Clarifying Data Analytics Concepts for IE

Clarifying Data Analytics Concepts for IE

1. Cost decrease of sensors, wireless connectivity, and the opportunity to store big amounts of data pushed a process towards a next generation of information technology industry.

2. In the last decade manufacturing experienced a shift towards digitalization.

3. 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.

4. The extraction of value from data is a new challenge that companies are now experiencing.

   • The need for analytical information system is growing, in order to explore datasets and discover useful and often hidden information.

5. 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.

6. Given the importance of extracting value from datat, it is clear that a deeper knowledge of the data analytics tools and concepts is highly desired also in the industrial engineering field.

   • This paper poses itself as a preliminary work that aims at clarifying the main differences of the data analytics concepts and techniques and, among the latter, at summarizing what are the machine learning techniques that are currently most used in the industrial engineering research and practice.

7. In this context, some interesting research statements arise and need to be carefully detailed

   • To understand how to capture data along the production process.
   • To understand how to analyse data in order to extract useful knowledge about product, production process and product life cycle.
   • To understand how to share information among all the different stakeholders, along all the product life cycle.