Abstract
In the age of Industry 4.0, data-driven approaches are increasingly being used to optimize industrial processes, enhancing efficiency, reducing waste, and improving product quality. By leveraging advanced analytics, machine learning algorithms, and real-time data collection, manufacturers can gain actionable insights that drive process improvements. This article explores the key data-driven techniques used in industrial process optimization, such as predictive analytics, process mining, and advanced control systems. We examine how these approaches are applied in real-world manufacturing environments and discuss their potential to transform production systems and create smarter, more efficient factories.
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