Citation

Kannan, Rathimala and Abdul Halim, Haq’ul Aqif and Ramakrishnan, Kannan and Ismail, Shahrinaz and Wijaya, Dedy Rahman (2022) Machine learning approach for predicting production delays: a quarry company case study. Journal of Big Data, 9 (1). ISSN 2196-1115

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Abstract

Predictive maintenance employing machine learning techniques and big data analytics is a benefit to the industrial business in the Industry 4.0 era. Companies, on the other hand, have difficulties as they move from reactive to predictive manufacturing processes. The purpose of this paper is to demonstrate how data analytics and machine learning approaches may be utilized to predict production delays in a quarry firm as a case study. The dataset contains production records for six months, with a total of 20 columns for each production record for two machines. Cross Industry Standard Process for Data Mining approach is followed to build the machine learning models. Five predictive models were created using machine learning algorithms such as Decision Tree, Neural Network, Random Forest, Nave Bayes and Logistic Regression. The results show that Multilayer Perceptron Neural Network and Logistic Regression outperform other techniques and accurately predicts production delays with a F-measure score of 0.973. The quarry company's improved decision-making reducing potential production line delays demonstrates the value of this study.

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Production delay
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2022 07:10
Last Modified:	01 Aug 2022 07:10
URII:	http://shdl.mmu.edu.my/id/eprint/10269

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