By Luís Gonçalves, Data Analytics & AI Director at Noesis
Predictive asset management or intelligence at the service of asset management is currently one of the most relevant applications of artificial intelligence in the manufacturing sector. Large industries are continually striving to optimize the performance of their investments and physical assets. It is in this context that a predictive asset management approach, with a development of Artificial Intelligence models oriented to data, monitoring equipment, issuing alerts and automating daily processes allows for significant efficiency gains.
In a context in which Industry 4.0 generates/produces more data and information than ever before - production data, data of related processes, loTs, etc - its collection allows to define and control processes more efficiently and optimize asset management. Developing analysis models to detect anomalies, making forecast analysis and, more interestingly, predictive maintenance, are among the main benefits of using Al.
Models based on artificial intelligence ensure a more accurate view of events throughout the production process, anticipate scenarios and predict future temporal events through the study of historical variables. Anomaly detection is also optimized, as the use of Artificial Intelligence allows the identification of changes in previously learned operating patterns and identifies potential failures, with the generation of real-time alerts. On the other hand, through the analysis of the historical pattern of operation of industrial assets, and other variables that influence them, it is also possible to predict maintenance needs and even to anticipate the probability of failure of this equipment.
Process Optimization with AI
If we consider the potential of Artificial Intelligence to predict component and energy consumption; forecast demand, market, production needs and others; optimize stocks, both of products and consumables; detecting faults in advance, whether they are of production, quality anomalies or of the production process; and the aforementioned ability to identify preventive maintenance needs, allowing to anticipate problems with equipment / machines, for example, we can easily see how AI has been revolutionizing the Industry landscape, with remarkable gains in the optimization of production, planning, management and, necessarily cost reduction.
Date analytics & ai to enhance efficiency - a practical example
In one of our most recent projects, the developed use case focused on the theme of energy efficiency.
The project, developed for one of the main players in the energy supply market in Portugal, consisted on the creation of a data ingestion system to forecast energy consumption. With the development of this solution, it was possible to provide the organization with the ability to forecast the energy consumption needs of its B2B customers, through a system of real-time analysis of various indicators. Being able to monitor consumption and draw conclusions for future decision-making was one of the goals of the developed predictive system, which analyzes all points of energy consumption by customers and allows the generation of individual forecasts for each of these points, whether they are, machinery, refrigeration or others. With a data ingestion system, the ability to analyze in real time and to generate individual analysis, by customer and/or by consumption point, associated to the "training" of the predictive model, using artificial intelligence, it was possible to implement a complete solution that has brought significant efficiency gains to this energy supplier. From the outset, having access to an energy consumption forecast, with confidence levels above 90%, enables to estimate the levels of energy demand and the possibility to make better decisions in the process of acquiring that energy. On the other hand, the detection of anomalous situations with the identification of significant deviations in the customer's consumption pattern, allowed to minimize situations of possible fraud. Finally, with the guarantee of a better supply, it was also possible to increase customer satisfaction rates, achieve levels of excellence in service provision and, consequently, increase revenues.
In conclusion, the higher level of data generation, the ability to capture and enrich data by using Artificial Intelligence, in order to facilitate decision-making, should be the main reference and immediate goals in any organization. The solutions and possibilities that Data Analytics and Artificial Intelligence allows, are a stronger allied to transform the manufacture sector.
