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The Application of Data Mining in the Production Processes

Received: 16 September 2018     Accepted: 28 September 2018     Published: 30 October 2018
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Abstract

Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Naïve Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data.

Published in Industrial Engineering (Volume 2, Issue 1)
DOI 10.11648/j.ie.20180201.14
Page(s) 26-33
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Data Mining Algorithms, Classification, Industrial Data, Accuracy, ROC Curve

References
[1] Saad HR (2018), “Use Bagging Algorithm to Improve Prediction Accuracy for Evaluation of Worker Performances at a Production Company”. Ind Eng Manage 7: 257. doi:10.4172/2169-0316.1000257.
[2] Han, J., and Kamber, M., 2001, “Data Mining: Concepts and Techniques, Morgan Kaufmann”, New York, 550 pp.
[3] Malkoff, D. B., (1987), “A Framwork for Real-Time Fault Detection and Diagnosis Using Temporal Data,” Artif. Intell. Eng., 22, pp. 97–111.
[4] Ramamoorthy, C. V., and Wah, B. W., (1989), “Knowledge and Data Engineering,” IEEE Trans. Knowl. Data Eng., 11, pp. 9–16.
[5] Lee, M. H., (1993), “Knowledge Based Factory,” Artif. Intell. Eng., 8, pp.109–125.
[6] Irani, K. B., Cheng, J., Fayyad, U. M., and Qian, Z., (1993), “Applying Machine Learning to Semiconductor Manufacturing,” IEEE Expert, 81, pp. 41–47.
[7] Piatetsky-Shapiro, G., (1999), “The Data Mining Industry Coming of Age,” IEEE Intell. Syst., 146, pp. 32–34.
[8] Foguem, Rigal, & Mauget (2013). Mining Association Rules for the Quality Improvement of the production process. Expert system with applications journal.
[9] Saed Sayad (2010-2018) “An Introduction to Data Science”. Copyright © 2010-2018, Dr. Saed Sayad.
[10] J. A. Harding, M. Shahbaz, Srinivas, A. Kusiak (2006) Data Mining in Manufacturing: A Review. Journal of Manufacturing Science and Engineering. DOI: 10.1115/1.2194554.
[11] H Saad and N Nagarur (2017), “Data Analysis of Early Detection and Clinical Stages of Breast Cancer in Libya”. The 6th Annual World Conference of the Society for Industrial and Systems Engineering.
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    Hamza Saad. (2018). The Application of Data Mining in the Production Processes. Industrial Engineering, 2(1), 26-33. https://doi.org/10.11648/j.ie.20180201.14

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    Hamza Saad. The Application of Data Mining in the Production Processes. Ind. Eng. 2018, 2(1), 26-33. doi: 10.11648/j.ie.20180201.14

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    Hamza Saad. The Application of Data Mining in the Production Processes. Ind Eng. 2018;2(1):26-33. doi: 10.11648/j.ie.20180201.14

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  • @article{10.11648/j.ie.20180201.14,
      author = {Hamza Saad},
      title = {The Application of Data Mining in the Production Processes},
      journal = {Industrial Engineering},
      volume = {2},
      number = {1},
      pages = {26-33},
      doi = {10.11648/j.ie.20180201.14},
      url = {https://doi.org/10.11648/j.ie.20180201.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ie.20180201.14},
      abstract = {Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Naïve Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - The Application of Data Mining in the Production Processes
    AU  - Hamza Saad
    Y1  - 2018/10/30
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ie.20180201.14
    DO  - 10.11648/j.ie.20180201.14
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    JF  - Industrial Engineering
    JO  - Industrial Engineering
    SP  - 26
    EP  - 33
    PB  - Science Publishing Group
    SN  - 2640-1118
    UR  - https://doi.org/10.11648/j.ie.20180201.14
    AB  - Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Naïve Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • System Science and Industrial Engineering, Binghamton University, New York, USA

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