Maintenance is important to support 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 diverse 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: conducted after the occurrence of the failure, with the aim to repair the machinery so that can do its required function;
Despite its significant importance, improving the maintenance process in the production line is still considered a big challenge. Industries have the mission of expert how to readjust their practices and planning to their future demands. In this case, technology is a great ally, as it enhances the productive ability, reduces errors and increase profits.
By investing in data science, it is possible to implement predictive maintenance, avoiding sudden downtime that directly severely 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 improve 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.
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