Clarifying Data Analytics Concepts for Industrial Engineering

Clarifying Data Analytics Concepts for Industrial Engineering

1. Data analytics wants to analyse raw data in order to discover hidden patterns and relationships among different variables

   • 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
   • Main concepts
     • Data mining: extraction of implicit, previously unkown and potentially useful information from data, the idea is to construct numerical algorithms that investigate datasets to extract hidden patterns and regularities
       • Data mining and machine learning can be grouped into two different sets:
         • Supervised learning: predicting an outcome value based on other input variables
         • Unsupervised learning: deals with description of associations and patterns among a set of input data
     • Machine learning: is the field that developed most of the algorithms to find and describe structural patterns in data
       • Machine learning techniques in smart manufacturing
         • Supervised learning
           • Linear methods for regression
           • Linear method for classification
           • Decision tree based method
           • Neutral networks
         • Unsupervised learning
           • Principal component analysis (PCA)
           • Association rules
           • Cluster analysis
           • Self-organizing maps (SOM)
     • Data analytics: it is a more comprehensive term respect to data mining, since it incorporates a great deal of statistical thinking
     • AI: computer programs that possess own decision making capability to solve a problem of interest
     • Big data: it refers to a type of datasets, in which variables are different in type and nature (volume, velocity, variety, variability, veracity. etc.)
   • Extraction of value from data
     • Thanks to digitalization, wireless connectivity and sensors, manufacturing has now the possibility to gather large quantity of data, which span from production status, to machinery condition and product quality detection.
     • Manufacturing companies need to embrace data analytics to remain competitive in the global market