5 Amazing Industrial Technologies
Manufacturing technologies have been consistently evolving since the very first industrial revolution, and now, as we are hitting the 4th industrial revolution, we are starting to see the potential of having more connected factories via machines and sensors.
Amazing Industrial Technologies
A good number of production plants are adapting to Industrial Internet of Things (IIoT), VR and AR are being executed in different hierarchies for R&D and safety concerns, and robotics are taking over the risky human labors. This change to production doesn’t only increase efficiency but also adds to production volume, reduces risks to the worker’s lives and enhances their living and working standard. 2018 is the year of revolution in industrial technology; not that there hadn’t been noticeable revolutions before but this year the technologies are to take a sheer rise in their presence across different production lines – technology, healthcare, consumer goods, automotive, transportation, cryptocurrency, and more!
Different technologies are making a widespread impact in all walks of life, and the line between industrial, commercial, and consumer technology is being blurred more and more everyday. We have sorted out 5 of these technologies to provide a glimpse of what’s going on.
Gone are the days when implementing a new design or plan for a production line would require hours of manual work, drawing and sketching out plans on paper, failed prototypes, and a good number of trial and error attempts. Instead, augmented reality now makes it easier for the manufacturing companies to have a grasp on their factory resources, and prepare a plan based on that.
In R&D, measurement and placement of a new tool or piece of machinery becomes instantaneous and more accurate with AR compared to the manual way of the past. For example, if a production line is to acquire a set of shadowed toolboxes for their production lines, crafting it all up on an AR interface and allowing the operators to get a simulated experience through AR gadgets first-hand makes factory flow of work much faster.
AR is no longer a marketing gimmick, rather a reality. Boeing has acquired Google Glass along with the Skylight platform for assembling their airplanes, Thyssenkrupp materials works with HoloLens for drafting designs on a holographic projection, ‘Porsche Production 4.0’ uses AR in the new factory revolution for QA testing cars, and Caterpillar has inaugurated a new maintenance ecosystem using augmented reality – these are only a few industry examples of AR being used in manufacturing.
Heavy Integration of ERP
Enterprise Resource Planning tools (ERPs) are not new, they have been around since the third industrial revolution but have evolved significantly. Most factories with an Integrated Work System technique adopt and use ERP tools like SAP, which is a quite popular name in consumer goods factories in particular. As the 4th industrial revolution wave comes through, companies are now in fact forced to adopt such tools as early as possible, otherwise, a significant amount of production could get lost resulting in bad sales output.
However, the current challenge for developers is how to integrate IIoT modules into these programs. People with different levels of computer literacy use ERPs, therefore it is important that the new generation ERPs are understandable to everyone involved in a factory.
Any equipment with an active user panel (e.g. HMI) should have partial or complete access to the designated ERP as a part of IIoT integration, to save time in manual data logging and vetting by the authority. The goal of using advanced ERPs in a production factory is to keep up with the flexible nature of technology growth in 2018.
New Metrics and Standards
Anyone working in a production facility knows about terms like Overall Equipment Effectiveness (OEE), Mean Time between Failures (MTBF) etc. These metrics were not utilized in old-school factory ecosystems but they are very prominent in newer, updated ones. The metrics are now being monitored in real-time, giving a series of usable statistics and data for each day, week, month, and year.
Although these have been proven to be useful in most production scenarios, they still tend to have inaccuracies and backlogs if due to components in a factory not being automated (e.g. OEE falsely recorded due to operator’s negligence) This results in a significant fall in OEE over time.
With the rise in data science, scientists and engineers need to come up with better metrics with predictive properties so that factories could be aware of any possible machine failure well ahead of the actual incident. With IoT sensors and cloud computing, introducing a new standard of metrics should not be difficult to do.
Cloud-Dependent Manufacturing Process
Application Programmer Interface (APIs) are now provided by a majority of software developers – this includes the manufacturing industry sphere as well. For example, Protos is a cigarette making machine used in various tobacco companies (e.g. British American Tobacco) with a Siemens PLC (Programmable Logic Controller) onboard; and Siemens would release an update API for this machine on a regular basis to ensure optimum manufacturing volume and production quality.
If for some reason there is an error that causes a major setback in manufactured cigarette volume, Siemens is able to tweak the API and issue a patch and, assuming it is not a hardware issue, it should be solved. Now, in 2018, issuing patches after encountering a drawback is really an old-school way of thinking, now the goal is not to be able to react to drawbacks, but to predict and prevent.
If we take the scenario we have just discussed, in a cloud-connected manufacturing ecosystem, Siemens should be able to sense an upcoming breakdown and instantly work on the API to provide a patch before the breakdown even happens due to a software issue. Now, this obviously is how it would work in a perfect world, but technology and connectivity are finally reaching the point where can predict issues with relatively good accuracy.
Robots to Take Over Manual Labor
No matter how much a person is being paid, a human will never be able to maintain the same amount of efficiency for the entire duration they are posted in a repetitive working post. After a month or two, they will eventually get bored and the efficiency rate might fall. They become experts of course, but there is a good number of negative impacts as well.
We have already seen robotic arms sweep through the industrial world, but humanoid robots are under development so that these dull jobs can be given to them, whereas the human workers get to spend their time on something less repetitive. Having a sense of accomplishment is important to keep the production level boosted, and incorporating humanoid robots in production lines would certainly make a good impact.
Robot Sophia is a recent example of intelligent humanoids, and companies like Honda have been working on making such robots for a long time. Honda Asimo is a very good demonstration of machine learning and AI in a human-shaped machine.
Industry 4.0 is opening doors for us all – it’s a matter of perspectives on how we are to embrace it. From a manual worker to the end consumers – the whole chain is impacted by how a new technology is integrated into a factory. Based on the rate of technological evolution in the past few years, I have no doubt that there will be major breakthroughs in 2018 that will forever change the industrial landscape.