Clarifying Data Analytics Concepts for Industrial Engineering

Clarifying Data Analytics Concepts for Industrial Engineering

1. Provide an overview of the machine learning techniques in the that could be used to extract knowledge from data along the manufacturing process

2. Main goal is to develope smart factories for higher competitiveness and flexibility, at the same time the technical development of data storages, computational power and analysis algorithms.

3. Main concepts

   • Data Mining
     • is the extraction of implicit, previously unknown and potentially useful information from the data
   • Machine learning
     • Is the field that developed most of the algorythms to find and describe structural pattern in data
       • Supervised learning
       • Unsupervised learning
   • Data analytics
     • is composed both of data mining and statistcal models to clean data at the beginning and to validate rules at the end.
       • has the following steps:
         • statistical techniques
         • selection of attributes
         • discarding outliers
   • Big data
     • type of database in which variables are different in type and nature, they compel to be analysed with a number of specific techniques

4. Machine learning techniques

   • Supervised learning
     • linear methods for regression
     • linear method for classification
     • decision tree based method
     • neutral networks
   • Unsupervised learning
     • Principal component analysis
     • association rules
     • Cluster analysis
     • self-organizing maps

5. To send the right information to the right person in the right moment is clear that a deeper knowledge of the data analytics tools and concepts is highly desired

   • This will aim at giving industrial engineering researches the right insights and competences for wider and more technicalyy robust investigation