Industry 4.0 is a hot topic at the moment, and with good reason. Experts believe that it will revolutionize the world of manufacturing, increasing efficiency, cutting costs, and allowing companies to push the limits of just-in-time manufacturing. But what exactly do we mean when we talk about Industry 4.0, and what makes it so important?
The story begins with the 3rd industrial revolution, also known as the digital revolution. This marked the beginning of the information age, when our working lives were drastically altered by the introduction of computers, digital systems, and robotics. This shift allowed concrete processes to be automated for the first time; at the push of a button, machines and computers could carry out tasks on their own, saving human workers time and effort. But these digital systems were usually separate from one another, and if they were linked, it was via a central hub that required human supervision.
Industry 4.0, also known as digital manufacturing, smart manufacturing, or the Industrial Internet of Things (IIoT), takes this process of digitization to an entirely new level. In Industry 4.0, the digital systems of Industry 3.0 are becoming increasingly interconnected. These new cyber-physical systems — physical components equipped with sensors and receivers — can monitor production processes in real-time, gathering data at all production stages and sharing it with other smart components and enterprise systems. The systems link disparate areas of the factory and the company so that the entire value chain can be monitored seamlessly. This monitoring can lead to improvements in efficiency, reduced costs, faster production cycles, and much more.
What is Industry 4.0 capable of?
Industry 4.0 is only in its infancy but, even now, it is possible to design systems that can autonomously “think” for themselves, using advanced algorithms to analyze data in real-time. Rather than creating an information bottleneck where a centralized control station reviews information and makes decisions, smart factories rely on decentralized decision-making processes. Data is shared and analyzed in the cloud so that machines can autonomously complete routine tasks in “collaboration” with other parts of the production system, reducing human intervention to a minimum.
This is all possible thanks to recent advances in robotics, computers, and data processing. The interconnected systems in the Industry 4.0 production environment send and receive data in a constant stream, and the systems act based on that data. Just as steam powered the first industrial revolution, data powers the fourth.
Already, we have smart part bins that can automatically re-order parts for themselves if they need to be re-filled. Rather than just triggering an alert that a human operator needs to respond to, the interconnected system actually places a digital order with the order management system, which is received and processed automatically.
In the high-value manufacturing sector, pilot projects have gathered data in all steps of the manufacturing process and analyzed it to identify areas that could be made more efficient. These efforts are making it possible to efficiently create low-volume, high-value products such as those used in aeronautics. Traditional automation was not very helpful in low-volume production environments, but I4.0 is helping digital production plants to reduce costs and improve efficiency while maintaining quality.
There is also enormous potential in the area of maintenance. Sensors can monitor production units for changes in temperature, humidity, and other parameters. With the help of this data, smart factories can often predict equipment failure before it actually happens. This ability, known as predictive analytics, uses machine learning algorithms to analyze existing data, learn from it, and identify patterns that occur before a component fails. The system can then identify components that are at risk of failure or that require maintenance. In this case, a human employee can be notified and maintenance can be carried out before failure occurs.
This can be a huge boon to efficiency and productivity in production environments. In fact, a study by the US National Institute of Standards and Technology (NIST) found that “up to 20% of production capacity is recovered as equipment is proactively tuned for reliability.” They also found that predictive data analytics in smart manufacturing systems can result in a 5% decrease in batch cycle time, 10% improvement in machine reliability, 10% reduction in water consumption, and 5% reduction in energy costs. It’s easy to see what a difference this could make in a large-scale production environment.
A fully-integrated value chain
Depending on the complexity of the system, this flow of data can extend from the production line to the entire company value chain, from product design to production and on to fulfillment, logistics, and service. This amounts to a continuous stream of real-time, company-wide status updates — a thought that would make most human employees want to tear their hair out, but which cyber-physical systems thrive on.
In a fully digitized and integrated system, the entire value chain can be interconnected, allowing other business units to react quickly to unexpected developments. In addition, the insights gleaned from the comprehensive stream of data can improve efficiency and quality management company-wide.
For example, if a shipment of parts or supplies is delayed, a digitized logistics chain could track the shipment, identify the delay, and find the best way to compensate. This could include intelligently rescheduling production processes or increasing/decreasing order quantities of related products as needed.
And the possibilities continue past the point of delivery. When companies offer digitized products and apps that collect usage data from the end-customer, the data can be analyzed and used in product development. This allows the company to take a more customer-centric approach and deliver greater value at a lower cost than ever before.
One car manufacturer, for example, analyzed data from its online configurator and actual purchase data to eliminate less-popular options from its offering. This drastically simplified product development and significantly lowered production costs without sacrificing customer satisfaction.
Why Industry 4.0 needs artificial intelligence to succeed
The potential benefits of this new industrial revolution are truly impressive. But for that potential to be realized, companies need to make sense of all the raw data generated by their I4.0 systems. And with omnipresent sensors sending continuous real-time updates, the volume of data is truly staggering. Traditional statistics and other analysis methods can be too inefficient for the vast sea of data because they need to be defined and optimized manually.
Machine learning algorithms and AI systems, on the other hand, can learn “on the job” — they take raw data and actually learn to extract meaningful insights from it. In a system where everything from component temperature to ambient humidity, infrared images to system logs could potentially deliver a gold nugget of useful information, it’s hard for even an analytics professional to know where to start. With machine learning, that’s OK.
Machine learning algorithms identify patterns that would otherwise remain invisible to us, and the more data they are given, the more useful insights they can provide. They can also learn to process “unstructured data” like images, which traditional analytics can’t support. And, with the right planning algorithms, an intelligent virtual entity (IVE) could even decide what steps to take if something in the environment changes — rerouting components from a non-working production unit to a working one, for example.
Looking to the future
Some aspects of Industry 4.0 are ready to roll out at scale, while some are still in development. Nonetheless, it’s safe to say that we can expect these systems to play a major role in the world of industry going forward. And they will continue to develop in complexity and in capability.
What might Industry 5.0, 6.0, or 7.0 look like a few decades down the road? The answer to that question will give an even better idea of what machine intelligence, combined with advanced robotic systems, might be capable of in the not-so-distant future.
It might sound far-fetched, but advances in automation could one day bring us fully automated supply chains that are controlled by intelligent virtual entities from the raw materials to the a finished product. One IVE could handle inventory control while another manages purchasing and logistics, self-driving forklifts would deliver the raw goods to the factory floor, and advanced robotic systems would take care of production, packaging, and fulfillment. Predictive maintenance could expand to include self-repairing machines and fully robotic maintenance procedures. This type of fully autonomous production chain could also push just-in-time production to the extreme.
We could see the Internet of Things (IoT) and Industrial Internet of Things (IIoT) become fully integrated. A car outfitted with an IVE could detect a problem and predict that it will require a certain spare part within the next 1000 kilometers. It could then autonomously notify the manufacturer, who would use Just in Time production to produce the part and deliver it to the specified mechanic. The car would then notify the driver when the part was ready, so the car can be brought in for maintenance.
This type of predictive maintenance would eliminate the need for regularly scheduled servicing. Rather than planning maintenance based on a certain number of kilometers driven, which is a rough guess at best, the car’s AI systems could constantly analyze the actual condition of the car’s components and notify the driver, mechanic, and parts factory whenever an issue is detected. This type of maintenance could even be used for household devices like washing machines or refrigerators. Imagine getting a new refrigerator light delivered to your door before you even realize the old one needs replacing!
Manufacturers’ recalls could become a thing of the past, at least for networked machines. If a machine senses that it has a defect, it could automatically notify other machines of the same production batch or type. These could, in turn, notify their owners of the defect or even schedule a visit from a mechanic.
These scenarios are obviously still a long way off, but AI specialists like those at Blackzendo are working towards a future where these types of technologies are our new reality. The possibilities are nearly endless, and it is thrilling to imagine the new technologies that will develop as we continue to push the envelope of possibility.