Artificial intelligence (AI) and machine learning (ML) technology is crucial to data collection. AI-powered data analytics analyse huge quantities of information from multiple sources and predict things like maintenance issues, resource optimisation, and energy consumption.
Similarly, machine learning algorithms identify data patterns that are crucial in minimising operational costs, driving down emissions, reducing waste, and optimising productivity.
One example of how AI-powered data analytics improve the utilisation of machinery and optimise operations is digital twin technology. This uses artificial intelligence and software analytics to collect real-time data from physical assets and turn it into a virtual simulation.
Machine learning algorithms then identify and analyse data patterns to predict how assets and equipment will perform. This empowers operators with better decision making by providing complete visibility of operations and showing them exactly how they can optimise production.
Key fact: Deloitte forecasts that the global market for digital twin technologies will reach $16 billion by 2023
Improves decision making
Enhances data quality
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