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Regression Models for Lean Production

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Recent Advances in Information Systems and Technologies (WorldCIST 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 569))

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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%.

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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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Authors and Affiliations

  1. Algoritmi Research Centre, University of Minho, Guimarães, Portugal

    Ricardo Bragança, Filipe Portela & Manuel Santos

Authors
  1. Ricardo Bragança
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  2. Filipe Portela
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  3. Manuel Santos
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Corresponding author

Correspondence to Filipe Portela .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Baixo Mondego, Portugal

    Álvaro Rocha

  2. Nova IMS, Universidade Nova de Lisboa, Lisboa, Portugal

    Ana Maria Correia

  3. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  4. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  5. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Bragança, R., Portela, F., Santos, M. (2017). Regression Models for Lean Production. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_40

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  • DOI: https://doi.org/10.1007/978-3-319-56535-4_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56534-7

  • Online ISBN: 978-3-319-56535-4

  • eBook Packages: EngineeringEngineering (R0)

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