Today, more and more devices and technologies for industrial digitalisation have become available with which manufacturing companies in Hungary can significantly increase their production and improve their operation.
An increasing number of companies build sensors in their machines and attempts to analyse the vast amount of data derived from the production procedure. Experts have set up a roadmap that illustrates the extent of advancement in industrial digitalisation. This model defines three levels of maturity and maps out the developments most efficiently realisable on each level.
The first level: Understanding
The majority of Hungarian industrial companies have already reached the first level or at least sees the advantages of digitalisation and wishes to take action in the forthcoming future regarding industrial digitalisation. This is the level at which companies already begin to build sensors in their machines and monitor their efficiency. They know and understand that by appropriate administration and monitoring of data, they can save themselves from significant losses (such as idle machine time). A good example for the first level of digitalisation is the practice today, as opposed to the earlier procedure (when a piece of industrial equipment malfunctioned, repairmen found the errors after long experimentation), by appropriate sensors, which can detect the malfunction with a quick search. For measurements, the cause of the malfunction can be detected instantly and repair can be done more efficiently. Furthermore, production can be resumed with a far less loss than in the past.
The first level of digitalisation maturity means that the manufacturers measure performance in real time and discover problems, thus reaching increased productivity and save costs.
The second level: Development and optimisation
For companies, the second level of digitalisation means manufacturing procedures are transformed. A leap forward is taken in the efficiency of decision making. At this stage, management decisions are based on the analysis of real data and the survey of interrelations among them. On this level, the measurement and analysis of several parameters are happening in a way, where not only conclusions can be drawn, but forecasts can also be made. A typical example is the so called “predictive maintenance” in the case of which intervention is not only happening when a piece of equipment malfunctions, but, with the analysis of data from various sensors, the possibility of future malfunction is forecast. This way, unexpected shutdowns can be avoided, or maintenance can be scheduled for the time of a shutdown planned for any other reason.
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