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Machine Learning for Smart Industry
Predictive Maintenance
Predictive maintenance uses IoT sensors to collect data on the operating parameters of industrial machines. This data is analyzed by machine learning software to identify correlations and predict maintenance needs or failure risks. Over time and with more data, the software improves its predictions. This approach changes the traditional method of periodic maintenance, preventing sudden failures and production stops. Additionally, machine learning can be used for monitoring and controlling the production process, recognizing products and defects with almost absolute precision.
Logistics and Supply Chain
Machine learning is widely used in risk management in logistics and industrial supply chains. Continuous data analysis of transport and product movements optimizes transport plans considering various parameters such as costs, distances, and sales time flexibility. Logistics 4.0, thanks to advanced data analysis enabled by machine learning, allows quick and precise decisions to meet customer demand timely and economically, promoting the creation of a 'global warehouse' through data cross-referencing from different operational centers. Integrating machine learning with Digital Twins, digital models of the production reality, allows efficient testing of products and services, reducing errors and improving the production chain.
Process Automation
Machine Learning algorithms enable the automation of many industrial processes, increasing efficiency and reducing human errors.
Product Quality
The analysis of data collected by sensors during production by machine learning models ensures more rigorous and immediate quality control.