70% of companies have already completed at least one IoT project or are considering one. According to Siemens data, applying IoT to processes means reducing asset downtime by 15% and increasing production by 8%.
The fourth industrial revolution is creating a faster, smarter and more efficient manufacturing process. Known as Industry 4.0, this stage that is spreading to manufacturing plants has taken automation to the next level. Using the Industrial Internet of Things (IoT), manufacturers can connect the physical world with the digital world and fully control all systems like never before. In addition, they can leverage data to reduce costs, improve performance, and increase productivity by using an open IoT operating system.
Although a digital transformation may seem somewhat overwhelming, the transition can be carried out in just four phases, called pillars: connectivity, control, digitalization and augmentation. Although few companies (4%) are fully integrated today, more than 70% have completed at least one IoT project or are looking for and implementing one. Almost 90% of those who have completed a project are looking for new plans.
Those who have completed an IoT project report reduced downtime, lower operating costs, increased productivity and a better understanding of the equipment (see graph below) and how to optimize it.
The following steps detail how to make the digital transition and take advantage of the opportunities offered by Industry 4.0.
Connectivity means connecting physical devices and business systems to the IoT to promote systems integration, increase transparency and improve remote processes between plants.
The first step of the digital transformation is to connect physical devices and systems to the IoT. Even companies with older equipment can do this with investments in sensors and hardware. Plants in several locations can be connected and then controlled remotely. Once the machines are connected, data can be collected in real time, and alarms can be set up to notify the manufacturer when an asset is not working properly. This reduces the chances of an expensive and urgent repair.
This control minimizes downtime and offers manufacturers the opportunity to continuously improve machine performance using real-time asset data. Siemens’ MindSphere customers, for example, report reducing service costs by 30%, decreasing downtime by 15% and increasing production by 8%.
Control allows an enterprise to use data from connected devices for full transparency and control of asset performance. The second step of the digital transformation is to use the data collected to optimize machine performance, using predictive and descriptive maintenance. This involves replacing traditional maintenance methods (reactive and scheduled maintenance) with evolving, data-driven approaches.
Predictive and prescriptive maintenance means carrying out maintenance on machinery at the right time to prevent any service failure. This eliminates untimely maintenance, reduces unnecessary downtime and allows the manufacturer to remotely monitor machines and identify the root cause of production problems.
Predictive maintenance saves 12% in costs compared to scheduled repairs, reduces maintenance costs by 30% and decreases breakdowns by 70%, according to an operational efficiency report conducted by Pacific Northwest National Laboratory for the U.S. Department of Energy. Customers using MindSphere report a 15% reduction in downtime.
The scanning process uses data to create a digital copy of a product or system to search for efficiencies, resolve issues, test solutions and improve product development. Later, real-time data from the field is incorporated into the digital twin for continuous innovation.
There are three types of digital twins: product, production and performance. A digital product twin allows the manufacturer to test several versions of a product before creating the physical prototype. This can shorten development cycles, allow more innovation and eliminate costs in product development.
A digital production twin recreates the entire manufacturing process. This allows the manufacturer to search for defects in the process without affecting the manufacturing outcome. A digital performance twin collects real-time data from operational products and the production line so that manufacturers can identify new ways to improve the product or process. Data can also be incorporated into the digital twin of the product or production for continuous improvement.
In this phase, the IoT and artificial intelligence combine to create intelligent machines that can use data to function independently of human influence. The final step of the digital transformation is to use data from the IoT to report on the operation of the machine without human interference. Artificial intelligence uses the IoT data using automatic language to predict the results and act accordingly.
By automating the operation of machines, manufacturers can increase productivity, reduce errors and gain a competitive advantage over others with less operational efficiency. Bringing artificial intelligence to manufacturing plants provides companies with a way to stop using current business models and look for new opportunities.
As the industry moves at full speed toward digitalization, manufacturers need to be prepared for disruption. Industrial IoT can completely transform the way a company does business. 70% of companies have already completed at least one IoT project or are considering one, and 4% of companies are fully integrated with IoT. Continuing with traditional ways of operating while competitors move to Industry 4.0 will leave manufacturers behind in that race for better operational efficiency.
The industrial IoT allows companies to take control of their factories like never before. By connecting devices and systems, manufacturers can learn more about their machines and how to manipulate them to work smarter, reducing costs and errors.
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