In Industry 4.0, predictive maintenance is probably the best-known application of what is known as IIoT (Industrial Internet Of Things). The costs of maintaining the production line in operational condition can be significantly reduced thanks to this automation of maintenance operations. But the possibilities of these new devices are not limited to that!
Industry 4.0: IIoT to manage inventory and supply chain
An innovation of Industry 4.0 is the use of connectivity to know the status of production and inventory in real time. Connected devices allow real-time monitoring of manufacturing progress, input consumption and actual availability of finished products.
For example, software can alert when an input is in danger of running out in the near future. Then simply place an order with the supplier without delay. Productivity is thus significantly improved by these systems, which make it possible to avoid the risk of production interruption due to a lack of supply.
The consolidation of the data collected by these connected equipments allows to follow the production as closely as possible, to analyze any evolution and to be proactive instead of being reactive. Managers of an industrial infrastructure can track activity and measure performance.
The information can even be shared with customers. Thus, a subcontractor of a large industry will be able to automatically communicate the progress of its production, which allows the downstream industrialist to adjust its planning according to the products that it will actually have available.
IIoT to anticipate the maintenance of remote machines
In an Industry 4.0, predictive maintenance has a simple definition: it is the application of new technologies to anticipate possible breakdowns, in order to maximize the availability of the production tool. The interest of this predictive maintenance is obvious for all manufacturers.
When a machine breaks down, the production line has to be stopped completely. Fixed costs (salaries, premises, etc.) continue to run, while the production to cover them is interrupted. In some cases, late penalties may even apply!
Predictive maintenance is based on the analysis of sensor data and on the processing by artificial intelligence of the mass of data already available to identify the elements likely to fail, even before the failure actually occurs!
Maintenance operators can intervene at the best time, for example, by working outside normal operating hours or during an off-peak period. They will change a part that is in danger of breaking or replace a component that is bound to fail, thus ensuring maximum availability of the production tool.