Manufacturing industry should make good use of data analysis, AI, machine learning and other emerging technologies to integrate long-term manufacturing strategies in order to expand the business benefits of production site, customer experience and service model, while moving towards industry 4.0. Predictive maintenance will become more and more important.
In order to be a leading competitor in manufacturing industry, it is necessary to take the lead. Data analysis combined with machine learning can help to improve the availability and life of assets, including predicting the possible location of the next production bottleneck, and how to arrange maintenance for failures that cannot be mastered in the most cost-effective way, so as to enable enterprises to reduce costs, improve operational efficiency and data driven decision-making.
If reactive maintenance is adopted, when the equipment goes offline unexpectedly due to faults, the impact on productivity is irreversible, and it will cause the chain effect of the whole production chain. Data is the lifeblood of digital transformation. With the increasing number of manufacturing industries using IIoT, using AI and machine learning aided data analysis, abnormal information can be filtered out from the large data collected in the production site, and potential models can be found to improve the accuracy of equipment reliability prediction, and further drive the transformation of maintenance operations into comprehensive test pattern.
Manufacturing industry can adjust maintenance schedule before equipment failure according to accurate prediction information, identify and solve potential problems in advance, avoid additional costs arising from emergency or over-maintenance, and improve normal operation time and production quality of equipment. Advanced data analysis also identifies and manages the benefits of newly deployed fully digital solutions and their impact on current business processes, helping forward-looking enterprises to counter future digital damage and shocks.