摘要：AbstractIn the last decade manufacturing experienced a shift towards digitalization. Cost decrease of sensors, wireless connectivity, and the opportunity to store big amounts of data pushed a process towards a next generation of IT industry. Manufacturing now has the opportunity to gather large quantities of data, coming from different areas, such as product and process design, assembly, material planning, quality control, scheduling, maintenance, fault detection and cover all the product life cycle phases. The extraction of value from data is a new challenge that companies are now experiencing. Therefore, the need for analytical information system is growing, in order to explore datasets and discover useful and often hidden information. Data analytics became a keyword in this context, but sometimes it is not clear how different methods or tools are defined and could be effectively used to analyze data in manufacturing. The paper aims to present and clarify the meaning of terms that are currently and frequently used in the context of analytics. The paper also provides an overview of the data analysis techniques that could be used to extract knowledge from data along the manufacturing process.
