The technologies that are driving this new era of transformation range from prototyping and R&D, production and performance of analytics, and robotics to innovations such as virtual reality, IoT, artificial intelligence and cloud computing
Digital transformation in manufacturing is the new current revolution bringing in a new face, and for the better. Today, unlike the previous industrial revolutions we’ve witnessed before, utilization of digital data, connectivity, and processing is at the core of every aspect of almost all manufacturing activities.
The technologies that are driving this new era of transformation range from prototyping and R&D, production and performance of analytics, and robotics to innovations such as virtual reality, IoT, artificial intelligence and cloud computing. These technological advancements are increasingly setting the digital transformation on a path to impact literally all aspects of business, including the organizational structures of companies and even how companies generate revenue.
And because this is something that is revolutionizing how manufacturing is done, we’ve decided to create a 2-part series explaining the core principles driving this digitization of manufacturing, how to define the digital transformation strategy, the challenges it faces and added some of the key trends poised to move manufacturing operations into the future.
So, let’s start here with part 1 of digital transformation in manufacturing.
If you were to list the benefits of digital transformation in manufacturing without categorizing the most targeted areas, it would probably be a book already. So, here are the 5 main areas that this transformation has touched on.
Tools such as 3D printing and augmented reality, combined with the power of behavioral data derived from users in real time have allowed design and development processes to be much faster and better informed.
In fact, it’s now possible to streamline production with very little to no downtime because of the capability brought forth by connected machines that give vital maintenance data used in optimizing output and preventing malfunctions.
Currently, digital transformation in manufacturing has allowed monitoring of production parameters using high-definition sensors that are set along the entire production line process.
It’s also possible to automatically determine the root causes of defects and predict waste-related issues early enough before they happen. This is made possible through the introduction of machine learning algorithms that are applied to the production data, a move that’s sometimes referred to as Quality 4.0.
Today, it’s possible to identify and predict new cost reduction opportunities by capturing and analyzing data throughout the stages of the manufacturing process, including logistics and transportation, production line and machine data.
As such and with IIoT, better management of inventories can be achieved which consequently allows demand to be met in a more accurate manner. The high level of flexibility brought about by machines creates the ability to quickly change between products.
If you haven’t noticed already, customization has become a darling among customers and for good reasons. Now, digitized manufacturing lines allow customers to have attractive customization options with the manufacturer still able to operate on a mass scale with a high level of efficiency keeping the prices competitive.
Currently, instead of having humans work in dangerous work environments, manufacturers have invested in robots that maneuver the dangers. Any potential hazards are relayed to the staff well in advance by dedicated sensors installed at key points throughout the factory (or plant).
And now, with the advantages brought about by the digitization of manufacturing already outline, let’s turn to the technologies or the digital transformations we are witnessing today.
Digital transformation in manufacturing is made possible thanks to a list of growing technological innovations and advancements. Below, we’ve focused on 10 of them that are widely observed in manufacturing today.
Additive manufacturing is commonly known to many as ‘3D printing’ and is a fascinating technology. Here, a wide variety of processes and materials all of which have the common property of direct transformation of 3D data into the physical realm are used.
The freedom of design that’s possible with this form of manufacturing has never been witnessed before. As this technology gets better by the day, its application is growing into sectors that were never imagined before such as aerospace, medical, automotive and consumer or lifestyle.
Briefly knowns as APM, the introduction of Industry 4.0 has redefined APM’s definition to included even broader sets of functions. Asset performance management is known to pool together numerous tools that allow it to enhance the overall reliability and availability of physical assets available within the manufacturing ecosystem.
These APM tools function to collect, organize, analyze and visualize data derived from the assets and make use of it, subjecting data to predictive forecasting, reliability maintenance, and condition monitoring.
If you still thought client-server data management still holds majority vote, you are part of the few already left behind. Today, instead of the old, heavy and more complex client-server data management, we have industrial cloud-based applications allowing development and deployment of software with easy updates and low-cost maintenance.
Through connected products, digitization of the supply chain puts manufacturers in the forefront as innovators right within their markets. Enhancements that manufacturers are making on their product’s features allows them to precisely meet the unique needs of their customers. As this connectivity advances, the manufacturer’s stance as a service is strengthened.
Today, more tasks can be accomplished by devices “on-site” because of the increasing power of computing. This means that the latency that’s normally the case can be reduced by lightening the load placed on the cloud and IoT network. Furthermore, the risks in data security can be mitigated and connectivity costs reduced.
This processing capability by devices located in the field is known as “edge computing” and brings with it numerous possibilities within the realm of IoT. These include face recognition, language processing, object detection and avoidance, and other machine learning applications.
Fog computing, also known simply as the Fog, takes the space between IoT endpoints and the cloud. This simply means that Fog is the network connecting where the data is stored to the points of data input and creation. As a midway processing zone, the fog takes care of the data from the edge and manages tasks that can’t be taken care of on-device but requires the cloud.
First coined in Germany in 2011, Industry 4.0 is helping stamp the digital disruption in manufacturing today. And as industrial IoT continues to advance day by day, manufacturers are also slowly getting to understand the immense power that this technological approach could impact how their operations are run. Industry 4.0’s leading use cases are predicted to include digital twins, predictive maintenance, condition monitoring, fleet management, and data-driven R&D.
Among the premises of industrial IoT that have proved to be very significant include the ability to receive and analyze endless updates and other data collection stations in real time, and provide a response with immediate action. As this happens, machine learning is proving to be a very powerful tool in harvesting the power of IoT for the benefit of industrial production.
It’s also possible through machine learning and predictive maintenance to learn and understand the behavior and performance of machines within the manufacturing setup. Consequently, algorithms are then able to adapt with new information yielded from all this interaction.
Till this century, automation in manufacturing systems are controlled through Distributed Control System (DCS) facilitated by programmable logic controllers (PLCs). The downside of this control system is in it being proprietary in nature. This system is hugely difficult to change and is normally customized to a specific production line that may not fit across the industries that need automation.
But now, a new innovation known as open process automation proposes a new generation of automation infrastructure that can be easily implemented and availed for use in industrial and consumer IoT applications.
Today, it’s not a surprise to see robots inhabiting industrial manufacturing spaces. In fact, there are almost 2 million industrial robots already on the floors of manufacturing plants across the globe.
And this is not news given the unparalleled efficiency that robots bring to the manufacturing process. Now, instead of humans doing unpleasant, monotonous and dangerous works that subject life to profound risks, robots have come in handy and to the rescue of the humans.
This sudden surge in the application of robots in industries is driven by the advancements that have been realized with The Internet of Robotic Things (IoRT). Going forward, more powerful and sophisticated robots are expected to hit the manufacturing space with production robots getting connected and fed with real-time data. This will facilitate decisions that regard synchronicity and performance on the actual factory floors.
Furthermore, the Internet of Robotic Things (IoRT) is projected to aid the manufacturer to better their responses to changes in the supply chain with more accurate solutions and meet their customers’ needs even better.