Most of us are familiar with physical devices that are connected to the internet, such as light switches, cameras, or thermostats that can be controlled using a smartphone app. This is termed the Internet of Things or IoT. The Industrial Internet of Things, or IIoT, refers to the industrial use of such devices; for example, sensors embedded in an airplane engine that collect and transmit data on its performance, or proximity sensors that can trigger processes on an assembly line.
Another important technology for Industry 4.0 is low-cost cloud computing, which allows the management of large volumes of data in open systems and enables real-time communication that can be used for production systems. Cloud computing, allowing access to information from anywhere in the globe at any time, increases the flexibility of manufacturing processes, customer processes, and financial processes.
While these tools enable processes to be smarter, more efficient, and more customized, the road to achieving Industry 4.0 is not a simple one, nor should it be undertaken blindly. A careful evaluation of the potential opportunities and obstacles needs to be undertaken, and implementation should be carried out strategically and stepwise. Here we discuss those opportunities and obstacles as well as some best practices to consider.
Industry 4.0 is a label meaning the Fourth Industrial Revolution. The term Industry 4.0 was invented about 2011 by a group within the German government, but the use of the term “fourth industrial revolution” goes back to 1948. The previous industrial revolutions are defined as:
Industry 4.0 represents the integration of tools that are connected to a global network by transmitting digital data. It includes the use of mobile devices, location detection technology based on either GPS (global positioning system) or NFC (near field communication), advanced human-machine interface, robotics, 3D printing, simulation, smart sensors, big data analytics, artificial intelligence, augmented reality, cloud computing, and other techniques.
Implementing Industry 4.0 tools into a “smart factory” enables integrated and real-time availability and control of systems across a company. Let’s dive into some of the opportunities that can be pursued by implementing an Industry 4.0 strategy.
An important objective of Industry 4.0 is to enable faster and more efficient manufacturing of products, due to easy standardization of assembly processes and instant feedback on quality. Raw material and energy savings are also possible in some cases.
Availability of instant feedback can enable easier decision-making and faster corrections, resulting in less rejected products.
Grade changes in continuous manufacturing can be made more easily, as a process can be controlled automatically to mirror the previous production of that grade. Just like in batch production of items, changeovers can be made more rapidly and error-free, especially during a pandemic.
Especially when manufacturing parts using 3D printing (additive manufacturing), individually-specified products can be made easily.
Faster, data-driven decision-making is enabled by having access to data from anywhere within an organization, and 3D printing can avoid the need for ordering parts from a remote central location.
As a result of improved efficiency and lower cost.
With automation, robotics, and data-driven decisions, processes can be more easily standardized and are less subject to errors or variability in quality.
When known failure modes can be predicted using online data, operators can react and take corrective actions.
With the right interface and process understanding, data analytics can be a powerful tool for process improvement.
Implementing Industry 4.0 is much more complex than simply purchasing and adding IIOT devices. Before implementing an Industry 4.0 strategy, it’s important not to underestimate the effort required. The collection of raw data needs to be converted into value-added information.
For instance, temperature measurements from a piece of machinery can be converted into information about when it is overheating. A further step is virtual simulation of the real process, also known as modeling or digital twinning. This gives better insight into potential opportunities for improvement. The first implementation will rarely be fully optimized. A system for collecting feedback should be included so as to continually improve the process.
Now, let’s have a look at 7 obstacles that may need to be overcome in an Industry 4.0 implementation.
Purchase of IIoT hardware and software is only part of the cost of an Industry 4.0 project. The time spent learning from the new data and turning this information into value for the business is the most important investment.
It’s very difficult to predict the return on investment before a project, but estimates can be made from expected gains in efficiency and operating costs based on industry benchmarks and the experience of experts. The additional business from improved flexibility and faster customer response is also a benefit that is difficult to quantify.
There will be a learning curve as data-collecting devices are installed and personnel learn how a process behaves.
In challenging situations such as heat and humidity, outdoor exposure, or contaminated environments, some devices can malfunction, and this should be taken into account when specifying equipment. In some situations, precautions must be taken to ensure false signals don’t result in breakdown or rejected products, by building in checks to the system. An extreme case of this would be the angle-of-attack sensors that appear to have led to two fatal Boeing 737 plane crashes.
Not only might data storage and speed of retrieval need to be improved, but the quality of connections such as wifi may need some bolstering.
Particularly for smaller businesses who have not previously built significant IT infrastructure. As the use of IIoT devices expands, new security concerns arise, because each new connected device or component can become a potential liability.
Training the workforce to work alongside machines with simulation programs, and to adopt an attitude of continuous improvement of the process, will be required. The more input the front-line workers have in designing the new process, the more the likelihood of success.
Managers need to understand the impact of the new technology on the existing workplace, encourage input from users, and support innovation, experimentation, and flexibility.
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