanced Hybrid Cloud and Virtualization

As data increases, local storage will not suffice, which brings us to Cloud Services and Virtualization. Elements of high-speed data analytics coupled with A.I. and machine learning enable real-time knowledge sharing. Advanced Cloud Services also enable anticipatory predict-and-prevent strategies.

Additive Manufacturing (3D Printing)

Advanced additive-manufacturing methods will be integrated into mass production systems, providing a new level of speed and customization along with the ability to solve complex manufacturing problems while also functioning as a standalone system for custom manufacturing.

Augmented Reality

According to my Hard Trend Methodology, this relatively new technology will gain more traction as augmented reality (A.R.) apps for business and industry are developed. For example, in Industry 4.0, AR can help quickly find parts in a warehouse by looking around from one location.

The adaptation of any of the new technologies in Industry 4.0 will face an uphill battle, as blue-collar manufacturing industries are not often open-minded about embracing new technology often seen as a job eliminator. Embracing the ever-changing spectrum of Industry 4.0 technologies allows acceleration of innovation, pre-solving seemingly impossible problems, and developing and implementing digital manufacturing solutions.
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Shaping the Future of A.I.

One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google’s DeepMind beat the world’s best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM’s Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today’s human jobs in the years ahead.

Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more.  

As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics. If we don’t have common definitions for and understanding of what we are talking about, it’s likely we will create an increasing number of problems going forward. With that in mind, I will try to add some clarity to this complex subject.

Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.  

Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.

Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learning and/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.

Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement.  For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.  

Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.   

A.I. advances represent a Hard Trend that will happen and continue to unfold in the years ahead. The benefits of A.I. are too big to ignore and include:

  1. Increasing speed
  2. Increasing accuracy
  3. 24/7 functionality
  4. High economic benefit
  5. Ability to be applied to a large and growing number of tasks
  6. Ability to make invisible patterns and opportunities visible

Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans. Yes, A.I. — like all technology — will take the place of many current jobs. But A.I. will also create many jobs if we are willing to learn new things. There is an old saying “You can’t teach an old dog new tricks.” With that said, it’s a good thing we aren’t dogs!

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