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Regression Models for Lean Production

  • Ricardo Bragança
  • Filipe PortelaEmail author
  • Manuel Santos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 569)

Abstract

Data mining models are an excellent tool to help companies that live from the sales of items they produce because it allows the company to optimize its production and reduce costs, for example in storage. When these models are combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM, and aims to reduce and, if possible, eliminate wastage. The following methods: average, mean and standard deviation, quartiles and Sturges rule regression, were techniques applied to this data to determine which one is the model is less likely to make mistakes, in other words, meaning that the model did correctly predict the target. Most common metrics used at the statistical level, which had already been proven to have good results in similar studies. After performing the tests, the M4 model is what is less likely to make mistakes in terms of regression with a RAE of 21,33%.

Keywords

Data mining Regression CRISP-DM DSR Lean production 

Notes

Acknowledgments

This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ricardo Bragança
    • 1
  • Filipe Portela
    • 1
    Email author
  • Manuel Santos
    • 1
  1. 1.Algoritmi Research CentreUniversity of MinhoGuimarãesPortugal

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