What is innovation in manufacturing today?
Innovation in factories of all sizes is driven by the introduction of new technologies. Industry 4.0, using large data for managing and growing production, is where it is. Together with sales data that are massively and in detail recorded and analyzed, it is possible to closely monitor the course of the entire production cycle of a product or component and to monitor deviations from the established standards, which can be recorded in real time by all those who need such information. The collection of data generated throughout the entire production cycle has many uses, but one of the main ones is the introduction of predictive maintenance.
Collection of data generated throughout the entire production cycle has many uses, but one of the main ones is the introduction of predictive maintenance.
How does predictive maintenance improve production?
By analyzing the operational data from machines located in production halls, any machine can be used to determine its behavior patterns in full operation, which will then allow for the foreseeability of maintenance. It is a more appropriate procedure than its reactive repair after a breakdown or after it begins to produce defective products. This situation can be prevented by a continuous analysis of the data from the operation of the equipment, which results in the detection of anomalies and deviations in machine's work before its failure causes distortion of production.
In this way, not only is the problem quickly and easily analyzed, but the maintenance of machine can be planned to cause the least possible disruption of production. This kind of planned downtime, in contrast to reactive repairs, is much cheaper and prolongs life of machine. Not only predictive maintenance is more effective in production efficiency, but more proactive quality control is comming.
Data capture also affects quality control
The quality of the output can be better controlled, allowing the machine to produce an order with a small amount of waste from the outlet.
Imagine this scenario without data-driven production: A 10,000-sheet order comes with shorter delivery times. The machines needed for this production cycle are involved, but in about half of the production, the production part of one of the machines starts vibrating. The operator can not see it and let the production cycle complete and up to a few hours later, the quality control check reveals that about 1,000 parts are unfinished. The parts thus produced must be discarded and the delivery time is delayed, resulting in customer dissatisfaction.
Let's imagine the scenario in with data monitoring: The machine will be known to have this problem through ongoing data collection and preventative control and will be involved in the production before the order is placed, to ensure the full cycle of the entire production cycle. Even if this did not happen and the vibrations mentioned would begin, the operator would be immediately informed that behavior of the machine is outside standard parameters. The operator would be alerted to this problem and would be able to have machine repaired before too many faulty components were manufactured, thus preventing large quantities of defective products from being produced, material damage and delayed delivery of the completed order.
Data-driven production is another step in development
The Internet of Things (IoT) and collection of data to analyze them are another ways for manufacturers to remain at the top in their industries. Collecting real-time data from independent machines in factory hall and making them available to anyone from the machine operator to CEO through new technologies will enable rapid and accurate decision-making, which should lead to lean manufacturing across the organization.
The more data will be collected and analyzed, the more accurate and efficient the processes involved in the production and processing of orders will be implemented, including operation, maintenance and delivery.
An important factor in profitability is timely delivery to customer, due to elimination of unplanned downtime and maintenance planning capability with minimal impact on production. Let's add a reduction in waste as a result of the timely detection of imminent failures and you are able to achieve a significantly positive effect even at basic production costs.
