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Maintenance is important to maintain the integrity of the machinery in industry and ensure the flow of processes on the shop floor. According to ABRAMAN (2018) – Brazilian Association of Maintenance and Asset Management – this area spends about 5% of the industry’s total revenue.
There are different types of maintenance present in the routine of a manufacture, as examples:
– Preventive maintenance: done on a scheduled basis, focusing on reducing the probability of failure or degradation of machinery;
– Predictive maintenance: based on the machine’s operating patterns to predict when maintenance should be executed;
– Corrective maintenance: carried out after the occurrence of the failure, with the objective to repair the machinery so that can do its required function;
Despite its great importance, optimizing the maintenance process in the production line is still considered a big challenge. Industries have the mission of master how to readjust their practices and planning to their future demands. In this case, technology is a great ally, as it enhances the productive capacity, reduces errors and increase profits.
By investing in data science, it is possible to implement predictive maintenance, avoiding sudden downtime that directly badly affects productivity. According to McKinsey(2017), this type of maintenance can increase the useful life of a machine by 20% to 40%, and reduce procedural costs by up to 25%.
This case exemplifies how a large multinational food company made use of the ST-One Solution. Here, the purpose was to optimize line maintenance and ensure the proper functioning of a valve matrix. With this purpose, the industry teamed up with a disruptive data technology, which assisted in act directly on the problem, resulting in gains in several areas. Learn more about us.
