Industry 4.0 and the digitalization of industry

Many software architects are sometimes wary of the hype around new technology. Great ideas and visions are pivots that lead us into the future and guide us in taking advantage of new technology in both our business and personal lives. However, the road to the current state of technology is paved with great ideas that never made it out of the concept phase, and overly aggressive marketing and sales around new (good and bad) technologies have made everyone just a little more cautious.

Usually, at the early stages of some technologies, marketing and sales teams jump in and take over, looking for any opportunity to push an idea or build a prototype with any potential customer, attempting to work together with customers to build a vision of what the future could be. But then comes the hard work of architecture, design, prototyping, rollout, testing, production, and support. Sometimes, the state of the technology isn’t quite ready, and reality intervenes. If you have been burned enough times, it gets harder to reach back in.

Fortunately for us, Industry 4.0 has made it well past the starting gate and into the reality of many organizations. Even though it has been making progress for most of the last decade, there is still a fair amount of work to be done before it can be considered mainstream technology in many organizations. The evolution and improvements in hardware, such as sensors and processors, software protocols, and integration tools, make retrieving real-time or near real-time data from almost any device or area more accessible and safer. The why of data capture and Industrial IoT is what we will be discussing in this chapter, while the how will be discussed in the rest of this book.

Industry 4.0, or the fourth Industrial Revolution, is commonly thought of as the automation and digitalization of industry and manufacturing systems. IoT and cloud technologies have become critical enablers of this effort and provide the ability to integrate and automate machinery to become more intelligent and adaptive. Ideally, this includes adopting artificial intelligence and machine learning to enable systems to self-monitor and diagnose or predict problems that may occur.

This description does provide a bit of futuristic vision, connotating a kind of rise of the machines approach, but it gives us a good starting point on which to base our discussion.